From 238657db2364cfb728c694470a4a81702afea760 Mon Sep 17 00:00:00 2001 From: kalomaze <66376113+kalomaze@users.noreply.github.com> Date: Tue, 31 Oct 2023 14:44:49 -0500 Subject: [PATCH 001/664] samplers : Min-P sampler implementation [alternative to Top P/Top K] (#3841) * Introduce the new Min-P sampler by @kalomaze The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. * Min-P enabled and set to 0.05 default --------- Co-authored-by: Georgi Gerganov Co-authored-by: cebtenzzre --- common/common.cpp | 8 ++++++++ common/sampling.cpp | 6 ++++-- common/sampling.h | 1 + examples/main/README.md | 8 ++++++++ llama.cpp | 26 ++++++++++++++++++++++++++ llama.h | 7 +++++++ 6 files changed, 54 insertions(+), 2 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index c187128d6..dc4865e80 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -218,6 +218,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } sparams.top_p = std::stof(argv[i]); + } else if (arg == "--min-p") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.min_p = std::stof(argv[i]); } else if (arg == "--temp") { if (++i >= argc) { invalid_param = true; @@ -679,6 +685,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k); printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p); + printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p); printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z); printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p); printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n); @@ -1275,6 +1282,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency()); fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); + fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); } diff --git a/common/sampling.cpp b/common/sampling.cpp index c4996c985..673d67a6d 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -89,10 +89,10 @@ std::string llama_sampling_print(const llama_sampling_params & params) { snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" - "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, typical_p = %.3f, temp = %.3f\n" + "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present, - params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, + params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau); return std::string(result); @@ -110,6 +110,7 @@ llama_token llama_sampling_sample( const float temp = params.temp; const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; const float top_p = params.top_p; + const float min_p = params.min_p; const float tfs_z = params.tfs_z; const float typical_p = params.typical_p; const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; @@ -190,6 +191,7 @@ llama_token llama_sampling_sample( llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); + llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); llama_sample_temp (ctx_main, &cur_p, temp); id = llama_sample_token(ctx_main, &cur_p); diff --git a/common/sampling.h b/common/sampling.h index 62ea6d4cf..7c9b8dcf2 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -14,6 +14,7 @@ typedef struct llama_sampling_params { int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. int32_t top_k = 40; // <= 0 to use vocab size float top_p = 0.95f; // 1.0 = disabled + float min_p = 0.05f; // 0.0 = disabled float tfs_z = 1.00f; // 1.0 = disabled float typical_p = 1.00f; // 1.0 = disabled float temp = 0.80f; // 1.0 = disabled diff --git a/examples/main/README.md b/examples/main/README.md index a9561c383..a3428b487 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -208,6 +208,14 @@ Top-p sampling, also known as nucleus sampling, is another text generation metho Example usage: `--top-p 0.95` +### Min P Sampling + +- `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.05). + +The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. + +Example usage: `--min-p 0.05` + ### Tail Free Sampling (TFS) - `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled). diff --git a/llama.cpp b/llama.cpp index e599917a8..7ee589298 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7368,6 +7368,32 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can } } +void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) { + if (p <= 0.0f || !candidates->size) { + return; + } + + llama_sample_softmax(ctx, candidates); + + const int64_t t_start_sample_us = ggml_time_us(); + + float scale = candidates->data[0].p; // scale by max prob + size_t i = 1; // first token always matches + + for (; i < candidates->size; ++i) { + if (candidates->data[i].p < p * scale && i >= min_keep) { + break; // prob too small + } + } + + // Resize the output vector to keep only the matching tokens + candidates->size = i; + + if (ctx) { + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + } +} + void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) { if (z >= 1.0f || candidates->size <= 2) { return; diff --git a/llama.h b/llama.h index d727dbd9f..75fe391ef 100644 --- a/llama.h +++ b/llama.h @@ -598,6 +598,13 @@ extern "C" { float p, size_t min_keep); + /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841 + LLAMA_API void llama_sample_min_p( + struct llama_context * ctx, + llama_token_data_array * candidates, + float p, + size_t min_keep); + /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/. LLAMA_API void llama_sample_tail_free( struct llama_context * ctx, From 71e3718abdb2771b50c9606d3a7569623a0b0afe Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 08:04:02 +0200 Subject: [PATCH 002/664] llama : refactor graph build code (#3837) * llama : factor out ggml-alloc from graph graph build functions ggml-ci * metal : disable kernel load log * llama : factor out tensor offloading outside the build call (wip) ggml-ci * llama : offload rest of the models ggml-ci * llama : update offload log messages to print node index * llama : comments * llama : support offloading result_norm + comments * llama : factor graph input into a function * llama : do tensor offload only with CUDA * llama : fix res_norm offloading * llama : try to optimize offloading code * llama : fix non-CUDA build * llama : try to fix build * llama : move refact in correct place + optimize graph input * llama : refactor tensor offloading as callback * llama : add layer index to all tensor names * llama : add functional header * llama : comment ggml-ci * llama : remove obsolete map for layer counting * llama : add llm_build helper functions (#3848) * llama : add llm_build_norm helper function ggml-ci * llama : add llm_build_ffn helper function (#3849) ggml-ci * llama : add llm_build_k_shift helper ggml-ci * llama : fix offloading after recent changes * llama : add llm_build_kv_store helper ggml-ci * llama : remove obsolete offload names * llama : fix llm_build_k_shift to use n_head_kv instead of n_head * llama : simplify falcon Q, K, V computation * llama : remove obsolete comments in build graphs * llama : add llm_build_kqv helper ggml-ci * llama : minor * llama : add LLAMA_OFFLOAD_DEBUG + fix starcoder offloading * llama : fix input allocation logic * llama : update offload functions for KQ tensors * llama : normalize tensor names ggml-ci * llama : enable warning about not offloaded tensors * llama : remove extra ; + deduplicate gate_b logic * llama : add llm_build_inp_embd helper --- ggml-metal.m | 11 +- ggml.h | 2 +- llama.cpp | 3655 ++++++++++++++++++++------------------------------ 3 files changed, 1477 insertions(+), 2191 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 2380c4310..bc881395a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -238,14 +238,17 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { // load kernels { NSError * error = nil; -#define GGML_METAL_ADD_KERNEL(name) \ - ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ - ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \ + + /* GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \ (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \ (int) ctx->pipeline_##name.threadExecutionWidth); \ + */ +#define GGML_METAL_ADD_KERNEL(name) \ + ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ + ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \ if (error) { \ - GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ + GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ return NULL; \ } diff --git a/ggml.h b/ggml.h index 8c954904e..9d16c5a72 100644 --- a/ggml.h +++ b/ggml.h @@ -709,7 +709,7 @@ extern "C" { // Context tensor enumeration and lookup GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor); - GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); + GGML_API struct ggml_tensor * ggml_get_tensor (struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); diff --git a/llama.cpp b/llama.cpp index 7ee589298..ead1d421d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -60,7 +60,9 @@ #include #include #include +#include #include +#include #include #include #include @@ -69,11 +71,10 @@ #include #include #include +#include #include #include #include -#include -#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -969,7 +970,7 @@ struct llama_mlock { typedef void (*offload_func_t)(struct ggml_tensor * tensor); -static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default +static void ggml_offload_nop(struct ggml_tensor * tensor) { (void) tensor; } @@ -1113,13 +1114,13 @@ struct llama_layer { struct ggml_tensor * ffn_norm_b; // ff - struct ggml_tensor * w1; // ffn_gate - struct ggml_tensor * w2; // ffn_down - struct ggml_tensor * w3; // ffn_up + struct ggml_tensor * ffn_gate; // w1 + struct ggml_tensor * ffn_down; // w2 + struct ggml_tensor * ffn_up; // w3 // ff bias - struct ggml_tensor * b2; // ffn_down - struct ggml_tensor * b3; // ffn_up + struct ggml_tensor * ffn_down_b; // b2 + struct ggml_tensor * ffn_up_b; // b3 }; struct llama_kv_cell { @@ -1225,8 +1226,8 @@ struct llama_model { llama_hparams hparams = {}; llama_vocab vocab; - struct ggml_tensor * tok_embeddings; - struct ggml_tensor * pos_embeddings; + struct ggml_tensor * tok_embd; + struct ggml_tensor * pos_embd; struct ggml_tensor * tok_norm; struct ggml_tensor * tok_norm_b; @@ -2482,7 +2483,7 @@ static void llm_load_tensors( case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: { - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); // output { @@ -2536,21 +2537,21 @@ static void llm_load_tensors( layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); if (backend == GGML_BACKEND_GPU) { vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + - ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + - ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3); + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + + ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); } } } break; case LLM_ARCH_BAICHUAN: { - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); { ggml_backend_type backend_norm; ggml_backend_type backend_output; @@ -2602,15 +2603,15 @@ static void llm_load_tensors( layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); if (backend == GGML_BACKEND_GPU) { vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + - ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + - ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3); + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + + ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); } } } break; @@ -2618,7 +2619,7 @@ static void llm_load_tensors( { // TODO: CPU-only for now - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); // output { @@ -2681,21 +2682,21 @@ static void llm_load_tensors( layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); if (backend == GGML_BACKEND_GPU) { vram_weights += ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.wo) + - ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3); + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); } } } break; case LLM_ARCH_STARCODER: { - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); // output { @@ -2754,11 +2755,11 @@ static void llm_load_tensors( layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); if (backend == GGML_BACKEND_GPU) { vram_weights += @@ -2766,14 +2767,14 @@ static void llm_load_tensors( ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) + - ggml_nbytes(layer.w2) + ggml_nbytes(layer.b2) + - ggml_nbytes(layer.w3) + ggml_nbytes(layer.b3); + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b) + + ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b); } } } break; case LLM_ARCH_PERSIMMON: { - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); { ggml_backend_type backend_norm; @@ -2814,31 +2815,31 @@ static void llm_load_tensors( const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); + layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend); - layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}, backend); + layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}, backend); layer.attn_k_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend); - layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend); + layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend); } } break; case LLM_ARCH_BLOOM: { // TODO: CPU-only for now - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.tok_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU); - model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU); + model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, GGML_BACKEND_CPU); // output { @@ -2897,11 +2898,11 @@ static void llm_load_tensors( layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); if (backend == GGML_BACKEND_GPU) { vram_weights += @@ -2909,14 +2910,14 @@ static void llm_load_tensors( ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) + - ggml_nbytes(layer.w3) + ggml_nbytes(layer.b3) + - ggml_nbytes(layer.w2) + ggml_nbytes(layer.b2); + ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b) + + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b); } } } break; case LLM_ARCH_MPT: { - model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); // output { @@ -2967,8 +2968,8 @@ static void llm_load_tensors( layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); if (backend == GGML_BACKEND_GPU) { vram_weights += @@ -2976,8 +2977,8 @@ static void llm_load_tensors( ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + - ggml_nbytes(layer.w2) + - ggml_nbytes(layer.w3); + ggml_nbytes(layer.ffn_down) + + ggml_nbytes(layer.ffn_up); } } } break; @@ -3007,10 +3008,10 @@ static void llm_load_tensors( #ifdef GGML_USE_CUBLAS const int max_backend_supported_layers = hparams.n_layer + 3; - const int max_offloadable_layers = hparams.n_layer + 3; -#elif defined(GGML_USE_CLBLAST) + const int max_offloadable_layers = hparams.n_layer + 3; +#elif GGML_USE_CLBLAST const int max_backend_supported_layers = hparams.n_layer + 1; - const int max_offloadable_layers = hparams.n_layer + 1; + const int max_offloadable_layers = hparams.n_layer + 1; #endif // GGML_USE_CUBLAS LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); @@ -3089,9 +3090,359 @@ static bool llama_model_load( return true; } +using llm_build_cb = std::function; + +enum llm_rope_type { + LLM_ROPE, + LLM_ROPE_NEOX, + LLM_ROPE_GLM, +}; + +static struct ggml_tensor * llm_build_inp_embd( + struct ggml_context * ctx, + const llama_batch & batch, + struct ggml_tensor * tok_embd, + int64_t n_embd, + int32_t n_tokens, + const llm_build_cb & cb) { + struct ggml_tensor * inpL; + + if (batch.token) { + struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_tokens); + cb(inp_tokens, "inp_tokens", -1); + + inpL = ggml_get_rows(ctx, tok_embd, inp_tokens); + } else { +#ifdef GGML_USE_MPI + GGML_ASSERT(false && "not implemented"); +#endif + + inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); + } + + return inpL; +} + +// Persimmon: n_rot = n_embd_head/2 +// Other: n_rot = n_embd_head +static void llm_build_k_shift( + const llama_context & lctx, + struct ggml_context * ctx, + struct ggml_cgraph * graph, + int64_t n_rot, + llm_rope_type type, + const llm_build_cb & cb) { + const auto & model = lctx.model; + const auto & kv_self = lctx.kv_self; + const auto & cparams = lctx.cparams; + + const auto & hparams = model.hparams; + + const int64_t n_layer = hparams.n_layer; + const int64_t n_head_kv = hparams.n_head_kv; + const int64_t n_embd_gqa = hparams.n_embd_gqa(); + const int64_t n_embd_head = hparams.n_embd_head(); + + const int64_t n_ctx = lctx.cparams.n_ctx; + + const float freq_base = cparams.rope_freq_base; + const float freq_scale = cparams.rope_freq_scale; + + GGML_ASSERT(n_embd_head % n_rot == 0); + + struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx); + cb(K_shift, "K_shift", -1); + + int rope_type = 0; + + switch (type) { + case LLM_ROPE: rope_type = 0; break; + case LLM_ROPE_NEOX: rope_type = 2; break; + case LLM_ROPE_GLM: rope_type = 4; break; + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * tmp = + // we rotate only the first n_rot dimensions + ggml_rope_custom_inplace(ctx, + ggml_view_3d(ctx, kv_self.k, + n_rot, n_head_kv, n_ctx, + ggml_element_size(kv_self.k)*n_embd_head, + ggml_element_size(kv_self.k)*n_embd_gqa, + ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il), + K_shift, n_rot, rope_type, 0, freq_base, freq_scale); + cb(tmp, "K_shifted", il); + ggml_build_forward_expand(graph, tmp); + } +} + +static void llm_build_kv_store( + const llama_context & lctx, + struct ggml_context * ctx, + struct ggml_cgraph * graph, + struct ggml_tensor * k_cur, + struct ggml_tensor * v_cur, + int32_t n_tokens, + int32_t kv_head, + const llm_build_cb & cb, + int64_t il) { + const auto & model = lctx.model; + const auto & kv_self = lctx.kv_self; + const auto & cparams = lctx.cparams; + + const auto & hparams = model.hparams; + + const int64_t n_ctx = cparams.n_ctx; + const int64_t n_embd_gqa = hparams.n_embd_gqa(); + + // compute the transposed [n_tokens, n_embd] V matrix + struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens)); + //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed + cb(v_cur_t, "v_cur_t", il); + + struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv_self.k, n_tokens*n_embd_gqa, + (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); + cb(k_cache_view, "k_cache_view", il); + + struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv_self.v, n_tokens, n_embd_gqa, + ( n_ctx)*ggml_element_size(kv_self.v), + (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); + cb(v_cache_view, "v_cache_view", il); + + // important: storing RoPE-ed version of K in the KV cache! + ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view)); + ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view)); +} + +enum llm_norm_type { + LLM_NORM, + LLM_NORM_RMS, +}; + +static struct ggml_tensor * llm_build_norm( + struct ggml_context * ctx, + struct ggml_tensor * cur, + struct ggml_tensor * mw, + struct ggml_tensor * mb, + llm_norm_type type, + float eps, + const llm_build_cb & cb, + int il) { + switch (type) { + case LLM_NORM: cur = ggml_norm (ctx, cur, eps); break; + case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, eps); break; + } + + if (mw || mb) { + cb(cur, "norm", il); + } + + if (mw) { + cur = ggml_mul(ctx, cur, mw); + if (mb) { + cb(cur, "norm_w", il); + } + } + + if (mb) { + cur = ggml_add(ctx, cur, mb); + } + + return cur; +} + +enum llm_ffn_op_type { + LLM_FFN_SILU, + LLM_FFN_GELU, + LLM_FFN_RELU, + LLM_FFN_RELU_SQR, +}; + +enum llm_ffn_gate_type { + LLM_FFN_SEQ, + LLM_FFN_PAR, // ffn_gate is parallel to ffn_up +}; + +static struct ggml_tensor * llm_build_ffn( + struct ggml_context * ctx, + struct ggml_tensor * cur, + struct ggml_tensor * up, + struct ggml_tensor * up_b, + struct ggml_tensor * gate, + struct ggml_tensor * gate_b, + struct ggml_tensor * down, + struct ggml_tensor * down_b, + llm_ffn_op_type type_op, + llm_ffn_gate_type type_gate, + const llm_build_cb & cb, + int il) { + struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur); + cb(tmp, "ffn_up", il); + + if (up_b) { + tmp = ggml_add(ctx, tmp, up_b); + cb(tmp, "ffn_up_b", il); + } + + if (gate) { + switch (type_gate) { + case LLM_FFN_SEQ: + { + cur = ggml_mul_mat(ctx, gate, tmp); + cb(cur, "ffn_gate", il); + } break; + case LLM_FFN_PAR: + { + cur = ggml_mul_mat(ctx, gate, cur); + cb(cur, "ffn_gate", il); + } break; + } + + if (gate_b) { + cur = ggml_add(ctx, cur, gate_b); + cb(cur, "ffn_gate_b", il); + } + } else { + cur = tmp; + } + + switch (type_op) { + case LLM_FFN_SILU: + { + cur = ggml_silu(ctx, cur); + cb(cur, "ffn_silu", il); + } break; + case LLM_FFN_GELU: + { + cur = ggml_gelu(ctx, cur); + cb(cur, "ffn_gelu", il); + } break; + case LLM_FFN_RELU: + { + cur = ggml_relu(ctx, cur); + cb(cur, "ffn_relu", il); + } break; + case LLM_FFN_RELU_SQR: + { + cur = ggml_relu(ctx, cur); + cb(cur, "ffn_relu", il); + + cur = ggml_sqr(ctx, cur); + cb(cur, "ffn_sqr(relu)", il); + } break; + } + + if (type_gate == LLM_FFN_PAR) { + cur = ggml_mul(ctx, cur, tmp); + cb(cur, "ffn_gate_par", il); + } + + cur = ggml_mul_mat(ctx, down, cur); + if (down_b) { + cb(cur, "ffn_down", il); + } + + if (down_b) { + cur = ggml_add(ctx, cur, down_b); + } + + return cur; +} + +// if max_alibi_bias > 0 then apply ALiBi +static struct ggml_tensor * llm_build_kqv( + const llama_context & lctx, + struct ggml_context * ctx, + struct ggml_tensor * cur, + struct ggml_tensor * wo, + struct ggml_tensor * wo_b, + struct ggml_tensor * q_cur, + struct ggml_tensor * kq_scale, + struct ggml_tensor * kq_mask, + int32_t n_tokens, + int32_t n_kv, + float alibi_bias_max, + const llm_build_cb & cb, + int il) { + const auto & model = lctx.model; + const auto & kv_self = lctx.kv_self; + const auto & cparams = lctx.cparams; + + const auto & hparams = model.hparams; + + const int64_t n_ctx = cparams.n_ctx; + const int64_t n_embd = hparams.n_embd; + const int64_t n_head = hparams.n_head; + const int64_t n_head_kv = hparams.n_head_kv; + const int64_t n_embd_head = hparams.n_embd_head(); + const int64_t n_embd_gqa = hparams.n_embd_gqa(); + + struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); + cb(q, "q", il); + + struct ggml_tensor * k = + ggml_view_3d(ctx, kv_self.k, + n_embd_head, n_kv, n_head_kv, + ggml_element_size(kv_self.k)*n_embd_gqa, + ggml_element_size(kv_self.k)*n_embd_head, + ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); + cb(k, "k", il); + + struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); + cb(kq, "kq", il); + + kq = ggml_scale(ctx, kq, kq_scale); + cb(kq, "kq_scaled", il); + + if (alibi_bias_max > 0.0f) { + // TODO: n_head or n_head_kv + // TODO: K-shift is likely not working + // TODO: change to ggml_add + kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, alibi_bias_max); + cb(kq, "kq_scaled_alibi", il); + } + + kq = ggml_add(ctx, kq, kq_mask); + cb(kq, "kq_masked", il); + + kq = ggml_soft_max(ctx, kq); + cb(kq, "kq_soft_max", il); + + // split cached v into n_head heads + struct ggml_tensor * v = + ggml_view_3d(ctx, kv_self.v, + n_kv, n_embd_head, n_head_kv, + ggml_element_size(kv_self.v)*n_ctx, + ggml_element_size(kv_self.v)*n_ctx*n_embd_head, + ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); + cb(v, "v", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); + cb(cur, "kqv_merged_cont", il); + + cur = ggml_mul_mat(ctx, wo, cur); + if (wo_b) { + cb(cur, "kqv_wo", il); + } + + if (wo_b) { + cur = ggml_add(ctx, cur, wo_b); + } + + return cur; +} + static struct ggml_cgraph * llm_build_llama( - llama_context & lctx, - const llama_batch & batch) { + llama_context & lctx, + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -3106,7 +3457,6 @@ static struct ggml_cgraph * llm_build_llama( const int64_t n_head = hparams.n_head; const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); GGML_ASSERT(n_embd_head == hparams.n_rot); @@ -3114,13 +3464,11 @@ static struct ggml_cgraph * llm_build_llama( const float freq_scale = cparams.rope_freq_scale; const float norm_rms_eps = hparams.f_norm_rms_eps; - const int n_gpu_layers = model.n_gpu_layers; - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; + const bool do_rope_shift = worst_case || kv_self.has_shift; //printf("n_kv = %d\n", n_kv); @@ -3139,314 +3487,81 @@ static struct ggml_cgraph * llm_build_llama( struct ggml_tensor * cur; struct ggml_tensor * inpL; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - - inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inpL); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL)); - } - } - - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - - // offload functions set the tensor output backend to GPU - // tensors are GPU-accelerated if any input or the output has been offloaded - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (n_gpu_layers > n_layer) { - offload_func_nr = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 1) { - offload_func_v = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 2) { - offload_func_kq = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head))); - } + cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - offload_func_kq(KQ_mask); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - // KQ_pos - contains the positions - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - offload_func_kq(KQ_pos); - ggml_set_name(KQ_pos, "KQ_pos"); - ggml_allocr_alloc(lctx.alloc, KQ_pos); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < n_tokens; ++i) { - data[i] = batch.pos[i]; - } - } + cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); - offload_func_kq(K_shift); - ggml_set_name(K_shift, "K_shift"); - ggml_allocr_alloc(lctx.alloc, K_shift); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) K_shift->data; - for (int i = 0; i < n_ctx; ++i) { - data[i] = kv_self.cells[i].delta; - } - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * tmp = - ggml_rope_custom_inplace(ctx0, - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_head_kv, n_ctx, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il), - K_shift, n_embd_head, 0, 0, freq_base, freq_scale); - offload_func_kq(tmp); - ggml_build_forward_expand(gf, tmp); - } + llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb); } for (int il = 0; il < n_layer; ++il) { - ggml_format_name(inpL, "layer_inp_%d", il); - - offload_func_t offload_func = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (il >= i_gpu_start) { - offload_func = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS - struct ggml_tensor * inpSA = inpL; // norm - { - cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps); - offload_func(cur); - ggml_set_name(cur, "rms_norm_0"); - - // cur = cur*attn_norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm); - offload_func(cur); - ggml_set_name(cur, "attention_norm_0"); - } + cur = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, il); + cb(cur, "attn_norm", il); // self-attention { // compute Q and K and RoPE them - struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - offload_func_kq(tmpk); - ggml_set_name(tmpk, "tmpk"); + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); - struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - offload_func_kq(tmpq); - ggml_set_name(tmpq, "tmpq"); + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); - struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); - offload_func_kq(Kcur); - ggml_set_name(Kcur, "Kcur"); + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); - struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); - offload_func_kq(Qcur); - ggml_set_name(Qcur, "Qcur"); + Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + cb(Qcur, "Qcur", il); - // store key and value to memory - { - // compute the transposed [n_tokens, n_embd] V matrix + Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + cb(Kcur, "Kcur", il); - struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - offload_func_v(tmpv); - ggml_set_name(tmpv, "tmpv"); + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens)); - offload_func_v(Vcur); - ggml_set_name(Vcur, "Vcur"); - - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - offload_func_kq(k); - ggml_set_name(k, "k"); - - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - ggml_set_name(v, "v"); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - offload_func_kq(Q); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - offload_func_kq(K); - ggml_set_name(K, "K"); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - // KQ_scaled = KQ / sqrt(n_embd_head) - // KQ_scaled shape [n_kv, n_tokens, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); - offload_func_kq(KQ_masked); - ggml_set_name(KQ_masked, "KQ_masked"); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - offload_func_v(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - // split cached V into n_head heads - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - offload_func_v(V); - ggml_set_name(V, "V"); - -#if 1 - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); -#else - // make V contiguous in memory to speed up the matmul, however we waste time on the copy - // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation - // is there a better way? - struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head)); - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max); -#endif - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - // cur = KQV_merged.contiguous().view(n_embd, n_tokens) - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); - - // projection (no bias) - cur = ggml_mul_mat(ctx0, - model.layers[il].wo, - cur); - offload_func(cur); - ggml_set_name(cur, "result_wo"); + cur = llm_build_kqv(lctx, ctx0, cur, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); } - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - offload_func(inpFF); - ggml_set_name(inpFF, "inpFF"); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); // feed-forward network { - // norm - { - cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps); - offload_func(cur); - ggml_set_name(cur, "rms_norm_1"); + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, il); + cb(cur, "ffn_norm", il); - // cur = cur*ffn_norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm); - offload_func(cur); - ggml_set_name(cur, "ffn_norm"); - } - - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model.layers[il].w3, - cur); - offload_func(tmp); - ggml_set_name(tmp, "result_w3"); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w1, - cur); - offload_func(cur); - ggml_set_name(cur, "result_w1"); - - // SILU activation - cur = ggml_silu(ctx0, cur); - offload_func(cur); - ggml_set_name(cur, "silu"); - - cur = ggml_mul(ctx0, cur, tmp); - offload_func(cur); - ggml_set_name(cur, "silu_x_result_w3"); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w2, - cur); - offload_func(cur); - ggml_set_name(cur, "result_w2"); + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); } - cur = ggml_add(ctx0, cur, inpFF); - offload_func(cur); - ggml_set_name(cur, "inpFF_+_result_w2"); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); // input for next layer inpL = cur; @@ -3454,21 +3569,14 @@ static struct ggml_cgraph * llm_build_llama( cur = inpL; - // norm - { - cur = ggml_rms_norm(ctx0, cur, norm_rms_eps); - offload_func_nr(cur); - ggml_set_name(cur, "rms_norm_2"); - - // cur = cur*norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.output_norm); - // offload_func_nr(cur); // TODO CPU + GPU mirrored backend - ggml_set_name(cur, "result_norm"); - } + cur = llm_build_norm(ctx0, cur, + model.output_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, -1); + cb(cur, "result_norm", -1); // lm_head cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -3479,7 +3587,9 @@ static struct ggml_cgraph * llm_build_llama( static struct ggml_cgraph * llm_build_baichaun( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -3494,7 +3604,6 @@ static struct ggml_cgraph * llm_build_baichaun( const int64_t n_head = hparams.n_head; const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); GGML_ASSERT(n_embd_head == hparams.n_rot); @@ -3502,13 +3611,11 @@ static struct ggml_cgraph * llm_build_baichaun( const float freq_scale = cparams.rope_freq_scale; const float norm_rms_eps = hparams.f_norm_rms_eps; - const int n_gpu_layers = model.n_gpu_layers; - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; + const bool do_rope_shift = worst_case || kv_self.has_shift; auto & buf_compute = lctx.buf_compute; @@ -3525,331 +3632,91 @@ static struct ggml_cgraph * llm_build_baichaun( struct ggml_tensor * cur; struct ggml_tensor * inpL; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - - inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inpL); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL)); - } - } - - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - - // offload functions set the tensor output backend to GPU - // tensors are GPU-accelerated if any input or the output has been offloaded - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (n_gpu_layers > n_layer) { - offload_func_nr = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 1) { - offload_func_v = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 2) { - offload_func_kq = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head)); - } + cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - offload_func_kq(KQ_mask); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - // KQ_pos - contains the positions - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - offload_func_kq(KQ_pos); - ggml_set_name(KQ_pos, "KQ_pos"); - ggml_allocr_alloc(lctx.alloc, KQ_pos); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < n_tokens; ++i) { - data[i] = batch.pos[i]; - } - } + cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); - offload_func_kq(K_shift); - ggml_set_name(K_shift, "K_shift"); - ggml_allocr_alloc(lctx.alloc, K_shift); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) K_shift->data; - for (int i = 0; i < n_ctx; ++i) { - data[i] = kv_self.cells[i].delta; - } - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * tmp = - ggml_rope_custom_inplace(ctx0, - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_head_kv, n_ctx, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il), - K_shift, n_embd_head, 0, 0, freq_base, freq_scale); - offload_func_kq(tmp); - ggml_build_forward_expand(gf, tmp); - } + llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb); } for (int il = 0; il < n_layer; ++il) { - ggml_format_name(inpL, "layer_inp_%d", il); - - offload_func_t offload_func = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (il >= i_gpu_start) { - offload_func = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS - struct ggml_tensor * inpSA = inpL; - // norm - { - cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps); - offload_func(cur); - ggml_set_name(cur, "rms_norm_0"); - - // cur = cur*attn_norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm); - offload_func(cur); - ggml_set_name(cur, "attention_norm_0"); - } + cur = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, il); + cb(cur, "attn_norm", il); // self-attention { - // compute Q and K and RoPE them - struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - offload_func_kq(tmpk); - ggml_set_name(tmpk, "tmpk"); + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); - struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - offload_func_kq(tmpq); - ggml_set_name(tmpq, "tmpq"); + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); - struct ggml_tensor * Kcur; - struct ggml_tensor * Qcur; switch (model.type) { case MODEL_7B: - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); break; case MODEL_13B: - Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, n_tokens); - Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, n_tokens); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens); break; default: GGML_ASSERT(false); } + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); - offload_func_kq(Kcur); - ggml_set_name(Kcur, "Kcur"); + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - offload_func_kq(Qcur); - ggml_set_name(Qcur, "Qcur"); + // apply ALiBi for 13B model + const float alibi_bias_max = model.type == MODEL_13B ? 8.0f : -1.0f; - // store key and value to memory - { - // compute the transposed [n_tokens, n_embd] V matrix - - struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - offload_func_v(tmpv); - ggml_set_name(tmpv, "tmpv"); - - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens)); - offload_func_v(Vcur); - ggml_set_name(Vcur, "Vcur"); - - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - offload_func_kq(k); - ggml_set_name(k, "k"); - - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - ggml_set_name(v, "v"); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - offload_func_kq(Q); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - offload_func_kq(K); - ggml_set_name(K, "K"); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - // KQ_scaled = KQ / sqrt(n_embd_head) - // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - struct ggml_tensor * KQ_masked; - struct ggml_tensor * KQ_scaled_alibi; - - switch (model.type) { - case MODEL_7B: - KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); - break; - case MODEL_13B: - // TODO: replace with ggml_add() - KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8); - ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi"); - KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask); - break; - default: - GGML_ASSERT(false); - } - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - offload_func_v(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - // split cached V into n_head heads - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - offload_func_v(V); - ggml_set_name(V, "V"); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - // cur = KQV_merged.contiguous().view(n_embd, n_tokens) - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); - - // projection (no bias) - cur = ggml_mul_mat(ctx0, - model.layers[il].wo, - cur); - offload_func(cur); - ggml_set_name(cur, "result_wo"); + cur = llm_build_kqv(lctx, ctx0, cur, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, alibi_bias_max, cb, il); + cb(cur, "kqv_out", il); } - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - offload_func(inpFF); - ggml_set_name(inpFF, "inpFF"); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); // feed-forward network { - // norm - { - cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps); - offload_func(cur); - ggml_set_name(cur, "rms_norm_1"); + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, il); + cb(cur, "ffn_norm", il); - // cur = cur*ffn_norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm); - offload_func(cur); - ggml_set_name(cur, "ffn_norm"); - } - - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model.layers[il].w3, - cur); - offload_func(tmp); - ggml_set_name(tmp, "result_w3"); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w1, - cur); - offload_func(cur); - ggml_set_name(cur, "result_w1"); - - // SILU activation - cur = ggml_silu(ctx0, cur); - offload_func(cur); - ggml_set_name(cur, "silu"); - - cur = ggml_mul(ctx0, cur, tmp); - offload_func(cur); - ggml_set_name(cur, "silu_x_result_w3"); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w2, - cur); - offload_func(cur); - ggml_set_name(cur, "result_w2"); + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); } - cur = ggml_add(ctx0, cur, inpFF); - offload_func(cur); - ggml_set_name(cur, "inpFF_+_result_w2"); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); // input for next layer inpL = cur; @@ -3857,366 +3724,14 @@ static struct ggml_cgraph * llm_build_baichaun( cur = inpL; - // norm - { - cur = ggml_rms_norm(ctx0, cur, norm_rms_eps); - offload_func_nr(cur); - ggml_set_name(cur, "rms_norm_2"); - - // cur = cur*norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.output_norm); - // offload_func_nr(cur); // TODO CPU + GPU mirrored backend - ggml_set_name(cur, "result_norm"); - } + cur = llm_build_norm(ctx0, cur, + model.output_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, -1); + cb(cur, "result_norm", -1); // lm_head cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_refact( - llama_context & lctx, - const llama_batch & batch) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - - const float norm_rms_eps = hparams.f_norm_rms_eps; - - const int n_gpu_layers = model.n_gpu_layers; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; - - // printf("n_kv = %d\n", n_kv); - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - - inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inpL); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL)); - } - } - - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - - // offload functions set the tensor output backend to GPU - // tensors are GPU-accelerated if any input or the output has been offloaded - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (n_gpu_layers > n_layer) { - offload_func_nr = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 1) { - offload_func_v = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 2) { - offload_func_kq = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head))); - } - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - offload_func_kq(KQ_mask); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - for (int il = 0; il < n_layer; ++il) { - ggml_format_name(inpL, "layer_inp_%d", il); - - offload_func_t offload_func = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (il >= i_gpu_start) { - offload_func = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS - - struct ggml_tensor * inpSA = inpL; - - // norm - { - cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps); - offload_func(cur); - ggml_set_name(cur, "rms_norm_0"); - - // cur = cur*attn_norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm); - offload_func(cur); - ggml_set_name(cur, "attention_norm_0"); - } - - // self-attention - { - // compute Q and K - struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - offload_func_kq(tmpk); - ggml_set_name(tmpk, "tmpk"); - - struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - offload_func_kq(tmpq); - ggml_set_name(tmpq, "tmpq"); - - struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens); - offload_func_kq(Kcur); - ggml_set_name(Kcur, "Kcur"); - - struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens); - offload_func_kq(Qcur); - ggml_set_name(Qcur, "Qcur"); - - // store key and value to memory - { - // compute the transposed [n_tokens, n_embd] V matrix - - struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - offload_func_v(tmpv); - ggml_set_name(tmpv, "tmpv"); - - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens)); - offload_func_v(Vcur); - ggml_set_name(Vcur, "Vcur"); - - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - offload_func_kq(k); - ggml_set_name(k, "k"); - - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - ggml_set_name(v, "v"); - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - offload_func_kq(Q); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - offload_func_kq(K); - ggml_set_name(K, "K"); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - // KQ_scaled = KQ / sqrt(n_embd_head) - // KQ_scaled shape [n_kv, n_tokens, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8); - ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi"); - - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask); - offload_func_kq(KQ_masked); - ggml_set_name(KQ_masked, "KQ_masked"); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - offload_func_v(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - // split cached V into n_head heads - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - offload_func_v(V); - ggml_set_name(V, "V"); - -#if 1 - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); -#else - // make V contiguous in memory to speed up the matmul, however we waste time on the copy - // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation - // is there a better way? - struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head)); - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max); -#endif - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - // cur = KQV_merged.contiguous().view(n_embd, n_tokens) - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); - - // projection (no bias) - cur = ggml_mul_mat(ctx0, - model.layers[il].wo, - cur); - offload_func(cur); - ggml_set_name(cur, "result_wo"); - } - - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - offload_func(inpFF); - ggml_set_name(inpFF, "inpFF"); - - // feed-forward network - { - // norm - { - cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps); - offload_func(cur); - ggml_set_name(cur, "rms_norm_1"); - - // cur = cur*ffn_norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm); - offload_func(cur); - ggml_set_name(cur, "ffn_norm"); - } - - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model.layers[il].w3, - cur); - offload_func(tmp); - ggml_set_name(tmp, "result_w3"); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w1, - cur); - offload_func(cur); - ggml_set_name(cur, "result_w1"); - - // SILU activation - cur = ggml_silu(ctx0, cur); - offload_func(cur); - ggml_set_name(cur, "silu"); - - cur = ggml_mul(ctx0, cur, tmp); - offload_func(cur); - ggml_set_name(cur, "silu_x_result_w3"); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w2, - cur); - offload_func(cur); - ggml_set_name(cur, "result_w2"); - } - - cur = ggml_add(ctx0, cur, inpFF); - offload_func(cur); - ggml_set_name(cur, "inpFF_+_result_w2"); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - // norm - { - cur = ggml_rms_norm(ctx0, cur, norm_rms_eps); - offload_func_nr(cur); - ggml_set_name(cur, "rms_norm_2"); - - // cur = cur*norm(broadcasted) - cur = ggml_mul(ctx0, cur, model.output_norm); - // offload_func_nr(cur); // TODO CPU + GPU mirrored backend - ggml_set_name(cur, "result_norm"); - } - - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -4227,7 +3742,9 @@ static struct ggml_cgraph * llm_build_refact( static struct ggml_cgraph * llm_build_falcon( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -4250,13 +3767,11 @@ static struct ggml_cgraph * llm_build_falcon( const float freq_scale = cparams.rope_freq_scale; const float norm_eps = hparams.f_norm_eps; - const int n_gpu_layers = model.n_gpu_layers; - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; + const bool do_rope_shift = worst_case || kv_self.has_shift; //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n", // kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift); @@ -4276,294 +3791,94 @@ static struct ggml_cgraph * llm_build_falcon( struct ggml_tensor * cur; struct ggml_tensor * inpL; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - - inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inpL); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL)); - } - } - - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - - // offload functions set the tensor output backend to GPU - // tensors are GPU-accelerated if any input or the output has been offloaded - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (n_gpu_layers > n_layer) { - offload_func_nr = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 1) { - offload_func_v = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 2) { - offload_func_kq = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head)); - } + cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - offload_func_kq(KQ_mask); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - // KQ_pos - contains the positions - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - offload_func_kq(KQ_pos); - ggml_set_name(KQ_pos, "KQ_pos"); - ggml_allocr_alloc(lctx.alloc, KQ_pos); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < n_tokens; ++i) { - data[i] = batch.pos[i]; - } - } + cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); - offload_func_kq(K_shift); - ggml_set_name(K_shift, "K_shift"); - ggml_allocr_alloc(lctx.alloc, K_shift); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) K_shift->data; - for (int i = 0; i < n_ctx; ++i) { - data[i] = kv_self.cells[i].delta; - } - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * tmp = - ggml_rope_custom_inplace(ctx0, - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_head_kv, n_ctx, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il), - K_shift, n_embd_head, 2, 0, freq_base, freq_scale); - offload_func_kq(tmp); - ggml_build_forward_expand(gf, tmp); - } + llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE_NEOX, cb); } for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; - offload_func_t offload_func = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (il >= i_gpu_start) { - offload_func = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + attn_norm = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(attn_norm, "attn_norm", il); // self-attention - // TODO: refactor into common function (shared with LLaMA) { - attn_norm = ggml_norm(ctx0, inpL, norm_eps); - offload_func(attn_norm); - - attn_norm = ggml_add(ctx0, - ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm), - model.layers[il].attn_norm_b); - offload_func(attn_norm->src[0]); - offload_func(attn_norm); - - if (model.layers[il].attn_norm_2) { // Falcon-40B - cur = ggml_norm(ctx0, inpL, norm_eps); - offload_func(cur); - - cur = ggml_add(ctx0, - ggml_mul(ctx0, cur, model.layers[il].attn_norm_2), - model.layers[il].attn_norm_2_b); - offload_func(cur->src[0]); - offload_func(cur); - } else { // Falcon 7B + if (model.layers[il].attn_norm_2) { + // Falcon-40B + cur = llm_build_norm(ctx0, attn_norm, + model.layers[il].attn_norm_2, + model.layers[il].attn_norm_2_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "attn_norm_2", il); + } else { cur = attn_norm; } - // compute QKV - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - offload_func_kq(cur); + cb(cur, "wqkv", il); - // Note that the strides for Kcur, Vcur are set up so that the - // resulting views are misaligned with the tensor's storage - // (by applying the K/V offset we shift the tensor's original - // view to stick out behind the viewed QKV tensor's allocated - // memory, so to say). This is ok because no actual accesses - // happen to that out-of-range memory, but it can require some - // trickery when trying to accurately dump these views for - // debugging. + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - const size_t wsize = ggml_type_size(cur->type); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); - // TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for - // non-contiguous views is added for the rope operator - struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d( - ctx0, cur, n_embd_head, n_head, n_tokens, - wsize * n_embd_head, - wsize * n_embd_head * (n_head + 2 * n_head_kv), - 0)); - offload_func_kq(tmpq); - - struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d( - ctx0, cur, n_embd_head, n_head_kv, n_tokens, - wsize * n_embd_head, - wsize * n_embd_head * (n_head + 2 * n_head_kv), - wsize * n_embd_head * n_head)); - offload_func_kq(tmpk); - - struct ggml_tensor * tmpv = ggml_view_3d( - ctx0, cur, n_embd_head, n_head_kv, n_tokens, - wsize * n_embd_head, - wsize * n_embd_head * (n_head + 2 * n_head_kv), - wsize * n_embd_head * (n_head + n_head_kv)); - offload_func_v(tmpv); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale); - offload_func_kq(Qcur); - struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale); - offload_func_kq(Kcur); + Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + cb(Qcur, "Qcur", il); - { - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens)); - offload_func_v(Vcur); - offload_func_v(Vcur->src[0]->src[0]); - ggml_set_name(Vcur, "Vcur"); + Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + cb(Kcur, "Kcur", il); - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - offload_func_kq(k); - ggml_set_name(k, "k"); + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - offload_func_kq(Q); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - offload_func_kq(K); - ggml_set_name(K, "K"); - - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); - offload_func_kq(KQ_masked); - ggml_set_name(KQ_masked, "KQ_masked"); - - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - offload_func_v(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - offload_func_v(V); - ggml_set_name(V, "V"); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); - - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); - - cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur); - offload_func(cur); - ggml_set_name(cur, "result_wo"); + cur = llm_build_kqv(lctx, ctx0, attn_norm, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); } - struct ggml_tensor * attn_out = cur; + struct ggml_tensor * ffn_inp = cur; // feed forward { - struct ggml_tensor * inpFF = attn_norm; - - cur = ggml_mul_mat(ctx0, model.layers[il].w3, inpFF); - offload_func(cur); - - cur = ggml_gelu(ctx0, cur); - offload_func(cur); - cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur); - offload_func(cur); + cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result + model.layers[il].ffn_up, NULL, + NULL, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); } - cur = ggml_add(ctx0, cur, attn_out); - offload_func(cur); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + cur = ggml_add(ctx0, cur, inpL); - offload_func(cur); + cb(cur, "l_out", il); // input for next layer inpL = cur; @@ -4572,18 +3887,14 @@ static struct ggml_cgraph * llm_build_falcon( cur = inpL; // norm - { - cur = ggml_norm(ctx0, cur, norm_eps); - offload_func_nr(cur); - - cur = ggml_add(ctx0, - ggml_mul(ctx0, cur, model.output_norm), - model.output_norm_b); - ggml_set_name(cur, "result_norm"); - } + cur = llm_build_norm(ctx0, cur, + model.output_norm, + model.output_norm_b, + LLM_NORM, norm_eps, cb, -1); + cb(cur, "result_norm", -1); cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -4594,7 +3905,9 @@ static struct ggml_cgraph * llm_build_falcon( static struct ggml_cgraph * llm_build_starcoder( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -4607,7 +3920,6 @@ static struct ggml_cgraph * llm_build_starcoder( const int64_t n_layer = hparams.n_layer; const int64_t n_ctx = cparams.n_ctx; const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head = hparams.n_embd_head(); const int64_t n_embd_gqa = hparams.n_embd_gqa(); @@ -4615,11 +3927,9 @@ static struct ggml_cgraph * llm_build_starcoder( const float norm_eps = hparams.f_norm_eps; - const int n_gpu_layers = model.n_gpu_layers; - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; auto & buf_compute = lctx.buf_compute; @@ -4634,266 +3944,95 @@ static struct ggml_cgraph * llm_build_starcoder( ggml_cgraph * gf = ggml_new_graph(ctx0); struct ggml_tensor * cur; - struct ggml_tensor * token; - struct ggml_tensor * position; + struct ggml_tensor * pos; struct ggml_tensor * inpL; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - - token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, token); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token)); - } - } - - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - - // offload functions set the tensor output backend to GPU - // tensors are GPU-accelerated if any input or the output has been offloaded - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (n_gpu_layers > n_layer) { - offload_func_nr = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 1) { - offload_func_v = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 2) { - offload_func_kq = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS - - { - // Compute position embeddings. - struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - ggml_allocr_alloc(lctx.alloc, inp_positions); - if (!ggml_allocr_is_measure(lctx.alloc)) { - for (int i = 0; i < n_tokens; ++i) { - ((int32_t *) inp_positions->data)[i] = batch.pos[i]; - } - } - ggml_set_name(inp_positions, "inp_positions"); - - position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions); - } + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head)); - } + cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - ggml_set_name(KQ_mask, "KQ_mask"); - offload_func_kq(KQ_mask); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); + cb(KQ_mask, "KQ_mask", -1); - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; + pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); + cb(pos, "pos_embd", -1); - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - inpL = ggml_add(ctx0, token, position); - ggml_set_name(inpL, "inpL"); + inpL = ggml_add(ctx0, inpL, pos); + cb(inpL, "inpL", -1); for (int il = 0; il < n_layer; ++il) { - offload_func_t offload_func = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (il >= i_gpu_start) { - offload_func = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + cur = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "attn_norm", il); + // self-attention { - // Norm - cur = ggml_norm(ctx0, inpL, norm_eps); - offload_func(cur); - - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b); - offload_func(cur); - } - - { - // Self Attention cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - offload_func_kq(cur); + cb(cur, "wqkv", il); cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - offload_func_kq(cur); + cb(cur, "bqkv", il); - struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * tmpv = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - ggml_set_name(tmpq, "tmpq"); - ggml_set_name(tmpk, "tmpk"); - ggml_set_name(tmpv, "tmpv"); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); - offload_func_kq(tmpq); - offload_func_kq(tmpk); - offload_func_v (tmpv); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens); - struct ggml_tensor * Kcur = tmpk; + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - { - struct ggml_tensor * Vcur = ggml_transpose(ctx0, tmpv); - offload_func_v(Vcur); - ggml_set_name(Vcur, "Vcur"); - - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - offload_func_kq(k); - ggml_set_name(k, "k"); - - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - ggml_set_name(v, "v"); - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - offload_func_kq(Q); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - offload_func_kq(K); - ggml_set_name(K, "K"); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - // KQ_scaled = KQ / sqrt(n_embd_head) - // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); - offload_func_kq(KQ_masked); - ggml_set_name(KQ_masked, "KQ_masked"); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - offload_func_v(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - // split cached V into n_head heads - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - ggml_set_name(V, "V"); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); - - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); + cur = llm_build_kqv(lctx, ctx0, cur, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); } - // Projection - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo); - offload_func(cur); - - // Add the input - cur = ggml_add(ctx0, cur, inpL); - offload_func(cur); - - struct ggml_tensor * inpFF = cur; + // add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); // FF { - // Norm - { - cur = ggml_norm(ctx0, inpFF, norm_eps); - offload_func_nr(cur); + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "ffn_norm", il); - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b); - offload_func_nr(cur); - } - - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3); - offload_func(cur); - - // GELU activation - cur = ggml_gelu(ctx0, cur); - offload_func(cur); - - // Projection - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2); - offload_func(cur); + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); } - inpL = ggml_add(ctx0, cur, inpFF); - + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); } - // Output Norm - { - cur = ggml_norm(ctx0, inpL, norm_eps); - offload_func_nr(cur); - - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b); - ggml_set_name(cur, "result_norm"); - } + cur = llm_build_norm(ctx0, inpL, + model.output_norm, + model.output_norm_b, + LLM_NORM, norm_eps, cb, -1); + cb(cur, "result_norm", -1); cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); ggml_free(ctx0); @@ -4903,7 +4042,9 @@ static struct ggml_cgraph * llm_build_starcoder( static struct ggml_cgraph * llm_build_persimmon( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; @@ -4912,29 +4053,27 @@ static struct ggml_cgraph * llm_build_persimmon( GGML_ASSERT(!!kv_self.ctx); const auto & cparams = lctx.cparams; + const int64_t n_embd = hparams.n_embd; const int64_t n_layer = hparams.n_layer; const int64_t n_ctx = cparams.n_ctx; const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_head = hparams.n_head; const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - const size_t n_rot = n_embd_head / 2; + const int64_t n_rot = n_embd_head / 2; const float freq_base = cparams.rope_freq_base; const float freq_scale = cparams.rope_freq_scale; - const float norm_eps = hparams.f_norm_eps; - - const int n_gpu_layers = model.n_gpu_layers; - + const float norm_eps = hparams.f_norm_eps; const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; + const bool do_rope_shift = worst_case || kv_self.has_shift; auto & buf_compute = lctx.buf_compute; + struct ggml_init_params params = { /*.mem_size =*/ buf_compute.size, /*.mem_buffer =*/ buf_compute.data, @@ -4948,146 +4087,77 @@ static struct ggml_cgraph * llm_build_persimmon( struct ggml_tensor * cur; struct ggml_tensor * inpL; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "imp_embd", -1); + + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { - inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - ggml_allocr_alloc(lctx.alloc, inpL); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL)); - } - } - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head))); - } - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); + cb(KQ_scale, "KQ_scale", -1); + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - offload_func_kq(KQ_mask); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); + cb(KQ_mask, "KQ_mask", -1); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - offload_func_kq(KQ_pos); - ggml_set_name(KQ_pos, "KQ_pos"); - ggml_allocr_alloc(lctx.alloc, KQ_pos); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < n_tokens; ++i) { - data[i] = batch.pos[i]; - } - } if (do_rope_shift) { - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); - offload_func_kq(K_shift); - ggml_set_name(K_shift, "K_shift"); - ggml_allocr_alloc(lctx.alloc, K_shift); - if (!ggml_allocr_is_measure(lctx.alloc)) { - int * data = (int *) K_shift->data; - for (int i = 0; i < n_ctx; ++i) { - data[i] = kv_self.cells[i].delta; - } - } - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * tmp = - // we rotate only the first n_rot dimensions. - ggml_rope_custom_inplace(ctx0, - ggml_view_3d(ctx0, kv_self.k, - n_rot, n_head, n_ctx, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*(n_embd_head*n_ctx*il) - ), - K_shift, n_rot, 2, 0, freq_base, freq_scale); - offload_func_kq(tmp); - ggml_build_forward_expand(gf, tmp); - } + llm_build_k_shift(lctx, ctx0, gf, n_rot, LLM_ROPE_NEOX, cb); } - for (int il=0; il < n_layer; ++il) { + + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * residual = inpL; - offload_func_t offload_func = llama_nop; - { - cur = ggml_norm(ctx0, inpL, norm_eps); - offload_func(cur); - cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm); - offload_func(cur); - cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b); - offload_func(cur); - ggml_format_name(cur, "input_layernorm_%d", il); - } + + cur = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "attn_norm", il); + // self attention { cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - offload_func_kq(cur); + cb(cur, "wqkv", il); + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - offload_func_kq(cur); + cb(cur, "bqkv", il); // split qkv GGML_ASSERT(n_head_kv == n_head); - ggml_set_name(cur, format("qkv_%d", il).c_str()); + struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens); - offload_func_kq(tmpqkv); + cb(tmpqkv, "tmpqkv", il); + struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2)); - offload_func_kq(tmpqkv_perm); - ggml_format_name(tmpqkv_perm, "tmpqkv_perm_%d", il); + cb(tmpqkv_perm, "tmpqkv", il); + struct ggml_tensor * tmpq = ggml_view_3d( ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, ggml_element_size(tmpqkv_perm) * n_embd_head, ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, 0 ); - offload_func_kq(tmpq); + cb(tmpq, "tmpq", il); + struct ggml_tensor * tmpk = ggml_view_3d( ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, ggml_element_size(tmpqkv_perm) * n_embd_head, ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens ); - offload_func_kq(tmpk); - // Q/K Layernorm - tmpq = ggml_norm(ctx0, tmpq, norm_eps); - offload_func_kq(tmpq); - tmpq = ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm); - offload_func_kq(tmpq); - tmpq = ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b); - offload_func_kq(tmpq); + cb(tmpk, "tmpk", il); - tmpk = ggml_norm(ctx0, tmpk, norm_eps); - offload_func_v(tmpk); - tmpk = ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm); - offload_func_v(tmpk); - tmpk = ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b); - offload_func_v(tmpk); + // Q/K Layernorm + tmpq = llm_build_norm(ctx0, tmpq, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(tmpq, "tmpq", il); + + tmpk = llm_build_norm(ctx0, tmpk, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(tmpk, "tmpk", il); // RoPE the first n_rot of q/k, pass the other half, and concat. struct ggml_tensor * qrot = ggml_view_3d( @@ -5096,16 +4166,15 @@ static struct ggml_cgraph * llm_build_persimmon( ggml_element_size(tmpq) * n_embd_head * n_head, 0 ); - offload_func_kq(qrot); - ggml_format_name(qrot, "qrot_%d", il); + cb(qrot, "qrot", il); + struct ggml_tensor * krot = ggml_view_3d( ctx0, tmpk, n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, 0 ); - offload_func_kq(krot); - ggml_format_name(krot, "krot_%d", il); + cb(krot, "krot", il); // get the second half of tmpq, e.g tmpq[n_rot:, :, :] struct ggml_tensor * qpass = ggml_view_3d( @@ -5114,193 +4183,117 @@ static struct ggml_cgraph * llm_build_persimmon( ggml_element_size(tmpq) * n_embd_head * n_head, ggml_element_size(tmpq) * n_rot ); - offload_func_kq(qpass); - ggml_format_name(qpass, "qpass_%d", il); + cb(qpass, "qpass", il); + struct ggml_tensor * kpass = ggml_view_3d( ctx0, tmpk, n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, ggml_element_size(tmpk) * n_rot ); - offload_func_kq(kpass); - ggml_format_name(kpass, "kpass_%d", il); + cb(kpass, "kpass", il); - struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale + struct ggml_tensor * qrotated = ggml_rope_custom( + ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale ); - offload_func_kq(qrotated); + cb(qrotated, "qrotated", il); + struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale + ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale ); - offload_func_kq(krotated); + cb(krotated, "krotated", il); + // ggml currently only supports concatenation on dim=2 // so we need to permute qrot, qpass, concat, then permute back. qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); - offload_func_kq(qrotated); + cb(qrotated, "qrotated", il); + krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); - offload_func_kq(krotated); + cb(krotated, "krotated", il); qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); - offload_func_kq(qpass); + cb(qpass, "qpass", il); + kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); - offload_func_kq(kpass); + cb(kpass, "kpass", il); struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); - offload_func_kq(Qcur); + cb(Qcur, "Qcur", il); + struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); - offload_func_kq(Kcur); + cb(Kcur, "Kcur", il); struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3)); - offload_func_kq(Q); + cb(Q, "Q", il); Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); - offload_func_kq(Kcur); - { - struct ggml_tensor * tmpv = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_view_3d( + ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, + ggml_element_size(tmpqkv_perm) * n_embd_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 ); - offload_func_v(tmpv); - // store K, V in cache - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens)); - offload_func_v(Vcur); - ggml_set_name(Vcur, "Vcur"); + cb(Vcur, "Vcur", il); - struct ggml_tensor * k = ggml_view_1d( - ctx0, kv_self.k, n_tokens*n_embd_gqa, - (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head) - ); - offload_func_kq(k); - ggml_set_name(k, "k"); + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - ggml_set_name(v, "v"); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - struct ggml_tensor * K = ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - - offload_func_kq(K); - ggml_format_name(K, "K_%d", il); - - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); - offload_func_kq(KQ_masked); - ggml_set_name(KQ_masked, "KQ_masked"); - - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - offload_func_kq(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - offload_func_v(V); - ggml_set_name(V, "V"); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); - - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); - - cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur); - offload_func(cur); - cur = ggml_add(ctx0, cur, model.layers[il].bo); - offload_func(cur); - ggml_set_name(cur, "result_wo"); + // TODO: not tested, could be broken + cur = llm_build_kqv(lctx, ctx0, Q, + model.layers[il].wo, model.layers[il].bo, + Q, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); } - struct ggml_tensor * inpFF = ggml_add(ctx0, residual, cur); - offload_func(inpFF); - ggml_set_name(inpFF, "inpFF"); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network { - // MLP - { - // Norm - cur = ggml_norm(ctx0, inpFF, norm_eps); - offload_func(cur); - cur = ggml_add(ctx0, - ggml_mul(ctx0, cur, model.layers[il].ffn_norm), - model.layers[il].ffn_norm_b - ); - ggml_set_name(cur, "ffn_norm"); - offload_func(cur); - } - cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur); - offload_func(cur); + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "ffn_norm", il); - cur = ggml_add(ctx0, cur, model.layers[il].b3); - offload_func(cur); - ggml_set_name(cur, "result_ffn_up"); - - cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur)); - ggml_set_name(cur, "result_ffn_act"); - offload_func(cur); - offload_func(cur->src[0]); - - cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur); - offload_func(cur); - cur = ggml_add(ctx0, - cur, - model.layers[il].b2); - offload_func(cur); - ggml_set_name(cur, "outFF"); + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); } - cur = ggml_add(ctx0, cur, inpFF); - offload_func(cur); - ggml_set_name(cur, "inpFF_+_outFF"); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + inpL = cur; } + cur = inpL; - { - cur = ggml_norm(ctx0, cur, norm_eps); - offload_func_nr(cur); - cur = ggml_mul(ctx0, cur, model.output_norm); - offload_func_nr(cur); - cur = ggml_add(ctx0, cur, model.output_norm_b); - // offload_func_nr(cur); + cur = llm_build_norm(ctx0, cur, + model.output_norm, + model.output_norm_b, + LLM_NORM, norm_eps, cb, -1); + cb(cur, "result_norm", -1); - ggml_set_name(cur, "result_norm"); - } cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); + ggml_build_forward_expand(gf, cur); + ggml_free(ctx0); + return gf; } -static struct ggml_cgraph * llm_build_bloom( +static struct ggml_cgraph * llm_build_refact( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -5315,6 +4308,133 @@ static struct ggml_cgraph * llm_build_bloom( const int64_t n_head = hparams.n_head; const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head = hparams.n_embd_head(); + + const float norm_rms_eps = hparams.f_norm_rms_eps; + + const int32_t n_tokens = batch.n_tokens; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; + + auto & buf_compute = lctx.buf_compute; + + struct ggml_init_params params = { + /*.mem_size =*/ buf_compute.size, + /*.mem_buffer =*/ buf_compute.data, + /*.no_alloc =*/ true, + }; + + struct ggml_context * ctx0 = ggml_init(params); + + ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + cur = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + cb(Kcur, "Kcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cb(Qcur, "Qcur", il); + + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(lctx, ctx0, Qcur, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, + model.output_norm, NULL, + LLM_NORM_RMS, norm_rms_eps, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + ggml_free(ctx0); + + return gf; +} + +static struct ggml_cgraph * llm_build_bloom( + llama_context & lctx, + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { + const auto & model = lctx.model; + const auto & hparams = model.hparams; + const auto & cparams = lctx.cparams; + + const auto & kv_self = lctx.kv_self; + + GGML_ASSERT(!!kv_self.ctx); + + const int64_t n_embd = hparams.n_embd; + const int64_t n_layer = hparams.n_layer; + const int64_t n_ctx = cparams.n_ctx; + const int64_t n_head = hparams.n_head; + const int64_t n_embd_head = hparams.n_embd_head(); const int64_t n_embd_gqa = hparams.n_embd_gqa(); GGML_ASSERT(n_embd_head == hparams.n_rot); @@ -5322,8 +4442,8 @@ static struct ggml_cgraph * llm_build_bloom( const float norm_eps = hparams.f_norm_eps; const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; auto & buf_compute = lctx.buf_compute; @@ -5340,198 +4460,90 @@ static struct ggml_cgraph * llm_build_bloom( ggml_cgraph * gf = ggml_new_graph(ctx0); struct ggml_tensor * cur; - struct ggml_tensor * token; struct ggml_tensor * inpL; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - } - ggml_set_name(inp_tokens, "inp_tokens"); - - token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, token); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token)); - } - } + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head)); - } + cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); + cb(KQ_mask, "KQ_mask", -1); - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } - - // norm - { - inpL = ggml_norm(ctx0, token, norm_eps); - inpL = ggml_add(ctx0, ggml_mul(ctx0, inpL, model.tok_norm), model.tok_norm_b); - } - - ggml_set_name(inpL, "inpL"); + inpL = llm_build_norm(ctx0, inpL, + model.tok_norm, + model.tok_norm_b, + LLM_NORM, norm_eps, cb, -1); + cb(inpL, "inp_norm", -1); for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "attn_norm", il); + + // self-attention { - // Norm - cur = ggml_norm(ctx0, inpL, norm_eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b); + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(lctx, ctx0, Qcur, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il); + cb(cur, "kqv_out", il); } - { - // Self Attention - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv); - - struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd); - struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd); - struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa)); - - struct ggml_tensor * Qcur = tmpq; - struct ggml_tensor * Kcur = tmpk; - - // store key and value to memory - { - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens)); - ggml_set_name(Vcur, "Vcur"); - - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - ggml_set_name(k, "k"); - - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = - ggml_permute(ctx0, - ggml_cpy(ctx0, - Qcur, - ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)), - 0, 2, 1, 3); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - ggml_set_name(K, "K"); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - ggml_set_name(KQ, "KQ"); - - // KQ_scaled = KQ / sqrt(n_embd_head) - // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ kv_head, n_head, 8); - ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi"); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask); - ggml_set_name(KQ_masked, "KQ_masked"); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - // split cached V into n_head heads - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - ggml_set_name(V, "V"); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - ggml_set_name(KQV, "KQV"); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - ggml_set_name(KQV_merged, "KQV_merged"); - - // cur = KQV_merged.contiguous().view(n_embd, n_tokens) - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - ggml_set_name(cur, "KQV_merged_contiguous"); - } - - // Projection - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo); - // Add the input - cur = ggml_add(ctx0, cur, inpL); - - struct ggml_tensor * inpFF = cur; + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); // FF { - // Norm - { - cur = ggml_norm(ctx0, inpFF, norm_eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b); - } + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, norm_eps, cb, il); + cb(cur, "ffn_norm", il); - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3); - - // GELU activation - cur = ggml_gelu(ctx0, cur); - - // Projection - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2); + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); } - inpL = ggml_add(ctx0, cur, inpFF); + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); } - // Output Norm - { - cur = ggml_norm(ctx0, inpL, norm_eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b); - } - ggml_set_name(cur, "result_norm"); + cur = llm_build_norm(ctx0, inpL, + model.output_norm, + model.output_norm_b, + LLM_NORM, norm_eps, cb, -1); + cb(cur, "result_norm", -1); cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -5542,7 +4554,9 @@ static struct ggml_cgraph * llm_build_bloom( static struct ggml_cgraph * llm_build_mpt( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + const llm_build_cb & cb, + bool worst_case) { const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -5555,7 +4569,6 @@ static struct ggml_cgraph * llm_build_mpt( const int64_t n_layer = hparams.n_layer; const int64_t n_ctx = cparams.n_ctx; const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head = hparams.n_embd_head(); const int64_t n_embd_gqa = hparams.n_embd_gqa(); @@ -5563,11 +4576,9 @@ static struct ggml_cgraph * llm_build_mpt( const float clamp_kqv = hparams.f_clamp_kqv; const float max_alibi_bias = hparams.f_max_alibi_bias; - const int n_gpu_layers = model.n_gpu_layers; - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; - const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; + const int32_t n_kv = worst_case ? n_ctx : kv_self.n; + const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; auto & buf_compute = lctx.buf_compute; @@ -5586,269 +4597,93 @@ static struct ggml_cgraph * llm_build_mpt( struct ggml_tensor * cur; struct ggml_tensor * inpL; - //int warmup = 0; - if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inp_tokens); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens)); - //warmup = ((uint32_t*) inp_tokens->data)[0] == 0; - } - - ggml_set_name(inp_tokens, "inp_tokens"); - - inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); - } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - - inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); - - ggml_allocr_alloc(lctx.alloc, inpL); - if (!ggml_allocr_is_measure(lctx.alloc)) { - memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL)); - } - } - - const int i_gpu_start = n_layer - n_gpu_layers; - (void) i_gpu_start; - - // offload functions set the tensor output backend to GPU - // tensors are GPU-accelerated if any input or the output has been offloaded - offload_func_t offload_func_nr = llama_nop; // nr = non-repeating - offload_func_t offload_func_kq = llama_nop; - offload_func_t offload_func_v = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (n_gpu_layers > n_layer) { - offload_func_nr = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 1) { - offload_func_v = ggml_cuda_assign_buffers_no_alloc; - } - if (n_gpu_layers > n_layer + 2) { - offload_func_kq = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); + cb(inpL, "inp_embd", -1); // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); - ggml_allocr_alloc(lctx.alloc, KQ_scale); - if (!ggml_allocr_is_measure(lctx.alloc)) { - ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head)); - } + cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - offload_func_kq(KQ_mask); - ggml_set_name(KQ_mask, "KQ_mask"); - ggml_allocr_alloc(lctx.alloc, KQ_mask); - if (!ggml_allocr_is_measure(lctx.alloc)) { - float * data = (float *) KQ_mask->data; - memset(data, 0, ggml_nbytes(KQ_mask)); - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; - } - } - } - } - } + cb(KQ_mask, "KQ_mask", -1); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; - offload_func_t offload_func = llama_nop; - -#ifdef GGML_USE_CUBLAS - if (il >= i_gpu_start) { - offload_func = ggml_cuda_assign_buffers_no_alloc; - } -#endif // GGML_USE_CUBLAS + attn_norm = llm_build_norm(ctx0, inpL, + model.layers[il].attn_norm, + NULL, + LLM_NORM, norm_eps, cb, il); + cb(attn_norm, "attn_norm", il); // self-attention - // TODO: refactor into common function (shared with LLaMA) { - attn_norm = ggml_norm(ctx0, inpL, norm_eps); - offload_func(attn_norm); - - attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm); - offload_func(attn_norm); - - if (1) { - cur = attn_norm; - } - - // compute QKV + cur = attn_norm; cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - offload_func_kq(cur); + cb(cur, "wqkv", il); if (clamp_kqv > 0.0f) { cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv); - offload_func_kq(cur); + cb(cur, "wqkv_clamped", il); } - const size_t wsize = ggml_type_size(cur->type); + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - struct ggml_tensor * Qcur = ggml_view_3d( - ctx0, cur, n_embd_head, n_head, n_tokens, - wsize * n_embd_head, - wsize * n_embd_head * (n_head + 2 * n_head_kv), - 0); - offload_func_kq(Qcur); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); - struct ggml_tensor * Kcur = ggml_view_3d( - ctx0, cur, n_embd_head, n_head_kv, n_tokens, - wsize * n_embd_head, - wsize * n_embd_head * (n_head + 2 * n_head_kv), - wsize * n_embd_head * n_head); - offload_func_kq(Kcur); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - struct ggml_tensor * tmpv = ggml_view_3d( - ctx0, cur, n_embd_head, n_head_kv, n_tokens, - wsize * n_embd_head, - wsize * n_embd_head * (n_head + 2 * n_head_kv), - wsize * n_embd_head * (n_head + n_head_kv)); - offload_func_kq(Kcur); + llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - ggml_set_name(Qcur, "Qcur"); - ggml_set_name(Kcur, "Kcur"); - - { - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens)); - offload_func_v(Vcur); - offload_func_v(Vcur->src[0]->src[0]); - ggml_set_name(Vcur, "Vcur"); - - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); - offload_func_kq(k); - ggml_set_name(k, "k"); - - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); - offload_func_v(v); - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - offload_func_kq(Q); - ggml_set_name(Q, "Q"); - - struct ggml_tensor * K = - ggml_view_3d(ctx0, kv_self.k, - n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); - offload_func_kq(K); - ggml_set_name(K, "K"); - - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - offload_func_kq(KQ); - ggml_set_name(KQ, "KQ"); - - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale); - offload_func_kq(KQ_scaled); - ggml_set_name(KQ_scaled, "KQ_scaled"); - - // TODO: replace with ggml_add() - struct ggml_tensor * KQ_scaled_alibi = - ggml_alibi(ctx0, KQ_scaled, 0, n_head, max_alibi_bias); - offload_func_kq(KQ_scaled_alibi); - ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi"); - - struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask); - offload_func_kq(KQ_masked); - ggml_set_name(KQ_masked, "KQ_masked"); - - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - offload_func_v(KQ_soft_max); - ggml_set_name(KQ_soft_max, "KQ_soft_max"); - - struct ggml_tensor * V = - ggml_view_3d(ctx0, kv_self.v, - n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); - offload_func_v(V); - ggml_set_name(V, "V"); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - offload_func_v(KQV); - ggml_set_name(KQV, "KQV"); - - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - offload_func_v(KQV_merged); - ggml_set_name(KQV_merged, "KQV_merged"); - - cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); - offload_func_v(cur); - ggml_set_name(cur, "KQV_merged_contiguous"); - - cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur); - offload_func(cur); - ggml_set_name(cur, "result_wo"); + cur = llm_build_kqv(lctx, ctx0, Qcur, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, max_alibi_bias, cb, il); + cb(cur, "kqv_out", il); } // Add the input - cur = ggml_add(ctx0, cur, inpL); - offload_func(cur); - - struct ggml_tensor * attn_out = cur; + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); // feed forward { - // Norm - { - cur = ggml_norm(ctx0, attn_out, norm_eps); - offload_func(cur); + cur = llm_build_norm(ctx0, ffn_inp, + model.layers[il].ffn_norm, + NULL, + LLM_NORM, norm_eps, cb, il); + cb(cur, "ffn_norm", il); - cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm); - offload_func(cur); - } - - cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur); - offload_func(cur); - - cur = ggml_gelu(ctx0, cur); - offload_func(cur); - cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur); - offload_func(cur); + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + NULL, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); } - cur = ggml_add(ctx0, cur, attn_out); - offload_func(cur); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + // input for next layer inpL = cur; } cur = inpL; - // norm - { - cur = ggml_norm(ctx0, cur, norm_eps); - offload_func_nr(cur); - - cur = ggml_mul(ctx0, cur, model.output_norm); - ggml_set_name(cur, "result_norm"); - } + cur = llm_build_norm(ctx0, cur, + model.output_norm, + NULL, + LLM_NORM, norm_eps, cb, -1); + cb(cur, "result_norm", -1); cur = ggml_mul_mat(ctx0, model.output, cur); - ggml_set_name(cur, "result_output"); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -5857,50 +4692,494 @@ static struct ggml_cgraph * llm_build_mpt( return gf; } +// +// tensor offloading helpers +// +// TODO: will be removed with backend v2 + +enum llm_offload_func_e { + OFFLOAD_FUNC_NOP, + OFFLOAD_FUNC, + OFFLOAD_FUNC_KQ, + OFFLOAD_FUNC_V, + OFFLOAD_FUNC_NR, + OFFLOAD_FUNC_EMB, + OFFLOAD_FUNC_OUT, +}; + +// TODO: will be removed with backend v2 +struct llm_offload_trie { + struct node { + ~node() { + for (int i = 0; i < 256; ++i) { + if (children[i]) { + delete children[i]; + } + } + } + + node * children[256] = { nullptr }; + llm_offload_func_e func = OFFLOAD_FUNC_NOP; + }; + + llm_offload_trie() { + root = new node; + } + + llm_offload_trie(const std::unordered_map & map) { + root = new node; + + for (const auto & kv : map) { + add(kv.first, kv.second); + } + } + + ~llm_offload_trie() { + delete root; + } + + void add(const char * name, llm_offload_func_e func) { + node * cur = root; + + for (int i = 0; ; ++i) { + const uint8_t c = name[i]; + + if (!c) { + break; + } + + if (!cur->children[c]) { + cur->children[c] = new node; + } + + cur = cur->children[c]; + } + + cur->func = func; + } + + llm_offload_func_e find(const char * name) const { + const node * cur = root; + + for (int i = 0; ; ++i) { + const uint8_t c = name[i]; + + if (!c) { + break; + } + + if (!cur->children[c]) { + return OFFLOAD_FUNC_NOP; + } + + cur = cur->children[c]; + } + + return cur->func; + } + + node * root = nullptr; +}; + +// TODO: will be removed with backend v2 +static const std::unordered_map k_offload_map = { + //{ "inp_tokens", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel + //{ "inp_embd", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel + { "pos_embd", OFFLOAD_FUNC_NR }, + + { "inp_pos", OFFLOAD_FUNC_KQ }, // this is often used for KQ ops (e.g. rope) + { "KQ_scale", OFFLOAD_FUNC_KQ }, + { "KQ_mask", OFFLOAD_FUNC_KQ }, + { "K_shift", OFFLOAD_FUNC_KQ }, + { "K_shifted", OFFLOAD_FUNC_KQ }, + + { "inp_norm", OFFLOAD_FUNC_NR }, + { "inp_norm_w", OFFLOAD_FUNC_NR }, + { "inp_norm_wb", OFFLOAD_FUNC_NR }, + + { "norm", OFFLOAD_FUNC }, + { "norm_w", OFFLOAD_FUNC }, + { "norm_wb", OFFLOAD_FUNC }, + + { "attn_norm", OFFLOAD_FUNC }, + { "attn_norm_2", OFFLOAD_FUNC }, + + { "wqkv", OFFLOAD_FUNC_KQ }, + { "bqkv", OFFLOAD_FUNC_KQ }, + { "wqkv_clamped", OFFLOAD_FUNC_KQ }, + + { "tmpk", OFFLOAD_FUNC_KQ }, + { "tmpq", OFFLOAD_FUNC_KQ }, + { "tmpv", OFFLOAD_FUNC_V }, + { "Kcur", OFFLOAD_FUNC_KQ }, + { "Qcur", OFFLOAD_FUNC_KQ }, + { "Vcur", OFFLOAD_FUNC_V }, + + { "krot", OFFLOAD_FUNC_KQ }, + { "qrot", OFFLOAD_FUNC_KQ }, + { "kpass", OFFLOAD_FUNC_KQ }, + { "qpass", OFFLOAD_FUNC_KQ }, + { "krotated", OFFLOAD_FUNC_KQ }, + { "qrotated", OFFLOAD_FUNC_KQ }, + + { "q", OFFLOAD_FUNC_KQ }, + { "k", OFFLOAD_FUNC_KQ }, + { "kq", OFFLOAD_FUNC_KQ }, + { "kq_scaled", OFFLOAD_FUNC_KQ }, + { "kq_scaled_alibi", OFFLOAD_FUNC_KQ }, + { "kq_masked", OFFLOAD_FUNC_KQ }, + { "kq_soft_max", OFFLOAD_FUNC_V }, + { "v", OFFLOAD_FUNC_V }, + { "kqv", OFFLOAD_FUNC_V }, + { "kqv_merged", OFFLOAD_FUNC_V }, + { "kqv_merged_cont", OFFLOAD_FUNC_V }, + { "kqv_wo", OFFLOAD_FUNC_V }, + { "kqv_out", OFFLOAD_FUNC_V }, + + { "ffn_inp", OFFLOAD_FUNC }, + { "ffn_norm", OFFLOAD_FUNC }, + + { "ffn_up", OFFLOAD_FUNC }, + { "ffn_up_b", OFFLOAD_FUNC }, + { "ffn_gate", OFFLOAD_FUNC }, + { "ffn_gate_b", OFFLOAD_FUNC }, + { "ffn_gate_par", OFFLOAD_FUNC }, + { "ffn_down", OFFLOAD_FUNC }, + { "ffn_down_b", OFFLOAD_FUNC }, + { "ffn_out", OFFLOAD_FUNC }, + + { "ffn_silu", OFFLOAD_FUNC }, + { "ffn_gelu", OFFLOAD_FUNC }, + { "ffn_relu", OFFLOAD_FUNC }, + { "ffn_sqr(relu)", OFFLOAD_FUNC }, + + { "l_out", OFFLOAD_FUNC }, + + { "result_norm", OFFLOAD_FUNC_EMB }, + { "result_output", OFFLOAD_FUNC_OUT }, +}; + +static llm_offload_trie k_offload_func_trie(k_offload_map); + static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch) { const auto & model = lctx.model; + // check if we should build the worst-case graph (for memory measurement) + const bool worst_case = ggml_allocr_is_measure(lctx.alloc); + + // keep track of the input that has already been allocated + bool alloc_inp_tokens = false; + bool alloc_inp_embd = false; + bool alloc_inp_pos = false; + bool alloc_inp_KQ_scale = false; + bool alloc_inp_KQ_mask = false; + bool alloc_inp_K_shift = false; + +#ifdef GGML_USE_CUBLAS + const bool do_offload = true; +#else + const bool do_offload = true; // TODO: set to false after finishing refactoring +#endif + + int n_non_view = 0; // number of non-view tensors that have been processed by the callback + + // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) + // TODO: will be removed with backend v2 + llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) { + if (il >= 0) { + ggml_format_name(cur, "%s-%d", name, il); + } else { + ggml_set_name(cur, name); + } + + // + // allocate input tensors and set input data + // + // TODO: will be removed with backend v2 + + if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) { + const int64_t n_tokens = cur->ne[0]; + + memcpy(cur->data, batch.token, n_tokens*ggml_element_size(cur)); + } + + alloc_inp_tokens = true; + } + + if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) { + const int64_t n_embd = cur->ne[0]; + const int64_t n_tokens = cur->ne[1]; + + memcpy(cur->data, batch.embd, n_tokens*n_embd*ggml_element_size(cur)); + } + + alloc_inp_embd = true; + } + + if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) { + const int64_t n_tokens = cur->ne[0]; + + int32_t * data = (int32_t *) cur->data; + + for (int i = 0; i < n_tokens; ++i) { + data[i] = batch.pos[i]; + } + } + + alloc_inp_pos = true; + } + + if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc)) { + const int64_t n_embd_head = model.hparams.n_embd_head(); + ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); + } + + alloc_inp_KQ_scale = true; + } + + if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc)) { + const int64_t n_kv = cur->ne[0]; + const int64_t n_tokens = cur->ne[1]; + + float * data = (float *) cur->data; + memset(data, 0, ggml_nbytes(cur)); + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_pos pos = batch.pos[j]; + const llama_seq_id seq_id = batch.seq_id[j][0]; + + for (int i = 0; i < n_kv; ++i) { + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { + data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; + } + } + } + } + } + + alloc_inp_KQ_mask = true; + } + + if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc)) { + const int64_t n_ctx = cur->ne[0]; + + int32_t * data = (int32_t *) cur->data; + + for (int i = 0; i < n_ctx; ++i) { + data[i] = lctx.kv_self.cells[i].delta; + } + } + + alloc_inp_K_shift = true; + } + + // view tensors are not processed further + if (cur->view_src != nullptr) { + return; + } + + if (cur->op != GGML_OP_NONE) { + n_non_view++; + } + + // + // offload layers + // + // TODO: will be removed with backend v2 + +//#define LLAMA_OFFLOAD_DEBUG + + if (!do_offload) { + return; + } + + const int n_layer = model.hparams.n_layer; + + const int n_gpu_layers = model.n_gpu_layers; + const int i_gpu_start = n_layer - n_gpu_layers; + + // should we offload the final norm? yes if we are not computing embeddings + const bool offload_emb = lctx.embedding.empty(); + + static const std::unordered_map> k_offload_func_name = { + { OFFLOAD_FUNC_NOP, "CPU" }, + { OFFLOAD_FUNC_OUT, "CPU" }, +#ifdef GGML_USE_CUBLAS + { OFFLOAD_FUNC, "GPU (CUDA)" }, + { OFFLOAD_FUNC_KQ, "GPU (CUDA) KQ" }, + { OFFLOAD_FUNC_V, "GPU (CUDA) V" }, + { OFFLOAD_FUNC_NR, "GPU (CUDA) NR" }, + { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" }, +#else + { OFFLOAD_FUNC, "CPU" }, + { OFFLOAD_FUNC_KQ, "CPU" }, + { OFFLOAD_FUNC_V, "CPU" }, + { OFFLOAD_FUNC_NR, "CPU" }, + { OFFLOAD_FUNC_EMB, "CPU" }, +#endif // GGML_USE_CUBLAS + }; + + // check the global map for what offload function to use for this tensor + llm_offload_func_e func_e = k_offload_func_trie.find(name); + + if (func_e == OFFLOAD_FUNC_NOP) { +#ifdef LLAMA_OFFLOAD_DEBUG + // if a tensor hasn't been offloaded, we warn the user + if (worst_case) { + LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__, + cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837"); + } +#endif + + return; + } + + // count the number of layers and respect the provided n_gpu_layers + switch (func_e) { + case OFFLOAD_FUNC_NOP: + case OFFLOAD_FUNC_OUT: + break; + case OFFLOAD_FUNC: + if (n_gpu_layers < n_layer) { + if (il < i_gpu_start) { + func_e = OFFLOAD_FUNC_NOP; + } + } + break; + case OFFLOAD_FUNC_NR: + if (n_gpu_layers <= n_layer + 0) { + func_e = OFFLOAD_FUNC_NOP; + } + break; + case OFFLOAD_FUNC_V: + if (n_gpu_layers <= n_layer + 1) { + func_e = OFFLOAD_FUNC_NOP; + } + break; + case OFFLOAD_FUNC_KQ: + if (n_gpu_layers <= n_layer + 2) { + func_e = OFFLOAD_FUNC_NOP; + } + break; + case OFFLOAD_FUNC_EMB: + if (!offload_emb || n_gpu_layers < n_layer) { + func_e = OFFLOAD_FUNC_NOP; + } + break; + default: GGML_ASSERT(false); + } + + offload_func_t func = ggml_offload_nop; + + // this is needed for compatibility with Metal for example +#ifdef GGML_USE_CUBLAS + static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc; +#else + static offload_func_t ggml_offload_gpu = ggml_offload_nop; +#endif + + switch (func_e) { + case OFFLOAD_FUNC_NOP: + case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break; + case OFFLOAD_FUNC: + case OFFLOAD_FUNC_KQ: + case OFFLOAD_FUNC_V: + case OFFLOAD_FUNC_NR: + case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break; + default: GGML_ASSERT(false); + } + + // apply offload function to the tensor + func(cur); + +#ifdef LLAMA_OFFLOAD_DEBUG + if (worst_case) { + LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str()); + } +#endif + }; + struct ggml_cgraph * result = NULL; switch (model.arch) { case LLM_ARCH_LLAMA: { - result = llm_build_llama(lctx, batch); + result = llm_build_llama(lctx, batch, cb, worst_case); } break; case LLM_ARCH_BAICHUAN: { - result = llm_build_baichaun(lctx, batch); + result = llm_build_baichaun(lctx, batch, cb, worst_case); } break; case LLM_ARCH_FALCON: { - result = llm_build_falcon(lctx, batch); + result = llm_build_falcon(lctx, batch, cb, worst_case); } break; case LLM_ARCH_STARCODER: { - result = llm_build_starcoder(lctx, batch); + result = llm_build_starcoder(lctx, batch, cb, worst_case); } break; case LLM_ARCH_PERSIMMON: { - result = llm_build_persimmon(lctx, batch); + result = llm_build_persimmon(lctx, batch, cb, worst_case); } break; case LLM_ARCH_REFACT: { - result = llm_build_refact(lctx, batch); + result = llm_build_refact(lctx, batch, cb, worst_case); } break; case LLM_ARCH_BLOOM: { - result = llm_build_bloom(lctx, batch); + result = llm_build_bloom(lctx, batch, cb, worst_case); } break; case LLM_ARCH_MPT: { - result = llm_build_mpt(lctx, batch); + result = llm_build_mpt(lctx, batch, cb, worst_case); } break; default: GGML_ASSERT(false); } + if (worst_case) { + int n_non_view_total = 0; + + for (int i = 0; i < result->n_nodes; ++i) { + if (result->nodes[i]->view_src == nullptr) { + n_non_view_total++; + } + } + + LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total); + + if (n_non_view != n_non_view_total) { + LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__); + LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n", __func__); + LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n", __func__); + LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n", __func__); + LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n", __func__); + LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__); + } + } + return result; } @@ -6043,11 +5322,13 @@ static int llama_decode_internal( } // If all tensors can be run on the GPU then using more than 1 thread is detrimental. - const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA || + const bool full_offload_supported = + model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_BAICHUAN || - model.arch == LLM_ARCH_FALCON || - model.arch == LLM_ARCH_REFACT || + model.arch == LLM_ARCH_FALCON || + model.arch == LLM_ARCH_REFACT || model.arch == LLM_ARCH_MPT; + const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3; if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) { n_threads = 1; @@ -6102,6 +5383,8 @@ static int llama_decode_internal( //} // extract logits + // TODO: do not compute and extract logits if only embeddings are needed + // need to update the graphs to skip "result_output" { auto & logits_out = lctx.logits; @@ -8713,8 +7996,8 @@ static int llama_apply_lora_from_file_internal( ggml_tensor * dest_t = model_tensors[base_name]; - offload_func_t offload_func = llama_nop; - offload_func_t offload_func_force_inplace = llama_nop; + offload_func_t offload_func = ggml_offload_nop; + offload_func_t offload_func_force_inplace = ggml_offload_nop; #ifdef GGML_USE_CUBLAS if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) { From ca190bca8e844d171020d6147687e71472d71734 Mon Sep 17 00:00:00 2001 From: Adrian Hesketh Date: Wed, 1 Nov 2023 09:28:28 +0000 Subject: [PATCH 003/664] server : re-enable completion and embedded at the same time (#3876) --- .gitignore | 1 + examples/server/server.cpp | 16 ++++++++++------ 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/.gitignore b/.gitignore index 545c28726..5d7c5479e 100644 --- a/.gitignore +++ b/.gitignore @@ -15,6 +15,7 @@ .DS_Store .build/ .cache/ +.ccls-cache/ .direnv/ .envrc .swiftpm diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c163c7f8e..47ae0d558 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -149,6 +149,7 @@ struct task_server { task_type type; json data; bool infill_mode = false; + bool embedding_mode = false; }; struct task_result { @@ -371,6 +372,7 @@ struct llama_client_slot std::vector generated_token_probs; bool infill = false; + bool embedding = false; bool has_next_token = true; bool truncated = false; bool stopped_eos = false; @@ -1244,13 +1246,14 @@ struct llama_server_context queue_results.push_back(res); } - int request_completion(json data, bool infill) + int request_completion(json data, bool infill, bool embedding) { std::lock_guard lock(mutex_tasks); task_server task; task.id = id_gen++; task.data = data; task.infill_mode = infill; + task.embedding_mode = embedding; task.type = COMPLETION_TASK; queue_tasks.push_back(task); return task.id; @@ -1376,7 +1379,7 @@ struct llama_server_context { LOG_TEE("slot unavailable\n"); // send error result - send_error(task.id, "slot unavaliable"); + send_error(task.id, "slot unavailable"); return; } @@ -1388,6 +1391,7 @@ struct llama_server_context slot->reset(); slot->infill = task.infill_mode; + slot->embedding = task.embedding_mode; slot->task_id = task.id; if (!launch_slot_with_data(slot, task.data)) @@ -1695,7 +1699,7 @@ struct llama_server_context } // prompt evaluated for embedding - if (params.embedding) + if (slot.embedding) { send_embedding(slot); slot.release(); @@ -2274,7 +2278,7 @@ int main(int argc, char **argv) svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res) { json data = json::parse(req.body); - const int task_id = llama.request_completion(data, false); + const int task_id = llama.request_completion(data, false, false); if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); @@ -2329,7 +2333,7 @@ int main(int argc, char **argv) svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) { json data = json::parse(req.body); - const int task_id = llama.request_completion(data, true); + const int task_id = llama.request_completion(data, true, false); if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); @@ -2433,7 +2437,7 @@ int main(int argc, char **argv) { prompt = ""; } - const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false); + const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true); task_result result = llama.next_result(task_id); return res.set_content(result.result_json.dump(), "application/json"); }); From f0e209324a7f663225791897877bf610f1af152d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 11:29:07 +0200 Subject: [PATCH 004/664] scripts : add server-llm.sh (#3868) * scripts : add deploy-server.sh * scripts : rename to server-llm.sh * scripts : working curl pipe --- scripts/server-llm.sh | 391 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 391 insertions(+) create mode 100644 scripts/server-llm.sh diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh new file mode 100644 index 000000000..7bf0929bb --- /dev/null +++ b/scripts/server-llm.sh @@ -0,0 +1,391 @@ +#!/bin/bash +# +# Helper script for deploying llama.cpp server with a single Bash command +# +# - Works on Linux and macOS +# - Supports: CPU, CUDA, Metal, OpenCL +# - Can run all GGUF models from HuggingFace +# - Can serve requests in parallel +# - Always builds latest llama.cpp from GitHub +# +# Limitations +# +# - Chat templates are poorly supported (base models recommended) +# - Might be unstable! +# +# Usage: +# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] +# +# --port: port number, default is 8888 +# --repo: path to a repo containing GGUF model files +# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input +# --backend: cpu, cuda, metal, opencl, depends on the OS +# --gpu-id: gpu id, default is 0 +# --n-parallel: number of parallel requests, default is 8 +# --n-kv: KV cache size, default is 4096 +# --verbose: verbose output +# +# Example: +# +# bash -c "$(curl -s https://ggml.ai/server-llm.sh)" +# + +set -e + +# required utils: curl, git, make +if ! command -v curl &> /dev/null; then + printf "[-] curl not found\n" + exit 1 +fi +if ! command -v git &> /dev/null; then + printf "[-] git not found\n" + exit 1 +fi +if ! command -v make &> /dev/null; then + printf "[-] make not found\n" + exit 1 +fi + +# parse arguments +port=8888 +repo="" +wtype="" +backend="cpu" + +# if macOS, use metal backend by default +if [[ "$OSTYPE" == "darwin"* ]]; then + backend="metal" +elif command -v nvcc &> /dev/null; then + backend="cuda" +fi + +gpu_id=0 +n_parallel=8 +n_kv=4096 +verbose=0 + +function print_usage { + printf "Usage:\n" + printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" + printf " --port: port number, default is 8888\n" + printf " --repo: path to a repo containing GGUF model files\n" + printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" + printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" + printf " --gpu-id: gpu id, default is 0\n" + printf " --n-parallel: number of parallel requests, default is 8\n" + printf " --n-kv: KV cache size, default is 4096\n" + printf " --verbose: verbose output\n\n" + printf "Example:\n\n" + printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' +} + +while [[ $# -gt 0 ]]; do + key="$1" + case $key in + --port) + port="$2" + shift + shift + ;; + --repo) + repo="$2" + shift + shift + ;; + --wtype) + wtype="$2" + shift + shift + ;; + --backend) + backend="$2" + shift + shift + ;; + --gpu-id) + gpu_id="$2" + shift + shift + ;; + --n-parallel) + n_parallel="$2" + shift + shift + ;; + --n-kv) + n_kv="$2" + shift + shift + ;; + --verbose) + verbose=1 + shift + ;; + --help) + print_usage + exit 0 + ;; + *) + echo "Unknown argument: $key" + print_usage + exit 1 + ;; + esac +done + +# available weights types +wtypes=("F16" "Q8_0" "Q4_0" "Q4_1" "Q5_0" "Q5_1" "Q6_K" "Q5_K_M" "Q5_K_S" "Q4_K_M" "Q4_K_S" "Q3_K_L" "Q3_K_M" "Q3_K_S" "Q2_K") + +wfiles=() +for wt in "${wtypes[@]}"; do + wfiles+=("") +done + +# sample repos +repos=( + "https://huggingface.co/TheBloke/Llama-2-7B-GGUF" + "https://huggingface.co/TheBloke/Llama-2-13B-GGUF" + "https://huggingface.co/TheBloke/Llama-2-70B-GGUF" + "https://huggingface.co/TheBloke/CodeLlama-7B-GGUF" + "https://huggingface.co/TheBloke/CodeLlama-13B-GGUF" + "https://huggingface.co/TheBloke/CodeLlama-34B-GGUF" + "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF" + "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF" + "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF" + "https://huggingface.co/TheBloke/CausalLM-7B-GGUF" +) + +printf "\n" +printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" +printf " Based on the options that follow, the script might download a model file\n" +printf " from the internet, which can be a few GBs in size. The script will also\n" +printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" +printf "\n" +printf " Upon success, an HTTP server will be started and it will serve the selected\n" +printf " model using llama.cpp for demonstration purposes.\n" +printf "\n" +printf " Please note:\n" +printf "\n" +printf " - All new data will be stored in the current folder\n" +printf " - The server will be listening on all network interfaces\n" +printf " - The server will run with default settings which are not always optimal\n" +printf " - Do not judge the quality of a model based on the results from this script\n" +printf " - Do not use this script to benchmark llama.cpp\n" +printf " - Do not use this script in production\n" +printf " - This script is only for demonstration purposes\n" +printf "\n" +printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" +printf "\n" +printf " Press Enter to continue ...\n\n" + +read + +if [[ -z "$repo" ]]; then + printf "[+] No repo provided from the command line\n" + printf " Please select a number from the list below or enter an URL:\n\n" + + is=0 + for r in "${repos[@]}"; do + printf " %2d) %s\n" $is "$r" + is=$((is+1)) + done + + # ask for repo until index of sample repo is provided or an URL + while [[ -z "$repo" ]]; do + printf "\n Or choose one from: https://huggingface.co/models?sort=trending&search=gguf\n\n" + read -p "[+] Select repo: " repo + + # check if the input is a number + if [[ "$repo" =~ ^[0-9]+$ ]]; then + if [[ "$repo" -ge 0 && "$repo" -lt ${#repos[@]} ]]; then + repo="${repos[$repo]}" + else + printf "[-] Invalid repo index: %s\n" "$repo" + repo="" + fi + elif [[ "$repo" =~ ^https?:// ]]; then + repo="$repo" + else + printf "[-] Invalid repo URL: %s\n" "$repo" + repo="" + fi + done +fi + +# remove suffix +repo=$(echo "$repo" | sed -E 's/\/tree\/main$//g') + +printf "[+] Checking for GGUF model files in %s\n" "$repo" + +# find GGUF files in the source +# TODO: better logic +model_tree="${repo%/}/tree/main" +model_files=$(curl -s "$model_tree" | grep -i "\\.gguf" | sed -E 's/.*(.*)<\/span><\/a>/\1/g') + +# list all files in the provided git repo +printf "[+] Model files:\n\n" +for file in $model_files; do + # determine iw by grepping the filename with wtypes + iw=-1 + is=0 + for wt in "${wtypes[@]}"; do + # uppercase + ufile=$(echo "$file" | tr '[:lower:]' '[:upper:]') + if [[ "$ufile" =~ "$wt" ]]; then + iw=$is + break + fi + is=$((is+1)) + done + + if [[ $iw -eq -1 ]]; then + continue + fi + + wfiles[$iw]="$file" + + have=" " + if [[ -f "$file" ]]; then + have="*" + fi + + printf " %2d) %s %s\n" $iw "$have" "$file" +done + +# ask for weights type until provided and available +while [[ -z "$wtype" ]]; do + printf "\n" + read -p "[+] Select weight type: " wtype + wfile="${wfiles[$wtype]}" + + if [[ -z "$wfile" ]]; then + printf "[-] Invalid weight type: %s\n" "$wtype" + wtype="" + fi +done + +printf "[+] Selected weight type: %s (%s)\n" "$wtype" "$wfile" + +url="${repo%/}/resolve/main/$wfile" + +# check file if the model has been downloaded before +chk="$wfile.chk" + +# check if we should download the file +# - if $wfile does not exist +# - if $wfile exists but $chk does not exist +# - if $wfile exists and $chk exists but $wfile is newer than $chk +# TODO: better logic using git lfs info + +do_download=0 + +if [[ ! -f "$wfile" ]]; then + do_download=1 +elif [[ ! -f "$chk" ]]; then + do_download=1 +elif [[ "$wfile" -nt "$chk" ]]; then + do_download=1 +fi + +if [[ $do_download -eq 1 ]]; then + printf "[+] Downloading weights from %s\n" "$url" + + # download the weights file + curl -o "$wfile" -# -L "$url" + + # create a check file if successful + if [[ $? -eq 0 ]]; then + printf "[+] Creating check file %s\n" "$chk" + touch "$chk" + fi +else + printf "[+] Using cached weights %s\n" "$wfile" +fi + +# get latest llama.cpp and build + +printf "[+] Downloading latest llama.cpp\n" + +llama_cpp_dir="__llama_cpp_port_${port}__" + +if [[ -d "$llama_cpp_dir" && ! -f "$llama_cpp_dir/__ggml_script__" ]]; then + # if the dir exists and there isn't a file "__ggml_script__" in it, abort + printf "[-] Directory %s already exists\n" "$llama_cpp_dir" + printf "[-] Please remove it and try again\n" + exit 1 +elif [[ -d "$llama_cpp_dir" ]]; then + printf "[+] Directory %s already exists\n" "$llama_cpp_dir" + printf "[+] Using cached llama.cpp\n" + + cd "$llama_cpp_dir" + git reset --hard + git fetch + git checkout origin/master + + cd .. +else + printf "[+] Cloning llama.cpp\n" + + git clone https://github.com/ggerganov/llama.cpp "$llama_cpp_dir" +fi + +# mark that that the directory is made by this script +touch "$llama_cpp_dir/__ggml_script__" + +if [[ $verbose -eq 1 ]]; then + set -x +fi + +# build +cd "$llama_cpp_dir" + +make clean + +log="--silent" +if [[ $verbose -eq 1 ]]; then + log="" +fi + +if [[ "$backend" == "cuda" ]]; then + printf "[+] Building with CUDA backend\n" + LLAMA_CUBLAS=1 make -j server $log +elif [[ "$backend" == "cpu" ]]; then + printf "[+] Building with CPU backend\n" + make -j server $log +elif [[ "$backend" == "metal" ]]; then + printf "[+] Building with Metal backend\n" + make -j server $log +elif [[ "$backend" == "opencl" ]]; then + printf "[+] Building with OpenCL backend\n" + LLAMA_CLBLAST=1 make -j server $log +else + printf "[-] Unknown backend: %s\n" "$backend" + exit 1 +fi + +# run the server + +printf "[+] Running server\n" + +args="" +if [[ "$backend" == "cuda" ]]; then + export CUDA_VISIBLE_DEVICES=$gpu_id + args="-ngl 999" +elif [[ "$backend" == "cpu" ]]; then + args="-ngl 0" +elif [[ "$backend" == "metal" ]]; then + args="-ngl 999" +elif [[ "$backend" == "opencl" ]]; then + args="-ngl 999" +else + printf "[-] Unknown backend: %s\n" "$backend" + exit 1 +fi + +if [[ $verbose -eq 1 ]]; then + args="$args --verbose" +fi + +./server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args + +exit 0 From 73bdcb395ef9a997d9c02950c7cd4249546162cd Mon Sep 17 00:00:00 2001 From: Andrew Godfrey Date: Wed, 1 Nov 2023 04:49:04 -0700 Subject: [PATCH 005/664] finetune : add -ngl parameter (#3762) * Add '-ngl' support to finetune.cpp * Add fprintf in ggml_cuda_op_add When I tried CUDA offloading during finetuning following the readme, I got an assert here. This probably isn't an important case because inference later gives a warning saying you should use f16 or f32 instead when using lora * Add 'finetune.sh', which currently fails when using GPU "error: operator (): Finetuning on tensors with type 'f16' is not yet supported" * tweak finetune.sh * Suppress some warnings in ggml.c * Add f16 implementation to ggml_compute_forward_add_f16_f32 * Add an f16 case to ggml_add_cast_impl and llama_build_lora_finetune_graphs * finetune.sh: Edit comments * Add "add_f16_f32_f32_cuda" * Tweak an error message * finetune.sh: Add an optional LLAMA_MODEL_DIR variable * finetune.sh: Add an optional LLAMA_TRAINING_DIR variable * train : minor * tabs to spaces --------- Co-authored-by: Georgi Gerganov Co-authored-by: cebtenzzre --- common/train.cpp | 2 ++ common/train.h | 1 + examples/finetune/finetune.cpp | 14 +++++++++- examples/finetune/finetune.sh | 34 +++++++++++++++++++++++ ggml-cuda.cu | 17 ++++++++++++ ggml-quants.c | 2 ++ ggml.c | 49 +++++++++++++++++++++++++--------- llama.cpp | 2 +- 8 files changed, 106 insertions(+), 15 deletions(-) create mode 100644 examples/finetune/finetune.sh diff --git a/common/train.cpp b/common/train.cpp index 3cce5da26..bc15b7a03 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1045,6 +1045,7 @@ struct train_params_common get_default_train_params_common() { params.n_batch = 8; params.n_gradient_accumulation = 1; params.n_epochs = -1; + params.n_gpu_layers = 0; params.custom_n_ctx = false; @@ -1080,6 +1081,7 @@ struct train_params_common get_default_train_params_common() { params.adam_beta2 = 0.999f; params.adam_gclip = 1.0f; params.adam_eps_f = 0.0f; + return params; } diff --git a/common/train.h b/common/train.h index 42fa704b8..d86c93cc4 100644 --- a/common/train.h +++ b/common/train.h @@ -44,6 +44,7 @@ struct train_params_common { int n_batch; int n_gradient_accumulation; int n_epochs; + int n_gpu_layers; bool custom_n_ctx; diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 35824cd2d..60c7faa79 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -652,7 +652,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( GGML_ASSERT(tokens_input->type == GGML_TYPE_I32); auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { - if (ggml_is_quantized(a->type)) { + if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) { return ggml_add_cast(ctx, a, b, GGML_TYPE_F32); } else if (a->type == GGML_TYPE_F32) { return ggml_add(ctx, a, b); @@ -1459,6 +1459,17 @@ static bool train_params_parse(int argc, char ** argv, struct train_params * par } params->n_rank_w3 = std::stoi(argv[i]); params->custom_n_rank_w3 = true; + } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { + invalid_param = true; + break; + } +#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD + params->common.n_gpu_layers = std::stoi(argv[i]); +#else + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); +#endif } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); train_print_usage(argc, argv, &default_params); @@ -1545,6 +1556,7 @@ int main(int argc, char ** argv) { srand(params.common.seed); struct llama_model_params llama_mparams = llama_model_default_params(); + llama_mparams.n_gpu_layers = params.common.n_gpu_layers; llama_mparams.vocab_only = false; printf("%s: model base = '%s'\n", __func__, params.fn_model_base); diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh new file mode 100644 index 000000000..079bfa113 --- /dev/null +++ b/examples/finetune/finetune.sh @@ -0,0 +1,34 @@ +#!/bin/bash +cd `dirname $0` +cd ../.. + +EXE="./finetune" + +if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi +if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi + +# MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf" # This is the model the readme uses. +MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "main --lora" with GPU inferencing. + +while getopts "dg" opt; do + case $opt in + d) + DEBUGGER="gdb --args" + ;; + g) + EXE="./build/bin/Release/finetune" + GPUARG="--gpu-layers 25" + ;; + esac +done + +$DEBUGGER $EXE \ + --model-base $MODEL \ + $GPUARG \ + --checkpoint-in chk-ol3b-shakespeare-LATEST.gguf \ + --checkpoint-out chk-ol3b-shakespeare-ITERATION.gguf \ + --lora-out lora-ol3b-shakespeare-ITERATION.bin \ + --train-data "$LLAMA_TRAINING_DIR\shakespeare.txt" \ + --save-every 10 \ + --threads 10 --adam-iter 30 --batch 4 --ctx 64 \ + --use-checkpointing diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 1ba951f68..4e6e7cd94 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -513,6 +513,15 @@ static __global__ void add_f16_f32_f16(const half * x, const float * y, half * d dst[i] = __hadd(x[i], __float2half(y[i])); } +static __global__ void add_f16_f32_f32(const half * x, const float * y, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = __half2float(x[i]) + y[i]; +} + static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -4693,6 +4702,11 @@ static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, co add_f16_f32_f16<<>>(x, y, dst, k); } +static void add_f16_f32_f32_cuda(const half * x, const float * y, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE; + add_f16_f32_f32<<>>(x, y, dst, k); +} + static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) { const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE; mul_f32<<>>(x, y, dst, kx, ky); @@ -5996,7 +6010,10 @@ inline void ggml_cuda_op_add( add_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(src0), ne10*ne11, main_stream); } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { add_f16_f32_f16_cuda((const half *) src0_dd, src1_dd, (half *) dst_dd, ggml_nelements(src0), main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + add_f16_f32_f32_cuda((const half *) src0_dd, src1_dd, dst_dd, ggml_nelements(src0), main_stream); } else { + fprintf(stderr, "src0->type: %d dst->type: %d\n", src0->type, dst->type); GGML_ASSERT(false); } diff --git a/ggml-quants.c b/ggml-quants.c index 721594467..255c89b6a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -716,6 +716,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { __riscv_vse8_v_i8m1(y[i].qs , vs, vl); } #else + UNUSED(nb); // scalar quantize_row_q8_0_reference(x, y, k); #endif @@ -969,6 +970,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { y[i].s = sum*d; } #else + UNUSED(nb); // scalar quantize_row_q8_1_reference(x, y, k); #endif diff --git a/ggml.c b/ggml.c index 84407b122..80d682255 100644 --- a/ggml.c +++ b/ggml.c @@ -3153,7 +3153,7 @@ static struct ggml_tensor * ggml_add_cast_impl( // TODO: support less-strict constraint // GGML_ASSERT(ggml_can_repeat(b, a)); GGML_ASSERT(ggml_can_repeat_rows(b, a)); - GGML_ASSERT(ggml_is_quantized(a->type)); // currently only supported for quantized input + GGML_ASSERT(ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16); // currently only supported for quantized input and f16 bool is_node = false; @@ -6927,9 +6927,15 @@ static void ggml_compute_forward_add_f16_f32( GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + if (dst->type == GGML_TYPE_F32) { + GGML_ASSERT( nb0 == sizeof(float)); + } + else { + GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + } + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread @@ -6940,18 +6946,35 @@ static void ggml_compute_forward_add_f16_f32( const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + if (dst->type == GGML_TYPE_F16) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + } + } + } else { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; + } } } } diff --git a/llama.cpp b/llama.cpp index ead1d421d..42cedc7a1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8003,7 +8003,7 @@ static int llama_apply_lora_from_file_internal( if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) { if (dest_t->type != GGML_TYPE_F16) { throw std::runtime_error(format( - "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__)); + "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type)); } offload_func = ggml_cuda_assign_buffers; offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace; From 9a3b4f6c86503c9cfc049d4d0fdeafef12806f5e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 13:50:45 +0200 Subject: [PATCH 006/664] ggml : fix UNUSED macro (#3762) --- ggml-quants.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 255c89b6a..740be6dc5 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -716,7 +716,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { __riscv_vse8_v_i8m1(y[i].qs , vs, vl); } #else - UNUSED(nb); + GGML_UNUSED(nb); // scalar quantize_row_q8_0_reference(x, y, k); #endif @@ -970,7 +970,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { y[i].s = sum*d; } #else - UNUSED(nb); + GGML_UNUSED(nb); // scalar quantize_row_q8_1_reference(x, y, k); #endif From e75dfdd31b6a3dfa0627ba4ac3bb4b36e9db588e Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Wed, 1 Nov 2023 21:40:43 +0800 Subject: [PATCH 007/664] sampling : null grammar field after reset (#3885) --- common/sampling.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/common/sampling.cpp b/common/sampling.cpp index 673d67a6d..1317024c2 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -39,6 +39,7 @@ void llama_sampling_free(struct llama_sampling_context * ctx) { void llama_sampling_reset(llama_sampling_context * ctx) { if (ctx->grammar != NULL) { llama_grammar_free(ctx->grammar); + ctx->grammar = NULL; } if (!ctx->parsed_grammar.rules.empty()) { From a2758d08e44ce3624d233af4d23c6843e2e735b5 Mon Sep 17 00:00:00 2001 From: staviq Date: Wed, 1 Nov 2023 15:18:27 +0100 Subject: [PATCH 008/664] log : make generating separate log files optional (#3787) * impl --log-new, --log-append * Update common/log.h Co-authored-by: cebtenzzre * Update common/log.h Co-authored-by: cebtenzzre * Apply suggestions from code review Co-authored-by: cebtenzzre --------- Co-authored-by: cebtenzzre --- common/log.h | 122 ++++++++++++++++++++++++++++++++++----------------- 1 file changed, 82 insertions(+), 40 deletions(-) diff --git a/common/log.h b/common/log.h index d2c864cea..c0e814861 100644 --- a/common/log.h +++ b/common/log.h @@ -97,38 +97,56 @@ #define LOG_TEE_TARGET stderr #endif -// NOTE: currently disabled as it produces too many log files +// Utility for synchronizing log configuration state +// since std::optional was introduced only in c++17 +enum LogTriState +{ + LogTriStateSame, + LogTriStateFalse, + LogTriStateTrue +}; + // Utility to obtain "pid" like unique process id and use it when creating log files. -//inline std::string log_get_pid() -//{ -// static std::string pid; -// if (pid.empty()) -// { -// // std::this_thread::get_id() is the most portable way of obtaining a "process id" -// // it's not the same as "pid" but is unique enough to solve multiple instances -// // trying to write to the same log. -// std::stringstream ss; -// ss << std::this_thread::get_id(); -// pid = ss.str(); -// } -// -// return pid; -//} +inline std::string log_get_pid() +{ + static std::string pid; + if (pid.empty()) + { + // std::this_thread::get_id() is the most portable way of obtaining a "process id" + // it's not the same as "pid" but is unique enough to solve multiple instances + // trying to write to the same log. + std::stringstream ss; + ss << std::this_thread::get_id(); + pid = ss.str(); + } + + return pid; +} // Utility function for generating log file names with unique id based on thread id. // invocation with log_filename_generator( "llama", "log" ) creates a string "llama..log" // where the number is a runtime id of the current thread. -#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(log_file_basename, log_file_extension) +#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(LogTriStateSame, log_file_basename, log_file_extension) // INTERNAL, DO NOT USE -inline std::string log_filename_generator_impl(const std::string & log_file_basename, const std::string & log_file_extension) +inline std::string log_filename_generator_impl(LogTriState multilog, const std::string & log_file_basename, const std::string & log_file_extension) { + static bool _multilog = false; + + if (multilog != LogTriStateSame) + { + _multilog = multilog == LogTriStateTrue; + } + std::stringstream buf; buf << log_file_basename; - //buf << "."; - //buf << log_get_pid(); + if (_multilog) + { + buf << "."; + buf << log_get_pid(); + } buf << "."; buf << log_file_extension; @@ -213,15 +231,6 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base #define LOG_TEE_FLF_VAL ,"" #endif -// Utility for synchronizing log configuration state -// since std::optional was introduced only in c++17 -enum LogTriState -{ - LogTriStateSame, - LogTriStateFalse, - LogTriStateTrue -}; - // INTERNAL, DO NOT USE // USE LOG() INSTEAD // @@ -315,16 +324,23 @@ enum LogTriState #endif // INTERNAL, DO NOT USE -inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr) +inline FILE *log_handler1_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr) { - static bool _initialized{false}; - static bool _disabled{(filename.empty() && target == nullptr)}; + static bool _initialized = false; + static bool _append = false; + static bool _disabled = filename.empty() && target == nullptr; static std::string log_current_filename{filename}; static FILE *log_current_target{target}; static FILE *logfile = nullptr; if (change) { + if (append != LogTriStateSame) + { + _append = append == LogTriStateTrue; + return logfile; + } + if (disable == LogTriStateTrue) { // Disable primary target @@ -377,7 +393,7 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri } } - logfile = fopen(filename.c_str(), "w"); + logfile = fopen(filename.c_str(), _append ? "a" : "w"); } if (!logfile) @@ -398,9 +414,9 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri } // INTERNAL, DO NOT USE -inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME) +inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME) { - return log_handler1_impl(change, disable, filename, target); + return log_handler1_impl(change, append, disable, filename, target); } // Disables logs entirely at runtime. @@ -411,7 +427,7 @@ inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTri // INTERNAL, DO NOT USE inline FILE *log_disable_impl() { - return log_handler1_impl(true, LogTriStateTrue); + return log_handler1_impl(true, LogTriStateSame, LogTriStateTrue); } // Enables logs at runtime. @@ -420,19 +436,31 @@ inline FILE *log_disable_impl() // INTERNAL, DO NOT USE inline FILE *log_enable_impl() { - return log_handler1_impl(true, LogTriStateFalse); + return log_handler1_impl(true, LogTriStateSame, LogTriStateFalse); } // Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*) #define log_set_target(target) log_set_target_impl(target) // INTERNAL, DO NOT USE -inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, filename); } -inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, target); } +inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, LogTriStateSame, filename); } +inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, LogTriStateSame, target); } // INTERNAL, DO NOT USE inline FILE *log_handler() { return log_handler1_impl(); } +// Enable or disable creating separate log files for each run. +// can ONLY be invoked BEFORE first log use. +#define log_multilog(enable) log_filename_generator_impl((enable) ? LogTriStateTrue : LogTriStateFalse, "", "") +// Enable or disable append mode for log file. +// can ONLY be invoked BEFORE first log use. +#define log_append(enable) log_append_impl(enable) +// INTERNAL, DO NOT USE +inline FILE *log_append_impl(bool enable) +{ + return log_handler1_impl(true, enable ? LogTriStateTrue : LogTriStateFalse, LogTriStateSame); +} + inline void log_test() { log_disable(); @@ -494,6 +522,18 @@ inline bool log_param_single_parse(const std::string & param) return true; } + if (param == "--log-new") + { + log_multilog(true); + return true; + } + + if (param == "--log-append") + { + log_append(true); + return true; + } + return false; } @@ -523,7 +563,9 @@ inline void log_print_usage() printf(" --log-disable Disable trace logs\n"); printf(" --log-enable Enable trace logs\n"); printf(" --log-file Specify a log filename (without extension)\n"); - printf(" Log file will be tagged with unique ID and written as \"..log\"\n"); /* */ + printf(" --log-new Create a separate new log file on start. " + "Each log file will have unique name: \"..log\"\n"); + printf(" --log-append Don't truncate the old log file.\n"); } #define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv) From 0e40806c1cb3bdf9955ed807ffbe212be85b4c67 Mon Sep 17 00:00:00 2001 From: bandoti <141645996+bandoti@users.noreply.github.com> Date: Wed, 1 Nov 2023 14:42:01 -0300 Subject: [PATCH 009/664] common : allow caller to handle help/argument exceptions (#3715) * Allow caller to handle help/argument exceptions * Prepend newline to usage output * Add new gpt_params_parse_ex function to hide arg-parse impl * Fix issue blocking success case * exit instead of returning false * Update common/common.h Co-authored-by: Georgi Gerganov * Update common/common.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 41 ++++++++++++++++++++++++++--------------- common/common.h | 2 ++ 2 files changed, 28 insertions(+), 15 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index dc4865e80..89be41261 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -103,9 +103,24 @@ void process_escapes(std::string& input) { } bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { + bool result = true; + try { + if (!gpt_params_parse_ex(argc, argv, params)) { + gpt_print_usage(argc, argv, gpt_params()); + exit(0); + } + } + catch (const std::invalid_argument& ex) { + fprintf(stderr, ex.what()); + gpt_print_usage(argc, argv, gpt_params()); + exit(1); + } + return result; +} + +bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { bool invalid_param = false; std::string arg; - gpt_params default_params; const std::string arg_prefix = "--"; llama_sampling_params & sparams = params.sparams; @@ -554,11 +569,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } } else if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, default_params); -#ifndef LOG_DISABLE_LOGS - log_print_usage(); -#endif // LOG_DISABLE_LOGS - exit(0); + return false; + } else if (arg == "--random-prompt") { params.random_prompt = true; } else if (arg == "--in-prefix-bos") { @@ -617,22 +629,17 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { // End of Parse args for logging parameters #endif // LOG_DISABLE_LOGS } else { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - gpt_print_usage(argc, argv, default_params); - exit(1); + throw std::invalid_argument("error: unknown argument: " + arg); } } if (invalid_param) { - fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - gpt_print_usage(argc, argv, default_params); - exit(1); + throw std::invalid_argument("error: invalid parameter for argument: " + arg); } if (params.prompt_cache_all && (params.interactive || params.interactive_first || params.instruct)) { - fprintf(stderr, "error: --prompt-cache-all not supported in interactive mode yet\n"); - gpt_print_usage(argc, argv, default_params); - exit(1); + + throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); } if (params.escape) { @@ -651,6 +658,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { const llama_sampling_params & sparams = params.sparams; + printf("\n"); printf("usage: %s [options]\n", argv[0]); printf("\n"); printf("options:\n"); @@ -762,6 +770,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -ld LOGDIR, --logdir LOGDIR\n"); printf(" path under which to save YAML logs (no logging if unset)\n"); printf("\n"); +#ifndef LOG_DISABLE_LOGS + log_print_usage(); +#endif // LOG_DISABLE_LOGS } std::string get_system_info(const gpt_params & params) { diff --git a/common/common.h b/common/common.h index 84523a4fb..343b27217 100644 --- a/common/common.h +++ b/common/common.h @@ -110,6 +110,8 @@ struct gpt_params { std::string image = ""; // path to an image file }; +bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); + bool gpt_params_parse(int argc, char ** argv, gpt_params & params); void gpt_print_usage(int argc, char ** argv, const gpt_params & params); From 50337961a678fce4081554b24e56e86b67660163 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 20:11:02 +0200 Subject: [PATCH 010/664] llm : add llm_build_context (#3881) * llm : add llm_build_context * llm : deduce norm eps based on type + explict max_alibi_bias, clamp_kqv * llm : restore the non-graph llm_build_ functional API ggml-ci * llm : cleanup + comments --- llama.cpp | 2338 ++++++++++++++++++++++++----------------------------- 1 file changed, 1042 insertions(+), 1296 deletions(-) diff --git a/llama.cpp b/llama.cpp index 42cedc7a1..d0c4ef101 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3090,6 +3090,10 @@ static bool llama_model_load( return true; } +// +// llm_build +// + using llm_build_cb = std::function; enum llm_rope_type { @@ -3098,17 +3102,35 @@ enum llm_rope_type { LLM_ROPE_GLM, }; +enum llm_ffn_op_type { + LLM_FFN_SILU, + LLM_FFN_GELU, + LLM_FFN_RELU, + LLM_FFN_RELU_SQR, +}; + +enum llm_ffn_gate_type { + LLM_FFN_SEQ, + LLM_FFN_PAR, // ffn_gate is parallel to ffn_up +}; + +enum llm_norm_type { + LLM_NORM, + LLM_NORM_RMS, +}; + static struct ggml_tensor * llm_build_inp_embd( struct ggml_context * ctx, + const llama_hparams & hparams, const llama_batch & batch, struct ggml_tensor * tok_embd, - int64_t n_embd, - int32_t n_tokens, const llm_build_cb & cb) { + const int64_t n_embd = hparams.n_embd; + struct ggml_tensor * inpL; if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens); cb(inp_tokens, "inp_tokens", -1); inpL = ggml_get_rows(ctx, tok_embd, inp_tokens); @@ -3117,7 +3139,7 @@ static struct ggml_tensor * llm_build_inp_embd( GGML_ASSERT(false && "not implemented"); #endif - inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); + inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); } return inpL; @@ -3126,28 +3148,21 @@ static struct ggml_tensor * llm_build_inp_embd( // Persimmon: n_rot = n_embd_head/2 // Other: n_rot = n_embd_head static void llm_build_k_shift( - const llama_context & lctx, - struct ggml_context * ctx, - struct ggml_cgraph * graph, - int64_t n_rot, - llm_rope_type type, - const llm_build_cb & cb) { - const auto & model = lctx.model; - const auto & kv_self = lctx.kv_self; - const auto & cparams = lctx.cparams; - - const auto & hparams = model.hparams; - + struct ggml_context * ctx, + const llama_hparams & hparams, + const llama_kv_cache & kv, + struct ggml_cgraph * graph, + llm_rope_type type, + int64_t n_ctx, + int64_t n_rot, + float freq_base, + float freq_scale, + const llm_build_cb & cb) { const int64_t n_layer = hparams.n_layer; const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_gqa = hparams.n_embd_gqa(); const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_ctx = lctx.cparams.n_ctx; - - const float freq_base = cparams.rope_freq_base; - const float freq_scale = cparams.rope_freq_scale; - GGML_ASSERT(n_embd_head % n_rot == 0); struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx); @@ -3165,11 +3180,11 @@ static void llm_build_k_shift( struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions ggml_rope_custom_inplace(ctx, - ggml_view_3d(ctx, kv_self.k, + ggml_view_3d(ctx, kv.k, n_rot, n_head_kv, n_ctx, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il), + ggml_element_size(kv.k)*n_embd_head, + ggml_element_size(kv.k)*n_embd_gqa, + ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il), K_shift, n_rot, rope_type, 0, freq_base, freq_scale); cb(tmp, "K_shifted", il); ggml_build_forward_expand(graph, tmp); @@ -3177,22 +3192,17 @@ static void llm_build_k_shift( } static void llm_build_kv_store( - const llama_context & lctx, struct ggml_context * ctx, + const llama_hparams & hparams, + const llama_kv_cache & kv, struct ggml_cgraph * graph, struct ggml_tensor * k_cur, struct ggml_tensor * v_cur, + int64_t n_ctx, int32_t n_tokens, int32_t kv_head, const llm_build_cb & cb, int64_t il) { - const auto & model = lctx.model; - const auto & kv_self = lctx.kv_self; - const auto & cparams = lctx.cparams; - - const auto & hparams = model.hparams; - - const int64_t n_ctx = cparams.n_ctx; const int64_t n_embd_gqa = hparams.n_embd_gqa(); // compute the transposed [n_tokens, n_embd] V matrix @@ -3200,13 +3210,13 @@ static void llm_build_kv_store( //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed cb(v_cur_t, "v_cur_t", il); - struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv_self.k, n_tokens*n_embd_gqa, - (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); + struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k, n_tokens*n_embd_gqa, + (ggml_element_size(kv.k)*n_embd_gqa)*(il*n_ctx + kv_head)); cb(k_cache_view, "k_cache_view", il); - struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv_self.v, n_tokens, n_embd_gqa, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); + struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v, n_tokens, n_embd_gqa, + ( n_ctx)*ggml_element_size(kv.v), + (il*n_ctx)*ggml_element_size(kv.v)*n_embd_gqa + kv_head*ggml_element_size(kv.v)); cb(v_cache_view, "v_cache_view", il); // important: storing RoPE-ed version of K in the KV cache! @@ -3214,23 +3224,18 @@ static void llm_build_kv_store( ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view)); } -enum llm_norm_type { - LLM_NORM, - LLM_NORM_RMS, -}; - static struct ggml_tensor * llm_build_norm( struct ggml_context * ctx, struct ggml_tensor * cur, + const llama_hparams & hparams, struct ggml_tensor * mw, struct ggml_tensor * mb, llm_norm_type type, - float eps, const llm_build_cb & cb, int il) { switch (type) { - case LLM_NORM: cur = ggml_norm (ctx, cur, eps); break; - case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, eps); break; + case LLM_NORM: cur = ggml_norm (ctx, cur, hparams.f_norm_eps); break; + case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break; } if (mw || mb) { @@ -3251,18 +3256,6 @@ static struct ggml_tensor * llm_build_norm( return cur; } -enum llm_ffn_op_type { - LLM_FFN_SILU, - LLM_FFN_GELU, - LLM_FFN_RELU, - LLM_FFN_RELU_SQR, -}; - -enum llm_ffn_gate_type { - LLM_FFN_SEQ, - LLM_FFN_PAR, // ffn_gate is parallel to ffn_up -}; - static struct ggml_tensor * llm_build_ffn( struct ggml_context * ctx, struct ggml_tensor * cur, @@ -3351,26 +3344,21 @@ static struct ggml_tensor * llm_build_ffn( // if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( - const llama_context & lctx, struct ggml_context * ctx, struct ggml_tensor * cur, + const llama_hparams & hparams, + const llama_kv_cache & kv, struct ggml_tensor * wo, struct ggml_tensor * wo_b, struct ggml_tensor * q_cur, struct ggml_tensor * kq_scale, struct ggml_tensor * kq_mask, + int64_t n_ctx, int32_t n_tokens, int32_t n_kv, - float alibi_bias_max, + float max_alibi_bias, const llm_build_cb & cb, - int il) { - const auto & model = lctx.model; - const auto & kv_self = lctx.kv_self; - const auto & cparams = lctx.cparams; - - const auto & hparams = model.hparams; - - const int64_t n_ctx = cparams.n_ctx; + int il) { const int64_t n_embd = hparams.n_embd; const int64_t n_head = hparams.n_head; const int64_t n_head_kv = hparams.n_head_kv; @@ -3381,11 +3369,11 @@ static struct ggml_tensor * llm_build_kqv( cb(q, "q", il); struct ggml_tensor * k = - ggml_view_3d(ctx, kv_self.k, + ggml_view_3d(ctx, kv.k, n_embd_head, n_kv, n_head_kv, - ggml_element_size(kv_self.k)*n_embd_gqa, - ggml_element_size(kv_self.k)*n_embd_head, - ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); + ggml_element_size(kv.k)*n_embd_gqa, + ggml_element_size(kv.k)*n_embd_head, + ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il); cb(k, "k", il); struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); @@ -3394,11 +3382,11 @@ static struct ggml_tensor * llm_build_kqv( kq = ggml_scale(ctx, kq, kq_scale); cb(kq, "kq_scaled", il); - if (alibi_bias_max > 0.0f) { + if (max_alibi_bias > 0.0f) { // TODO: n_head or n_head_kv // TODO: K-shift is likely not working // TODO: change to ggml_add - kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, alibi_bias_max); + kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias); cb(kq, "kq_scaled_alibi", il); } @@ -3410,11 +3398,11 @@ static struct ggml_tensor * llm_build_kqv( // split cached v into n_head heads struct ggml_tensor * v = - ggml_view_3d(ctx, kv_self.v, + ggml_view_3d(ctx, kv.v, n_kv, n_embd_head, n_head_kv, - ggml_element_size(kv_self.v)*n_ctx, - ggml_element_size(kv_self.v)*n_ctx*n_embd_head, - ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il); + ggml_element_size(kv.v)*n_ctx, + ggml_element_size(kv.v)*n_ctx*n_embd_head, + ggml_element_size(kv.v)*n_ctx*n_embd_gqa*il); cb(v, "v", il); struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq); @@ -3438,1259 +3426,1011 @@ static struct ggml_tensor * llm_build_kqv( return cur; } -static struct ggml_cgraph * llm_build_llama( +struct llm_build_context { + const llama_model & model; + const llama_hparams & hparams; + const llama_cparams & cparams; + const llama_batch & batch; + const llama_kv_cache & kv_self; + + const int64_t n_embd; + const int64_t n_layer; + const int64_t n_ctx; // user-specified context size (can be different from n_ctx_train) + const int64_t n_head; + const int64_t n_head_kv; + const int64_t n_embd_head; + const int64_t n_embd_gqa; + + const float freq_base; + const float freq_scale; + const float norm_eps; + const float norm_rms_eps; + + const int32_t n_tokens; + const int32_t n_kv; // size of KV cache to consider (n_kv <= n_ctx) + const int32_t kv_head; // index of where we store new KV data in the cache + + const bool do_rope_shift; + + const llm_build_cb & cb; + + llama_buffer & buf_compute; + + struct ggml_context * ctx0 = nullptr; + + // TODO: consider making the entire interface noexcept + llm_build_context( llama_context & lctx, const llama_batch & batch, const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; + bool worst_case) : + model (lctx.model), + hparams (model.hparams), + cparams (lctx.cparams), + batch (batch), + kv_self (lctx.kv_self), + n_embd (hparams.n_embd), + n_layer (hparams.n_layer), + n_ctx (cparams.n_ctx), + n_head (hparams.n_head), + n_head_kv (hparams.n_head_kv), + n_embd_head (hparams.n_embd_head()), + n_embd_gqa (hparams.n_embd_gqa()), + freq_base (cparams.rope_freq_base), + freq_scale (cparams.rope_freq_scale), + norm_eps (hparams.f_norm_eps), + norm_rms_eps (hparams.f_norm_rms_eps), + n_tokens (batch.n_tokens), + n_kv (worst_case ? n_ctx : kv_self.n), + kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), + do_rope_shift (worst_case || kv_self.has_shift), + cb (cb), + buf_compute (lctx.buf_compute) { + GGML_ASSERT(!!kv_self.ctx); - const auto & kv_self = lctx.kv_self; + // all initializations should be done in init() + } - GGML_ASSERT(!!kv_self.ctx); + void init() { + struct ggml_init_params params = { + /*.mem_size =*/ buf_compute.size, + /*.mem_buffer =*/ buf_compute.data, + /*.no_alloc =*/ true, + }; - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head = hparams.n_embd_head(); - - GGML_ASSERT(n_embd_head == hparams.n_rot); - - const float freq_base = cparams.rope_freq_base; - const float freq_scale = cparams.rope_freq_scale; - const float norm_rms_eps = hparams.f_norm_rms_eps; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - const bool do_rope_shift = worst_case || kv_self.has_shift; - - //printf("n_kv = %d\n", n_kv); - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - cb(inp_pos, "inp_pos", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb); + ctx0 = ggml_init(params); } - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; + void free() { + if (ctx0) { + ggml_free(ctx0); + ctx0 = nullptr; + } + } + + struct ggml_cgraph * build_llama() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_baichuan() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + switch (model.type) { + case MODEL_7B: + Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + break; + case MODEL_13B: + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens); + break; + default: + GGML_ASSERT(false); + } + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + // apply ALiBi for 13B model + const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f; + + cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_falcon() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * attn_norm; + + attn_norm = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(attn_norm, "attn_norm", il); + + // self-attention + { + if (model.layers[il].attn_norm_2) { + // Falcon-40B + cur = llm_build_norm(ctx0, attn_norm, hparams, + model.layers[il].attn_norm_2, + model.layers[il].attn_norm_2_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm_2", il); + } else { + cur = attn_norm; + } + + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + // using mode = 2 for neox mode + Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, attn_norm, hparams, kv_self, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = cur; + + // feed forward + { + cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result + model.layers[il].ffn_up, NULL, + NULL, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; // norm - cur = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, il); - cb(cur, "attn_norm", il); + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); - // self-attention - { - // compute Q and K and RoPE them - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); + ggml_build_forward_expand(gf, cur); - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); - cb(Qcur, "Qcur", il); - - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); - cb(Kcur, "Kcur", il); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - cur = llm_build_kqv(lctx, ctx0, cur, - model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; + return gf; } - cur = inpL; + struct ggml_cgraph * build_starcoder() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); - cur = llm_build_norm(ctx0, cur, - model.output_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, -1); - cb(cur, "result_norm", -1); + struct ggml_tensor * cur; + struct ggml_tensor * pos; + struct ggml_tensor * inpL; - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); - ggml_build_forward_expand(gf, cur); + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); - ggml_free(ctx0); + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); - return gf; -} + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); -static struct ggml_cgraph * llm_build_baichaun( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; + pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); + cb(pos, "pos_embd", -1); - const auto & kv_self = lctx.kv_self; + inpL = ggml_add(ctx0, inpL, pos); + cb(inpL, "inpL", -1); - GGML_ASSERT(!!kv_self.ctx); + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head = hparams.n_embd_head(); + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); - GGML_ASSERT(n_embd_head == hparams.n_rot); + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); - const float freq_base = cparams.rope_freq_base; - const float freq_scale = cparams.rope_freq_scale; - const float norm_rms_eps = hparams.f_norm_rms_eps; + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); - const bool do_rope_shift = worst_case || kv_self.has_shift; + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - auto & buf_compute = lctx.buf_compute; + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - cb(inp_pos, "inp_pos", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb); - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - cur = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); - - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - - switch (model.type) { - case MODEL_7B: - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); - break; - case MODEL_13B: - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens); - break; - default: - GGML_ASSERT(false); - } - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - // apply ALiBi for 13B model - const float alibi_bias_max = model.type == MODEL_13B ? 8.0f : -1.0f; - - cur = llm_build_kqv(lctx, ctx0, cur, - model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, alibi_bias_max, cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, - model.output_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, -1); - cb(cur, "result_norm", -1); - - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_falcon( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - - GGML_ASSERT(n_embd_head == hparams.n_rot); - - const float freq_base = cparams.rope_freq_base; - const float freq_scale = cparams.rope_freq_scale; - const float norm_eps = hparams.f_norm_eps; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - const bool do_rope_shift = worst_case || kv_self.has_shift; - - //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n", - // kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift); - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - cb(inp_pos, "inp_pos", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE_NEOX, cb); - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * attn_norm; - - attn_norm = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, - model.layers[il].attn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(attn_norm, "attn_norm", il); - - // self-attention - { - if (model.layers[il].attn_norm_2) { - // Falcon-40B - cur = llm_build_norm(ctx0, attn_norm, - model.layers[il].attn_norm_2, - model.layers[il].attn_norm_2_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "attn_norm_2", il); - } else { - cur = attn_norm; + cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); } - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - - // using mode = 2 for neox mode - Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); - cb(Qcur, "Qcur", il); - - Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); - cb(Kcur, "Kcur", il); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - cur = llm_build_kqv(lctx, ctx0, attn_norm, - model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = cur; - - // feed forward - { - cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result - model.layers[il].ffn_up, NULL, - NULL, NULL, - model.layers[il].ffn_down, NULL, - LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - cur = ggml_add(ctx0, cur, inpL); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - // norm - cur = llm_build_norm(ctx0, cur, - model.output_norm, - model.output_norm_b, - LLM_NORM, norm_eps, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_starcoder( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - - GGML_ASSERT(n_embd_head == hparams.n_rot); - - const float norm_eps = hparams.f_norm_eps; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * pos; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - cb(inp_pos, "inp_pos", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); - cb(pos, "pos_embd", -1); - - inpL = ggml_add(ctx0, inpL, pos); - cb(inpL, "inpL", -1); - - for (int il = 0; il < n_layer; ++il) { - cur = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, - model.layers[il].attn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); - - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - cur = llm_build_kqv(lctx, ctx0, cur, - model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); - cb(cur, "kqv_out", il); - } - - // add the input - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); - cb(ffn_inp, "ffn_inp", il); - - // FF - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - inpL = ggml_add(ctx0, cur, ffn_inp); - cb(inpL, "l_out", il); - } - - cur = llm_build_norm(ctx0, inpL, - model.output_norm, - model.output_norm_b, - LLM_NORM, norm_eps, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_persimmon( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const auto & cparams = lctx.cparams; - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_head = hparams.n_head; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_rot = n_embd_head / 2; - - const float freq_base = cparams.rope_freq_base; - const float freq_scale = cparams.rope_freq_scale; - const float norm_eps = hparams.f_norm_eps; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - const bool do_rope_shift = worst_case || kv_self.has_shift; - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "imp_embd", -1); - - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); - cb(inp_pos, "inp_pos", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - if (do_rope_shift) { - llm_build_k_shift(lctx, ctx0, gf, n_rot, LLM_ROPE_NEOX, cb); - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * residual = inpL; - - cur = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, - model.layers[il].attn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "attn_norm", il); - - // self attention - { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); - - // split qkv - GGML_ASSERT(n_head_kv == n_head); - - struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens); - cb(tmpqkv, "tmpqkv", il); - - struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2)); - cb(tmpqkv_perm, "tmpqkv", il); - - struct ggml_tensor * tmpq = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - 0 - ); - cb(tmpq, "tmpq", il); - - struct ggml_tensor * tmpk = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens - ); - cb(tmpk, "tmpk", il); - - // Q/K Layernorm - tmpq = llm_build_norm(ctx0, tmpq, - model.layers[il].attn_q_norm, - model.layers[il].attn_q_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(tmpq, "tmpq", il); - - tmpk = llm_build_norm(ctx0, tmpk, - model.layers[il].attn_k_norm, - model.layers[il].attn_k_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(tmpk, "tmpk", il); - - // RoPE the first n_rot of q/k, pass the other half, and concat. - struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - 0 - ); - cb(qrot, "qrot", il); - - struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - 0 - ); - cb(krot, "krot", il); - - // get the second half of tmpq, e.g tmpq[n_rot:, :, :] - struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * n_rot - ); - cb(qpass, "qpass", il); - - struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * n_rot - ); - cb(kpass, "kpass", il); - - struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale - ); - cb(qrotated, "qrotated", il); - - struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale - ); - cb(krotated, "krotated", il); - - // ggml currently only supports concatenation on dim=2 - // so we need to permute qrot, qpass, concat, then permute back. - qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); - cb(qrotated, "qrotated", il); - - krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); - cb(krotated, "krotated", il); - - qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); - cb(qpass, "qpass", il); - - kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); - cb(kpass, "kpass", il); - - struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); - cb(Qcur, "Qcur", il); - - struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3)); - cb(Q, "Q", il); - - Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Vcur = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 - ); - cb(Vcur, "Vcur", il); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - // TODO: not tested, could be broken - cur = llm_build_kqv(lctx, ctx0, Q, - model.layers[il].wo, model.layers[il].bo, - Q, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, - model.output_norm, - model.output_norm_b, - LLM_NORM, norm_eps, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_refact( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head = hparams.n_embd_head(); - - const float norm_rms_eps = hparams.f_norm_rms_eps; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ true, - }; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - cur = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); - - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - cb(Kcur, "Kcur", il); - - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - cb(Qcur, "Qcur", il); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - cur = llm_build_kqv(lctx, ctx0, Qcur, - model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, - model.output_norm, NULL, - LLM_NORM_RMS, norm_rms_eps, cb, -1); - cb(cur, "result_norm", -1); - - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_bloom( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - - GGML_ASSERT(n_embd_head == hparams.n_rot); - - const float norm_eps = hparams.f_norm_eps; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ false, - }; - - params.no_alloc = true; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - inpL = llm_build_norm(ctx0, inpL, - model.tok_norm, - model.tok_norm_b, - LLM_NORM, norm_eps, cb, -1); - cb(inpL, "inp_norm", -1); - - for (int il = 0; il < n_layer; ++il) { - cur = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, - model.layers[il].attn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); - - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - cur = llm_build_kqv(lctx, ctx0, Qcur, - model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il); - cb(cur, "kqv_out", il); - } - - // Add the input - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); - cb(ffn_inp, "ffn_inp", il); - - // FF - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, norm_eps, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - inpL = ggml_add(ctx0, cur, ffn_inp); - cb(inpL, "l_out", il); - } - - cur = llm_build_norm(ctx0, inpL, - model.output_norm, - model.output_norm_b, - LLM_NORM, norm_eps, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} - -static struct ggml_cgraph * llm_build_mpt( - llama_context & lctx, - const llama_batch & batch, - const llm_build_cb & cb, - bool worst_case) { - const auto & model = lctx.model; - const auto & hparams = model.hparams; - const auto & cparams = lctx.cparams; - - const auto & kv_self = lctx.kv_self; - - GGML_ASSERT(!!kv_self.ctx); - - const int64_t n_embd = hparams.n_embd; - const int64_t n_layer = hparams.n_layer; - const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - - const float norm_eps = hparams.f_norm_eps; - const float clamp_kqv = hparams.f_clamp_kqv; - const float max_alibi_bias = hparams.f_max_alibi_bias; - - const int32_t n_tokens = batch.n_tokens; - const int32_t n_kv = worst_case ? n_ctx : kv_self.n; - const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head; - - auto & buf_compute = lctx.buf_compute; - - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ false, - }; - - params.no_alloc = true; - - struct ggml_context * ctx0 = ggml_init(params); - - ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb); - cb(inpL, "inp_embd", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); - cb(KQ_mask, "KQ_mask", -1); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * attn_norm; - - attn_norm = llm_build_norm(ctx0, inpL, - model.layers[il].attn_norm, - NULL, - LLM_NORM, norm_eps, cb, il); - cb(attn_norm, "attn_norm", il); - - // self-attention - { - cur = attn_norm; - - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - if (clamp_kqv > 0.0f) { - cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv); - cb(cur, "wqkv_clamped", il); + // add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // FF + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); } - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - - llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - - cur = llm_build_kqv(lctx, ctx0, Qcur, - model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, max_alibi_bias, cb, il); - cb(cur, "kqv_out", il); + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); } - // Add the input - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); - cb(ffn_inp, "ffn_inp", il); + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); - // feed forward - { - cur = llm_build_norm(ctx0, ffn_inp, - model.layers[il].ffn_norm, + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_persimmon() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + const int64_t n_rot = n_embd_head / 2; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "imp_embd", -1); + + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * residual = inpL; + + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + // split qkv + GGML_ASSERT(n_head_kv == n_head); + + struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens); + cb(tmpqkv, "tmpqkv", il); + + struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2)); + cb(tmpqkv_perm, "tmpqkv", il); + + struct ggml_tensor * tmpq = ggml_view_3d( + ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, + ggml_element_size(tmpqkv_perm) * n_embd_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, + 0 + ); + cb(tmpq, "tmpq", il); + + struct ggml_tensor * tmpk = ggml_view_3d( + ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, + ggml_element_size(tmpqkv_perm) * n_embd_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens + ); + cb(tmpk, "tmpk", il); + + // Q/K Layernorm + tmpq = llm_build_norm(ctx0, tmpq, hparams, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, cb, il); + cb(tmpq, "tmpq", il); + + tmpk = llm_build_norm(ctx0, tmpk, hparams, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, cb, il); + cb(tmpk, "tmpk", il); + + // RoPE the first n_rot of q/k, pass the other half, and concat. + struct ggml_tensor * qrot = ggml_view_3d( + ctx0, tmpq, n_rot, n_head, n_tokens, + ggml_element_size(tmpq) * n_embd_head, + ggml_element_size(tmpq) * n_embd_head * n_head, + 0 + ); + cb(qrot, "qrot", il); + + struct ggml_tensor * krot = ggml_view_3d( + ctx0, tmpk, n_rot, n_head, n_tokens, + ggml_element_size(tmpk) * n_embd_head, + ggml_element_size(tmpk) * n_embd_head * n_head, + 0 + ); + cb(krot, "krot", il); + + // get the second half of tmpq, e.g tmpq[n_rot:, :, :] + struct ggml_tensor * qpass = ggml_view_3d( + ctx0, tmpq, n_rot, n_head, n_tokens, + ggml_element_size(tmpq) * n_embd_head, + ggml_element_size(tmpq) * n_embd_head * n_head, + ggml_element_size(tmpq) * n_rot + ); + cb(qpass, "qpass", il); + + struct ggml_tensor * kpass = ggml_view_3d( + ctx0, tmpk, n_rot, n_head, n_tokens, + ggml_element_size(tmpk) * n_embd_head, + ggml_element_size(tmpk) * n_embd_head * n_head, + ggml_element_size(tmpk) * n_rot + ); + cb(kpass, "kpass", il); + + struct ggml_tensor * qrotated = ggml_rope_custom( + ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale + ); + cb(qrotated, "qrotated", il); + + struct ggml_tensor * krotated = ggml_rope_custom( + ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale + ); + cb(krotated, "krotated", il); + + // ggml currently only supports concatenation on dim=2 + // so we need to permute qrot, qpass, concat, then permute back. + qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); + cb(qrotated, "qrotated", il); + + krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); + cb(krotated, "krotated", il); + + qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); + cb(qpass, "qpass", il); + + kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); + cb(kpass, "kpass", il); + + struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3)); + cb(Q, "Q", il); + + Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_view_3d( + ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, + ggml_element_size(tmpqkv_perm) * n_embd_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, + ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 + ); + cb(Vcur, "Vcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + // TODO: not tested, could be broken + cur = llm_build_kqv(ctx0, Q, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_refact() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + cb(Kcur, "Kcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cb(Qcur, "Qcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_bloom() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + inpL = llm_build_norm(ctx0, inpL, hparams, + model.tok_norm, + model.tok_norm_b, + LLM_NORM, cb, -1); + cb(inpL, "inp_norm", -1); + + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); + cb(cur, "kqv_out", il); + } + + // Add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // FF + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + } + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_mpt() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * attn_norm; + + attn_norm = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, - LLM_NORM, norm_eps, cb, il); - cb(cur, "ffn_norm", il); + LLM_NORM, cb, il); + cb(attn_norm, "attn_norm", il); - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - NULL, NULL, - model.layers[il].ffn_down, NULL, - LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); + // self-attention + { + cur = attn_norm; + + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + if (hparams.f_clamp_kqv > 0.0f) { + cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); + cb(cur, "wqkv_clamped", il); + } + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + model.layers[il].wo, NULL, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il); + cb(cur, "kqv_out", il); + } + + // Add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // feed forward + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + NULL, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + NULL, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; } - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); + cur = inpL; - // input for next layer - inpL = cur; + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, + NULL, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, - model.output_norm, - NULL, - LLM_NORM, norm_eps, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - ggml_free(ctx0); - - return gf; -} +}; // // tensor offloading helpers @@ -5122,43 +4862,49 @@ static struct ggml_cgraph * llama_build_graph( struct ggml_cgraph * result = NULL; + struct llm_build_context llm(lctx, batch, cb, worst_case); + + llm.init(); + switch (model.arch) { case LLM_ARCH_LLAMA: { - result = llm_build_llama(lctx, batch, cb, worst_case); + result = llm.build_llama(); } break; case LLM_ARCH_BAICHUAN: { - result = llm_build_baichaun(lctx, batch, cb, worst_case); + result = llm.build_baichuan(); } break; case LLM_ARCH_FALCON: { - result = llm_build_falcon(lctx, batch, cb, worst_case); + result = llm.build_falcon(); } break; case LLM_ARCH_STARCODER: { - result = llm_build_starcoder(lctx, batch, cb, worst_case); + result = llm.build_starcoder(); } break; case LLM_ARCH_PERSIMMON: { - result = llm_build_persimmon(lctx, batch, cb, worst_case); + result = llm.build_persimmon(); } break; case LLM_ARCH_REFACT: { - result = llm_build_refact(lctx, batch, cb, worst_case); + result = llm.build_refact(); } break; case LLM_ARCH_BLOOM: { - result = llm_build_bloom(lctx, batch, cb, worst_case); + result = llm.build_bloom(); } break; case LLM_ARCH_MPT: { - result = llm_build_mpt(lctx, batch, cb, worst_case); + result = llm.build_mpt(); } break; default: GGML_ASSERT(false); } + llm.free(); + if (worst_case) { int n_non_view_total = 0; From ff8f9a88da0018972dfdf6fe64b5c8992caabd9c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 21:15:55 +0200 Subject: [PATCH 011/664] common : minor (#3715) --- common/common.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 89be41261..7a48e9d11 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -110,8 +110,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { exit(0); } } - catch (const std::invalid_argument& ex) { - fprintf(stderr, ex.what()); + catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); gpt_print_usage(argc, argv, gpt_params()); exit(1); } From e16b9fa4baa8a09c6619b116159830e898050942 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 21:25:00 +0200 Subject: [PATCH 012/664] metal : multi-simd softmax (#3710) ggml-ci --- ggml-metal.m | 9 +++- ggml-metal.metal | 129 +++++++++++++++++++++++++++++++++++++---------- 2 files changed, 108 insertions(+), 30 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index bc881395a..1f0341507 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1001,11 +1001,15 @@ void ggml_metal_graph_compute( } break; case GGML_OP_SOFT_MAX: { - const int nth = MIN(32, ne00); + int nth = 32; // SIMD width if (ne00%4 == 0) { [encoder setComputePipelineState:ctx->pipeline_soft_max_4]; } else { + do { + nth *= 2; + } while (nth <= ne00 && nth <= 1024); + nth /= 2; [encoder setComputePipelineState:ctx->pipeline_soft_max]; } [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1013,8 +1017,9 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; case GGML_OP_DIAG_MASK_INF: { diff --git a/ggml-metal.metal b/ggml-metal.metal index f4b460564..f3152778a 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -184,36 +184,73 @@ kernel void kernel_soft_max( constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + threadgroup float * buf [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + uint tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint ntg[[threads_per_threadgroup]]) { + const int64_t i03 = (tgpig) / (ne02*ne01); + const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01; + const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01); device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; // parallel max - float lmax = tpitg[0] < ne00 ? psrc0[tpitg[0]] : -INFINITY; - for (int i00 = tpitg[0] + ntg[0]; i00 < ne00; i00 += ntg[0]) { + float lmax = tpitg < ne00 ? psrc0[tpitg] : -INFINITY; + + for (int i00 = tpitg + ntg; i00 < ne00; i00 += ntg) { lmax = MAX(lmax, psrc0[i00]); } - const float max = simd_max(lmax); + + float max = simd_max(lmax); + if (tiisg == 0) { + buf[sgitg] = max; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // broadcast, simd group number is ntg / 32 + for (uint i = ntg / 32 / 2; i > 0; i /= 2) { + if (tpitg < i) { + buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]); + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + max = buf[0]; // parallel sum float lsum = 0.0f; - for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { + for (int i00 = tpitg; i00 < ne00; i00 += ntg) { const float exp_psrc0 = exp(psrc0[i00] - max); lsum += exp_psrc0; // Remember the result of exp here. exp is expensive, so we really do not - // whish to compute it twice. + // wish to compute it twice. pdst[i00] = exp_psrc0; } - const float sum = simd_sum(lsum); + float sum = simd_sum(lsum); + if (tiisg == 0) { + buf[sgitg] = sum; + } - for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { + threadgroup_barrier(mem_flags::mem_threadgroup); + + // broadcast, simd group number is ntg / 32 + for (uint i = ntg / 32 / 2; i > 0; i /= 2) { + if (tpitg < i) { + buf[tpitg] += buf[tpitg + i]; + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sum = buf[0]; + + for (int i00 = tpitg; i00 < ne00; i00 += ntg) { pdst[i00] /= sum; } } @@ -224,37 +261,73 @@ kernel void kernel_soft_max_4( constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + threadgroup float * buf [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + uint tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint ntg[[threads_per_threadgroup]]) { + const int64_t i03 = (tgpig) / (ne02*ne01); + const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01; + const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01); device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); // parallel max - float4 lmax4 = tpitg[0] < ne00/4 ? psrc4[tpitg[0]] : -INFINITY; - for (int i00 = tpitg[0] + ntg[0]; i00 < ne00/4; i00 += ntg[0]) { + float4 lmax4 = tpitg < ne00/4 ? psrc4[tpitg] : -INFINITY; + + for (int i00 = tpitg + ntg; i00 < ne00/4; i00 += ntg) { lmax4 = fmax(lmax4, psrc4[i00]); } - float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); - const float max = simd_max(lmax); + const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); + float max = simd_max(lmax); + if (tiisg == 0) { + buf[sgitg] = max; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // broadcast, simd group number is ntg / 32 + for (uint i = ntg / 32 / 2; i > 0; i /= 2) { + if (tpitg < i) { + buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]); + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + max = buf[0]; // parallel sum float4 lsum4 = 0.0f; - for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) { + for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { const float4 exp_psrc4 = exp(psrc4[i00] - max); lsum4 += exp_psrc4; pdst4[i00] = exp_psrc4; } - float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3]; - const float sum = simd_sum(lsum); + const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3]; + float sum = simd_sum(lsum); + if (tiisg == 0) { + buf[sgitg] = sum; + } - for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) { + threadgroup_barrier(mem_flags::mem_threadgroup); + + // broadcast, simd group number is ntg / 32 + for (uint i = ntg / 32 / 2; i > 0; i /= 2) { + if (tpitg < i) { + buf[tpitg] += buf[tpitg + i]; + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sum = buf[0]; + + for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { pdst4[i00] /= sum; } } @@ -274,7 +347,7 @@ kernel void kernel_diag_mask_inf( dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY; } else { dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00]; - } + } } kernel void kernel_diag_mask_inf_8( From 523e49b11174368cd73460fa5eae7b39d856f300 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 23:00:50 +0200 Subject: [PATCH 013/664] llm : fix falcon norm after refactoring (#3837) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index d0c4ef101..17cf364bb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3763,7 +3763,7 @@ struct llm_build_context { { if (model.layers[il].attn_norm_2) { // Falcon-40B - cur = llm_build_norm(ctx0, attn_norm, hparams, + cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il); From c43c2da8afacaddfe51c09b21dbd9922cd0ea46b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 23:08:30 +0200 Subject: [PATCH 014/664] llm : fix llm_build_kqv taking unused tensor (benign, #3837) --- llama.cpp | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/llama.cpp b/llama.cpp index 17cf364bb..1c6d482f8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3345,7 +3345,6 @@ static struct ggml_tensor * llm_build_ffn( // if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, - struct ggml_tensor * cur, const llama_hparams & hparams, const llama_kv_cache & kv, struct ggml_tensor * wo, @@ -3411,7 +3410,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); cb(kqv_merged, "kqv_merged", il); - cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); + struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); cb(cur, "kqv_merged_cont", il); cur = ggml_mul_mat(ctx, wo, cur); @@ -3565,7 +3564,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -3677,7 +3676,7 @@ struct llm_build_context { // apply ALiBi for 13B model const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f; - cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il); cb(cur, "kqv_out", il); @@ -3795,7 +3794,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, attn_norm, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -3895,7 +3894,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -4100,7 +4099,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); // TODO: not tested, could be broken - cur = llm_build_kqv(ctx0, Q, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -4191,7 +4190,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); cb(cur, "kqv_out", il); @@ -4288,7 +4287,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); cb(cur, "kqv_out", il); @@ -4382,7 +4381,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il); cb(cur, "kqv_out", il); From 898aeca90a9bb992f506234cf3b8b7f7fa28a1df Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Wed, 1 Nov 2023 18:04:33 -0400 Subject: [PATCH 015/664] llama : implement YaRN RoPE scaling (#2268) Co-authored-by: cebtenzzre Co-authored-by: Jeffrey Quesnelle --- common/common.cpp | 79 +++++- common/common.h | 7 + convert-baichuan-hf-to-gguf.py | 3 +- convert.py | 97 ++++--- examples/finetune/finetune.cpp | 5 +- examples/server/server.cpp | 59 ++++- .../train-text-from-scratch.cpp | 6 +- ggml-cuda.cu | 153 ++++++++--- ggml-metal.m | 22 +- ggml-metal.metal | 61 ++++- ggml.c | 241 +++++++++++++----- ggml.h | 20 +- gguf-py/gguf/gguf.py | 29 ++- llama.cpp | 220 ++++++++++++---- llama.h | 18 +- 15 files changed, 763 insertions(+), 257 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 7a48e9d11..b182ffaae 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -219,12 +219,52 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.rope_freq_scale = std::stof(argv[i]); + } else if (arg == "--rope-scaling") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; } + else { invalid_param = true; break; } } else if (arg == "--rope-scale") { if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = 1.0f/std::stof(argv[i]); + } else if (arg == "--yarn-orig-ctx") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_orig_ctx = std::stoi(argv[i]); + } else if (arg == "--yarn-ext-factor") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_ext_factor = std::stof(argv[i]); + } else if (arg == "--yarn-attn-factor") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_attn_factor = std::stof(argv[i]); + } else if (arg == "--yarn-beta-fast") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_fast = std::stof(argv[i]); + } else if (arg == "--yarn-beta-slow") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_slow = std::stof(argv[i]); } else if (arg == "--memory-f32") { params.memory_f16 = false; } else if (arg == "--top-p") { @@ -716,9 +756,16 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --cfg-negative-prompt-file FNAME\n"); printf(" negative prompt file to use for guidance. (default: empty)\n"); printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale); - printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale\n"); + printf(" --rope-scaling {none,linear,yarn}\n"); + printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); + printf(" --rope-scale N RoPE context scaling factor, expands context by a factor of N\n"); printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency linear scaling factor (default: loaded from model)\n"); + printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); + printf(" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\n"); + printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); + printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); + printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); + printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); @@ -826,17 +873,23 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { auto cparams = llama_context_default_params(); - cparams.n_ctx = params.n_ctx; - cparams.n_batch = params.n_batch; - cparams.n_threads = params.n_threads; - cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; - cparams.seed = params.seed; - cparams.f16_kv = params.memory_f16; - cparams.logits_all = params.logits_all; - cparams.embedding = params.embedding; - cparams.rope_freq_base = params.rope_freq_base; - cparams.rope_freq_scale = params.rope_freq_scale; + cparams.n_ctx = params.n_ctx; + cparams.n_batch = params.n_batch; + cparams.n_threads = params.n_threads; + cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + cparams.mul_mat_q = params.mul_mat_q; + cparams.seed = params.seed; + cparams.f16_kv = params.memory_f16; + cparams.logits_all = params.logits_all; + cparams.embedding = params.embedding; + cparams.rope_scaling_type = params.rope_scaling_type; + cparams.rope_freq_base = params.rope_freq_base; + cparams.rope_freq_scale = params.rope_freq_scale; + cparams.yarn_ext_factor = params.yarn_ext_factor; + cparams.yarn_attn_factor = params.yarn_attn_factor; + cparams.yarn_beta_fast = params.yarn_beta_fast; + cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.yarn_orig_ctx = params.yarn_orig_ctx; return cparams; } diff --git a/common/common.h b/common/common.h index 343b27217..7be69f925 100644 --- a/common/common.h +++ b/common/common.h @@ -9,6 +9,7 @@ #define LOG_NO_FILE_LINE_FUNCTION #include "log.h" +#include #include #include #include @@ -54,6 +55,12 @@ struct gpt_params { int32_t n_beams = 0; // if non-zero then use beam search of given width. float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = NAN; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f;// YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters struct llama_sampling_params sparams; diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 5ee99be73..67ccbe99f 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -163,7 +163,8 @@ gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: if "type" in hparams["rope_scaling"]: if hparams["rope_scaling"]["type"] == "linear": - gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) + gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"]) # TOKENIZATION diff --git a/convert.py b/convert.py index bfbfab283..9110f1580 100755 --- a/convert.py +++ b/convert.py @@ -151,8 +151,11 @@ class Params: n_head_kv: int f_norm_eps: float + rope_scaling_type: gguf.RopeScalingType | None = None f_rope_freq_base: float | None = None f_rope_scale: float | None = None + n_orig_ctx: int | None = None + rope_finetuned: bool | None = None ftype: GGMLFileType | None = None @@ -198,20 +201,20 @@ class Params: def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) - n_vocab = config["vocab_size"] - n_embd = config["hidden_size"] - n_layer = config["num_hidden_layers"] - n_ff = config["intermediate_size"] - n_head = config["num_attention_heads"] - n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head - f_norm_eps = config["rms_norm_eps"] - f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - + rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None rope_scaling = config.get("rope_scaling") - if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear": - f_rope_scale = config["rope_scaling"].get("factor") - else: - f_rope_scale = None + + if rope_scaling is not None and (typ := rope_scaling.get("type")): + rope_factor = rope_scaling.get("factor") + f_rope_scale = rope_factor + if typ == "linear": + rope_scaling_type = gguf.RopeScalingType.LINEAR + elif typ == "yarn": + rope_scaling_type = gguf.RopeScalingType.YARN + n_orig_ctx = rope_scaling['original_max_position_embeddings'] + rope_finetuned = rope_scaling['finetuned'] + else: + raise NotImplementedError(f'Unknown rope scaling type: {typ}') if "max_sequence_length" in config: n_ctx = config["max_sequence_length"] @@ -222,16 +225,19 @@ class Params: "Suggestion: provide 'config.json' of the model in the same directory containing model files.") return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_layer = n_layer, - n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, - f_rope_freq_base = f_rope_freq_base, - f_rope_scale = f_rope_scale, + n_vocab = config["vocab_size"], + n_embd = config["hidden_size"], + n_layer = config["num_hidden_layers"], + n_ctx = n_ctx, + n_ff = config["intermediate_size"], + n_head = (n_head := config["num_attention_heads"]), + n_head_kv = config.get("num_key_value_heads", n_head), + f_norm_eps = config["rms_norm_eps"], + f_rope_freq_base = config.get("rope_theta"), + rope_scaling_type = rope_scaling_type, + f_rope_scale = f_rope_scale, + n_orig_ctx = n_orig_ctx, + rope_finetuned = rope_finetuned, ) # LLaMA v2 70B params.json @@ -240,17 +246,8 @@ class Params: def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) - n_vocab = config["vocab_size"] if "vocab_size" in config else -1 - n_embd = config["dim"] - n_layer = config["n_layers"] - n_ff = -1 - n_head = config["n_heads"] - n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head - f_norm_eps = config["norm_eps"] - f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - # hack to determine LLaMA v1 vs v2 vs CodeLlama - if f_rope_freq_base == 1000000: + if config.get("rope_theta") == 1000000: # CodeLlama n_ctx = 16384 elif config["norm_eps"] == 1e-05: @@ -260,22 +257,16 @@ class Params: # LLaMA v1 n_ctx = 2048 - if n_vocab == -1: - n_vocab = model["tok_embeddings.weight"].shape[0] - - if n_ff == -1: - n_ff = model["layers.0.feed_forward.w1.weight"].shape[0] - return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_layer = n_layer, + n_vocab = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]), + n_embd = config["dim"], + n_layer = config["n_layers"], n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, - f_rope_freq_base = f_rope_freq_base, + n_ff = model["layers.0.feed_forward.w1.weight"].shape[0], + n_head = (n_head := config["n_heads"]), + n_head_kv = config.get("n_kv_heads", n_head), + f_norm_eps = config["norm_eps"], + f_rope_freq_base = config.get("rope_theta"), ) @staticmethod @@ -831,8 +822,16 @@ class OutputFile: if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) - if params.f_rope_scale is not None: - self.gguf.add_rope_scale_linear(params.f_rope_scale) + if params.rope_scaling_type: + assert params.f_rope_scale is not None + self.gguf.add_rope_scaling_type(params.rope_scaling_type) + self.gguf.add_rope_scaling_factor(params.f_rope_scale) + + if params.n_orig_ctx is not None: + self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx) + + if params.rope_finetuned is not None: + self.gguf.add_rope_scaling_finetuned(params.rope_finetuned) if params.ftype is not None: self.gguf.add_file_type(params.ftype) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 60c7faa79..649a3b7c1 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -642,8 +642,9 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( const int rope_mode = 0; return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, - rope_freq_base, rope_freq_scale); + t, KQ_pos, n_rot, rope_mode, n_ctx, 0, + rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f + ); }; set_name(tokens_input, "tokens_input"); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 47ae0d558..84b04d5a0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1755,12 +1755,18 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("options:\n"); printf(" -h, --help show this help message and exit\n"); printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" --rope-scaling {none,linear,yarn}\n"); + printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); + printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); + printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); + printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); + printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) @@ -1881,6 +1887,19 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_ctx = std::stoi(argv[i]); } + else if (arg == "--rope-scaling") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; } + else { invalid_param = true; break; } + } else if (arg == "--rope-freq-base") { if (++i >= argc) @@ -1899,6 +1918,38 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.rope_freq_scale = std::stof(argv[i]); } + else if (arg == "--yarn-ext-factor") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_ext_factor = std::stof(argv[i]); + } + else if (arg == "--yarn-attn-factor") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_attn_factor = std::stof(argv[i]); + } + else if (arg == "--yarn-beta-fast") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_fast = std::stof(argv[i]); + } + else if (arg == "--yarn-beta-slow") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_slow = std::stof(argv[i]); + } else if (arg == "--memory-f32" || arg == "--memory_f32") { params.memory_f16 = false; diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 1ce6cef29..2a257e632 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -349,9 +349,9 @@ static struct ggml_tensor * llama_build_train_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, - rope_freq_base, rope_freq_scale); + return ggml_rope_custom( + ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f + ); }; set_name(tokens_input, "tokens_input"); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 4e6e7cd94..12ee10e3d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4493,11 +4493,41 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, cpy_1(cx + x_offset, cdst + dst_offset); } -// rope == RoPE == rotary positional embedding +static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} +struct rope_corr_dims { + float v[4]; +}; + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static __device__ void rope_yarn( + float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +// rope == RoPE == rotary positional embedding template -static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale) { +static __global__ void rope( + const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + float ext_factor, float attn_factor, rope_corr_dims corr_dims +) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); if (col >= ncols) { @@ -4509,10 +4539,10 @@ static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t const int i2 = row/p_delta_rows; const int p = has_pos ? pos[i2] : 0; - const float p0 = p*freq_scale; - const float theta = p0*powf(theta_scale, col/2); - const float sin_theta = sinf(theta); - const float cos_theta = cosf(theta); + const float theta_base = p*powf(freq_base, -col/ncols); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + 1]; @@ -4522,8 +4552,10 @@ static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t } template -static __global__ void rope_neox(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale) { +static __global__ void rope_neox( + const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + float ext_factor, float attn_factor, rope_corr_dims corr_dims +) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); if (col >= ncols) { @@ -4534,11 +4566,14 @@ static __global__ void rope_neox(const T * x, T * dst, const int ncols, const in const int i = row*ncols + col/2; const int i2 = row/p_delta_rows; + // simplified from `(row * ncols + col) * (-1 / ncols)` + const float cur_rot = -col/ncols - row; + const int p = has_pos ? pos[i2] : 0; - const float p0 = p*freq_scale; - const float theta = p0*powf(theta_scale, col/2); - const float sin_theta = sinf(theta); - const float cos_theta = cosf(theta); + const float theta_base = p*powf(freq_base, cur_rot); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + ncols/2]; @@ -4547,8 +4582,10 @@ static __global__ void rope_neox(const T * x, T * dst, const int ncols, const in dst[i + ncols/2] = x0*sin_theta + x1*cos_theta; } -static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, const int n_ctx) { +static __global__ void rope_glm_f32( + const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + int n_ctx +) { const int col = blockDim.x*blockIdx.x + threadIdx.x; const int half_n_dims = ncols/4; @@ -4560,7 +4597,7 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol const int i = row*ncols + col; const int i2 = row/p_delta_rows; - const float col_theta_scale = powf(theta_scale, col); + const float col_theta_scale = powf(freq_base, -2.0f*col/ncols); // FIXME: this is likely wrong const int p = pos != nullptr ? pos[i2] : 0; @@ -5584,40 +5621,54 @@ static void clamp_f32_cuda(const float * x, float * dst, const float min, const } template -static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, cudaStream_t stream) { +static void rope_cuda( + const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream +) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const dim3 block_nums(nrows, num_blocks_x, 1); if (pos == nullptr) { - rope<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } else { - rope<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } } template -static void rope_neox_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, cudaStream_t stream) { +static void rope_neox_cuda( + const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream +) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const dim3 block_nums(nrows, num_blocks_x, 1); if (pos == nullptr) { - rope_neox<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope_neox<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } else { - rope_neox<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope_neox<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } } -static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) { +static void rope_glm_f32_cuda( + const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, int n_ctx, cudaStream_t stream +) { GGML_ASSERT(ncols % 4 == 0); const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1); const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE; const dim3 block_nums(num_blocks_x, nrows, 1); - rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale, n_ctx); + rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx); } static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, @@ -6477,17 +6528,20 @@ inline void ggml_cuda_op_rope( const int64_t ne2 = dst->ne[2]; const int64_t nrows = ggml_nrows(src0); - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + // RoPE alteration for extended context - - float freq_base, freq_scale; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - - const float theta_scale = powf(freq_base, -2.0f/n_dims); + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); const int32_t * pos = nullptr; if ((mode & 1) == 0) { @@ -6499,24 +6553,39 @@ inline void ggml_cuda_op_rope( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + rope_corr_dims corr_dims; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); + // compute if (is_glm) { GGML_ASSERT(false); - rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, n_ctx, main_stream); + rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream); } else if (is_neox) { GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet"); if (src0->type == GGML_TYPE_F32) { - rope_neox_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_neox_cuda( + (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else if (src0->type == GGML_TYPE_F16) { - rope_neox_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_neox_cuda( + (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else { GGML_ASSERT(false); } } else { if (src0->type == GGML_TYPE_F32) { - rope_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_cuda( + (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else if (src0->type == GGML_TYPE_F16) { - rope_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_cuda( + (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else { GGML_ASSERT(false); } diff --git a/ggml-metal.m b/ggml-metal.m index 1f0341507..611d5e173 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1400,14 +1400,18 @@ void ggml_metal_graph_compute( const int nth = MIN(1024, ne00); - const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[3]; - float freq_base; - float freq_scale; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); switch (src0->type) { case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break; @@ -1439,6 +1443,10 @@ void ggml_metal_graph_compute( [encoder setBytes:&mode length:sizeof( int) atIndex:21]; [encoder setBytes:&freq_base length:sizeof(float) atIndex:22]; [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23]; + [encoder setBytes:&ext_factor length:sizeof(float) atIndex:24]; + [encoder setBytes:&attn_factor length:sizeof(float) atIndex:25]; + [encoder setBytes:&beta_fast length:sizeof(float) atIndex:26]; + [encoder setBytes:&beta_slow length:sizeof(float) atIndex:27]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index f3152778a..471d7d390 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1061,6 +1061,45 @@ kernel void kernel_alibi_f32( } } +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) { + return n_dims * log(n_orig_ctx / (n_rot * 2 * M_PI_F)) / (2 * log(base)); +} + +static void rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base))); + dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base))); +} + typedef void (rope_t)( device const void * src0, device const int32_t * src1, @@ -1116,6 +1155,10 @@ kernel void kernel_rope( constant int & mode, constant float & freq_base, constant float & freq_scale, + constant float & ext_factor, + constant float & attn_factor, + constant float & beta_fast, + constant float & beta_slow, uint tiitg[[thread_index_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]], uint3 tgpig[[threadgroup_position_in_grid]]) { @@ -1125,19 +1168,22 @@ kernel void kernel_rope( const bool is_neox = mode & 2; + float corr_dims[2]; + rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); + device const int32_t * pos = src1; const int64_t p = pos[i2]; - const float theta_0 = freq_scale * (float)p; + const float theta_0 = (float)p; const float inv_ndims = -1.f/n_dims; if (!is_neox) { for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { const float theta = theta_0 * pow(freq_base, inv_ndims*i0); - const float cos_theta = cos(theta); - const float sin_theta = sin(theta); + float cos_theta, sin_theta; + rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -1152,9 +1198,12 @@ kernel void kernel_rope( for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) { - const float theta = theta_0 * pow(freq_base, inv_ndims*ic - ib); - const float cos_theta = cos(theta); - const float sin_theta = sin(theta); + // simplified from `(ib * n_dims + ic) * inv_ndims` + const float cur_rot = inv_ndims*ic - ib; + + const float theta = theta_0 * pow(freq_base, cur_rot); + float cos_theta, sin_theta; + rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); const int64_t i0 = ib*n_dims + ic/2; diff --git a/ggml.c b/ggml.c index 80d682255..2c7fe476b 100644 --- a/ggml.c +++ b/ggml.c @@ -1,4 +1,5 @@ #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows +#define _USE_MATH_DEFINES // For M_PI on MSVC #include "ggml-impl.h" #include "ggml-quants.h" @@ -4845,8 +4846,13 @@ static struct ggml_tensor * ggml_rope_impl( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, float xpos_base, bool xpos_down, bool inplace) { @@ -4862,11 +4868,15 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx }; - memcpy(params + 4, &freq_base, sizeof(float)); - memcpy(params + 5, &freq_scale, sizeof(float)); - memcpy(params + 6, &xpos_base, sizeof(float)); - memcpy(params + 7, &xpos_down, sizeof(bool)); + int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; + memcpy(params + 5, &freq_base, sizeof(float)); + memcpy(params + 6, &freq_scale, sizeof(float)); + memcpy(params + 7, &ext_factor, sizeof(float)); + memcpy(params + 8, &attn_factor, sizeof(float)); + memcpy(params + 9, &beta_fast, sizeof(float)); + memcpy(params + 10, &beta_slow, sizeof(float)); + memcpy(params + 11, &xpos_base, sizeof(float)); + memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE; @@ -4884,7 +4894,9 @@ struct ggml_tensor * ggml_rope( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false); + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false + ); } struct ggml_tensor * ggml_rope_inplace( @@ -4894,7 +4906,9 @@ struct ggml_tensor * ggml_rope_inplace( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true); + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ); } struct ggml_tensor * ggml_rope_custom( @@ -4904,9 +4918,17 @@ struct ggml_tensor * ggml_rope_custom( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ); } struct ggml_tensor * ggml_rope_custom_inplace( @@ -4916,9 +4938,17 @@ struct ggml_tensor * ggml_rope_custom_inplace( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true + ); } struct ggml_tensor * ggml_rope_xpos_inplace( @@ -4928,7 +4958,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace( int n_dims, float base, bool down) { - return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true); + return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); } // ggml_rope_back @@ -10901,6 +10931,45 @@ static void ggml_compute_forward_clamp( // ggml_compute_forward_rope +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / MAX(0.001f, high - low); + return 1 - MIN(1, MAX(0, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) { + return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); +} + +void ggml_rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + dims[0] = MAX(0, floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base))); + dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base))); +} + static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -10910,21 +10979,26 @@ static void ggml_compute_forward_rope_f32( return; } - float freq_base; - float freq_scale; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; // these two only relevant for xPos RoPE: float xpos_base; bool xpos_down; - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool)); + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + memcpy(&xpos_base, (int32_t *) dst->op_params + 11, sizeof(float)); + memcpy(&xpos_down, (int32_t *) dst->op_params + 12, sizeof(bool)); GGML_TENSOR_UNARY_OP_LOCALS @@ -10952,6 +11026,9 @@ static void ggml_compute_forward_rope_f32( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -10965,18 +11042,18 @@ static void ggml_compute_forward_rope_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = freq_scale * (float)p; + float theta_base = (float)p; if (is_glm) { - theta = MIN(p, n_ctx - 2); + theta_base = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta); - theta *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -10994,13 +11071,16 @@ static void ggml_compute_forward_rope_f32( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta + ); + // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; - theta *= theta_scale; + theta_base *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11014,12 +11094,19 @@ static void ggml_compute_forward_rope_f32( } else { // TODO: this might be wrong for ne0 != n_dims - need double check // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 + theta_base *= freq_scale; for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + // simplified from `(ib * n_dims + ic) * inv_ndims` + float cur_rot = inv_ndims * ic - ib; - theta *= theta_scale; + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + &cos_theta, &sin_theta + ); + + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -11048,15 +11135,19 @@ static void ggml_compute_forward_rope_f16( return; } - float freq_base; - float freq_scale; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS @@ -11084,6 +11175,9 @@ static void ggml_compute_forward_rope_f16( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -11097,18 +11191,18 @@ static void ggml_compute_forward_rope_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = freq_scale * (float)p; + float theta_base = (float)p; if (is_glm) { - theta = MIN(p, n_ctx - 2); + theta_base = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta); - theta *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -11126,10 +11220,12 @@ static void ggml_compute_forward_rope_f16( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta + ); - theta *= theta_scale; + theta_base *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11143,12 +11239,19 @@ static void ggml_compute_forward_rope_f16( } else { // TODO: this might be wrong for ne0 != n_dims - need double check // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 + theta_base *= freq_scale; for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + // simplified from `(ib * n_dims + ic) * inv_ndims` + float cur_rot = inv_ndims * ic - ib; - theta *= theta_scale; + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + &cos_theta, &sin_theta + ); + + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -11256,17 +11359,18 @@ static void ggml_compute_forward_rope_back_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = freq_scale * (float)p; + float theta_base = freq_scale * (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); + // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; - theta *= theta_scale; + theta_base *= theta_scale; const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11280,10 +11384,10 @@ static void ggml_compute_forward_rope_back_f32( } else { for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); - theta *= theta_scale; + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -11356,14 +11460,14 @@ static void ggml_compute_forward_rope_back_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = (float)p; + float theta_base = (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); - theta *= theta_scale; + theta_base *= theta_scale; const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11377,10 +11481,10 @@ static void ggml_compute_forward_rope_back_f16( } else { for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); - theta *= theta_scale; + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -15505,9 +15609,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src1, n_dims, mode, + 0, n_ctx, freq_base, freq_scale, + 0.0f, + 1.0f, + 0.0f, + 0.0f, xpos_base, xpos_down, false), diff --git a/ggml.h b/ggml.h index 9d16c5a72..70eb25a6b 100644 --- a/ggml.h +++ b/ggml.h @@ -219,7 +219,7 @@ #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 6 #define GGML_MAX_NAME 64 -#define GGML_MAX_OP_PARAMS 32 +#define GGML_MAX_OP_PARAMS 64 #define GGML_DEFAULT_N_THREADS 4 #if UINTPTR_MAX == 0xFFFFFFFF @@ -1326,8 +1326,13 @@ extern "C" { int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_custom_inplace( @@ -1337,8 +1342,17 @@ extern "C" { int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow); + + // compute correction dims for YaRN RoPE scaling + void ggml_rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); // xPos RoPE, in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_xpos_inplace( diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 6b7d65429..727b4e554 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -7,7 +7,7 @@ import shutil import struct import sys import tempfile -from enum import IntEnum, auto +from enum import Enum, IntEnum, auto from io import BufferedWriter from pathlib import Path from typing import IO, Any, BinaryIO, Callable, Sequence @@ -53,9 +53,12 @@ KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" # RoPE -KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" -KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" -KEY_ROPE_SCALE_LINEAR = "{arch}.rope.scale_linear" +KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" +KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" +KEY_ROPE_SCALING_TYPE = "{arch}.rope.scaling.type" +KEY_ROPE_SCALING_FACTOR = "{arch}.rope.scaling.factor" +KEY_ROPE_SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" +KEY_ROPE_SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" # tokenization KEY_TOKENIZER_MODEL = "tokenizer.ggml.model" @@ -577,6 +580,11 @@ class TokenType(IntEnum): UNUSED = 5 BYTE = 6 +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + # # implementation # @@ -948,8 +956,17 @@ class GGUFWriter: def add_rope_freq_base(self, value: float): self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value) - def add_rope_scale_linear(self, value: float): - self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value) + def add_rope_scaling_type(self, value: RopeScalingType): + self.add_string(KEY_ROPE_SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float): + self.add_float32(KEY_ROPE_SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int): + self.add_uint32(KEY_ROPE_SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool): + self.add_bool(KEY_ROPE_SCALING_FINETUNED.format(arch=self.arch), value) def add_tokenizer_model(self, model: str): self.add_string(KEY_TOKENIZER_MODEL, model) diff --git a/llama.cpp b/llama.cpp index 1c6d482f8..685882c20 100644 --- a/llama.cpp +++ b/llama.cpp @@ -54,6 +54,7 @@ #include #include #include +#include #include #include #include @@ -235,6 +236,10 @@ enum llm_kv { LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, LLM_KV_ROPE_SCALE_LINEAR, + LLM_KV_ROPE_SCALING_TYPE, + LLM_KV_ROPE_SCALING_FACTOR, + LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, + LLM_KV_ROPE_SCALING_FINETUNED, LLM_KV_TOKENIZER_MODEL, LLM_KV_TOKENIZER_LIST, @@ -276,9 +281,13 @@ static std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, - { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, - { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, - { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, + { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, + { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, + { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, + { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, + { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, + { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, + { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, @@ -552,6 +561,22 @@ do { \ } \ } while (0) +static std::map LLAMA_ROPE_SCALING_TYPES = { + { LLAMA_ROPE_SCALING_NONE, "none" }, + { LLAMA_ROPE_SCALING_LINEAR, "linear" }, + { LLAMA_ROPE_SCALING_YARN, "yarn" }, +}; + +static int8_t llama_rope_scaling_type_from_string(const std::string & name) { + for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { + if (kv.second == name) { + return kv.first; + } + } + + return LLAMA_ROPE_SCALING_UNSPECIFIED; +} + // // ggml helpers // @@ -1035,8 +1060,11 @@ struct llama_hparams { float f_norm_eps; float f_norm_rms_eps; - float rope_freq_base_train; - float rope_freq_scale_train; + float rope_freq_base_train; + float rope_freq_scale_train; + uint32_t n_yarn_orig_ctx; + int8_t rope_scaling_type_train : 3; + bool rope_finetuned : 1; float f_clamp_kqv; float f_max_alibi_bias; @@ -1051,6 +1079,8 @@ struct llama_hparams { if (this->n_layer != other.n_layer) return true; if (this->n_rot != other.n_rot) return true; if (this->n_ff != other.n_ff) return true; + if (this->rope_finetuned != other.rope_finetuned) return true; + if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; const float EPSILON = 1e-9; @@ -1081,8 +1111,16 @@ struct llama_cparams { uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - float rope_freq_base; - float rope_freq_scale; + float rope_freq_base; + float rope_freq_scale; + + uint32_t n_yarn_orig_ctx; + // These hyperparameters are not exposed in GGUF, because all + // existing YaRN models use the same values for them. + float yarn_ext_factor; + float yarn_attn_factor; + float yarn_beta_fast; + float yarn_beta_slow; bool mul_mat_q; }; @@ -2014,14 +2052,30 @@ static void llm_load_hparams( hparams.n_head_kv = hparams.n_head; GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV)); + hparams.rope_finetuned = false; + GGUF_GET_KEY(ctx, hparams.rope_finetuned, gguf_get_val_bool, GGUF_TYPE_BOOL, false, + kv(LLM_KV_ROPE_SCALING_FINETUNED)); + + hparams.n_yarn_orig_ctx = hparams.n_ctx_train; + GGUF_GET_KEY(ctx, hparams.n_yarn_orig_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, + kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN)); + // rope_freq_base (optional) hparams.rope_freq_base_train = 10000.0f; GGUF_GET_KEY(ctx, hparams.rope_freq_base_train, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); + std::string rope_scaling("linear"); + GGUF_GET_KEY(ctx, rope_scaling, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_ROPE_SCALING_TYPE)); + hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling); + GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED); + // rope_freq_scale (inverse of the kv) is optional - float ropescale = 1.0f; - GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); - hparams.rope_freq_scale_train = 1.0f/ropescale; + float ropescale = 0.0f; + GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALING_FACTOR)); + if (ropescale == 0.0f) { // try the old key name + GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); + } + hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale; // sanity check for n_rot (optional) { @@ -2371,6 +2425,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { const auto & hparams = model.hparams; const auto & vocab = model.vocab; + const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); @@ -2389,8 +2445,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv); LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias); LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str()); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); + LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); + LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9); @@ -3047,21 +3106,11 @@ static void llm_load_tensors( model.t_load_us = ggml_time_us() - model.t_start_us; } -static bool llama_model_load( - const std::string & fname, - llama_model & model, - int n_gpu_layers, - int main_gpu, - const float * tensor_split, - bool use_mmap, - bool use_mlock, - bool vocab_only, - llama_progress_callback progress_callback, - void *progress_callback_user_data) { +static bool llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) { try { - llama_model_loader ml(fname, use_mmap); + llama_model_loader ml(fname, params.use_mmap); - model.hparams.vocab_only = vocab_only; + model.hparams.vocab_only = params.vocab_only; llm_load_arch (ml, model); llm_load_hparams(ml, model); @@ -3073,15 +3122,15 @@ static bool llama_model_load( throw std::runtime_error("vocab size mismatch"); } - if (vocab_only) { + if (params.vocab_only) { LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__); return true; } llm_load_tensors( - ml, model, n_gpu_layers, - main_gpu, tensor_split, - use_mlock, progress_callback, progress_callback_user_data); + ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock, + params.progress_callback, params.progress_callback_user_data + ); } catch (const std::exception & err) { LLAMA_LOG_ERROR("error loading model: %s\n", err.what()); return false; @@ -3150,6 +3199,7 @@ static struct ggml_tensor * llm_build_inp_embd( static void llm_build_k_shift( struct ggml_context * ctx, const llama_hparams & hparams, + const llama_cparams & cparams, const llama_kv_cache & kv, struct ggml_cgraph * graph, llm_rope_type type, @@ -3162,6 +3212,11 @@ static void llm_build_k_shift( const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_gqa = hparams.n_embd_gqa(); const int64_t n_embd_head = hparams.n_embd_head(); + const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; + const float ext_factor = cparams.yarn_ext_factor; + const float attn_factor = cparams.yarn_attn_factor; + const float beta_fast = cparams.yarn_beta_fast; + const float beta_slow = cparams.yarn_beta_slow; GGML_ASSERT(n_embd_head % n_rot == 0); @@ -3185,7 +3240,8 @@ static void llm_build_k_shift( ggml_element_size(kv.k)*n_embd_head, ggml_element_size(kv.k)*n_embd_gqa, ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il), - K_shift, n_rot, rope_type, 0, freq_base, freq_scale); + K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); cb(tmp, "K_shifted", il); ggml_build_forward_expand(graph, tmp); } @@ -3442,12 +3498,17 @@ struct llm_build_context { const float freq_base; const float freq_scale; + const float ext_factor; + const float attn_factor; + const float beta_fast; + const float beta_slow; const float norm_eps; const float norm_rms_eps; const int32_t n_tokens; const int32_t n_kv; // size of KV cache to consider (n_kv <= n_ctx) const int32_t kv_head; // index of where we store new KV data in the cache + const int32_t n_orig_ctx; const bool do_rope_shift; @@ -3477,11 +3538,16 @@ struct llm_build_context { n_embd_gqa (hparams.n_embd_gqa()), freq_base (cparams.rope_freq_base), freq_scale (cparams.rope_freq_scale), + ext_factor (cparams.yarn_ext_factor), + attn_factor (cparams.yarn_attn_factor), + beta_fast (cparams.yarn_beta_fast), + beta_slow (cparams.yarn_beta_slow), norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), n_tokens (batch.n_tokens), n_kv (worst_case ? n_ctx : kv_self.n), kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), + n_orig_ctx (cparams.n_yarn_orig_ctx), do_rope_shift (worst_case || kv_self.has_shift), cb (cb), buf_compute (lctx.buf_compute) { @@ -3532,7 +3598,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -3556,10 +3622,18 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Kcur, "Kcur", il); llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); @@ -3634,7 +3708,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -3658,8 +3732,16 @@ struct llm_build_context { switch (model.type) { case MODEL_7B: - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); break; case MODEL_13B: Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens); @@ -3746,7 +3828,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -3786,10 +3868,16 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom( + ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom( + ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Kcur, "Kcur", il); llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); @@ -3960,7 +4048,7 @@ struct llm_build_context { cb(KQ_mask, "KQ_mask", -1); if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4053,13 +4141,15 @@ struct llm_build_context { cb(kpass, "kpass", il); struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale - ); + ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(qrotated, "qrotated", il); struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale - ); + ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(krotated, "krotated", il); // ggml currently only supports concatenation on dim=2 @@ -7883,8 +7973,13 @@ struct llama_context_params llama_context_default_params() { /*.n_batch =*/ 512, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, + /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, + /*.yarn_ext_factor =*/ NAN, + /*.yarn_attn_factor =*/ 1.0f, + /*.yarn_beta_fast =*/ 32.0f, + /*.yarn_beta_slow =*/ 1.0f, /*.mul_mat_q =*/ true, /*.f16_kv =*/ true, /*.logits_all =*/ false, @@ -7971,10 +8066,7 @@ struct llama_model * llama_load_model_from_file( }; } - if (!llama_model_load(path_model, *model, params.n_gpu_layers, - params.main_gpu, params.tensor_split, - params.use_mmap, params.use_mlock, params.vocab_only, - params.progress_callback, params.progress_callback_user_data)) { + if (!llama_model_load(path_model, *model, params)) { LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); delete model; return nullptr; @@ -8000,13 +8092,35 @@ struct llama_context * llama_new_context_with_model( const auto & hparams = model->hparams; auto & cparams = ctx->cparams; - cparams.n_batch = params.n_batch; - cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; - cparams.rope_freq_base = params.rope_freq_base == 0 ? hparams.rope_freq_base_train : params.rope_freq_base; - cparams.rope_freq_scale = params.rope_freq_scale == 0 ? hparams.rope_freq_scale_train : params.rope_freq_scale; - cparams.n_threads = params.n_threads; - cparams.n_threads_batch = params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; + cparams.n_batch = params.n_batch; + cparams.n_threads = params.n_threads; + cparams.n_threads_batch = params.n_threads_batch; + cparams.yarn_ext_factor = params.yarn_ext_factor; + cparams.yarn_attn_factor = params.yarn_attn_factor; + cparams.yarn_beta_fast = params.yarn_beta_fast; + cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.mul_mat_q = params.mul_mat_q; + + cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; + cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; + cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; + + cparams.n_yarn_orig_ctx = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx : + hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx : + hparams.n_ctx_train; + + auto rope_scaling_type = params.rope_scaling_type; + if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) { + rope_scaling_type = hparams.rope_scaling_type_train; + } + + if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) { + cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none + } + + if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set' + cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f; + } if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); diff --git a/llama.h b/llama.h index 75fe391ef..3f1becd76 100644 --- a/llama.h +++ b/llama.h @@ -106,6 +106,14 @@ extern "C" { LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; + enum llama_rope_scaling_type { + LLAMA_ROPE_SCALING_UNSPECIFIED = -1, + LLAMA_ROPE_SCALING_NONE = 0, + LLAMA_ROPE_SCALING_LINEAR = 1, + LLAMA_ROPE_SCALING_YARN = 2, + LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, + }; + typedef struct llama_token_data { llama_token id; // token id float logit; // log-odds of the token @@ -172,10 +180,16 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing + int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 - float rope_freq_base; // RoPE base frequency, 0 = from model - float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model + float rope_freq_base; // RoPE base frequency, 0 = from model + float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model + float yarn_ext_factor; // YaRN extrapolation mix factor, NaN = from model + float yarn_attn_factor; // YaRN magnitude scaling factor + float yarn_beta_fast; // YaRN low correction dim + float yarn_beta_slow; // YaRN high correction dim + uint32_t yarn_orig_ctx; // YaRN original context size // Keep the booleans together to avoid misalignment during copy-by-value. bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) From d02e98cde035d91ed8032ab943d1d504fe9da394 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 1 Nov 2023 23:10:09 +0100 Subject: [PATCH 016/664] ggml-cuda : compute ptrs for cublasGemmBatchedEx in a kernel (#3891) * ggml-cuda : compute ptrs for cublasGemmBatchedEx in a kernel * fix warnings --- ggml-cuda.cu | 78 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 45 insertions(+), 33 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 12ee10e3d..61cd1747c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6696,8 +6696,10 @@ inline void ggml_cuda_op_clamp( GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const float min = ((float *) dst->op_params)[0]; - const float max = ((float *) dst->op_params)[1]; + float min; + float max; + memcpy(&min, dst->op_params, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream); CUDA_CHECK(cudaGetLastError()); @@ -7221,6 +7223,30 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); } +__global__ void k_compute_batched_ptrs( + const half * src0_as_f16, const half * src1_as_f16, half * dst_f16, + void ** ptrs, + int ne12, int ne13, + int ne23, + int nb02, int nb03, + int nb12, int nb13, + int nb2, int nb3, + int r2, int r3) { + int i13 = blockIdx.x * blockDim.x + threadIdx.x; + int i12 = blockIdx.y * blockDim.y + threadIdx.y; + + if (i13 >= ne13 || i12 >= ne12) { + return; + } + + int i03 = i13 / r3; + int i02 = i12 / r2; + + ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; + ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* nb2/2 + i13* nb3/2; +} + static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); @@ -7322,49 +7348,35 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } else { // use cublasGemmBatchedEx - // TODO: https://github.com/ggerganov/llama.cpp/pull/3749#discussion_r1369997000 const int ne23 = ne12*ne13; - // TODO: avoid this alloc - void ** ptrs = (void **) malloc(3*ne23*sizeof(void *)); - - for (int i13 = 0; i13 < ne13; ++i13) { - for (int i12 = 0; i12 < ne12; ++i12) { - int i03 = i13 / r3; - int i02 = i12 / r2; - - ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3]; - ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2; - ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2; - } - } - - // allocate device memory for pointers void ** ptrs_as = nullptr; - CUDA_CHECK(cudaMalloc(&ptrs_as, 3*ne23*sizeof(void *))); + size_t ptrs_s = 0; + ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s); - // TODO: this does not work for some reason -- not sure why? - //size_t ptrs_s = 0; - //ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s); - - // copy pointers to device - CUDA_CHECK(cudaMemcpy(ptrs_as, ptrs, 3*ne23*sizeof(void *), cudaMemcpyHostToDevice)); - - free(ptrs); + dim3 block_dims(ne13, ne12); + k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( + src0_as_f16, src1_as_f16, dst_f16, + ptrs_as, + ne12, ne13, + ne23, + nb02, nb03, + nb12, nb13, + dst->nb[2], dst->nb[3], + r2, r3); + CUDA_CHECK(cudaGetLastError()); CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const void **) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void **) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float), - &beta_f16, ( void **) (ptrs_as + 2*ne23), CUDA_R_16F, ne01, + &alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half), + (const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + &beta_f16, ( void ** ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01, ne23, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - // free device memory for pointers - CUDA_CHECK(cudaFree(ptrs_as)); - //ggml_cuda_pool_free(ptrs_as, ptrs_s); + ggml_cuda_pool_free(ptrs_as, ptrs_s); } #endif From 0eb332a10f3f14a3746c391bf80ff5e7bdf29d5d Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Wed, 1 Nov 2023 19:29:14 -0400 Subject: [PATCH 017/664] llama : fix llama_context_default_params after #2268 (#3893) --- llama.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/llama.cpp b/llama.cpp index 685882c20..32d7d23de 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7980,6 +7980,7 @@ struct llama_context_params llama_context_default_params() { /*.yarn_attn_factor =*/ 1.0f, /*.yarn_beta_fast =*/ 32.0f, /*.yarn_beta_slow =*/ 1.0f, + /*.yarn_orig_ctx =*/ 0, /*.mul_mat_q =*/ true, /*.f16_kv =*/ true, /*.logits_all =*/ false, From 2fffa0d61fa10e4b466e78cabcc6a4e16717b580 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Thu, 2 Nov 2023 01:49:44 -0400 Subject: [PATCH 018/664] cuda : fix RoPE after #2268 (#3897) --- ggml-cuda.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 61cd1747c..57a528ede 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4539,7 +4539,7 @@ static __global__ void rope( const int i2 = row/p_delta_rows; const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*powf(freq_base, -col/ncols); + const float theta_base = p*powf(freq_base, -float(col)/ncols); float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -4566,8 +4566,8 @@ static __global__ void rope_neox( const int i = row*ncols + col/2; const int i2 = row/p_delta_rows; - // simplified from `(row * ncols + col) * (-1 / ncols)` - const float cur_rot = -col/ncols - row; + // simplified from `(ib * ncols + col) * (-1 / ncols)`, where ib is assumed to be zero + const float cur_rot = -float(col)/ncols; const int p = has_pos ? pos[i2] : 0; const float theta_base = p*powf(freq_base, cur_rot); From 183b3fac6c28e65d23ac0230c1dd6fb84bf0154d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 08:33:37 +0200 Subject: [PATCH 019/664] metal : fix build errors and kernel sig after #2268 (#3898) --- ggml-metal.m | 57 ++++++++++++++++++++++++------------------------ ggml-metal.metal | 16 +++++++++----- 2 files changed, 40 insertions(+), 33 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 611d5e173..b33a3cb8f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1419,34 +1419,35 @@ void ggml_metal_graph_compute( default: GGML_ASSERT(false); }; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&freq_base length:sizeof(float) atIndex:22]; - [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23]; - [encoder setBytes:&ext_factor length:sizeof(float) atIndex:24]; - [encoder setBytes:&attn_factor length:sizeof(float) atIndex:25]; - [encoder setBytes:&beta_fast length:sizeof(float) atIndex:26]; - [encoder setBytes:&beta_slow length:sizeof(float) atIndex:27]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; + [encoder setBytes:&mode length:sizeof( int) atIndex:21]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 471d7d390..7c35f23a7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1070,20 +1070,20 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta + thread float * cos_theta, thread float * sin_theta ) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; if (ext_factor != 0.0f) { - ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + mscale *= 1.0f + 0.1f * log(1.0f / freq_scale); } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; + *cos_theta = cos(theta) * mscale; + *sin_theta = sin(theta) * mscale; } // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get @@ -1123,8 +1123,13 @@ typedef void (rope_t)( constant int & n_past, constant int & n_dims, constant int & mode, + constant int & n_orig_ctx, constant float & freq_base, constant float & freq_scale, + constant float & ext_factor, + constant float & attn_factor, + constant float & beta_fast, + constant float & beta_slow, uint tiitg[[thread_index_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]], uint3 tgpig[[threadgroup_position_in_grid]]); @@ -1153,6 +1158,7 @@ kernel void kernel_rope( constant int & n_past, constant int & n_dims, constant int & mode, + constant int & n_orig_ctx, constant float & freq_base, constant float & freq_scale, constant float & ext_factor, From 4d719a6d4e74b9a98e75f826f865f3153717d54b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 08:35:10 +0200 Subject: [PATCH 020/664] cuda : check if this fixes Pascal card regression (#3882) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 57a528ede..e46295126 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7420,7 +7420,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { From b12fa0d1c13596869c512f49a526b979c94787cc Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Thu, 2 Nov 2023 02:50:16 -0400 Subject: [PATCH 021/664] build : link against build info instead of compiling against it (#3879) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * cmake : fix build when .git does not exist * cmake : simplify BUILD_INFO target * cmake : add missing dependencies on BUILD_INFO * build : link against build info instead of compiling against it * zig : make build info a .cpp source instead of a header Co-authored-by: Matheus C. França * cmake : revert change to CMP0115 --------- Co-authored-by: Matheus C. França --- .gitignore | 2 +- CMakeLists.txt | 33 --------- Makefile | 71 ++++++++++---------- build.zig | 38 +++++------ common/CMakeLists.txt | 42 +++++++++++- common/build-info.cpp.in | 4 ++ common/common.cpp | 5 +- common/common.h | 12 +++- examples/benchmark/CMakeLists.txt | 5 +- examples/benchmark/benchmark-matmult.cpp | 1 - examples/embedding/CMakeLists.txt | 3 - examples/embedding/embedding.cpp | 1 - examples/infill/CMakeLists.txt | 3 - examples/infill/infill.cpp | 5 +- examples/llama-bench/CMakeLists.txt | 3 - examples/llama-bench/llama-bench.cpp | 5 +- examples/llava/CMakeLists.txt | 6 -- examples/main/CMakeLists.txt | 3 - examples/main/main.cpp | 5 +- examples/parallel/CMakeLists.txt | 3 - examples/parallel/parallel.cpp | 2 - examples/perplexity/CMakeLists.txt | 3 - examples/perplexity/perplexity.cpp | 1 - examples/quantize-stats/CMakeLists.txt | 2 +- examples/quantize-stats/quantize-stats.cpp | 1 - examples/quantize/CMakeLists.txt | 5 +- examples/quantize/quantize.cpp | 1 - examples/save-load-state/CMakeLists.txt | 3 - examples/save-load-state/save-load-state.cpp | 1 - examples/server/CMakeLists.txt | 3 - examples/server/server.cpp | 5 +- examples/speculative/CMakeLists.txt | 3 - examples/speculative/speculative.cpp | 2 - scripts/build-info.cmake | 30 +++++---- scripts/build-info.h.in | 9 --- scripts/build-info.sh | 13 ++-- 36 files changed, 143 insertions(+), 191 deletions(-) create mode 100644 common/build-info.cpp.in delete mode 100644 scripts/build-info.h.in diff --git a/.gitignore b/.gitignore index 5d7c5479e..50cbd0b47 100644 --- a/.gitignore +++ b/.gitignore @@ -65,7 +65,7 @@ models-mnt /parallel /train-text-from-scratch /vdot -build-info.h +/common/build-info.cpp arm_neon.h compile_commands.json CMakeSettings.json diff --git a/CMakeLists.txt b/CMakeLists.txt index 3659279e2..611ed3f4d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -100,39 +100,6 @@ option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALO option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ON) -# -# Build info header -# - -# Generate initial build-info.h -include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) - -if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.git") - set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/.git") - - # Is git submodule - if(NOT IS_DIRECTORY "${GIT_DIR}") - file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) - string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) - set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}") - endif() - - # Add a custom target for build-info.h - add_custom_target(BUILD_INFO ALL DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h") - - # Add a custom command to rebuild build-info.h when .git/index changes - add_custom_command( - OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h" - COMMENT "Generating build details from Git" - COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake" - WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS "${GIT_DIR}/index" - VERBATIM - ) -else() - message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") -endif() - # # Compile flags # diff --git a/Makefile b/Makefile index c53c1e726..300c1e6c7 100644 --- a/Makefile +++ b/Makefile @@ -542,9 +542,9 @@ llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h l $(CXX) $(CXXFLAGS) -c $< -o $@ COMMON_H_DEPS = common/common.h common/sampling.h common/log.h -COMMON_DEPS = common.o sampling.o grammar-parser.o +COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o -common.o: common/common.cpp build-info.h $(COMMON_H_DEPS) +common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ sampling.o: common/sampling.cpp $(COMMON_H_DEPS) @@ -563,46 +563,46 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples # -main: examples/main/main.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @echo -infill: examples/infill/infill.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -simple: examples/simple/simple.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -batched: examples/batched/batched.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -batched-bench: examples/batched-bench/batched-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS) +batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS) +quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o llama.o $(OBJS) +quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) @@ -614,7 +614,7 @@ train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratc convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) @@ -623,19 +623,19 @@ llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -finetune: examples/finetune/finetune.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) +finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -export-lora: examples/export-lora/export-lora.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -parallel: examples/parallel/parallel.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) ifdef LLAMA_METAL @@ -648,7 +648,7 @@ swift: examples/batched.swift (cd examples/batched.swift; make build) endif -build-info.h: $(wildcard .git/index) scripts/build-info.sh +common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh @sh scripts/build-info.sh $(CC) > $@.tmp @if ! cmp -s $@.tmp $@; then \ mv $@.tmp $@; \ @@ -656,13 +656,16 @@ build-info.h: $(wildcard .git/index) scripts/build-info.sh rm $@.tmp; \ fi +build-info.o: common/build-info.cpp + $(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@ + # # Tests # tests: $(TEST_TARGETS) -benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(OBJS) +benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) run-benchmark-matmult: benchmark-matmult @@ -676,40 +679,40 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) -tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-c.o: tests/test-c.c llama.h diff --git a/build.zig b/build.zig index 9b58b74ca..699738f3d 100644 --- a/build.zig +++ b/build.zig @@ -10,7 +10,6 @@ const Maker = struct { builder: *std.build.Builder, target: CrossTarget, optimize: Mode, - config_header: *ConfigHeader, enable_lto: bool, include_dirs: ArrayList([]const u8), @@ -41,26 +40,24 @@ const Maker = struct { const commit_hash = try std.ChildProcess.exec( .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } }, ); - const config_header = builder.addConfigHeader( - .{ .style = .blank, .include_path = "build-info.h" }, - .{ - .BUILD_NUMBER = 0, - .BUILD_COMMIT = commit_hash.stdout[0 .. commit_hash.stdout.len - 1], // omit newline - .BUILD_COMPILER = builder.fmt("Zig {s}", .{zig_version}), - .BUILD_TARGET = try target.allocDescription(builder.allocator), - }, - ); + try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt( + \\int LLAMA_BUILD_NUMBER = {}; + \\char const *LLAMA_COMMIT = "{s}"; + \\char const *LLAMA_COMPILER = "Zig {s}"; + \\char const *LLAMA_BUILD_TARGET = "{s}"; + \\ + , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) })); var m = Maker{ .builder = builder, .target = target, .optimize = builder.standardOptimizeOption(.{}), - .config_header = config_header, .enable_lto = false, .include_dirs = ArrayList([]const u8).init(builder.allocator), .cflags = ArrayList([]const u8).init(builder.allocator), .cxxflags = ArrayList([]const u8).init(builder.allocator), .objs = ArrayList(*Compile).init(builder.allocator), }; + try m.addCFlag("-std=c11"); try m.addCxxFlag("-std=c++11"); try m.addProjectInclude(&.{}); @@ -72,7 +69,7 @@ const Maker = struct { const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize }); if (o.target.getAbi() != .msvc) o.defineCMacro("_GNU_SOURCE", null); - o.addConfigHeader(m.config_header); + if (std.mem.endsWith(u8, src, ".c")) { o.addCSourceFiles(&.{src}, m.cflags.items); o.linkLibC(); @@ -85,7 +82,6 @@ const Maker = struct { o.linkLibCpp(); } } - o.addConfigHeader(m.config_header); for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i }); o.want_lto = m.enable_lto; return o; @@ -105,7 +101,6 @@ const Maker = struct { // linkLibCpp already add (libc++ + libunwind + libc) e.linkLibCpp(); } - e.addConfigHeader(m.config_header); m.builder.installArtifact(e); e.want_lto = m.enable_lto; return e; @@ -121,6 +116,7 @@ pub fn build(b: *std.build.Builder) !void { const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); const llama = make.obj("llama", "llama.cpp"); + const buildinfo = make.obj("common", "common/build-info.cpp"); const common = make.obj("common", "common/common.cpp"); const console = make.obj("console", "common/console.cpp"); const sampling = make.obj("sampling", "common/sampling.cpp"); @@ -128,14 +124,14 @@ pub fn build(b: *std.build.Builder) !void { const train = make.obj("train", "common/train.cpp"); const clip = make.obj("clip", "examples/llava/clip.cpp"); - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); + _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, grammar_parser, clip }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index fbb0ff095..0150114e3 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -1,8 +1,46 @@ # common + +# Build info header +# + +if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") + set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../.git") + + # Is git submodule + if(NOT IS_DIRECTORY "${GIT_DIR}") + file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) + string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) + set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}") + endif() + + set(GIT_INDEX "${GIT_DIR}/index") +else() + message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") + set(GIT_INDEX "") +endif() + +# Add a custom command to rebuild build-info.cpp when .git/index changes +add_custom_command( + OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp" + COMMENT "Generating build details from Git" + COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} + -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake" + WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.." + DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX} + VERBATIM +) +set(TARGET build_info) +add_library(${TARGET} OBJECT build-info.cpp) +if (BUILD_SHARED_LIBS) + set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) +endif() + + set(TARGET common) -add_library(${TARGET} OBJECT +add_library(${TARGET} STATIC common.h common.cpp sampling.h @@ -21,4 +59,4 @@ endif() target_include_directories(${TARGET} PUBLIC .) target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE llama) +target_link_libraries(${TARGET} PRIVATE llama build_info) diff --git a/common/build-info.cpp.in b/common/build-info.cpp.in new file mode 100644 index 000000000..0b945aa68 --- /dev/null +++ b/common/build-info.cpp.in @@ -0,0 +1,4 @@ +int LLAMA_BUILD_NUMBER = @BUILD_NUMBER@; +char const *LLAMA_COMMIT = "@BUILD_COMMIT@"; +char const *LLAMA_COMPILER = "@BUILD_COMPILER@"; +char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@"; diff --git a/common/common.cpp b/common/common.cpp index b182ffaae..e938dee16 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,5 +1,4 @@ #include "common.h" -#include "build-info.h" #include "llama.h" #include @@ -1199,8 +1198,8 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { const llama_sampling_params & sparams = params.sparams; - fprintf(stream, "build_commit: %s\n", BUILD_COMMIT); - fprintf(stream, "build_number: %d\n", BUILD_NUMBER); + fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); + fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); diff --git a/common/common.h b/common/common.h index 7be69f925..72a49b890 100644 --- a/common/common.h +++ b/common/common.h @@ -26,11 +26,17 @@ #define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) -#define print_build_info() do { \ - fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); \ - fprintf(stderr, "%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); \ +#define print_build_info() do { \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) +// build info +extern int LLAMA_BUILD_NUMBER; +extern char const *LLAMA_COMMIT; +extern char const *LLAMA_COMPILER; +extern char const *LLAMA_BUILD_TARGET; + // // CLI argument parsing // diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt index 14916d831..2bb47bab5 100644 --- a/examples/benchmark/CMakeLists.txt +++ b/examples/benchmark/CMakeLists.txt @@ -1,9 +1,6 @@ set(TARGET benchmark) add_executable(${TARGET} benchmark-matmult.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index f1c382aa9..76e3f57cc 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "ggml.h" diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 0c752c7bb..8ffc33868 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} embedding.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 14075609e..3295cd240 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index 57d01cb0b..e4e8028da 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} infill.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 9c52b7bba..62f5ce3c1 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -2,7 +2,6 @@ #include "console.h" #include "llama.h" -#include "build-info.h" #include "grammar-parser.h" #include @@ -184,8 +183,8 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); } - LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); + LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); diff --git a/examples/llama-bench/CMakeLists.txt b/examples/llama-bench/CMakeLists.txt index 7e395afd0..5bdbea4e2 100644 --- a/examples/llama-bench/CMakeLists.txt +++ b/examples/llama-bench/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} llama-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 780398184..9bd82d565 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -19,7 +19,6 @@ #include "ggml.h" #include "llama.h" #include "common.h" -#include "build-info.h" #include "ggml-cuda.h" // utils @@ -641,8 +640,8 @@ struct test { } }; -const std::string test::build_commit = BUILD_COMMIT; -const int test::build_number = BUILD_NUMBER; +const std::string test::build_commit = LLAMA_COMMIT; +const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cublas(); const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::metal = !!ggml_cpu_has_metal(); diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 2d7979ecd..03d32c26e 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -5,9 +5,6 @@ target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if (NOT MSVC) target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h - endif() -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) endif() set(TARGET llava) @@ -15,6 +12,3 @@ add_executable(${TARGET} llava.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index cc1888948..d532980b7 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} main.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 8a43b6ab8..8d985c82a 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -2,7 +2,6 @@ #include "console.h" #include "llama.h" -#include "build-info.h" #include #include @@ -153,8 +152,8 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); } - LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); + LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index 0bbf89eae..319535a6e 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} parallel.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 9a0b9c183..a78df305f 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -1,8 +1,6 @@ // A basic application simulating a server with multiple clients. // The clients submite requests to the server and they are processed in parallel. -#include "build-info.h" - #include "common.h" #include "llama.h" diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index af00b4e16..3c76d3221 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} perplexity.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bd2c73d87..de60c5227 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index db182e263..e31cf5e38 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -1,6 +1,6 @@ set(TARGET quantize-stats) add_executable(${TARGET} quantize-stats.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index dd76b1cee..271282477 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -1,5 +1,4 @@ #define LLAMA_API_INTERNAL -#include "build-info.h" #include "common.h" #include "ggml.h" #include "llama.h" diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 4a8eed544..6f374a2bd 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -1,9 +1,6 @@ set(TARGET quantize) add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index be0b2fe1e..d27ea5e91 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index eadd13cdf..cc6ed8554 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} save-load-state.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 38d05f4d3..48d801110 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index a23ddcc55..1f0d26f77 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -11,6 +11,3 @@ if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 84b04d5a0..fd755327a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,5 @@ #include "common.h" #include "llama.h" -#include "build-info.h" #include "grammar-parser.h" #include "../llava/clip.h" @@ -2264,8 +2263,8 @@ int main(int argc, char **argv) llama_backend_init(params.numa); - LOG_INFO("build info", {{"build", BUILD_NUMBER}, - {"commit", BUILD_COMMIT}}); + LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER}, + {"commit", LLAMA_COMMIT}}); LOG_INFO("system info", { {"n_threads", params.n_threads}, diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt index 6c5c9456e..810f3c46a 100644 --- a/examples/speculative/CMakeLists.txt +++ b/examples/speculative/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} speculative.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 323c74652..798684f66 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -1,5 +1,3 @@ -#include "build-info.h" - #include "common.h" #include "llama.h" diff --git a/scripts/build-info.cmake b/scripts/build-info.cmake index c86ab4379..73853dfa4 100644 --- a/scripts/build-info.cmake +++ b/scripts/build-info.cmake @@ -1,5 +1,5 @@ -set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.h.in") -set(HEADER_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h") +set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in") +set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp") set(BUILD_NUMBER 0) set(BUILD_COMMIT "unknown") set(BUILD_COMPILER "unknown") @@ -24,15 +24,21 @@ if(Git_FOUND) WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} OUTPUT_VARIABLE HEAD OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE RES ) + if (RES EQUAL 0) + set(BUILD_COMMIT ${HEAD}) + endif() execute_process( COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} OUTPUT_VARIABLE COUNT OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE RES ) - set(BUILD_COMMIT ${HEAD}) - set(BUILD_NUMBER ${COUNT}) + if (RES EQUAL 0) + set(BUILD_NUMBER ${COUNT}) + endif() endif() if(MSVC) @@ -53,22 +59,22 @@ else() set(BUILD_TARGET ${OUT}) endif() -# Only write the header if it's changed to prevent unnecessary recompilation -if(EXISTS ${HEADER_FILE}) - file(READ ${HEADER_FILE} CONTENTS) - string(REGEX MATCH "BUILD_COMMIT \"([^\"]*)\"" _ ${CONTENTS}) +# Only write the build info if it changed +if(EXISTS ${OUTPUT_FILE}) + file(READ ${OUTPUT_FILE} CONTENTS) + string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS}) set(OLD_COMMIT ${CMAKE_MATCH_1}) - string(REGEX MATCH "BUILD_COMPILER \"([^\"]*)\"" _ ${CONTENTS}) + string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS}) set(OLD_COMPILER ${CMAKE_MATCH_1}) - string(REGEX MATCH "BUILD_TARGET \"([^\"]*)\"" _ ${CONTENTS}) + string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS}) set(OLD_TARGET ${CMAKE_MATCH_1}) if ( NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR NOT OLD_TARGET STREQUAL BUILD_TARGET ) - configure_file(${TEMPLATE_FILE} ${HEADER_FILE}) + configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) endif() else() - configure_file(${TEMPLATE_FILE} ${HEADER_FILE}) + configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) endif() diff --git a/scripts/build-info.h.in b/scripts/build-info.h.in deleted file mode 100644 index e996faef0..000000000 --- a/scripts/build-info.h.in +++ /dev/null @@ -1,9 +0,0 @@ -#ifndef BUILD_INFO_H -#define BUILD_INFO_H - -#define BUILD_NUMBER @BUILD_NUMBER@ -#define BUILD_COMMIT "@BUILD_COMMIT@" -#define BUILD_COMPILER "@BUILD_COMPILER@" -#define BUILD_TARGET "@BUILD_TARGET@" - -#endif // BUILD_INFO_H diff --git a/scripts/build-info.sh b/scripts/build-info.sh index 3c8b1fb85..32682afbd 100755 --- a/scripts/build-info.sh +++ b/scripts/build-info.sh @@ -24,12 +24,7 @@ if out=$($CC -dumpmachine); then build_target=$out fi -echo "#ifndef BUILD_INFO_H" -echo "#define BUILD_INFO_H" -echo -echo "#define BUILD_NUMBER $build_number" -echo "#define BUILD_COMMIT \"$build_commit\"" -echo "#define BUILD_COMPILER \"$build_compiler\"" -echo "#define BUILD_TARGET \"$build_target\"" -echo -echo "#endif // BUILD_INFO_H" +echo "int LLAMA_BUILD_NUMBER = ${build_number};" +echo "char const *LLAMA_COMMIT = \"${build_commit}\";" +echo "char const *LLAMA_COMPILER = \"${build_compiler}\";" +echo "char const *LLAMA_BUILD_TARGET = \"${build_target}\";" From 1efae9b7dca2a5cc5aa21c1997b538022964ea19 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 09:54:18 +0200 Subject: [PATCH 022/664] llm : prevent from 1-D tensors being GPU split (#3697) --- llama.cpp | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 32d7d23de..bb60044b4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1837,6 +1837,12 @@ struct llama_model_loader { throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } + if (backend == GGML_BACKEND_GPU_SPLIT) { + if (ne.size() == 1) { + throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str())); + } + } + { bool is_ok = true; for (size_t i = 0; i < ne.size(); ++i) { @@ -2817,8 +2823,8 @@ static void llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); if (backend == GGML_BACKEND_GPU) { vram_weights += @@ -2877,13 +2883,13 @@ static void llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend); @@ -2949,19 +2955,19 @@ static void llm_load_tensors( layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); if (backend == GGML_BACKEND_GPU) { vram_weights += From 2756c4fbffab097736d5116007872d86456a544a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 11:20:21 +0200 Subject: [PATCH 023/664] gguf : remove special-case code for GGUFv1 (#3901) ggml-ci --- ggml.c | 58 +++-------------------------------- models/ggml-vocab-llama.gguf | Bin 595423 -> 723676 bytes 2 files changed, 5 insertions(+), 53 deletions(-) diff --git a/ggml.c b/ggml.c index 2c7fe476b..d5a49d8e4 100644 --- a/ggml.c +++ b/ggml.c @@ -18811,8 +18811,7 @@ static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) return n == size; } -// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 -static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) { +static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { p->n = 0; p->data = NULL; @@ -18824,19 +18823,6 @@ static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset return ok; } -static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) { - p->n = 0; - p->data = NULL; - - bool ok = true; - - uint32_t n = 0; - ok = ok && gguf_fread_el(file, &n, sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n; - ok = ok && gguf_fread_el(file, p->data, p->n, offset); - - return ok; -} - struct gguf_context * gguf_init_empty(void) { struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); @@ -18895,21 +18881,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ctx->data = NULL; ok = ok && gguf_fread_el(file, &ctx->header.version, sizeof(ctx->header.version), &offset); - - if (ctx->header.version == 1) { - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - uint32_t n_tensors = 0; - uint32_t n_kv = 0; - - ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset); - ok = ok && gguf_fread_el(file, &n_kv, sizeof(n_kv), &offset); - - ctx->header.n_tensors = n_tensors; - ctx->header.n_kv = n_kv; - } else { - ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); - ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); - } + ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); + ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); @@ -18919,12 +18892,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p } } - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - bool (* gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur; - if (ctx->header.version == 1) { - gguf_fread_str = gguf_fread_str_v1; - } - // read the kv pairs { ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv)); @@ -18955,15 +18922,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_ARRAY: { ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset); - - if (ctx->header.version == 1) { - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - uint32_t n = 0; - ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset); - kv->value.arr.n = n; - } else { - ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); - } + ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); switch (kv->value.arr.type) { case GGUF_TYPE_UINT8: @@ -19022,14 +18981,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_str(file, &info->name, &offset); ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims), &offset); for (uint32_t j = 0; j < info->n_dims; ++j) { - if (ctx->header.version == 1) { - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - uint32_t t = 0; - ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset); - info->ne[j] = t; - } else { - ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); - } + ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); } ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); diff --git a/models/ggml-vocab-llama.gguf b/models/ggml-vocab-llama.gguf index 63bfaf672f382c0f5bbcffe54736e2698ef3ac55..549eed8c53f438a61f1b00c9bd3b7d02325f2479 100644 GIT binary patch literal 723676 zcma&P`*UR1ap$Sn_e(o78#^2Oh~2PTy=H?Vo96o=l6K+M4GQRn05sb+Nl~||E>O2o zRkv6VpwVKREyR-=F{ggK}7oi~i+e-04-*vNN5H%m0Gk 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More options diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fd755327a..cbf36ad67 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -679,6 +679,7 @@ struct llama_server_context slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); @@ -1113,6 +1114,7 @@ struct llama_server_context {"temp", slot.sparams.temp}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, + {"min_p", slot.sparams.min_p}, {"tfs_z", slot.sparams.tfs_z}, {"typical_p", slot.sparams.typical_p}, {"repeat_last_n", slot.sparams.penalty_last_n}, From a75fa576abba9d37f463580c379e4bbf1e1ad03c Mon Sep 17 00:00:00 2001 From: Galunid Date: Thu, 9 Nov 2023 11:09:29 +0100 Subject: [PATCH 054/664] scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py --- convert-bloom-hf-to-gguf.py | 247 --------- convert-falcon-hf-to-gguf.py | 253 --------- convert-gptneox-hf-to-gguf.py | 221 -------- convert-hf-to-gguf.py | 890 ++++++++++++++++++++++++++++++++ convert-mpt-hf-to-gguf.py | 227 -------- convert-refact-hf-to-gguf.py | 272 ---------- convert-starcoder-hf-to-gguf.py | 210 -------- convert.py | 4 +- mypy.ini | 1 + 9 files changed, 893 insertions(+), 1432 deletions(-) delete mode 100755 convert-bloom-hf-to-gguf.py delete mode 100755 convert-falcon-hf-to-gguf.py delete mode 100755 convert-gptneox-hf-to-gguf.py create mode 100755 convert-hf-to-gguf.py delete mode 100755 convert-mpt-hf-to-gguf.py delete mode 100755 convert-refact-hf-to-gguf.py delete mode 100755 convert-starcoder-hf-to-gguf.py diff --git a/convert-bloom-hf-to-gguf.py b/convert-bloom-hf-to-gguf.py deleted file mode 100755 index 6e866d943..000000000 --- a/convert-bloom-hf-to-gguf.py +++ /dev/null @@ -1,247 +0,0 @@ -#!/usr/bin/env python3 -# HF bloom --> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import re -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -# Supported Models: -# https://huggingface.co/bigscience/bloom-1b7 -# https://huggingface.co/bigscience/bloom-3b -# https://huggingface.co/bigscience/bloom-7b1 -# https://huggingface.co/Langboat/bloom-1b4-zh -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a Bloom model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "BloomForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.BLOOM -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["n_layer"] - -gguf_writer.add_name("Bloom") -n_embed = hparams.get("hidden_size", hparams.get("n_embed")) -n_head = hparams.get("n_head", hparams.get("num_attention_heads")) -gguf_writer.add_context_length(hparams.get("seq_length", n_embed)) -gguf_writer.add_embedding_length(n_embed) -gguf_writer.add_feed_forward_length(4 * n_embed) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(n_head) -gguf_writer.add_head_count_kv(n_head) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH, block_count) - -# params for qkv transform -n_head_kv = hparams.get("n_head_kv", n_head) -head_dim = n_embed // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") - - has_lm_head = True - if "lm_head.weight" not in model_part.keys() and "output.weight" not in model_part.keys(): - has_lm_head = False - - for original_name in model_part.keys(): - data = model_part[original_name] - name = re.sub(r'transformer\.', '', original_name) - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): - # Map bloom-style qkv_linear to gpt-style qkv_linear - # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa - # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa - qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed)) - data = np.concatenate( - (qkv_weights[:, 0, :, :].reshape((-1, n_embed)), - qkv_weights[:, 1, :, :].reshape((-1, n_embed)), - qkv_weights[:, 2, :, :].reshape((-1, n_embed))), - axis=0 - ) - print("re-format attention.linear_qkv.weight") - elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): - qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) - data = np.concatenate( - (qkv_bias[:, 0, :].reshape((n_embed,)), - qkv_bias[:, 1, :].reshape((n_embed,)), - qkv_bias[:, 2, :].reshape((n_embed,))), - axis=0 - ) - print("re-format attention.linear_qkv.bias") - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - if not has_lm_head and name == "word_embeddings.weight": - gguf_writer.add_tensor("output.weight", data) - print(name, "=>", "output.weight" + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) # noqa - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py deleted file mode 100755 index 8e8f3c3f8..000000000 --- a/convert-falcon-hf-to-gguf.py +++ /dev/null @@ -1,253 +0,0 @@ -#!/usr/bin/env python3 -# HF falcon--> gguf conversion - -from __future__ import annotations - -import argparse -import contextlib -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path, prefix: str) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith(prefix): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] not in ("RWForCausalLM", "FalconForCausalLM"): - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model, "model-00") -if num_parts: - is_safetensors = True - from safetensors import safe_open -else: - is_safetensors = False - num_parts = count_model_parts(dir_model, "pytorch_model-") - -ARCH=gguf.MODEL_ARCH.FALCON -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams.get("num_hidden_layers") -if block_count is None: - block_count = hparams["n_layer"] # old name - -n_head = hparams.get("num_attention_heads") -if n_head is None: - n_head = hparams["n_head"] # old name - -n_head_kv = hparams.get("num_kv_heads") -if n_head_kv is None: - n_head_kv = hparams.get("n_head_kv", 1) # old name - -gguf_writer.add_name("Falcon") -gguf_writer.add_context_length(2048) # not in config.json -gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(n_head) -gguf_writer.add_head_count_kv(n_head_kv) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - tokens.append(reverse_vocab[i]) - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_scores(scores) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -head_dim = hparams["hidden_size"] // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -elif is_safetensors: - part_names = ( - f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1) - ) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - if is_safetensors: - ctx = safe_open(dir_model / part_name, framework="pt", device="cpu") - else: - ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu")) - - with ctx as model_part: - for name in model_part.keys(): - data = model_part.get_tensor(name) if is_safetensors else model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - # QKV tensor transform - # The original query_key_value tensor contains n_head_kv "kv groups", - # each consisting of n_head/n_head_kv query weights followed by one key - # and one value weight (shared by all query heads in the kv group). - # This layout makes it a big pain to work with in GGML. - # So we rearrange them here,, so that we have n_head query weights - # followed by n_head_kv key weights followed by n_head_kv value weights, - # in contiguous fashion. - # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py - - if "query_key_value" in name: - qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) - q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head) - k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) - v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) - data = torch.cat((q,k,v)).reshape_as(data) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py deleted file mode 100755 index 02d1fdf16..000000000 --- a/convert-gptneox-hf-to-gguf.py +++ /dev/null @@ -1,221 +0,0 @@ -#!/usr/bin/env python3 -# HF gptneox--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTNeoXForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.GPTNEOX -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_context_length(hparams["max_position_embeddings"]) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"]))) -gguf_writer.add_head_count(hparams["num_attention_heads"]) -gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"): - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py new file mode 100755 index 000000000..f7fe29fd4 --- /dev/null +++ b/convert-hf-to-gguf.py @@ -0,0 +1,890 @@ +#!/usr/bin/env python3 + +from __future__ import annotations + +import argparse +import contextlib +import json +import os +import re +import sys +from enum import IntEnum +from pathlib import Path +from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast + +import numpy as np +import torch + +if TYPE_CHECKING: + from torch import Tensor + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + + +###### MODEL DEFINITIONS ###### + +class SentencePieceTokenTypes(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class Model: + def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): + self.dir_model = dir_model + self.ftype = ftype + self.fname_out = fname_out + self.is_big_endian = is_big_endian + self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE + self.is_safetensors = self._is_model_safetensors() + self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") + self.part_names = self._get_part_names() + self.hparams = Model.load_hparams(self.dir_model) + self.model_arch = self._get_model_architecture() + self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess) + + def set_vocab(self): + self._set_vocab_gpt2() + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + for part_name in self.part_names: + print(f"gguf: loading model part '{part_name}'") + ctx: ContextManager[Any] + if self.is_safetensors: + from safetensors import safe_open + ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) + else: + ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu")) + + with ctx as model_part: + for name in model_part.keys(): + data = model_part.get_tensor(name) if self.is_safetensors else model_part[name] + yield name, data + + def set_gguf_parameters(self): + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_block_count(self.hparams.get( + "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")), + )) + if (n_ctx := self.hparams.get("max_position_embeddings")) is not None: + self.gguf_writer.add_context_length(n_ctx) + if (n_embd := self.hparams.get("hidden_size")) is not None: + self.gguf_writer.add_embedding_length(n_embd) + if (n_ff := self.hparams.get("intermediate_size")) is not None: + self.gguf_writer.add_feed_forward_length(n_ff) + if (n_head := self.hparams.get("num_attention_head")) is not None: + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + def write(self): + self.write_tensors() + self.gguf_writer.write_header_to_file() + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.write_tensors_to_file() + self.gguf_writer.close() + + def write_vocab(self): + self.gguf_writer.write_header_to_file() + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.close() + + @staticmethod + def count_model_parts(dir_model: Path, prefix: str) -> int: + num_parts = 0 + for filename in os.listdir(dir_model): + if filename.endswith(prefix): + num_parts += 1 + + return num_parts + + @staticmethod + def load_hparams(dir_model): + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + return json.load(f) + + @staticmethod + def from_model_architecture(model_architecture): + if model_architecture == "StableLMEpochForCausalLM": + return StableLMModel + if model_architecture == "GPTNeoXForCausalLM": + return GPTNeoXModel + if model_architecture == "BloomForCausalLM": + return BloomModel + if model_architecture == "MPTForCausalLM": + return MPTModel + if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): + return BaichuanModel + if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): + return FalconModel + if model_architecture == "GPTBigCodeForCausalLM": + return StarCoderModel + if model_architecture == "GPTRefactForCausalLM": + return RefactModel + if model_architecture == "PersimmonForCausalLM": + return PersimmonModel + return Model + + def _is_model_safetensors(self) -> bool: + return Model.count_model_parts(self.dir_model, ".safetensors") > 0 + + def _get_part_names(self): + if self.is_safetensors: + if self.num_parts == 1: # there's only one .safetensors file + return ("model.safetensors",) + return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1)) + + if self.num_parts == 1: # there's only one .bin file + return ("pytorch_model.bin",) + return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) + + def _get_model_architecture(self) -> gguf.MODEL_ARCH: + arch = self.hparams["architectures"][0] + if arch == "GPTNeoXForCausalLM": + return gguf.MODEL_ARCH.GPTNEOX + if arch == "BloomForCausalLM": + return gguf.MODEL_ARCH.BLOOM + if arch == "MPTForCausalLM": + return gguf.MODEL_ARCH.MPT + if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): + return gguf.MODEL_ARCH.BAICHUAN + if arch == "FalconForCausalLM": + return gguf.MODEL_ARCH.FALCON + if arch == "GPTBigCodeForCausalLM": + return gguf.MODEL_ARCH.STARCODER + if arch == "GPTRefactForCausalLM": + return gguf.MODEL_ARCH.REFACT + if arch == "PersimmonForCausalLM": + return gguf.MODEL_ARCH.PERSIMMON + + raise NotImplementedError(f'Architecture "{arch}" not supported!') + + def _set_vocab_gpt2(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[bytearray] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer # type: ignore[attr-defined] + tokenizer = AutoTokenizer.from_pretrained(dir_model) + vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) + assert max(tokenizer.vocab.values()) < vocab_size + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + for i in range(vocab_size): + if i not in reverse_vocab: + pad_token = f"[PAD{i}]".encode('utf-8') + tokens.append(bytearray(pad_token)) + toktypes.append(gguf.TokenType.USER_DEFINED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_sentencepiece(self): + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + sys.exit(1) + + tokenizer = SentencePieceProcessor(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.id_to_piece(token_id) + text = piece.encode("utf-8") + score = tokenizer.get_score(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + +class StableLMModel(Model): + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_dimension_count( + int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), + ) + self.gguf_writer.add_layer_norm_eps(1e-5) + + +class GPTNeoXModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count( + int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), + ) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + + +class BloomModel(Model): + def set_gguf_parameters(self): + self.gguf_writer.add_name("Bloom") + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(4 * n_embed) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams["n_layer"] + tensors = dict(self.get_tensors()) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + has_lm_head = True + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + for name, data_torch in tensors.items(): + if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys(): + has_lm_head = False + + name = re.sub(r'transformer\.', '', name) + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed)) + data = np.concatenate( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + axis=0, + ) + print("re-format attention.linear_qkv.weight") + elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): + qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) + data = np.concatenate( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + axis=0, + ) + print("re-format attention.linear_qkv.bias") + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + if not has_lm_head and name == "word_embeddings.weight": + self.gguf_writer.add_tensor("output.weight", data) + print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + +class MPTModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["n_layers"] + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"]) + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"): + self.gguf_writer.add_head_count_kv(kv_n_heads) + self.gguf_writer.add_layer_norm_eps(1e-5) + if self.hparams["attn_config"]["clip_qkv"] is not None: + self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) + self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + # note: MPT output is tied to (same as) wte in original model; + # for easier implementation in llama.cpp it's duplicated in GGUF, though :/ + if new_name == "token_embd.weight": + self.gguf_writer.add_tensor("output.weight", data) + + +class BaichuanModel(Model): + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + hf_repo = self.hparams.get("_name_or_path", "") + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + print("gguf: can not find ctx length parameter.") + sys.exit() + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_source_hf_repo(hf_repo) + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def write_tensors(self): + # Collect tensors from generator object + model_kv = dict(self.get_tensors()) + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + for i in range(block_count): + if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None: + print(f"Unpacking and permuting layer {i}") + model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \ + self._reverse_hf_permute_part(w, 0, head_count, head_count) + model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \ + self._reverse_hf_permute_part(w, 1, head_count, head_count_kv) + model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \ + self._reverse_hf_part(w, 2) + del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"] + + for name, data_torch in model_kv.items(): + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + def _reverse_hf_permute_part( + self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None, + ) -> Tensor: + r = weights.shape[0] // 3 + return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv) + + def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: + r = weights.shape[0] // 3 + return weights[r * n_part:r * n_part + r, ...] + + +class FalconModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams.get("num_hidden_layers") + if block_count is None: + block_count = self.hparams["n_layer"] # old name + + n_head = self.hparams.get("num_attention_heads") + if n_head is None: + n_head = self.hparams["n_head"] # old name + + n_head_kv = self.hparams.get("num_kv_heads") + if n_head_kv is None: + n_head_kv = self.hparams.get("n_head_kv", 1) # old name + + self.gguf_writer.add_name("Falcon") + self.gguf_writer.add_context_length(2048) # not in config.json + self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams.get("num_hidden_layers") + if block_count is None: + block_count = self.hparams["n_layer"] # old name + + n_head = self.hparams.get("num_attention_heads") + if n_head is None: + n_head = self.hparams["n_head"] # old name + + n_head_kv = self.hparams.get("num_kv_heads") + if n_head_kv is None: + n_head_kv = self.hparams.get("n_head_kv", 1) # old name + + head_dim = self.hparams["hidden_size"] // n_head + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # QKV tensor transform + # The original query_key_value tensor contains n_head_kv "kv groups", + # each consisting of n_head/n_head_kv query weights followed by one key + # and one value weight (shared by all query heads in the kv group). + # This layout makes it a big pain to work with in GGML. + # So we rearrange them here,, so that we have n_head query weights + # followed by n_head_kv key weights followed by n_head_kv value weights, + # in contiguous fashion. + # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py + + if "query_key_value" in name: + qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) + q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) + k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) + v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) + data_torch = torch.cat((q, k, v)).reshape_as(data_torch) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + +class StarCoderModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_name("StarCoder") + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + +class RefactModel(Model): + def set_gguf_parameters(self): + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_name("Refact") + # refact uses Alibi. So this is from config.json which might be used by training. + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + + self.gguf_writer.add_feed_forward_length(ff_dim) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + n_head = self.hparams["n_head"] + n_head_kv = 1 + head_dim = self.hparams["n_embd"] // n_head + block_count = self.hparams["n_layer"] + + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + tensors = dict(self.get_tensors()) + for i in range(block_count): + if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None: + tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim] + tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:] + del tensors[f"transformer.h.{i}.attn.kv.weight"] + if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None: + tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w + del tensors[f"transformer.h.{i}.attn.q.weight"] + if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None: + tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim] + tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:] + del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"] + + for name, data_torch in tensors.items(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + +class PersimmonModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) + head_count = self.hparams["num_attention_heads"] + head_count_kv = head_count + hidden_size = self.hparams["hidden_size"] + + self.gguf_writer.add_name('persimmon-8b-chat') + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + + def set_vocab(self): + self._set_vocab_sentencepiece() + # self.gguf_writer.add_bos_token_id(71013) + # self.gguf_writer.add_eos_token_id(71013) + + def write_tensors(self): + block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + if name.endswith(".self_attention.rotary_emb.inv_freq"): + continue + old_dtype = data_torch.dtype + # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) + data = data_torch.to(torch.float32).squeeze().numpy() + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + n_dims = len(data.shape) + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + +###### CONVERSION LOGIC ###### + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file") + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + parser.add_argument( + "--outfile", type=Path, + help="path to write to; default: based on input", + ) + parser.add_argument( + "--outtype", type=str, choices=["f32", "f16"], default="f16", + help="output format - use f32 for float32, f16 for float16", + ) + parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") + parser.add_argument( + "model", type=Path, + help="directory containing model file", + ) + + return parser.parse_args() + + +args = parse_args() + +dir_model = args.model +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file=sys.stderr) + sys.exit(1) + +ftype_map = { + "f32": gguf.GGMLQuantizationType.F32, + "f16": gguf.GGMLQuantizationType.F16, +} + +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + +print(f"Loading model: {dir_model.name}") + +hparams = Model.load_hparams(dir_model) + +model_class = Model.from_model_architecture(hparams["architectures"][0]) +model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + +print("Set model parameters") +model_instance.set_gguf_parameters() + +print("Set model tokenizer") +model_instance.set_vocab() + +if args.vocab_only: + print(f"Exporting model vocab to '{fname_out}'") + model_instance.write_vocab() +else: + print(f"Exporting model to '{fname_out}'") + model_instance.write() + +print(f"Model successfully exported to '{fname_out}'") diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py deleted file mode 100755 index 70d154b3f..000000000 --- a/convert-mpt-hf-to-gguf.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -# HF mpt--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert an MPT model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "MPTForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.MPT -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["n_layers"] - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_context_length(hparams["max_seq_len"]) -gguf_writer.add_embedding_length(hparams["d_model"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(4 * hparams["d_model"]) -gguf_writer.add_head_count(hparams["n_heads"]) -if kv_n_heads := hparams["attn_config"].get("kv_n_heads"): - gguf_writer.add_head_count_kv(kv_n_heads) -gguf_writer.add_layer_norm_eps(1e-05) -if hparams["attn_config"]["clip_qkv"] is not None: - gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"]) -gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"]) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but -# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to -# accomodate some "reserved" tokens; this is causing problems down the line in -# llama.cpp, so we pad the vocab with dummy tokens: - -vocab_size = hparams["vocab_size"] - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Cannot map tensor '" + name + "'") - continue # for the sake of compatibility with some old published models, don't quit - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - # note: MPT output is tied to (same as) wte in original model; - # for easier implementation in llama.cpp it's duplicated in GGUF, though :/ - if new_name == "token_embd.weight": - gguf_writer.add_tensor("output.weight", data) - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-refact-hf-to-gguf.py b/convert-refact-hf-to-gguf.py deleted file mode 100755 index f0cfe84d8..000000000 --- a/convert-refact-hf-to-gguf.py +++ /dev/null @@ -1,272 +0,0 @@ -#!/usr/bin/env python3 -# HF refact--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import sys -from pathlib import Path - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if "NO_LOCAL_GGUF" not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / "gguf-py" / "gguf")) -import gguf - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Convert a Refact model to a GGML compatible file" - ) - parser.add_argument( - "--vocab-only", - action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", - type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", - type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", - type=int, - choices=[0, 1], - default=1, - nargs="?", - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f"Error: {args.model} is not a directory", file=sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f"ggml-model-{ftype_str[ftype]}.gguf" - -print("gguf: loading model " + dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTRefactForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH = gguf.MODEL_ARCH.REFACT -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -# Get refact feed forward dimension -hidden_dim = hparams["n_embd"] -inner_dim = 4 * hidden_dim -hidden_dim = int(2 * inner_dim / 3) -multiple_of = 256 -ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - -block_count = hparams["n_layer"] - -gguf_writer.add_name("Refact") -# refact uses Alibi. So this is from config.json which might be used by training. -gguf_writer.add_context_length(hparams["n_positions"]) -gguf_writer.add_embedding_length(hparams["n_embd"]) - -gguf_writer.add_feed_forward_length(ff_dim) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(hparams["n_head"]) -gguf_writer.add_head_count_kv(1) -gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH, block_count) - -# params for qkv transform -n_head = hparams["n_head"] -n_head_kv = 1 - -head_dim = hparams["n_embd"] // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") - - for i in range(block_count): - if f"transformer.h.{i}.attn.kv.weight" in model_part: - data = model_part[f"transformer.h.{i}.attn.kv.weight"] - model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[ - : n_head_kv * head_dim - ] - model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[ - n_head_kv * head_dim : - ] - del model_part[f"transformer.h.{i}.attn.kv.weight"] - if f"transformer.h.{i}.attn.q.weight" in model_part: - model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[ - f"transformer.h.{i}.attn.q.weight" - ] - del model_part[f"transformer.h.{i}.attn.q.weight"] - if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part: - data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"] - model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim] - model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:] - del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"] - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ( - ftype == 1 - and data_dtype == np.float32 - and name.endswith(".weight") - and n_dims == 2 - ): - data = data.astype(np.float16) - - print( - new_name - + ", n_dims = " - + str(n_dims) - + ", " - + str(old_dtype) - + " --> " - + str(data.dtype) - ) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-starcoder-hf-to-gguf.py b/convert-starcoder-hf-to-gguf.py deleted file mode 100755 index a9bfed85e..000000000 --- a/convert-starcoder-hf-to-gguf.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -# HF starcoder --> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a StarCoder model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTBigCodeForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.STARCODER -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["n_layer"] - -gguf_writer.add_name("StarCoder") -gguf_writer.add_context_length(hparams["n_positions"]) -gguf_writer.add_embedding_length(hparams["n_embd"]) -gguf_writer.add_feed_forward_length(4 * hparams["n_embd"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(hparams["n_head"]) -gguf_writer.add_head_count_kv(1) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# params for qkv transform -n_head = hparams["n_head"] -n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1 - -head_dim = hparams["n_embd"] // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert.py b/convert.py index 9110f1580..b0f44dbef 100755 --- a/convert.py +++ b/convert.py @@ -26,7 +26,7 @@ from pathlib import Path from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar import numpy as np -from sentencepiece import SentencePieceProcessor # type: ignore[import] +from sentencepiece import SentencePieceProcessor import os if 'NO_LOCAL_GGUF' not in os.environ: @@ -328,7 +328,7 @@ class BpeVocab: def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.bpe_tokenizer - from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] + from transformers.models.gpt2 import tokenization_gpt2 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()} for i, _ in enumerate(tokenizer): diff --git a/mypy.ini b/mypy.ini index 55c168f2d..7215a05dd 100644 --- a/mypy.ini +++ b/mypy.ini @@ -3,3 +3,4 @@ strict = true allow_untyped_calls = true allow_untyped_defs = true allow_incomplete_defs = true +disable_error_code = import-untyped From df9d1293defe783f42bc83af732d3c670552c541 Mon Sep 17 00:00:00 2001 From: Galunid Date: Fri, 10 Nov 2023 14:24:54 +0100 Subject: [PATCH 055/664] Unbreak persimmon after #3837 (#4010) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index d220ff3e9..d682d2864 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4209,7 +4209,7 @@ struct llm_build_context { struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); cb(Kcur, "Kcur", il); - struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3)); + struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); cb(Q, "Q", il); Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); From 4a4fd3eefad5bd17ab6bcd8e2181b4f62eae76cf Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 11 Nov 2023 06:49:33 +0800 Subject: [PATCH 056/664] server : allow continue edit on completion mode (#3950) * server : allow continue edit on completion mode * server : handle abort case in runCompletion * server : style improvement --- examples/server/index.html.hpp | 4807 +++++++++++++++-------------- examples/server/public/index.html | 38 +- 2 files changed, 2468 insertions(+), 2377 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 207412513..f22b77e7f 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -229,850 +229,860 @@ unsigned char index_html[] = { 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x5d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x65, 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x30, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, 0x6b, 0x65, 0x79, 0x66, 0x72, - 0x61, 0x6d, 0x65, 0x73, 0x20, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, - 0x2d, 0x62, 0x67, 0x2d, 0x77, 0x69, 0x70, 0x65, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x30, 0x25, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, - 0x6f, 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transcriptUpdate([...session.value.transcript, ["", prompt]]); - await runLlama(prompt, { + runLlama(prompt, { ...params.value, slot_id: slot_id, stop: [], - }, ""); + }, "").finally(() => { + session.value.prompt = session.value.transcript.map(([_, data]) => + Array.isArray(data) ? data.map(msg => msg.content).join('') : data + ).join(''); + session.value.transcript = []; + }) } const stop = (e) => { @@ -573,6 +583,7 @@ } }, [messages]) + const isCompletionMode = session.value.type === 'completion' const chatLine = ([user, data], index) => { let message const isArrayMessage = Array.isArray(data) @@ -582,20 +593,31 @@ const text = isArrayMessage ? data.map(msg => msg.content).join('').replace(/^\s+/, '') : data; - message = html`<${Markdownish} text=${template(text)} />` + message = isCompletionMode ? + text : + html`<${Markdownish} text=${template(text)} />` } if (user) { return html`

${template(user)}: ${message}

` } else { - return html`

${message}

` + return isCompletionMode ? + html`${message}` : + html`

${message}

` } }; + const handleCompletionEdit = (e) => { + session.value.prompt = e.target.innerText; + session.value.transcript = []; + } + return html` -
+
- ${messages.flatMap(chatLine)} -
`; + + ${messages.flatMap(chatLine)} + + `; }; const ConfigForm = (props) => { From 34b0a082074b073eb14c2bd93c0c070e20ddcd16 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Fri, 10 Nov 2023 22:04:50 -0700 Subject: [PATCH 057/664] gguf-py: Refactor and allow reading/modifying existing GGUF files (#3981) * gguf-py: Refactor and add file reading support * Replay changes from #3871 Credit to @cebtenzzre for that pull * Various type annotation fixes. * sort imports with isort (again) * Fix missing return statement in add_tensor * style cleanup with flake8 * fix NamedTuple and Enum usage * Fix an issue with state init in GGUFReader Move examples to an examples/ directory Clean up examples Add an example of modifying keys in a GGUF file Update documentation with info on examples Try to support people importing gguf/gguf.py directly * Damagage is not a word. * Clean up gguf-py/examples/modify_gguf.py whitespace Co-authored-by: Jared Van Bortel * Update gguf-py/examples/modify_gguf.py formatting Co-authored-by: Jared Van Bortel * Update gguf-py/gguf/gguf_reader.py type hint Co-authored-by: Jared Van Bortel * Make examples executable, formatting changes * Add more information to GGUFReader and examples comments * Include a gguf Python package version bump * Add convert-gguf-endian.py script * cleanup * gguf-py : bump minor version * Reorganize scripts * Make GGUFReader endian detection less arbitrary * Add JSON dumping support to gguf-dump.py Which I kind of regret now * A few for gguf-dump.py cleanups * Murder accidental tuple in gguf-py/scripts/gguf-dump.py Co-authored-by: Jared Van Bortel * cleanup * constants : remove unneeded type annotations * fix python 3.8 compat * Set up gguf- scripts in pyproject.toml * And include scripts/__init__.py, derp * convert.py: We can't currently support Q8_0 on big endian. * gguf-py: SpecialVocab: Always try available sources for special token ids gguf-py: SpecialVocab: Try to load merges from merges.txt if not in tokenizer.json gguf-py: SpecialVocab: Add 'add_bos_token' type bools to GGUF metadata u * cleanup * Promote add_X_token to GGUF metadata for BOS and EOS --------- Co-authored-by: Jared Van Bortel Co-authored-by: Jared Van Bortel --- convert-baichuan-hf-to-gguf.py | 2 +- convert-llama-ggml-to-gguf.py | 24 +- convert-persimmon-to-gguf.py | 2 +- convert.py | 16 +- .../convert-train-checkpoint-to-gguf.py | 2 +- gguf-py/README.md | 10 + gguf-py/examples/writer.py | 40 + gguf-py/gguf/__init__.py | 6 +- gguf-py/gguf/constants.py | 470 +++++++ gguf-py/gguf/gguf.py | 1149 +---------------- gguf-py/gguf/gguf_reader.py | 264 ++++ gguf-py/gguf/gguf_writer.py | 409 ++++++ gguf-py/gguf/tensor_mapping.py | 257 ++++ gguf-py/gguf/vocab.py | 164 +++ gguf-py/pyproject.toml | 8 +- gguf-py/scripts/__init__.py | 12 + gguf-py/scripts/gguf-convert-endian.py | 113 ++ gguf-py/scripts/gguf-dump.py | 116 ++ gguf-py/scripts/gguf-set-metadata.py | 90 ++ gguf-py/tests/test_gguf.py | 4 +- 20 files changed, 1982 insertions(+), 1176 deletions(-) create mode 100755 gguf-py/examples/writer.py create mode 100644 gguf-py/gguf/constants.py create mode 100644 gguf-py/gguf/gguf_reader.py create mode 100644 gguf-py/gguf/gguf_writer.py create mode 100644 gguf-py/gguf/tensor_mapping.py create mode 100644 gguf-py/gguf/vocab.py create mode 100644 gguf-py/scripts/__init__.py create mode 100755 gguf-py/scripts/gguf-convert-endian.py create mode 100755 gguf-py/scripts/gguf-dump.py create mode 100755 gguf-py/scripts/gguf-set-metadata.py diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 67ccbe99f..789602351 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -16,7 +16,7 @@ import torch from sentencepiece import SentencePieceProcessor # type: ignore[import] if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index 871add64d..d898d81c4 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -12,29 +12,9 @@ import numpy as np import os if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -# Note: Does not support GGML_QKK_64 -QK_K = 256 -# Items here are (block size, type size) -GGML_QUANT_SIZES = { - gguf.GGMLQuantizationType.F32 : (1, 4), - gguf.GGMLQuantizationType.F16 : (1, 2), - gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16), - gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16), - gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16), - gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16), - gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32), - gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32), - gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4), - gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12), - gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8), -} - class GGMLFormat(IntEnum): GGML = 0 GGMF = 1 @@ -125,7 +105,7 @@ class Tensor: (n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12]) assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}' assert name_len < 4096, 'Absurd tensor name length' - quant = GGML_QUANT_SIZES.get(dtype) + quant = gguf.GGML_QUANT_SIZES.get(dtype) assert quant is not None, 'Unknown tensor type' (blksize, tysize) = quant offset += 12 diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index e022ffe46..240f87306 100644 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -6,7 +6,7 @@ import argparse from pathlib import Path from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf def _flatten_dict(dct, tensors, prefix=None): diff --git a/convert.py b/convert.py index b0f44dbef..a4b87e088 100755 --- a/convert.py +++ b/convert.py @@ -3,11 +3,9 @@ from __future__ import annotations import argparse import concurrent.futures -import copy import enum import faulthandler import functools -import io import itertools import json import math @@ -23,14 +21,14 @@ from abc import ABCMeta, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar +from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar import numpy as np from sentencepiece import SentencePieceProcessor import os if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf if TYPE_CHECKING: @@ -851,7 +849,7 @@ class OutputFile: elif isinstance(vocab, BpeVocab): self.gguf.add_tokenizer_model("gpt2") else: - raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab') + raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab') self.gguf.add_token_list(tokens) self.gguf.add_token_scores(scores) self.gguf.add_token_types(toktypes) @@ -905,7 +903,7 @@ class OutputFile: return dt.quantize(arr) @staticmethod - def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None: + def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out, endianess=endianess) @@ -1114,11 +1112,15 @@ def do_dump_model(model_plus: ModelPlus) -> None: def main(args_in: list[str] | None = None) -> None: + output_choices = ["f32", "f16"] + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # We currently only support Q8_0 output on little endian systems. + output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") + parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py index 887ed2e21..ed93673bc 100644 --- a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py +++ b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py @@ -9,7 +9,7 @@ import numpy as np from pathlib import Path if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py')) import gguf # gguf constants diff --git a/gguf-py/README.md b/gguf-py/README.md index a28d8c57a..502b6a510 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -11,6 +11,16 @@ as an example for its usage. pip install gguf ``` +## API Examples/Simple Tools + +[examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model. + +[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console. + +[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key. + +[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files. + ## Development Maintainers who participate in development of this package are advised to install it in editable mode: diff --git a/gguf-py/examples/writer.py b/gguf-py/examples/writer.py new file mode 100755 index 000000000..f39eed1af --- /dev/null +++ b/gguf-py/examples/writer.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFWriter # noqa: E402 + + +# Example usage: +def writer_example() -> None: + # Example usage with a file + gguf_writer = GGUFWriter("example.gguf", "llama") + + gguf_writer.add_architecture() + gguf_writer.add_block_count(12) + gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer + gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float + gguf_writer.add_custom_alignment(64) + + tensor1 = np.ones((32,), dtype=np.float32) * 100.0 + tensor2 = np.ones((64,), dtype=np.float32) * 101.0 + tensor3 = np.ones((96,), dtype=np.float32) * 102.0 + + gguf_writer.add_tensor("tensor1", tensor1) + gguf_writer.add_tensor("tensor2", tensor2) + gguf_writer.add_tensor("tensor3", tensor3) + + gguf_writer.write_header_to_file() + gguf_writer.write_kv_data_to_file() + gguf_writer.write_tensors_to_file() + + gguf_writer.close() + + +if __name__ == '__main__': + writer_example() diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py index f9b70a85b..110ab342c 100644 --- a/gguf-py/gguf/__init__.py +++ b/gguf-py/gguf/__init__.py @@ -1 +1,5 @@ -from .gguf import * +from .constants import * +from .gguf_reader import * +from .gguf_writer import * +from .tensor_mapping import * +from .vocab import * diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py new file mode 100644 index 000000000..bf1ccf669 --- /dev/null +++ b/gguf-py/gguf/constants.py @@ -0,0 +1,470 @@ +from __future__ import annotations + +import sys +from enum import Enum, IntEnum, auto +from typing import Any + +# +# constants +# + +GGUF_MAGIC = 0x46554747 # "GGUF" +GGUF_VERSION = 3 +GGUF_DEFAULT_ALIGNMENT = 32 + +# +# metadata keys +# + + +class Keys: + class General: + ARCHITECTURE = "general.architecture" + QUANTIZATION_VERSION = "general.quantization_version" + ALIGNMENT = "general.alignment" + NAME = "general.name" + AUTHOR = "general.author" + URL = "general.url" + DESCRIPTION = "general.description" + LICENSE = "general.license" + SOURCE_URL = "general.source.url" + SOURCE_HF_REPO = "general.source.huggingface.repository" + FILE_TYPE = "general.file_type" + + class LLM: + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + + class Attention: + HEAD_COUNT = "{arch}.attention.head_count" + HEAD_COUNT_KV = "{arch}.attention.head_count_kv" + MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" + CLAMP_KQV = "{arch}.attention.clamp_kqv" + LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" + LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + + class Rope: + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + + class Tokenizer: + MODEL = "tokenizer.ggml.model" + LIST = "tokenizer.ggml.tokens" + TOKEN_TYPE = "tokenizer.ggml.token_type" + SCORES = "tokenizer.ggml.scores" + MERGES = "tokenizer.ggml.merges" + BOS_ID = "tokenizer.ggml.bos_token_id" + EOS_ID = "tokenizer.ggml.eos_token_id" + UNK_ID = "tokenizer.ggml.unknown_token_id" + SEP_ID = "tokenizer.ggml.seperator_token_id" + PAD_ID = "tokenizer.ggml.padding_token_id" + ADD_BOS = "tokenizer.ggml.add_bos_token" + ADD_EOS = "tokenizer.ggml.add_eos_token" + HF_JSON = "tokenizer.huggingface.json" + RWKV = "tokenizer.rwkv.world" + + +# +# recommended mapping of model tensor names for storage in gguf +# + + +class MODEL_ARCH(IntEnum): + LLAMA = auto() + FALCON = auto() + BAICHUAN = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + PERSIMMON = auto() + REFACT = auto() + BERT = auto() + BLOOM = auto() + + +class MODEL_TENSOR(IntEnum): + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + OUTPUT_NORM = auto() + ROPE_FREQS = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_ROT_EMBD = auto() + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_NORM = auto() + ATTN_Q_NORM = auto() + ATTN_K_NORM = auto() + + +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { + MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.FALCON: "falcon", + MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GPT2: "gpt2", + MODEL_ARCH.GPTJ: "gptj", + MODEL_ARCH.GPTNEOX: "gptneox", + MODEL_ARCH.MPT: "mpt", + MODEL_ARCH.STARCODER: "starcoder", + MODEL_ARCH.PERSIMMON: "persimmon", + MODEL_ARCH.REFACT: "refact", + MODEL_ARCH.BERT: "bert", + MODEL_ARCH.BLOOM: "bloom", +} + +TENSOR_NAMES: dict[MODEL_TENSOR, str] = { + MODEL_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", +} + +MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPTNEOX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STARCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MPT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPTJ: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PERSIMMON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.REFACT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BLOOM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPT2: [ + # TODO + ], + # TODO +} + +# tensors that will not be serialized +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.PERSIMMON: [ + MODEL_TENSOR.ROPE_FREQS, + ], +} + +# +# types +# + + +class TokenType(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + + +class GGMLQuantizationType(IntEnum): + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + + +class GGUFEndian(IntEnum): + LITTLE = 0 + BIG = 1 + + +class GGUFValueType(IntEnum): + UINT8 = 0 + INT8 = 1 + UINT16 = 2 + INT16 = 3 + UINT32 = 4 + INT32 = 5 + FLOAT32 = 6 + BOOL = 7 + STRING = 8 + ARRAY = 9 + UINT64 = 10 + INT64 = 11 + FLOAT64 = 12 + + @staticmethod + def get_type(val: Any) -> GGUFValueType: + if isinstance(val, (str, bytes, bytearray)): + return GGUFValueType.STRING + elif isinstance(val, list): + return GGUFValueType.ARRAY + elif isinstance(val, float): + return GGUFValueType.FLOAT32 + elif isinstance(val, bool): + return GGUFValueType.BOOL + elif isinstance(val, int): + return GGUFValueType.INT32 + # TODO: need help with 64-bit types in Python + else: + print("Unknown type:", type(val)) + sys.exit() + + +# Note: Does not support GGML_QKK_64 +QK_K = 256 +# Items here are (block size, type size) +GGML_QUANT_SIZES = { + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), +} + + +# Aliases for backward compatibility. + +# general +KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE +KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION +KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT +KEY_GENERAL_NAME = Keys.General.NAME +KEY_GENERAL_AUTHOR = Keys.General.AUTHOR +KEY_GENERAL_URL = Keys.General.URL +KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION +KEY_GENERAL_LICENSE = Keys.General.LICENSE +KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL +KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO +KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE + +# LLM +KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH +KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH +KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT +KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH +KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL +KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT + +# attention +KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT +KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV +KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS +KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV +KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS +KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS + +# RoPE +KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT +KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE +KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE +KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR +KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN +KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED + +# tokenization +KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL +KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST +KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE +KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES +KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES +KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID +KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID +KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID +KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID +KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID +KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON +KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 7e495cb19..651a81eb8 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -1,1146 +1,15 @@ -#!/usr/bin/env python3 -from __future__ import annotations +# This file left for compatibility. If you want to use the GGUF API from Python +# then don't import gguf/gguf.py directly. If you're looking for examples, see the +# examples/ directory for gguf-py -import json -import os -import shutil -import struct +import importlib import sys -import tempfile -from enum import Enum, IntEnum, auto -from io import BufferedWriter from pathlib import Path -from typing import IO, Any, BinaryIO, Callable, Sequence -import numpy as np +sys.path.insert(0, str(Path(__file__).parent.parent)) -# -# constants -# +# Compatibility for people trying to import gguf/gguf.py directly instead of as a package. +importlib.invalidate_caches() +import gguf # noqa: E402 -GGUF_MAGIC = 0x46554747 -GGUF_VERSION = 3 -GGUF_DEFAULT_ALIGNMENT = 32 - - -# general -KEY_GENERAL_ARCHITECTURE = "general.architecture" -KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version" -KEY_GENERAL_ALIGNMENT = "general.alignment" -KEY_GENERAL_NAME = "general.name" -KEY_GENERAL_AUTHOR = "general.author" -KEY_GENERAL_URL = "general.url" -KEY_GENERAL_DESCRIPTION = "general.description" -KEY_GENERAL_LICENSE = "general.license" -KEY_GENERAL_SOURCE_URL = "general.source.url" -KEY_GENERAL_SOURCE_HF_REPO = "general.source.huggingface.repository" -KEY_GENERAL_FILE_TYPE = "general.file_type" - -# LLM -KEY_CONTEXT_LENGTH = "{arch}.context_length" -KEY_EMBEDDING_LENGTH = "{arch}.embedding_length" -KEY_BLOCK_COUNT = "{arch}.block_count" -KEY_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" -KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" -KEY_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" - -# attention -KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count" -KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv" -KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" -KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv" -KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" -KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" - -# RoPE -KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" -KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" -KEY_ROPE_SCALING_TYPE = "{arch}.rope.scaling.type" -KEY_ROPE_SCALING_FACTOR = "{arch}.rope.scaling.factor" -KEY_ROPE_SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" -KEY_ROPE_SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" - -# tokenization -KEY_TOKENIZER_MODEL = "tokenizer.ggml.model" -KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens" -KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type" -KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores" -KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges" -KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id" -KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id" -KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id" -KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id" -KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id" -KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json" -KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world" - - -# -# recommended mapping of model tensor names for storage in gguf -# - - -class MODEL_ARCH(IntEnum): - LLAMA : int = auto() - FALCON : int = auto() - BAICHUAN : int = auto() - GPT2 : int = auto() - GPTJ : int = auto() - GPTNEOX : int = auto() - MPT : int = auto() - STARCODER : int = auto() - PERSIMMON : int = auto() - REFACT : int = auto() - BERT : int = auto() - BLOOM : int = auto() - - -class MODEL_TENSOR(IntEnum): - TOKEN_EMBD : int = auto() - TOKEN_EMBD_NORM : int = auto() - TOKEN_TYPES : int = auto() - POS_EMBD : int = auto() - OUTPUT : int = auto() - OUTPUT_NORM : int = auto() - ROPE_FREQS : int = auto() - ATTN_Q : int = auto() - ATTN_K : int = auto() - ATTN_V : int = auto() - ATTN_QKV : int = auto() - ATTN_OUT : int = auto() - ATTN_NORM : int = auto() - ATTN_NORM_2 : int = auto() - ATTN_ROT_EMBD : int = auto() - FFN_GATE : int = auto() - FFN_DOWN : int = auto() - FFN_UP : int = auto() - FFN_NORM : int = auto() - ATTN_Q_NORM : int = auto() - ATTN_K_NORM : int = auto() - - -MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { - MODEL_ARCH.LLAMA: "llama", - MODEL_ARCH.FALCON: "falcon", - MODEL_ARCH.BAICHUAN: "baichuan", - MODEL_ARCH.GPT2: "gpt2", - MODEL_ARCH.GPTJ: "gptj", - MODEL_ARCH.GPTNEOX: "gptneox", - MODEL_ARCH.MPT: "mpt", - MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.PERSIMMON: "persimmon", - MODEL_ARCH.REFACT: "refact", - MODEL_ARCH.BERT: "bert", - MODEL_ARCH.BLOOM: "bloom", -} - -TENSOR_NAMES: dict[MODEL_TENSOR, str] = { - MODEL_TENSOR.TOKEN_EMBD: "token_embd", - MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", - MODEL_TENSOR.TOKEN_TYPES: "token_types", - MODEL_TENSOR.POS_EMBD: "position_embd", - MODEL_TENSOR.OUTPUT_NORM: "output_norm", - MODEL_TENSOR.OUTPUT: "output", - MODEL_TENSOR.ROPE_FREQS: "rope_freqs", - MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", - MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", - MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", - MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", - MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", - MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", - MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", - MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", - MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", - MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", - MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", - MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", - MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", - MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", -} - -MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { - MODEL_ARCH.LLAMA: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.ATTN_ROT_EMBD, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPTNEOX: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.FALCON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_NORM_2, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BAICHUAN: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.ATTN_ROT_EMBD, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.STARCODER: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.POS_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BERT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.TOKEN_TYPES, - MODEL_TENSOR.POS_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.MPT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPTJ: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - MODEL_TENSOR.ATTN_Q_NORM, - MODEL_TENSOR.ATTN_K_NORM, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.REFACT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BLOOM: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.TOKEN_EMBD_NORM, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPT2: [ - # TODO - ], - # TODO -} - -# tensors that will not be serialized -MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { - MODEL_ARCH.LLAMA: [ - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.BAICHUAN: [ - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.ROPE_FREQS, - ] -} - - -class TensorNameMap: - mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { - # Token embeddings - MODEL_TENSOR.TOKEN_EMBD: ( - "gpt_neox.embed_in", # gptneox - "transformer.wte", # gpt2 gpt-j mpt refact - "transformer.word_embeddings", # falcon - "word_embeddings", # bloom - "model.embed_tokens", # llama-hf - "tok_embeddings", # llama-pth - "embeddings.word_embeddings", # bert - "language_model.embedding.word_embeddings", # persimmon - ), - - # Token type embeddings - MODEL_TENSOR.TOKEN_TYPES: ( - "embeddings.token_type_embeddings", # bert - ), - - # Normalization of token embeddings - MODEL_TENSOR.TOKEN_EMBD_NORM: ( - "word_embeddings_layernorm", # bloom - ), - - # Position embeddings - MODEL_TENSOR.POS_EMBD: ( - "transformer.wpe", # gpt2 - "embeddings.position_embeddings", # bert - ), - - # Output - MODEL_TENSOR.OUTPUT: ( - "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan - "output", # llama-pth bloom - "word_embeddings_for_head", # persimmon - ), - - # Output norm - MODEL_TENSOR.OUTPUT_NORM: ( - "gpt_neox.final_layer_norm", # gptneox - "transformer.ln_f", # gpt2 gpt-j falcon - "model.norm", # llama-hf baichuan - "norm", # llama-pth - "embeddings.LayerNorm", # bert - "transformer.norm_f", # mpt - "ln_f", # refact bloom - "language_model.encoder.final_layernorm", # persimmon - ), - - # Rope frequencies - MODEL_TENSOR.ROPE_FREQS: ( - "rope.freqs", # llama-pth - ), - } - - block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { - # Attention norm - MODEL_TENSOR.ATTN_NORM: ( - "gpt_neox.layers.{bid}.input_layernorm", # gptneox - "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact - "transformer.blocks.{bid}.norm_1", # mpt - "transformer.h.{bid}.input_layernorm", # falcon7b - "h.{bid}.input_layernorm", # bloom - "transformer.h.{bid}.ln_mlp", # falcon40b - "model.layers.{bid}.input_layernorm", # llama-hf - "layers.{bid}.attention_norm", # llama-pth - "encoder.layer.{bid}.attention.output.LayerNorm", # bert - "language_model.encoder.layers.{bid}.input_layernorm", # persimmon - "model.layers.{bid}.ln1", # yi - ), - - # Attention norm 2 - MODEL_TENSOR.ATTN_NORM_2: ( - "transformer.h.{bid}.ln_attn", # falcon40b - ), - - # Attention query-key-value - MODEL_TENSOR.ATTN_QKV: ( - "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox - "transformer.h.{bid}.attn.c_attn", # gpt2 - "transformer.blocks.{bid}.attn.Wqkv", # mpt - "transformer.h.{bid}.self_attention.query_key_value", # falcon - "h.{bid}.self_attention.query_key_value", # bloom - "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon - ), - - # Attention query - MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf - "layers.{bid}.attention.wq", # llama-pth - "encoder.layer.{bid}.attention.self.query", # bert - "transformer.h.{bid}.attn.q_proj", # gpt-j - ), - - # Attention key - MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf - "layers.{bid}.attention.wk", # llama-pth - "encoder.layer.{bid}.attention.self.key", # bert - "transformer.h.{bid}.attn.k_proj", # gpt-j - ), - - # Attention value - MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf - "layers.{bid}.attention.wv", # llama-pth - "encoder.layer.{bid}.attention.self.value", # bert - "transformer.h.{bid}.attn.v_proj", # gpt-j - ), - - # Attention output - MODEL_TENSOR.ATTN_OUT: ( - "gpt_neox.layers.{bid}.attention.dense", # gptneox - "transformer.h.{bid}.attn.c_proj", # gpt2 refact - "transformer.blocks.{bid}.attn.out_proj", # mpt - "transformer.h.{bid}.self_attention.dense", # falcon - "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf - "layers.{bid}.attention.wo", # llama-pth - "encoder.layer.{bid}.attention.output.dense", # bert - "transformer.h.{bid}.attn.out_proj", # gpt-j - "language_model.encoder.layers.{bid}.self_attention.dense" # persimmon - ), - - # Rotary embeddings - MODEL_TENSOR.ATTN_ROT_EMBD: ( - "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf - "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth - ), - - # Feed-forward norm - MODEL_TENSOR.FFN_NORM: ( - "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox - "transformer.h.{bid}.ln_2", # gpt2 refact - "h.{bid}.post_attention_layernorm", # bloom - "transformer.blocks.{bid}.norm_2", # mpt - "model.layers.{bid}.post_attention_layernorm", # llama-hf - "layers.{bid}.ffn_norm", # llama-pth - "encoder.layer.{bid}.output.LayerNorm", # bert - "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon - "model.layers.{bid}.ln2", # yi - ), - - # Feed-forward up - MODEL_TENSOR.FFN_UP: ( - "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox - "transformer.h.{bid}.mlp.c_fc", # gpt2 - "transformer.blocks.{bid}.ffn.up_proj", # mpt - "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon - "h.{bid}.mlp.dense_h_to_4h", # bloom - "model.layers.{bid}.mlp.up_proj", # llama-hf refact - "layers.{bid}.feed_forward.w3", # llama-pth - "encoder.layer.{bid}.intermediate.dense", # bert - "transformer.h.{bid}.mlp.fc_in", # gpt-j - "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon - ), - - # Feed-forward gate - MODEL_TENSOR.FFN_GATE: ( - "model.layers.{bid}.mlp.gate_proj", # llama-hf refact - "layers.{bid}.feed_forward.w1", # llama-pth - ), - - # Feed-forward down - MODEL_TENSOR.FFN_DOWN: ( - "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox - "transformer.h.{bid}.mlp.c_proj", # gpt2 refact - "transformer.blocks.{bid}.ffn.down_proj", # mpt - "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon - "h.{bid}.mlp.dense_4h_to_h", # bloom - "model.layers.{bid}.mlp.down_proj", # llama-hf - "layers.{bid}.feed_forward.w2", # llama-pth - "encoder.layer.{bid}.output.dense", # bert - "transformer.h.{bid}.mlp.fc_out", # gpt-j - "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon - ), - - MODEL_TENSOR.ATTN_Q_NORM: ( - "language_model.encoder.layers.{bid}.self_attention.q_layernorm", - ), - - MODEL_TENSOR.ATTN_K_NORM: ( - "language_model.encoder.layers.{bid}.self_attention.k_layernorm", - ), - - MODEL_TENSOR.ROPE_FREQS: ( - "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon - ) - } - - mapping: dict[str, tuple[MODEL_TENSOR, str]] - - def __init__(self, arch: MODEL_ARCH, n_blocks: int): - self.mapping = {} - for tensor, keys in self.mappings_cfg.items(): - if tensor not in MODEL_TENSORS[arch]: - continue - tensor_name = TENSOR_NAMES[tensor] - self.mapping[tensor_name] = (tensor, tensor_name) - for key in keys: - self.mapping[key] = (tensor, tensor_name) - for bid in range(n_blocks): - for tensor, keys in self.block_mappings_cfg.items(): - if tensor not in MODEL_TENSORS[arch]: - continue - tensor_name = TENSOR_NAMES[tensor].format(bid = bid) - self.mapping[tensor_name] = (tensor, tensor_name) - for key in keys: - key = key.format(bid = bid) - self.mapping[key] = (tensor, tensor_name) - - def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None: - result = self.mapping.get(key) - if result is not None: - return result - for suffix in try_suffixes: - if key.endswith(suffix): - result = self.mapping.get(key[:-len(suffix)]) - if result is not None: - return (result[0], result[1] + suffix) - return None - - def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None: - result = self.get_type_and_name(key, try_suffixes = try_suffixes) - if result is None: - return None - return result[1] - - def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None: - result = self.get_type_and_name(key, try_suffixes = try_suffixes) - if result is None: - return None - return result[0] - - def __getitem__(self, key: str) -> str: - try: - return self.mapping[key][1] - except KeyError: - raise KeyError(key) - - def __contains__(self, key: str) -> bool: - return key in self.mapping - - def __repr__(self) -> str: - return repr(self.mapping) - -def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: - return TensorNameMap(arch, n_blocks) - -class TokenType(IntEnum): - NORMAL = 1 - UNKNOWN = 2 - CONTROL = 3 - USER_DEFINED = 4 - UNUSED = 5 - BYTE = 6 - -class RopeScalingType(Enum): - NONE = 'none' - LINEAR = 'linear' - YARN = 'yarn' - -# -# implementation -# - - -class GGMLQuantizationType(IntEnum): - F32 = 0 - F16 = 1 - Q4_0 = 2 - Q4_1 = 3 - Q5_0 = 6 - Q5_1 = 7 - Q8_0 = 8 - Q8_1 = 9 - Q2_K = 10 - Q3_K = 11 - Q4_K = 12 - Q5_K = 13 - Q6_K = 14 - Q8_K = 15 - -class GGUFEndian(IntEnum): - LITTLE = 0 - BIG = 1 - - -class GGUFValueType(IntEnum): - UINT8 = 0 - INT8 = 1 - UINT16 = 2 - INT16 = 3 - UINT32 = 4 - INT32 = 5 - FLOAT32 = 6 - BOOL = 7 - STRING = 8 - ARRAY = 9 - UINT64 = 10 - INT64 = 11 - FLOAT64 = 12 - - @staticmethod - def get_type(val): - if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray): - return GGUFValueType.STRING - elif isinstance(val, list): - return GGUFValueType.ARRAY - elif isinstance(val, float): - return GGUFValueType.FLOAT32 - elif isinstance(val, bool): - return GGUFValueType.BOOL - elif isinstance(val, int): - return GGUFValueType.INT32 - # TODO: need help with 64-bit types in Python - else: - print("Unknown type: "+str(type(val))) - sys.exit() - - -class WriterState(Enum): - EMPTY = auto() - HEADER = auto() - KV_DATA = auto() - TI_DATA = auto() - - -class GGUFWriter: - fout: BufferedWriter - temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any]] - - @property - def pack_prefix(self): - if self.endianess==GGUFEndian.LITTLE: - return "<" - else: - return ">" - - def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True, endianess=GGUFEndian.LITTLE): - self.fout = open(path, "wb") - self.arch = arch - self.endianess = endianess - self._simple_value_packing = { - GGUFValueType.UINT8: f"{self.pack_prefix}B", - GGUFValueType.INT8: f"{self.pack_prefix}b", - GGUFValueType.UINT16: f"{self.pack_prefix}H", - GGUFValueType.INT16: f"{self.pack_prefix}h", - GGUFValueType.UINT32: f"{self.pack_prefix}I", - GGUFValueType.INT32: f"{self.pack_prefix}i", - GGUFValueType.FLOAT32: f"{self.pack_prefix}f", - GGUFValueType.UINT64: f"{self.pack_prefix}Q", - GGUFValueType.INT64: f"{self.pack_prefix}q", - GGUFValueType.FLOAT64: f"{self.pack_prefix}d", - GGUFValueType.BOOL: "?" , - } - self.offset_tensor = 0 - self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" - self.kv_data_count = 0 - self.ti_data = b"" - self.ti_data_count = 0 - self.use_temp_file = use_temp_file - self.temp_file = None - self.tensors = [] - endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian" - print(f"This gguf file is for {endianess_str} only") - self.state = WriterState.EMPTY - - self.add_architecture() - - def write_header_to_file(self): - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') - - self.fout.write(struct.pack(" 0: - ltype = GGUFValueType.get_type(val[0]) - if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): - raise ValueError("All items in a GGUF array should be of the same type") - self.kv_data += struct.pack(f"{self.pack_prefix}I", ltype) - self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(val)) - for item in val: - self.add_val(item, add_vtype=False) - else: - raise ValueError("Invalid GGUF metadata value type or value") - - @staticmethod - def ggml_pad(x: int, n: int) -> int: - return ((x + n - 1) // n) * n - - def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None): - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') - - assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now" - - encoded_name = name.encode("utf8") - self.ti_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_name)) - self.ti_data += encoded_name - n_dims = len(tensor_shape) - self.ti_data += struct.pack(f"{self.pack_prefix}I", n_dims) - for i in range(n_dims): - self.ti_data += struct.pack(f"{self.pack_prefix}Q", tensor_shape[n_dims - 1 - i]) - if raw_dtype is None: - dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 - else: - dtype = raw_dtype - self.ti_data += struct.pack(f"{self.pack_prefix}I", dtype) - self.ti_data += struct.pack(f"{self.pack_prefix}Q", self.offset_tensor) - self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) - self.ti_data_count += 1 - - def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None): - if self.endianess == GGUFEndian.BIG: - tensor.byteswap(inplace=True) - if self.use_temp_file and self.temp_file is None: - fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) - fp.seek(0) - self.temp_file = fp - - shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape - self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) - - if self.temp_file is None: - self.tensors.append(tensor) - return - - tensor.tofile(self.temp_file) - self.write_padding(self.temp_file, tensor.nbytes) - - def write_padding(self, fp: IO[bytes], n: int, align: int | None = None): - pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n - if pad != 0: - fp.write(bytes([0] * pad)) - - def write_tensor_data(self, tensor: np.ndarray[Any, Any]): - if self.state is not WriterState.TI_DATA: - raise ValueError(f'Expected output file to contain tensor info, got {self.state}') - - if self.endianess==GGUFEndian.BIG: - tensor.byteswap(inplace=True) - self.write_padding(self.fout, self.fout.tell()) - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - - def write_tensors_to_file(self): - self.write_ti_data_to_file() - - self.write_padding(self.fout, self.fout.tell()) - - if self.temp_file is None: - while True: - try: - tensor = self.tensors.pop(0) - except IndexError: - break - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - return - - self.temp_file.seek(0) - - shutil.copyfileobj(self.temp_file, self.fout) - self.flush() - self.temp_file.close() - - def flush(self): - self.fout.flush() - - def close(self): - self.fout.close() - - def add_architecture(self): - self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch) - - def add_author(self, author: str): - self.add_string(KEY_GENERAL_AUTHOR, author) - - def add_tensor_data_layout(self, layout: str): - self.add_string(KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) - - def add_url(self, url: str): - self.add_string(KEY_GENERAL_URL, url) - - def add_description(self, description: str): - self.add_string(KEY_GENERAL_DESCRIPTION, description) - - def add_source_url(self, url: str): - self.add_string(KEY_GENERAL_SOURCE_URL, url) - - def add_source_hf_repo(self, repo: str): - self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo) - - def add_file_type(self, ftype: int): - self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype) - - def add_name(self, name: str): - self.add_string(KEY_GENERAL_NAME, name) - - def add_quantization_version(self, quantization_version: GGMLQuantizationType): - self.add_uint32( - KEY_GENERAL_QUANTIZATION_VERSION, quantization_version) - - def add_custom_alignment(self, alignment: int): - self.data_alignment = alignment - self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment) - - def add_context_length(self, length: int): - self.add_uint32( - KEY_CONTEXT_LENGTH.format(arch=self.arch), length) - - def add_embedding_length(self, length: int): - self.add_uint32( - KEY_EMBEDDING_LENGTH.format(arch=self.arch), length) - - def add_block_count(self, length: int): - self.add_uint32( - KEY_BLOCK_COUNT.format(arch=self.arch), length) - - def add_feed_forward_length(self, length: int): - self.add_uint32( - KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length) - - def add_parallel_residual(self, use: bool): - self.add_bool( - KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) - - def add_head_count(self, count: int): - self.add_uint32( - KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count) - - def add_head_count_kv(self, count: int): - self.add_uint32( - KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count) - - def add_max_alibi_bias(self, bias: float): - self.add_float32( - KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias) - - def add_clamp_kqv(self, value: float): - self.add_float32( - KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value) - - def add_layer_norm_eps(self, value: float): - self.add_float32( - KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value) - - def add_layer_norm_rms_eps(self, value: float): - self.add_float32( - KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value) - - def add_rope_dimension_count(self, count: int): - self.add_uint32( - KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count) - - def add_rope_freq_base(self, value: float): - self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value) - - def add_rope_scaling_type(self, value: RopeScalingType): - self.add_string(KEY_ROPE_SCALING_TYPE.format(arch=self.arch), value.value) - - def add_rope_scaling_factor(self, value: float): - self.add_float32(KEY_ROPE_SCALING_FACTOR.format(arch=self.arch), value) - - def add_rope_scaling_orig_ctx_len(self, value: int): - self.add_uint32(KEY_ROPE_SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) - - def add_rope_scaling_finetuned(self, value: bool): - self.add_bool(KEY_ROPE_SCALING_FINETUNED.format(arch=self.arch), value) - - def add_tokenizer_model(self, model: str): - self.add_string(KEY_TOKENIZER_MODEL, model) - - def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): - self.add_array(KEY_TOKENIZER_LIST, tokens) - - def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): - self.add_array(KEY_TOKENIZER_MERGES, merges) - - def add_token_types(self, types: Sequence[TokenType] | Sequence[int]): - self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types) - - def add_token_scores(self, scores: Sequence[float]): - self.add_array(KEY_TOKENIZER_SCORES, scores) - - def add_bos_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_BOS_ID, id) - - def add_eos_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_EOS_ID, id) - - def add_unk_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_UNK_ID, id) - - def add_sep_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_SEP_ID, id) - - def add_pad_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_PAD_ID, id) - - -class SpecialVocab: - merges: list[str] - special_token_ids: dict[str, int] - - def __init__( - self, path: str | os.PathLike[str], load_merges: bool = False, - special_token_types: tuple[str, ...] | None = None, - n_vocab: int | None = None, - ): - self.special_token_ids = {} - self.n_vocab = n_vocab - self.load_merges = load_merges - self.merges = [] - if special_token_types is not None: - self.special_token_types = special_token_types - else: - self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') - self._load(Path(path)) - - def _load(self, path: Path) -> None: - if not self._try_load_from_tokenizer_json(path): - self._try_load_from_config_json(path) - - def _set_special_token(self, typ: str, tid: Any): - if not isinstance(tid, int) or tid < 0: - return - if self.n_vocab is None or tid < self.n_vocab: - self.special_token_ids[typ] = tid - return - print(f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', - file = sys.stderr) - - - def _try_load_from_tokenizer_json(self, path: Path) -> bool: - tokenizer_file = path / 'tokenizer.json' - if not tokenizer_file.is_file(): - return False - with open(tokenizer_file, encoding = 'utf-8') as f: - tokenizer = json.load(f) - if self.load_merges: - merges = tokenizer.get('model', {}).get('merges') - if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str): - self.merges = merges - tokenizer_config_file = path / 'tokenizer_config.json' - added_tokens = tokenizer.get('added_tokens') - if added_tokens is None or not tokenizer_config_file.is_file(): - return True - with open(tokenizer_config_file, encoding = 'utf-8') as f: - tokenizer_config = json.load(f) - for typ in self.special_token_types: - entry = tokenizer_config.get(f'{typ}_token') - if isinstance(entry, str): - tc_content = entry - elif isinstance(entry, dict): - entry_content = entry.get('content') - if not isinstance(entry_content, str): - continue - tc_content = entry_content - else: - continue - # We only need the first match here. - maybe_token_id = next(( - atok.get('id') for atok in added_tokens - if atok.get('content') == tc_content), None) - self._set_special_token(typ, maybe_token_id) - return True - - def _try_load_from_config_json(self, path: Path) -> bool: - config_file = path / 'config.json' - if not config_file.is_file(): - return False - with open(config_file, encoding = 'utf-8') as f: - config = json.load(f) - for typ in self.special_token_types: - self._set_special_token(typ, config.get(f'{typ}_token_id')) - return True - - def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: - if len(self.merges) > 0: - if not quiet: - print(f'gguf: Adding {len(self.merges)} merge(s).') - gw.add_token_merges(self.merges) - for typ, tokid in self.special_token_ids.items(): - handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) - if handler is None: - print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', file = sys.stderr) - continue - if not quiet: - print(f'gguf: Setting special token type {typ} to {tokid}') - handler(tokid) - - def __repr__(self) -> str: - return f'' - - -# Example usage: -if __name__ == "__main__": - # Example usage with a file - gguf_writer = GGUFWriter("example.gguf", "llama") - - gguf_writer.add_architecture() - gguf_writer.add_block_count(12) - gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer - gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float - gguf_writer.add_custom_alignment(64) - - tensor1 = np.ones((32,), dtype=np.float32) * 100.0 - tensor2 = np.ones((64,), dtype=np.float32) * 101.0 - tensor3 = np.ones((96,), dtype=np.float32) * 102.0 - - gguf_writer.add_tensor("tensor1", tensor1) - gguf_writer.add_tensor("tensor2", tensor2) - gguf_writer.add_tensor("tensor3", tensor3) - - gguf_writer.write_header_to_file() - gguf_writer.write_kv_data_to_file() - gguf_writer.write_tensors_to_file() - - gguf_writer.close() +importlib.reload(gguf) diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py new file mode 100644 index 000000000..8682765ed --- /dev/null +++ b/gguf-py/gguf/gguf_reader.py @@ -0,0 +1,264 @@ +# +# GGUF file reading/modification support. For API usage information, +# please see the files scripts/ for some fairly simple examples. +# +from __future__ import annotations + +import os +from collections import OrderedDict +from typing import Any, Literal, NamedTuple, TypeVar, Union + +import numpy as np +import numpy.typing as npt + +if __name__ == "__main__": + import sys + from pathlib import Path + + # Allow running file in package as a script. + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf.constants import ( + GGML_QUANT_SIZES, + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFValueType, +) + + +READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION] + + +class ReaderField(NamedTuple): + # Offset to start of this field. + offset: int + + # Name of the field (not necessarily from file data). + name: str + + # Data parts. Some types have multiple components, such as strings + # that consist of a length followed by the string data. + parts: list[npt.NDArray[Any]] = [] + + # Indexes into parts that we can call the actual data. For example + # an array of strings will be populated with indexes to the actual + # string data. + data: list[int] = [-1] + + types: list[GGUFValueType] = [] + + +class ReaderTensor(NamedTuple): + name: str + tensor_type: GGMLQuantizationType + shape: npt.NDArray[np.uint32] + n_elements: int + n_bytes: int + data_offset: int + data: npt.NDArray[Any] + field: ReaderField + + +class GGUFReader: + # I - same as host, S - swapped + byte_order: Literal['I' | 'S'] = 'I' + alignment: int = GGUF_DEFAULT_ALIGNMENT + + # Note: Internal helper, API may change. + gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = { + GGUFValueType.UINT8: np.uint8, + GGUFValueType.INT8: np.int8, + GGUFValueType.UINT16: np.uint16, + GGUFValueType.INT16: np.int16, + GGUFValueType.UINT32: np.uint32, + GGUFValueType.INT32: np.int32, + GGUFValueType.FLOAT32: np.float32, + GGUFValueType.UINT64: np.uint64, + GGUFValueType.INT64: np.int64, + GGUFValueType.FLOAT64: np.float64, + GGUFValueType.BOOL: np.bool_, + } + + def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'): + self.data = np.memmap(path, mode = mode) + offs = 0 + if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC: + raise ValueError('GGUF magic invalid') + offs += 4 + temp_version = self._get(offs, np.uint32) + if temp_version[0] & 65535 == 0: + # If we get 0 here that means it's (probably) a GGUF file created for + # the opposite byte order of the machine this script is running on. + self.byte_order = 'S' + temp_version = temp_version.newbyteorder(self.byte_order) + version = temp_version[0] + if version not in READER_SUPPORTED_VERSIONS: + raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle') + self.fields: OrderedDict[str, ReaderField] = OrderedDict() + self.tensors: list[ReaderTensor] = [] + offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32])) + temp_counts = self._get(offs, np.uint64, 2) + offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64])) + offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64])) + tensor_count, kv_count = temp_counts + offs = self._build_fields(offs, kv_count) + offs, tensors_fields = self._build_tensors_fields(offs, tensor_count) + new_align = self.fields.get('general.alignment') + if new_align is not None: + if new_align.types != [GGUFValueType.UINT64]: + raise ValueError('Bad type for general.alignment field') + self.alignment = new_align.parts[-1][0] + padding = offs % self.alignment + if padding != 0: + offs += self.alignment - padding + self._build_tensors(offs, tensors_fields) + + _DT = TypeVar('_DT', bound = npt.DTypeLike) + + # Fetch a key/value metadata field by key. + def get_field(self, key: str) -> Union[ReaderField, None]: + return self.fields.get(key, None) + + # Fetch a tensor from the list by index. + def get_tensor(self, idx: int) -> ReaderTensor: + return self.tensors[idx] + + def _get( + self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None, + ) -> npt.NDArray[Any]: + count = int(count) + itemsize = int(np.empty([], dtype = dtype).itemsize) + end_offs = offset + itemsize * count + return ( + self.data[offset:end_offs] + .view(dtype = dtype)[:count] + .newbyteorder(override_order or self.byte_order) + ) + + def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: + if field.name in self.fields: + raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + self.fields[field.name] = field + return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) + + def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]: + slen = self._get(offset, np.uint64) + return slen, self._get(offset + 8, np.uint8, slen[0]) + + def _get_field_parts( + self, orig_offs: int, raw_type: int, + ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]: + offs = orig_offs + types: list[GGUFValueType] = [] + gtype = GGUFValueType(raw_type) + types.append(gtype) + # Handle strings. + if gtype == GGUFValueType.STRING: + sparts: list[npt.NDArray[Any]] = list(self._get_str(offs)) + size = sum(int(part.nbytes) for part in sparts) + return size, sparts, [1], types + # Check if it's a simple scalar type. + nptype = self.gguf_scalar_to_np.get(gtype) + if nptype is not None: + val = self._get(offs, nptype) + return int(val.nbytes), [val], [0], types + # Handle arrays. + if gtype == GGUFValueType.ARRAY: + raw_itype = self._get(offs, np.uint32) + offs += int(raw_itype.nbytes) + alen = self._get(offs, np.uint64) + offs += int(alen.nbytes) + aparts: list[npt.NDArray[Any]] = [raw_itype, alen] + data_idxs: list[int] = [] + for idx in range(alen[0]): + curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0]) + if idx == 0: + types += curr_types + idxs_offs = len(aparts) + aparts += curr_parts + data_idxs += (idx + idxs_offs for idx in curr_idxs) + offs += curr_size + return offs - orig_offs, aparts, data_idxs, types + # We can't deal with this one. + raise ValueError('Unknown/unhandled field type {gtype}') + + def _get_tensor(self, orig_offs: int) -> ReaderField: + offs = orig_offs + name_len, name_data = self._get_str(offs) + offs += int(name_len.nbytes + name_data.nbytes) + n_dims = self._get(offs, np.uint32) + offs += int(n_dims.nbytes) + dims = self._get(offs, np.uint64, n_dims[0]) + offs += int(dims.nbytes) + raw_dtype = self._get(offs, np.uint32) + offs += int(raw_dtype.nbytes) + offset_tensor = self._get(offs, np.uint64) + offs += int(offset_tensor.nbytes) + return ReaderField( + orig_offs, + str(bytes(name_data), encoding = 'utf-8'), + [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor], + [1, 3, 4, 5], + ) + + def _build_fields(self, offs: int, count: int) -> int: + for _ in range(count): + orig_offs = offs + kv_klen, kv_kdata = self._get_str(offs) + offs += int(kv_klen.nbytes + kv_kdata.nbytes) + raw_kv_type = self._get(offs, np.uint32) + offs += int(raw_kv_type.nbytes) + parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type] + idxs_offs = len(parts) + field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0]) + parts += field_parts + self._push_field(ReaderField( + orig_offs, + str(bytes(kv_kdata), encoding = 'utf-8'), + parts, + [idx + idxs_offs for idx in field_idxs], + field_types, + ), skip_sum = True) + offs += field_size + return offs + + def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]: + tensor_fields = [] + for _ in range(count): + field = self._get_tensor(offs) + offs += sum(int(part.nbytes) for part in field.parts) + tensor_fields.append(field) + return offs, tensor_fields + + def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: + tensors = [] + for field in fields: + _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts + ggml_type = GGMLQuantizationType(raw_dtype[0]) + n_elems = np.prod(dims) + block_size, type_size = GGML_QUANT_SIZES[ggml_type] + n_bytes = n_elems * type_size // block_size + data_offs = int(start_offs + offset_tensor[0]) + item_type: npt.DTypeLike + if ggml_type == GGMLQuantizationType.F32: + item_count = n_elems + item_type = np.float32 + elif ggml_type == GGMLQuantizationType.F16: + item_count = n_elems + item_type = np.float16 + else: + item_count = n_bytes + item_type = np.uint8 + tensors.append(ReaderTensor( + name = str(bytes(name_data), encoding = 'utf-8'), + tensor_type = ggml_type, + shape = dims, + n_elements = n_elems, + n_bytes = n_bytes, + data_offset = data_offs, + data = self._get(data_offs, item_type, item_count), + field = field, + )) + self.tensors = tensors diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py new file mode 100644 index 000000000..75fb6976f --- /dev/null +++ b/gguf-py/gguf/gguf_writer.py @@ -0,0 +1,409 @@ +from __future__ import annotations + +import os +import shutil +import struct +import tempfile +from enum import Enum, auto +from io import BufferedWriter +from typing import IO, Any, Sequence + +import numpy as np + +from .constants import ( + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFEndian, + GGUFValueType, + Keys, + RopeScalingType, + TokenType, +) + + +class WriterState(Enum): + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + + +class GGUFWriter: + fout: BufferedWriter + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[np.ndarray[Any, Any]] + _simple_value_packing = { + GGUFValueType.UINT8: "B", + GGUFValueType.INT8: "b", + GGUFValueType.UINT16: "H", + GGUFValueType.INT16: "h", + GGUFValueType.UINT32: "I", + GGUFValueType.INT32: "i", + GGUFValueType.FLOAT32: "f", + GGUFValueType.UINT64: "Q", + GGUFValueType.INT64: "q", + GGUFValueType.FLOAT64: "d", + GGUFValueType.BOOL: "?", + } + + def __init__( + self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True, + endianess: GGUFEndian = GGUFEndian.LITTLE, + ): + self.fout = open(path, "wb") + self.arch = arch + self.endianess = endianess + self.offset_tensor = 0 + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.kv_data = b"" + self.kv_data_count = 0 + self.ti_data = b"" + self.ti_data_count = 0 + self.use_temp_file = use_temp_file + self.temp_file = None + self.tensors = [] + print("gguf: This GGUF file is for {0} Endian only".format( + "Big" if self.endianess == GGUFEndian.BIG else "Little", + )) + self.state = WriterState.EMPTY + + self.add_architecture() + + def write_header_to_file(self) -> None: + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + self._write_packed(" None: + if self.state is not WriterState.HEADER: + raise ValueError(f'Expected output file to contain the header, got {self.state}') + + self.fout.write(self.kv_data) + self.flush() + self.state = WriterState.KV_DATA + + def write_ti_data_to_file(self) -> None: + if self.state is not WriterState.KV_DATA: + raise ValueError(f'Expected output file to contain KV data, got {self.state}') + + self.fout.write(self.ti_data) + self.flush() + self.state = WriterState.TI_DATA + + def add_key(self, key: str) -> None: + self.add_val(key, GGUFValueType.STRING, add_vtype=False) + + def add_uint8(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT8) + + def add_int8(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT8) + + def add_uint16(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT16) + + def add_int16(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT16) + + def add_uint32(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT32) + + def add_int32(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT32) + + def add_float32(self, key: str, val: float) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.FLOAT32) + + def add_uint64(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT64) + + def add_int64(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT64) + + def add_float64(self, key: str, val: float) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.FLOAT64) + + def add_bool(self, key: str, val: bool) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.BOOL) + + def add_string(self, key: str, val: str) -> None: + if not val: + return + self.add_key(key) + self.add_val(val, GGUFValueType.STRING) + + def add_array(self, key: str, val: Sequence[Any]) -> None: + if not isinstance(val, Sequence): + raise ValueError("Value must be a sequence for array type") + + self.add_key(key) + self.add_val(val, GGUFValueType.ARRAY) + + def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None: + if vtype is None: + vtype = GGUFValueType.get_type(val) + + if add_vtype: + self.kv_data += self._pack("I", vtype) + self.kv_data_count += 1 + + pack_fmt = self._simple_value_packing.get(vtype) + if pack_fmt is not None: + self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) + elif vtype == GGUFValueType.STRING: + encoded_val = val.encode("utf8") if isinstance(val, str) else val + self.kv_data += self._pack("Q", len(encoded_val)) + self.kv_data += encoded_val + elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + self.kv_data += self._pack("I", ltype) + self.kv_data += self._pack("Q", len(val)) + for item in val: + self.add_val(item, add_vtype=False) + else: + raise ValueError("Invalid GGUF metadata value type or value") + + @staticmethod + def ggml_pad(x: int, n: int) -> int: + return ((x + n - 1) // n) * n + + def add_tensor_info( + self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], + tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + if raw_dtype is None and tensor_dtype not in (np.float32, np.float16): + raise ValueError("Only F32 and F16 tensors are supported for now") + + encoded_name = name.encode("utf8") + self.ti_data += self._pack("Q", len(encoded_name)) + self.ti_data += encoded_name + n_dims = len(tensor_shape) + self.ti_data += self._pack("I", n_dims) + for i in range(n_dims): + self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) + if raw_dtype is None: + dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 + else: + dtype = raw_dtype + self.ti_data += self._pack("I", dtype) + self.ti_data += self._pack("Q", self.offset_tensor) + self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) + self.ti_data_count += 1 + + def add_tensor( + self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, + raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + if self.use_temp_file and self.temp_file is None: + fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) + fp.seek(0) + self.temp_file = fp + + shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) + + if self.temp_file is None: + self.tensors.append(tensor) + return + + tensor.tofile(self.temp_file) + self.write_padding(self.temp_file, tensor.nbytes) + + def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None: + pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n + if pad != 0: + fp.write(bytes([0] * pad)) + + def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: + if self.state is not WriterState.TI_DATA: + raise ValueError(f'Expected output file to contain tensor info, got {self.state}') + + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + self.write_padding(self.fout, self.fout.tell()) + tensor.tofile(self.fout) + self.write_padding(self.fout, tensor.nbytes) + + def write_tensors_to_file(self) -> None: + self.write_ti_data_to_file() + + self.write_padding(self.fout, self.fout.tell()) + + if self.temp_file is None: + while True: + try: + tensor = self.tensors.pop(0) + except IndexError: + break + tensor.tofile(self.fout) + self.write_padding(self.fout, tensor.nbytes) + return + + self.temp_file.seek(0) + + shutil.copyfileobj(self.temp_file, self.fout) + self.flush() + self.temp_file.close() + + def flush(self) -> None: + self.fout.flush() + + def close(self) -> None: + self.fout.close() + + def add_architecture(self) -> None: + self.add_string(Keys.General.ARCHITECTURE, self.arch) + + def add_author(self, author: str) -> None: + self.add_string(Keys.General.AUTHOR, author) + + def add_tensor_data_layout(self, layout: str) -> None: + self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) + + def add_url(self, url: str) -> None: + self.add_string(Keys.General.URL, url) + + def add_description(self, description: str) -> None: + self.add_string(Keys.General.DESCRIPTION, description) + + def add_source_url(self, url: str) -> None: + self.add_string(Keys.General.SOURCE_URL, url) + + def add_source_hf_repo(self, repo: str) -> None: + self.add_string(Keys.General.SOURCE_HF_REPO, repo) + + def add_file_type(self, ftype: int) -> None: + self.add_uint32(Keys.General.FILE_TYPE, ftype) + + def add_name(self, name: str) -> None: + self.add_string(Keys.General.NAME, name) + + def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None: + self.add_uint32( + Keys.General.QUANTIZATION_VERSION, quantization_version) + + def add_custom_alignment(self, alignment: int) -> None: + self.data_alignment = alignment + self.add_uint32(Keys.General.ALIGNMENT, alignment) + + def add_context_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) + + def add_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + + def add_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_parallel_residual(self, use: bool) -> None: + self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) + + def add_head_count(self, count: int) -> None: + self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + + def add_head_count_kv(self, count: int) -> None: + self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + + def add_max_alibi_bias(self, bias: float) -> None: + self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) + + def add_clamp_kqv(self, value: float) -> None: + self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) + + def add_layer_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) + + def add_layer_norm_rms_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + + def add_rope_dimension_count(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + + def add_rope_freq_base(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) + + def add_rope_scaling_type(self, value: RopeScalingType) -> None: + self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: + self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool) -> None: + self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + + def add_tokenizer_model(self, model: str) -> None: + self.add_string(Keys.Tokenizer.MODEL, model) + + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.LIST, tokens) + + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.MERGES, merges) + + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: + self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) + + def add_token_scores(self, scores: Sequence[float]) -> None: + self.add_array(Keys.Tokenizer.SCORES, scores) + + def add_bos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.BOS_ID, id) + + def add_eos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOS_ID, id) + + def add_unk_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.UNK_ID, id) + + def add_sep_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SEP_ID, id) + + def add_pad_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PAD_ID, id) + + def add_add_bos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_BOS, value) + + def add_add_eos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_EOS, value) + + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: + pack_prefix = '' + if not skip_pack_prefix: + pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' + return struct.pack(f'{pack_prefix}{fmt}', value) + + def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None: + self.fout.write(self._pack(fmt, value, skip_pack_prefix)) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py new file mode 100644 index 000000000..22ad8b8fc --- /dev/null +++ b/gguf-py/gguf/tensor_mapping.py @@ -0,0 +1,257 @@ +from __future__ import annotations + +from typing import Sequence + +from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES + + +class TensorNameMap: + mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Token embeddings + MODEL_TENSOR.TOKEN_EMBD: ( + "gpt_neox.embed_in", # gptneox + "transformer.wte", # gpt2 gpt-j mpt refact + "transformer.word_embeddings", # falcon + "word_embeddings", # bloom + "model.embed_tokens", # llama-hf + "tok_embeddings", # llama-pth + "embeddings.word_embeddings", # bert + "language_model.embedding.word_embeddings", # persimmon + ), + + # Token type embeddings + MODEL_TENSOR.TOKEN_TYPES: ( + "embeddings.token_type_embeddings", # bert + ), + + # Normalization of token embeddings + MODEL_TENSOR.TOKEN_EMBD_NORM: ( + "word_embeddings_layernorm", # bloom + ), + + # Position embeddings + MODEL_TENSOR.POS_EMBD: ( + "transformer.wpe", # gpt2 + "embeddings.position_embeddings", # bert + ), + + # Output + MODEL_TENSOR.OUTPUT: ( + "embed_out", # gptneox + "lm_head", # gpt2 mpt falcon llama-hf baichuan + "output", # llama-pth bloom + "word_embeddings_for_head", # persimmon + ), + + # Output norm + MODEL_TENSOR.OUTPUT_NORM: ( + "gpt_neox.final_layer_norm", # gptneox + "transformer.ln_f", # gpt2 gpt-j falcon + "model.norm", # llama-hf baichuan + "norm", # llama-pth + "embeddings.LayerNorm", # bert + "transformer.norm_f", # mpt + "ln_f", # refact bloom + "language_model.encoder.final_layernorm", # persimmon + ), + + # Rope frequencies + MODEL_TENSOR.ROPE_FREQS: ( + "rope.freqs", # llama-pth + ), + } + + block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact + "transformer.blocks.{bid}.norm_1", # mpt + "transformer.h.{bid}.input_layernorm", # falcon7b + "h.{bid}.input_layernorm", # bloom + "transformer.h.{bid}.ln_mlp", # falcon40b + "model.layers.{bid}.input_layernorm", # llama-hf + "layers.{bid}.attention_norm", # llama-pth + "encoder.layer.{bid}.attention.output.LayerNorm", # bert + "language_model.encoder.layers.{bid}.input_layernorm", # persimmon + "model.layers.{bid}.ln1", # yi + ), + + # Attention norm 2 + MODEL_TENSOR.ATTN_NORM_2: ( + "transformer.h.{bid}.ln_attn", # falcon40b + ), + + # Attention query-key-value + MODEL_TENSOR.ATTN_QKV: ( + "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox + "transformer.h.{bid}.attn.c_attn", # gpt2 + "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.h.{bid}.self_attention.query_key_value", # falcon + "h.{bid}.self_attention.query_key_value", # bloom + "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + ), + + # Attention query + MODEL_TENSOR.ATTN_Q: ( + "model.layers.{bid}.self_attn.q_proj", # llama-hf + "layers.{bid}.attention.wq", # llama-pth + "encoder.layer.{bid}.attention.self.query", # bert + "transformer.h.{bid}.attn.q_proj", # gpt-j + ), + + # Attention key + MODEL_TENSOR.ATTN_K: ( + "model.layers.{bid}.self_attn.k_proj", # llama-hf + "layers.{bid}.attention.wk", # llama-pth + "encoder.layer.{bid}.attention.self.key", # bert + "transformer.h.{bid}.attn.k_proj", # gpt-j + ), + + # Attention value + MODEL_TENSOR.ATTN_V: ( + "model.layers.{bid}.self_attn.v_proj", # llama-hf + "layers.{bid}.attention.wv", # llama-pth + "encoder.layer.{bid}.attention.self.value", # bert + "transformer.h.{bid}.attn.v_proj", # gpt-j + ), + + # Attention output + MODEL_TENSOR.ATTN_OUT: ( + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 refact + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "h.{bid}.self_attention.dense", # bloom + "model.layers.{bid}.self_attn.o_proj", # llama-hf + "layers.{bid}.attention.wo", # llama-pth + "encoder.layer.{bid}.attention.output.dense", # bert + "transformer.h.{bid}.attn.out_proj", # gpt-j + "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + ), + + # Rotary embeddings + MODEL_TENSOR.ATTN_ROT_EMBD: ( + "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf + "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + ), + + # Feed-forward norm + MODEL_TENSOR.FFN_NORM: ( + "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox + "transformer.h.{bid}.ln_2", # gpt2 refact + "h.{bid}.post_attention_layernorm", # bloom + "transformer.blocks.{bid}.norm_2", # mpt + "model.layers.{bid}.post_attention_layernorm", # llama-hf + "layers.{bid}.ffn_norm", # llama-pth + "encoder.layer.{bid}.output.LayerNorm", # bert + "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon + "model.layers.{bid}.ln2", # yi + ), + + # Feed-forward up + MODEL_TENSOR.FFN_UP: ( + "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox + "transformer.h.{bid}.mlp.c_fc", # gpt2 + "transformer.blocks.{bid}.ffn.up_proj", # mpt + "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon + "h.{bid}.mlp.dense_h_to_4h", # bloom + "model.layers.{bid}.mlp.up_proj", # llama-hf refact + "layers.{bid}.feed_forward.w3", # llama-pth + "encoder.layer.{bid}.intermediate.dense", # bert + "transformer.h.{bid}.mlp.fc_in", # gpt-j + "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon + ), + + # Feed-forward gate + MODEL_TENSOR.FFN_GATE: ( + "model.layers.{bid}.mlp.gate_proj", # llama-hf refact + "layers.{bid}.feed_forward.w1", # llama-pth + ), + + # Feed-forward down + MODEL_TENSOR.FFN_DOWN: ( + "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox + "transformer.h.{bid}.mlp.c_proj", # gpt2 refact + "transformer.blocks.{bid}.ffn.down_proj", # mpt + "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon + "h.{bid}.mlp.dense_4h_to_h", # bloom + "model.layers.{bid}.mlp.down_proj", # llama-hf + "layers.{bid}.feed_forward.w2", # llama-pth + "encoder.layer.{bid}.output.dense", # bert + "transformer.h.{bid}.mlp.fc_out", # gpt-j + "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + ), + + MODEL_TENSOR.ATTN_Q_NORM: ( + "language_model.encoder.layers.{bid}.self_attention.q_layernorm", + ), + + MODEL_TENSOR.ATTN_K_NORM: ( + "language_model.encoder.layers.{bid}.self_attention.k_layernorm", + ), + + MODEL_TENSOR.ROPE_FREQS: ( + "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon + ), + } + + mapping: dict[str, tuple[MODEL_TENSOR, str]] + + def __init__(self, arch: MODEL_ARCH, n_blocks: int): + self.mapping = {} + for tensor, keys in self.mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor] + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + self.mapping[key] = (tensor, tensor_name) + for bid in range(n_blocks): + for tensor, keys in self.block_mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor].format(bid = bid) + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + key = key.format(bid = bid) + self.mapping[key] = (tensor, tensor_name) + + def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None: + result = self.mapping.get(key) + if result is not None: + return result + for suffix in try_suffixes: + if key.endswith(suffix): + result = self.mapping.get(key[:-len(suffix)]) + if result is not None: + return result[0], result[1] + suffix + return None + + def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[1] + + def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[0] + + def __getitem__(self, key: str) -> str: + try: + return self.mapping[key][1] + except KeyError: + raise KeyError(key) + + def __contains__(self, key: str) -> bool: + return key in self.mapping + + def __repr__(self) -> str: + return repr(self.mapping) + + +def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: + return TensorNameMap(arch, n_blocks) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py new file mode 100644 index 000000000..71192a928 --- /dev/null +++ b/gguf-py/gguf/vocab.py @@ -0,0 +1,164 @@ +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path +from typing import Any, Callable + +from .gguf_writer import GGUFWriter + + +class SpecialVocab: + merges: list[str] + add_special_token: dict[str, bool] + special_token_ids: dict[str, int] + + def __init__( + self, path: str | os.PathLike[str], load_merges: bool = False, + special_token_types: tuple[str, ...] | None = None, + n_vocab: int | None = None, + ): + self.special_token_ids = {} + self.add_special_token = {} + self.n_vocab = n_vocab + self.load_merges = load_merges + self.merges = [] + if special_token_types is not None: + self.special_token_types = special_token_types + else: + self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') + self._load(Path(path)) + + def __repr__(self) -> str: + return ''.format( + len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset", + ) + + def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: + if self.merges: + if not quiet: + print(f'gguf: Adding {len(self.merges)} merge(s).') + gw.add_token_merges(self.merges) + elif self.load_merges: + print( + 'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.', + file = sys.stderr, + ) + for typ, tokid in self.special_token_ids.items(): + id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) + if id_handler is None: + print( + f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', + file = sys.stderr, + ) + continue + if not quiet: + print(f'gguf: Setting special token type {typ} to {tokid}') + id_handler(tokid) + for typ, value in self.add_special_token.items(): + add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None) + if add_handler is None: + print( + f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping', + file = sys.stderr, + ) + continue + if not quiet: + print(f'gguf: Setting add_{typ}_token to {value}') + add_handler(value) + + def _load(self, path: Path) -> None: + self._try_load_from_tokenizer_json(path) + self._try_load_from_config_json(path) + if self.load_merges and not self.merges: + self._try_load_merges_txt(path) + + def _try_load_merges_txt(self, path: Path) -> bool: + merges_file = path / 'merges.txt' + if not merges_file.is_file(): + return False + with open(merges_file, 'r') as fp: + first_line = next(fp, '').strip() + if not first_line.startswith('#'): + fp.seek(0) + line_num = 0 + else: + line_num = 1 + merges = [] + for line in fp: + line_num += 1 + line = line.strip() + if not line: + continue + parts = line.split(None, 3) + if len(parts) != 2: + print( + f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring', + file = sys.stderr, + ) + continue + merges.append(f'{parts[0]} {parts[1]}') + self.merges = merges + return True + + def _set_special_token(self, typ: str, tid: Any) -> None: + if not isinstance(tid, int) or tid < 0: + return + if self.n_vocab is None or tid < self.n_vocab: + if typ in self.special_token_ids: + return + self.special_token_ids[typ] = tid + return + print( + f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', + file = sys.stderr, + ) + + def _try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer_file = path / 'tokenizer.json' + if not tokenizer_file.is_file(): + return False + with open(tokenizer_file, encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and merges and isinstance(merges[0], str): + self.merges = merges + tokenizer_config_file = path / 'tokenizer_config.json' + added_tokens = tokenizer.get('added_tokens') + if added_tokens is None or not tokenizer_config_file.is_file(): + return True + with open(tokenizer_config_file, encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + for typ in self.special_token_types: + add_entry = tokenizer_config.get(f'add_{typ}_token') + if isinstance(add_entry, bool): + self.add_special_token[typ] = add_entry + entry = tokenizer_config.get(f'{typ}_token') + if isinstance(entry, str): + tc_content = entry + elif isinstance(entry, dict): + entry_content = entry.get('content') + if not isinstance(entry_content, str): + continue + tc_content = entry_content + else: + continue + # We only need the first match here. + maybe_token_id = next( + (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content), + None, + ) + self._set_special_token(typ, maybe_token_id) + return True + + def _try_load_from_config_json(self, path: Path) -> bool: + config_file = path / 'config.json' + if not config_file.is_file(): + return False + with open(config_file, encoding = 'utf-8') as f: + config = json.load(f) + for typ in self.special_token_types: + self._set_special_token(typ, config.get(f'{typ}_token_id')) + return True diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index c6cb2c37a..624e1cda6 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,11 +1,12 @@ [tool.poetry] name = "gguf" -version = "0.4.6" +version = "0.5.0" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, {include = "gguf/py.typed"}, + {include = "scripts"}, ] readme = "README.md" homepage = "https://ggml.ai" @@ -27,3 +28,8 @@ pytest = "^5.2" [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint" +gguf-dump = "scripts:gguf_dump_entrypoint" +gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint" diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py new file mode 100644 index 000000000..77132db7a --- /dev/null +++ b/gguf-py/scripts/__init__.py @@ -0,0 +1,12 @@ +import os + +from importlib import import_module + + +os.environ["NO_LOCAL_GGUF"] = "TRUE" + +gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main +gguf_dump_entrypoint = import_module("scripts.gguf-dump").main +gguf_set_metadata_entrypoint = import_module("scripts.gguf-set-metadata").main + +del import_module, os diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py new file mode 100755 index 000000000..b79d86e07 --- /dev/null +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf + + +def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None: + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # Host is little endian + host_endian = "little" + swapped_endian = "big" + else: + # Sorry PDP or other weird systems that don't use BE or LE. + host_endian = "big" + swapped_endian = "little" + if reader.byte_order == "S": + file_endian = swapped_endian + else: + file_endian = host_endian + if args.order == "native": + order = host_endian + print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") + if file_endian == order: + print(f"* File is already {order.upper()} endian. Nothing to do.") + sys.exit(0) + print("* Checking tensors for conversion compatibility") + for tensor in reader.tensors: + if tensor.tensor_type not in ( + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, + gguf.GGMLQuantizationType.Q8_0, + ): + raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") + print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}") + if args.dry_run: + return + print("\n*** Warning *** Warning *** Warning **") + print("* This conversion process may damage the file. Ensure you have a backup.") + if order != host_endian: + print("* Requested endian differs from host, you will not be able to load the model on this machine.") + print("* The file will be modified immediately, so if conversion fails or is interrupted") + print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") + response = input("YES, I am sure> ") + if response != "YES": + print("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + print(f"\n* Converting fields ({len(reader.fields)})") + for idx, field in enumerate(reader.fields.values()): + print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") + for part in field.parts: + part.byteswap(inplace=True) + print(f"\n* Converting tensors ({len(reader.tensors)})") + for idx, tensor in enumerate(reader.tensors): + print( + f" - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, " + f"elements={tensor.n_elements}... ", + end="", + ) + tensor_type = tensor.tensor_type + for part in tensor.field.parts: + part.byteswap(inplace=True) + if tensor_type != gguf.GGMLQuantizationType.Q8_0: + tensor.data.byteswap(inplace=True) + print() + continue + # A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes + block_size = 34 + n_blocks = len(tensor.data) // block_size + for block_num in range(n_blocks): + block_offs = block_num * block_size + # I know I said f16, but it doesn't matter here - any simple 16 bit type works. + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + if block_num % 100000 == 0: + print(f"[{(n_blocks - block_num) // 1000}K]", end="") + sys.stdout.flush() + print() + print("* Completion") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert GGUF file byte order") + parser.add_argument( + "model", type=str, + help="GGUF format model filename", + ) + parser.add_argument( + "order", type=str, choices=['big', 'little', 'native'], + help="Requested byte order", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="Don't actually change anything", + ) + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + print(f'* Loading: {args.model}') + reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+') + convert_byteorder(reader, args) + + +if __name__ == "__main__": + main() diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py new file mode 100755 index 000000000..5141873de --- /dev/null +++ b/gguf-py/scripts/gguf-dump.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path +from typing import Any + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader, GGUFValueType # noqa: E402 + + +def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]: + host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG' + if reader.byte_order == 'S': + file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE' + else: + file_endian = host_endian + return (host_endian, file_endian) + + +# For more information about what field.parts and field.data represent, +# please see the comments in the modify_gguf.py example. +def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + host_endian, file_endian = get_file_host_endian(reader) + print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') + print(f'\n* Dumping {len(reader.fields)} key/value pair(s)') + for n, field in enumerate(reader.fields.values(), 1): + if not field.types: + pretty_type = 'N/A' + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count + else: + pretty_type = str(field.types[-1].name) + print(f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '') + if len(field.types) == 1: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '') + elif field.types[0] in reader.gguf_scalar_to_np: + print(' = {0}'.format(field.parts[-1][0]), end = '') + print() + if args.no_tensors: + return + print(f'\n* Dumping {len(reader.tensors)} tensor(s)') + for n, tensor in enumerate(reader.tensors, 1): + prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape))) + print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') + + +def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: + import json + host_endian, file_endian = get_file_host_endian(reader) + metadata: dict[str, Any] = {} + tensors: dict[str, Any] = {} + result = { + "filename": args.model, + "endian": file_endian, + "metadata": metadata, + "tensors": tensors, + } + for idx, field in enumerate(reader.fields.values()): + curr: dict[str, Any] = { + "index": idx, + "type": field.types[0].name if field.types else 'UNKNOWN', + "offset": field.offset, + } + metadata[field.name] = curr + if field.types[:1] == [GGUFValueType.ARRAY]: + curr["array_types"] = [t.name for t in field.types][1:] + if not args.json_array: + continue + itype = field.types[-1] + if itype == GGUFValueType.STRING: + curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data] + else: + curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()] + elif field.types[0] == GGUFValueType.STRING: + curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8") + else: + curr["value"] = field.parts[-1].tolist()[0] + for idx, tensor in enumerate(reader.tensors): + tensors[tensor.name] = { + "index": idx, + "shape": tensor.shape.tolist(), + "type": tensor.tensor_type.name, + "offset": tensor.field.offset, + } + json.dump(result, sys.stdout) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata") + parser.add_argument("--json", action="store_true", help="Produce JSON output") + parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + if not args.json: + print(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r') + if args.json: + dump_metadata_json(reader, args) + else: + dump_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf-set-metadata.py new file mode 100755 index 000000000..3ebdfa898 --- /dev/null +++ b/gguf-py/scripts/gguf-set-metadata.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +import argparse +import os +import sys +from pathlib import Path + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader # noqa: E402 + + +def minimal_example(filename: str) -> None: + reader = GGUFReader(filename, 'r+') + field = reader.fields['tokenizer.ggml.bos_token_id'] + if field is None: + return + part_index = field.data[0] + field.parts[part_index][0] = 2 # Set tokenizer.ggml.bos_token_id to 2 + # + # So what's this field.data thing? It's helpful because field.parts contains + # _every_ part of the GGUF field. For example, tokenizer.ggml.bos_token_id consists + # of: + # + # Part index 0: Key length (27) + # Part index 1: Key data ("tokenizer.ggml.bos_token_id") + # Part index 2: Field type (4, the id for GGUFValueType.UINT32) + # Part index 3: Field value + # + # Note also that each part is an NDArray slice, so even a part that + # is only a single value like the key length will be a NDArray of + # the key length type (numpy.uint32). + # + # The .data attribute in the Field is a list of relevant part indexes + # and doesn't contain internal GGUF details like the key length part. + # In this case, .data will be [3] - just the part index of the + # field value itself. + + +def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + field = reader.get_field(args.key) + if field is None: + print(f'! Field {repr(args.key)} not found', file = sys.stderr) + sys.exit(1) + # Note that field.types is a list of types. This is because the GGUF + # format supports arrays. For example, an array of UINT32 would + # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32] + handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None + if handler is None: + print( + f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}', + file = sys.stderr, + ) + sys.exit(1) + current_value = field.parts[field.data[0]][0] + new_value = handler(args.value) + print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}') + if current_value == new_value: + print(f'- Key {repr(args.key)} already set to requested value {current_value}') + sys.exit(0) + if args.dry_run: + sys.exit(0) + if not args.force: + print('*** Warning *** Warning *** Warning **') + print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.') + print('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + print("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + field.parts[field.data[0]][0] = new_value + print('* Field changed. Successful completion.') + + +def main() -> None: + parser = argparse.ArgumentParser(description="Set a simple value in GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("key", type=str, help="Metadata key to set") + parser.add_argument("value", type=str, help="Metadata value to set") + parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything") + parser.add_argument("--force", action="store_true", help="Change the field without confirmation") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + print(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+') + set_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/tests/test_gguf.py b/gguf-py/tests/test_gguf.py index 512531dd2..0adeb7d55 100644 --- a/gguf-py/tests/test_gguf.py +++ b/gguf-py/tests/test_gguf.py @@ -1,7 +1,7 @@ -import gguf +import gguf # noqa: F401 # TODO: add tests -def test_write_gguf(): +def test_write_gguf() -> None: pass From d96ca7ded77df764db797b68b4a29e34c5b56285 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 11 Nov 2023 05:48:21 +0000 Subject: [PATCH 058/664] server : fix crash when prompt exceeds context size (#3996) --- examples/server/server.cpp | 58 +++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index cbf36ad67..46862a84b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1557,6 +1557,35 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); + if (slot.params.n_keep < 0) + { + slot.params.n_keep = slot.num_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); + + // if input prompt is too big, truncate it + if (slot.num_prompt_tokens >= slot.n_ctx) + { + const int n_left = slot.n_ctx - slot.params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + + LOG_VERBOSE("input truncated", { + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); + slot.truncated = true; + prompt_tokens = new_tokens; + + slot.num_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + } + if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); @@ -1566,35 +1595,6 @@ struct llama_server_context } else { - if (slot.params.n_keep < 0) - { - slot.params.n_keep = slot.num_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it - if (slot.num_prompt_tokens >= slot.n_ctx) - { - const int n_left = slot.n_ctx - slot.params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - slot.truncated = true; - prompt_tokens = new_tokens; - - slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); - } - // push the prompt into the sampling context (do not apply grammar) for (auto &token : prompt_tokens) { From e86fc56f7521ca4b18d1d9939e82abd40c2f1c01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sat, 11 Nov 2023 18:35:31 +0300 Subject: [PATCH 059/664] Fix gguf-convert-endian script (#4037) * Fix gguf-convert-endian script * Bump version and update description --- gguf-py/pyproject.toml | 4 ++-- gguf-py/scripts/gguf-convert-endian.py | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 624e1cda6..e21c3cd94 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] name = "gguf" -version = "0.5.0" -description = "Write ML models in GGUF for GGML" +version = "0.5.1" +description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py index b79d86e07..10a16ad06 100755 --- a/gguf-py/scripts/gguf-convert-endian.py +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -28,8 +28,7 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None file_endian = swapped_endian else: file_endian = host_endian - if args.order == "native": - order = host_endian + order = host_endian if args.order == "native" else args.order print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") if file_endian == order: print(f"* File is already {order.upper()} endian. Nothing to do.") From 532dd74e38c29e16ea1cfc4e7eedb4f2fab3f3cd Mon Sep 17 00:00:00 2001 From: Richard Kiss Date: Sat, 11 Nov 2023 22:04:58 -0800 Subject: [PATCH 060/664] Fix some documentation typos/grammar mistakes (#4032) * typos * Update examples/parallel/README.md Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --------- Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --- README.md | 2 +- docs/token_generation_performance_tips.md | 2 +- examples/main/README.md | 2 +- examples/parallel/README.md | 2 +- grammars/README.md | 4 ++-- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9c9e36ad0..af39e8c0e 100644 --- a/README.md +++ b/README.md @@ -424,7 +424,7 @@ Building the program with BLAS support may lead to some performance improvements ``` The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. - If your GPU is not officialy supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. + If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): | Option | Legal values | Default | Description | diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md index c9acff7d4..d7e863dff 100644 --- a/docs/token_generation_performance_tips.md +++ b/docs/token_generation_performance_tips.md @@ -17,7 +17,7 @@ llama_model_load_internal: [cublas] total VRAM used: 17223 MB If you see these lines, then the GPU is being used. ## Verifying that the CPU is not oversaturated -llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physicial CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. +llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. # Example of runtime flags effect on inference speed benchmark These runs were tested on the following machine: diff --git a/examples/main/README.md b/examples/main/README.md index a3428b487..c7997f665 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -142,7 +142,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by ### Extended Context Size -Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. +Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. - `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. diff --git a/examples/parallel/README.md b/examples/parallel/README.md index 4d0fe5cef..df0456733 100644 --- a/examples/parallel/README.md +++ b/examples/parallel/README.md @@ -1,3 +1,3 @@ # llama.cpp/example/parallel -Simplified simluation for serving incoming requests in parallel +Simplified simulation of serving incoming requests in parallel diff --git a/grammars/README.md b/grammars/README.md index 7f3b11ca5..e1383fa5c 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -55,7 +55,7 @@ The order of symbols in a sequence matter. For example, in `"1. " move " " move Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`. -Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optptional symbols (below) to a sequence. +Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optional symbols (below) to a sequence. ## Repetition and Optional Symbols @@ -67,7 +67,7 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte Comments can be specified with `#`: ``` -# defines optional whitspace +# defines optional whitespace ws ::= [ \t\n]+ ``` From 21fd874c8d2a14dea2d56724e4357c0824aee6a8 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 12 Nov 2023 16:39:37 -0700 Subject: [PATCH 061/664] gguf-py: gguf_writer: Use bytearray to build metadata (#4051) * gguf-py: gguf_writer: Use BytesIO to build metadata * Use bytearray instead Bump gguf-py package version --- gguf-py/gguf/gguf_writer.py | 4 ++-- gguf-py/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 75fb6976f..c3b8c588f 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -57,9 +57,9 @@ class GGUFWriter: self.endianess = endianess self.offset_tensor = 0 self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" + self.kv_data = bytearray() self.kv_data_count = 0 - self.ti_data = b"" + self.ti_data = bytearray() self.ti_data_count = 0 self.use_temp_file = use_temp_file self.temp_file = None diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index e21c3cd94..af777c3e0 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.5.1" +version = "0.5.2" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From bb50a792ec2a49944470c82694fa364345e95170 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Mon, 13 Nov 2023 01:58:15 -0700 Subject: [PATCH 062/664] Add ReLU and SQR CUDA ops to (partially) fix Persimmon offloading (#4041) * Add ReLU and SQR CUDA ops to fix Persimmon offloading * Persimmon loader: More helpful error on CUDA/ROCM when offloading too many layers --- ggml-cuda.cu | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++ llama.cpp | 7 +++++ 2 files changed, 79 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f87f18802..8d03ba664 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -433,6 +433,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define CUDA_MUL_BLOCK_SIZE 256 #define CUDA_GELU_BLOCK_SIZE 256 #define CUDA_SILU_BLOCK_SIZE 256 +#define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_CPY_BLOCK_SIZE 32 #define CUDA_SCALE_BLOCK_SIZE 256 #define CUDA_CLAMP_BLOCK_SIZE 256 @@ -553,6 +555,24 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } +static __global__ void relu_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = fmaxf(x[i], 0); +} + +static __global__ void sqr_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} + static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -4759,6 +4779,16 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ silu_f32<<>>(x, dst, k); } +static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; + relu_f32<<>>(x, dst, k); +} + +static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE; + sqr_f32<<>>(x, dst, k); +} + static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); if (ncols < 1024) { @@ -6128,6 +6158,34 @@ inline void ggml_cuda_op_silu( (void) src1_dd; } +inline void ggml_cuda_op_relu( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_cuda_op_sqr( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_cuda_op_norm( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { @@ -7160,6 +7218,14 @@ static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu); } +static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); +} + +static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr); +} + static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm); } @@ -7891,6 +7957,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_UNARY_OP_SILU: func = ggml_cuda_silu; break; + case GGML_UNARY_OP_RELU: + func = ggml_cuda_relu; + break; default: return false; } break; @@ -7909,6 +7978,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_SCALE: func = ggml_cuda_scale; break; + case GGML_OP_SQR: + func = ggml_cuda_sqr; + break; case GGML_OP_CLAMP: if (!any_on_device) { return false; diff --git a/llama.cpp b/llama.cpp index d682d2864..a5f3876cc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2877,6 +2877,13 @@ static void llm_load_tensors( ggml_backend_type backend_output; if (n_gpu_layers > int(n_layer)) { +#ifdef GGML_USE_CUBLAS + if (n_gpu_layers > int(n_layer + 1)) { + LLAMA_LOG_ERROR("%s: CUDA backend missing Persimmon CUDA ops, can offload at most %ld layers. See: https://github.com/ggerganov/llama.cpp/issues/4038\n", + __func__, n_layer + 1); + throw std::runtime_error("Persimmon CUDA offload failed"); + } +#endif // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 From 4760e7cc0b68570d58f55e8dda469805d1759d0d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 13 Nov 2023 14:16:23 +0200 Subject: [PATCH 063/664] sync : ggml (backend v2) (#3912) * sync : ggml (backend v2) (wip) * sync : migrate examples and llama.cpp to dynamic graphs (wip) * sync : update tests + fix max op params to 64 ggml-ci * sync : ggml-cuda ggml-ci * llama : fix save/load state context size ggml-ci * sync : try to fix build on tvOS * sync : pass custom graph sizes in training examples * sync : update graph copies to new ggml API * sync : update sync-ggml.sh with new files * scripts : fix header in sync script * train : fix context size calculations * llama : increase inference graph size up to 4096 nodes * train : allocate grads for backward graphs * train : allocate grads for gb_tmp --- common/train.cpp | 1 + common/train.h | 2 + examples/benchmark/benchmark-matmult.cpp | 21 +- examples/export-lora/export-lora.cpp | 4 +- examples/finetune/finetune.cpp | 23 +- examples/llava/clip.cpp | 2 +- examples/metal/metal.cpp | 10 +- .../train-text-from-scratch.cpp | 23 +- ggml-alloc.c | 594 ++++++---- ggml-alloc.h | 80 +- ggml-backend-impl.h | 87 ++ ggml-backend.c | 591 ++++++++- ggml-backend.h | 151 ++- ggml-cuda.cu | 16 +- ggml-impl.h | 14 +- ggml-metal.m | 25 +- ggml.c | 1055 ++++++++++------- ggml.h | 89 +- llama.cpp | 40 +- scripts/sync-ggml.sh | 12 +- tests/test-grad0.cpp | 7 +- tests/test-opt.cpp | 11 +- 22 files changed, 1994 insertions(+), 864 deletions(-) create mode 100644 ggml-backend-impl.h diff --git a/common/train.cpp b/common/train.cpp index bc15b7a03..964b156b5 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -32,6 +32,7 @@ struct train_state * init_train_state() { state->opt = new struct ggml_opt_context; state->opt->ctx = NULL; state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM); + state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; state->opt->loss_after = 0.0f; return state; diff --git a/common/train.h b/common/train.h index d86c93cc4..263d940c0 100644 --- a/common/train.h +++ b/common/train.h @@ -9,6 +9,8 @@ #include "ggml.h" #include "llama.h" +#define LLAMA_TRAIN_MAX_NODES 16384 + typedef std::string mt19937_state; struct train_state { diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index 76e3f57cc..284733b10 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -171,7 +171,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2); // printf("Creating compute graph\n"); - struct ggml_cgraph gf = ggml_build_forward(m11xm2); + struct ggml_cgraph * gf = ggml_new_graph(ctx); + ggml_build_forward_expand(gf, m11xm2); printf("n_threads=%i\n", benchmark_params.n_threads); @@ -180,9 +181,9 @@ int main(int argc, char ** argv) { std::vector work_buffer; - ggml_graph_compute_helper(work_buffer, &gf, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads); - TENSOR_DUMP(gf.nodes[0]); + TENSOR_DUMP(gf->nodes[0]); printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype)); @@ -200,7 +201,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2); // printf("Creating compute graph\n"); - struct ggml_cgraph gf31 = ggml_build_forward(q31); + struct ggml_cgraph * gf31 = ggml_new_graph(ctx); + ggml_build_forward_expand(gf31, q31); // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); @@ -211,7 +213,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); //printf("Creating compute graph\n"); - struct ggml_cgraph gf32 = ggml_build_forward(q32); + struct ggml_cgraph * gf32 = ggml_new_graph(ctx); + ggml_build_forward_expand(gf32, q32); printf("n_threads=%i\n", benchmark_params.n_threads); const int dimx = sizex; @@ -223,7 +226,7 @@ int main(int argc, char ** argv) { // Let's use the F32 result from above as a reference for the quantized multiplication - float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]); + float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]); printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n"); printf("=====================================================================================\n"); @@ -233,7 +236,7 @@ int main(int argc, char ** argv) { long long int start = ggml_time_us(); //printf("Running ggml_graph_compute\n"); - ggml_graph_compute_helper(work_buffer, &gf31, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf31, benchmark_params.n_threads); long long int stop = ggml_time_us(); long long int usec = stop-start; @@ -251,7 +254,7 @@ int main(int argc, char ** argv) { // Check that the matrix multiplication result is in the right ballpark // We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different - float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]); + float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]); float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference); float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6 @@ -266,7 +269,7 @@ int main(int argc, char ** argv) { } // Running a different graph computation to make sure we override the CPU cache lines - ggml_graph_compute_helper(work_buffer, &gf32, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads); } printf("\n"); printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations)); diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index d803cfd5c..c8754ce70 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -240,7 +240,7 @@ static struct lora_data * load_lora(struct lora_info * info) { } struct ggml_init_params params_ggml; - params_ggml.mem_size = ggml_tensor_overhead() * GGML_MAX_NODES; + params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE; params_ggml.mem_buffer = NULL; params_ggml.no_alloc = true; result->ctx = ggml_init(params_ggml); @@ -334,7 +334,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r; struct ggml_init_params params; - params.mem_size = GGML_OBJECT_SIZE + GGML_GRAPH_SIZE + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; + params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; params.mem_buffer = NULL; params.no_alloc = true; struct ggml_context * ctx = NULL; diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index fa7dbe496..5a6cf22ce 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -772,7 +772,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( if (enable_checkpointing) { ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); } else { - *gb = *gf; + ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx, gf, gb, true); } @@ -1615,6 +1615,7 @@ int main(int argc, char ** argv) { opt->params = ggml_opt_default_params(GGML_OPT_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; + opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; opt->params.n_threads = params.common.n_threads; opt->params.past = params.common.opt_past; opt->params.delta = params.common.opt_delta; @@ -1741,11 +1742,9 @@ int main(int argc, char ** argv) { ggml_allocr_free(alloc); // context for compute tensors without their data - size_t estimated_compute_size_wo_data = ( - ggml_tensor_overhead()*GGML_MAX_NODES*2 - + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*( - params.common.use_checkpointing ? 3 : 2 - ) + const size_t estimated_compute_size_wo_data = ( + 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() + + (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true)) ); struct ggml_init_params ctx_compute_params = { estimated_compute_size_wo_data, // mem_size @@ -1768,11 +1767,11 @@ int main(int argc, char ** argv) { for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new_measure(tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_lora_finetune_graphs( &model, &lora, alloc, ctx_compute, @@ -1801,11 +1800,11 @@ int main(int argc, char ** argv) { mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_lora_finetune_graphs( &model, &lora, alloc, ctx_compute, diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 3c909c7d3..c26ee4957 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -664,7 +664,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // measure mem requirement and allocate { static const size_t tensor_alignment = 32; - new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead()); + new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead()); new_clip->alloc = ggml_allocr_new_measure(tensor_alignment); clip_image_f32_batch batch; batch.size = 1; diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp index c05a4fa93..16c1146f9 100644 --- a/examples/metal/metal.cpp +++ b/examples/metal/metal.cpp @@ -34,7 +34,7 @@ int main(int argc, char ** argv) { struct ggml_context * ctx_data = NULL; struct ggml_context * ctx_eval = NULL; - struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); + struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); // this allocates all Metal resources and memory buffers auto * ctx_metal = ggml_metal_init(1); @@ -46,13 +46,13 @@ int main(int argc, char ** argv) { // main { - struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd"); + struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd"); *(int32_t *) input->data = 1; // BOS ggml_metal_set_tensor(ctx_metal, input); // warmup - ggml_metal_graph_compute(ctx_metal, &gf); + ggml_metal_graph_compute(ctx_metal, gf); const int n_iter = 16; @@ -60,7 +60,7 @@ int main(int argc, char ** argv) { // the actual inference happens here for (int i = 0; i < n_iter; ++i) { - ggml_metal_graph_compute(ctx_metal, &gf); + ggml_metal_graph_compute(ctx_metal, gf); } const int64_t t1 = ggml_time_us(); @@ -70,7 +70,7 @@ int main(int argc, char ** argv) { // debug output { - struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1]; + struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1]; ggml_metal_get_tensor(ctx_metal, logits); float * ptr = (float *) ggml_get_data(logits); diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 2a257e632..f049a3923 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -436,7 +436,7 @@ static struct ggml_tensor * llama_build_train_graphs( if (enable_checkpointing) { ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); } else { - *gb = *gf; + ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx, gf, gb, true); } @@ -1006,6 +1006,7 @@ int main(int argc, char ** argv) { opt->params = ggml_opt_default_params(GGML_OPT_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; + opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; opt->params.n_threads = params.common.n_threads; opt->params.past = params.common.opt_past; opt->params.delta = params.common.opt_delta; @@ -1108,11 +1109,9 @@ int main(int argc, char ** argv) { ggml_allocr_free(alloc); // context for compute tensors without their data - size_t estimated_compute_size_wo_data = ( - ggml_tensor_overhead()*GGML_MAX_NODES*2 - + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*( - params.common.use_checkpointing ? 3 : 2 - ) + const size_t estimated_compute_size_wo_data = ( + 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() + + (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true)) ); struct ggml_init_params ctx_compute_params = { estimated_compute_size_wo_data, // mem_size @@ -1135,11 +1134,11 @@ int main(int argc, char ** argv) { for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new_measure(tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_train_graphs( &model, alloc, ctx_compute, @@ -1168,11 +1167,11 @@ int main(int argc, char ** argv) { mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_train_graphs( &model, alloc, ctx_compute, diff --git a/ggml-alloc.c b/ggml-alloc.c index b553eb7c1..cdfe4caf6 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -1,51 +1,21 @@ #include "ggml-alloc.h" -#include "ggml-backend.h" +#include "ggml-backend-impl.h" #include "ggml.h" +#include "ggml-impl.h" #include +#include #include #include #include #include - -#define UNUSED(x) (void)(x) #define MAX(a, b) ((a) > (b) ? (a) : (b)) -#define GGML_MAX_CONCUR (2*GGML_MAX_NODES) +#define MAX_FREE_BLOCKS 256 //#define GGML_ALLOCATOR_DEBUG -//#define AT_PRINTF printf -#define AT_PRINTF(...) ((void)0) - -struct hash_node { - struct ggml_tensor * t; - int n_children; - int n_views; -}; - -static size_t hash(void * p) { - return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; -} - -static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) { - size_t h = hash(t); - - // linear probing - size_t i = h; - while (hash_table[i].t != NULL) { - if (hash_table[i].t == t) { - return &hash_table[i]; - } - i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE; - if (i == h) { - // hash table is full - GGML_ASSERT(false); - } - } - - hash_table[i].t = t; - return &hash_table[i]; -} +//#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__) +#define AT_PRINTF(...) // TODO: GGML_PAD ? static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) { @@ -59,20 +29,18 @@ struct free_block { size_t size; }; -#define MAX_FREE_BLOCKS 256 - -struct ggml_allocr { +struct ggml_tallocr { struct ggml_backend_buffer * buffer; bool buffer_owned; - void * data; + void * base; size_t alignment; + int n_free_blocks; struct free_block free_blocks[MAX_FREE_BLOCKS]; - struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE]; + size_t max_size; + bool measure; - int parse_seq[GGML_MAX_CONCUR]; - int parse_seq_len; #ifdef GGML_ALLOCATOR_DEBUG struct ggml_tensor * allocated_tensors[1024]; @@ -80,7 +48,7 @@ struct ggml_allocr { }; #ifdef GGML_ALLOCATOR_DEBUG -static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { for (int i = 0; i < 1024; i++) { if (alloc->allocated_tensors[i] == NULL) { alloc->allocated_tensors[i] = tensor; @@ -89,7 +57,7 @@ static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor } GGML_ASSERT(!"out of allocated_tensors"); } -static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { for (int i = 0; i < 1024; i++) { if (alloc->allocated_tensors[i] == tensor || (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) { @@ -103,7 +71,7 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens #endif // check if a tensor is allocated by this buffer -static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) { +static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) { return tensor->buffer == alloc->buffer; } @@ -111,7 +79,7 @@ static bool ggml_is_view(struct ggml_tensor * t) { return t->view_src != NULL; } -void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated @@ -162,9 +130,10 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } tensor->data = addr; - AT_PRINTF("%s: allocated data at %p\n", __func__, tensor->data); tensor->buffer = alloc->buffer; - ggml_backend_buffer_init_tensor(alloc->buffer, tensor); + if (!alloc->measure) { + ggml_backend_buffer_init_tensor(alloc->buffer, tensor); + } #ifdef GGML_ALLOCATOR_DEBUG add_allocated_tensor(alloc, tensor); @@ -180,16 +149,16 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } #endif - alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size); + alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->base + size); } // this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { - if (ggml_allocr_is_own(alloc, tensor) == false) { +static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { + if (ggml_tallocr_is_own(alloc, tensor) == false) { // the tensor was not allocated in this buffer // this can happen because the graph allocator will try to free weights and other tensors from different buffers // the easiest way to deal with this is just to ignore it - AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer); + // AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer); return; } @@ -199,7 +168,9 @@ static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tens size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks); - ggml_backend_buffer_free_tensor(alloc->buffer, tensor); + if (!alloc->measure) { + ggml_backend_buffer_free_tensor(alloc->buffer, tensor); + } #ifdef GGML_ALLOCATOR_DEBUG remove_allocated_tensor(alloc, tensor); @@ -253,91 +224,180 @@ static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tens alloc->n_free_blocks++; } -void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) { - for (int i = 0; i < n; i++) { - alloc->parse_seq[i] = list[i]; - } - alloc->parse_seq_len = n; -} - -void ggml_allocr_reset(struct ggml_allocr * alloc) { +void ggml_tallocr_reset(ggml_tallocr_t alloc) { alloc->n_free_blocks = 1; - size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment); - alloc->free_blocks[0].addr = (char *)alloc->data + align_offset; - alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; + size_t align_offset = aligned_offset(alloc->base, 0, alloc->alignment); + alloc->free_blocks[0].addr = (char *)alloc->base + align_offset; + + if (alloc->measure) { + alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows + } else { + alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; + } } -struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) { +ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) { struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size); - struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr)); + ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); - *alloc = (struct ggml_allocr){ + *alloc = (struct ggml_tallocr) { /*.buffer = */ buffer, /*.buffer_owned = */ true, /*.base = */ ggml_backend_buffer_get_base(buffer), /*.alignment = */ alignment, /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, - /*.hash_table = */ {{0}}, /*.max_size = */ 0, /*.measure = */ false, - /*.parse_seq = */ {0}, - /*.parse_seq_len = */ 0, #ifdef GGML_ALLOCATOR_DEBUG /*.allocated_tensors = */ {0}, #endif }; - ggml_allocr_reset(alloc); + ggml_tallocr_reset(alloc); return alloc; } -struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { - struct ggml_allocr * alloc = ggml_allocr_new((void *)0x1000, (size_t)-0x1001, alignment); +ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) { + ggml_tallocr_t alloc = ggml_tallocr_new((void *)0x1000, SIZE_MAX/2, alignment); alloc->measure = true; return alloc; } -struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) { - struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr)); +ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { + // create a backend buffer to get the correct tensor allocation sizes + ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, 1); - *alloc = (struct ggml_allocr){ + // TODO: move alloc initialization to a common ggml_tallocr_new_impl function + ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); + alloc->buffer_owned = true; + alloc->measure = true; + ggml_tallocr_reset(alloc); + return alloc; +} + +ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { + ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, size); + ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); + alloc->buffer_owned = true; + return alloc; +} + +ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) { + ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); + + *alloc = (struct ggml_tallocr) { /*.buffer = */ buffer, /*.buffer_owned = */ false, /*.base = */ ggml_backend_buffer_get_base(buffer), /*.alignment = */ ggml_backend_buffer_get_alignment(buffer), /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, - /*.hash_table = */ {{0}}, /*.max_size = */ 0, /*.measure = */ false, - /*.parse_seq = */ {0}, - /*.parse_seq_len = */ 0, #ifdef GGML_ALLOCATOR_DEBUG /*.allocated_tensors = */ {0}, #endif }; - ggml_allocr_reset(alloc); + ggml_tallocr_reset(alloc); return alloc; } -void ggml_allocr_free(struct ggml_allocr * alloc) { +struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t alloc) { + return alloc->buffer; +} + +void ggml_tallocr_free(ggml_tallocr_t alloc) { + if (alloc == NULL) { + return; + } + if (alloc->buffer_owned) { ggml_backend_buffer_free(alloc->buffer); } free(alloc); } -bool ggml_allocr_is_measure(struct ggml_allocr * alloc) { +bool ggml_tallocr_is_measure(ggml_tallocr_t alloc) { return alloc->measure; } -//////////// compute graph allocator +size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) { + return alloc->max_size; +} + +// graph allocator + +struct hash_node { + int n_children; + int n_views; +}; + +struct ggml_gallocr { + ggml_tallocr_t talloc; + struct ggml_hash_set hash_set; + struct hash_node * hash_values; + size_t hash_values_size; + ggml_tallocr_t * hash_allocs; + int * parse_seq; + int parse_seq_len; +}; + +ggml_gallocr_t ggml_gallocr_new(void) { + ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr)); + + *galloc = (struct ggml_gallocr) { + /*.talloc = */ NULL, + /*.hash_set = */ {0}, + /*.hash_values = */ NULL, + /*.hash_values_size = */ 0, + /*.hash_allocs = */ NULL, + /*.parse_seq = */ NULL, + /*.parse_seq_len = */ 0, + }; + + return galloc; +} + +void ggml_gallocr_free(ggml_gallocr_t galloc) { + if (galloc == NULL) { + return; + } + + if (galloc->hash_set.keys != NULL) { + free(galloc->hash_set.keys); + } + if (galloc->hash_values != NULL) { + free(galloc->hash_values); + } + if (galloc->hash_allocs != NULL) { + free(galloc->hash_allocs); + } + if (galloc->parse_seq != NULL) { + free(galloc->parse_seq); + } + free(galloc); +} + +void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n) { + free(galloc->parse_seq); + galloc->parse_seq = malloc(sizeof(int) * n); + + for (int i = 0; i < n; i++) { + galloc->parse_seq[i] = list[i]; + } + galloc->parse_seq_len = n; +} + +static struct hash_node * hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) { + size_t i = ggml_hash_find_or_insert(galloc->hash_set, t); + return &galloc->hash_values[i]; +} static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { if (a->type != b->type) { @@ -378,27 +438,40 @@ static bool ggml_op_can_inplace(enum ggml_op op) { } } -static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view, bool update_backend) { - assert(view->view_src != NULL && view->view_src->data != NULL); +static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * node) { + if (galloc->talloc != NULL) { + return galloc->talloc; + } + return galloc->hash_allocs[ggml_hash_find_or_insert(galloc->hash_set, node)]; +} + +static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) { + ggml_tallocr_t alloc = node_tallocr(galloc, view); + + //printf("init_view: %s from src %s\n", view->name, view->view_src->name); + GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL); if (update_backend) { view->backend = view->view_src->backend; } - view->buffer = view->view_src->buffer; view->data = (char *)view->view_src->data + view->view_offs; // FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend // due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras - assert(ggml_allocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend); - ggml_backend_buffer_init_tensor(alloc->buffer, view); + assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend); + + if (!alloc->measure) { + ggml_backend_buffer_init_tensor(alloc->buffer, view); + } } -static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) { - struct hash_node * ht = alloc->hash_table; +static void allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { + ggml_tallocr_t alloc = node_tallocr(galloc, node); + if (node->data == NULL) { if (ggml_is_view(node)) { - init_view(alloc, node, true); + init_view(galloc, node, true); } else { // see if we can reuse a parent's buffer (inplace) if (ggml_op_can_inplace(node->op)) { @@ -409,16 +482,16 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } // if the node's data is external, then we cannot re-use it - if (ggml_allocr_is_own(alloc, parent) == false) { + if (ggml_tallocr_is_own(alloc, parent) == false) { AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); continue; } - struct hash_node * p_hn = hash_get(ht, parent); + struct hash_node * p_hn = hash_get(galloc, parent); if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) { if (ggml_is_view(parent)) { struct ggml_tensor * view_src = parent->view_src; - struct hash_node * view_src_hn = hash_get(ht, view_src); + struct hash_node * view_src_hn = hash_get(galloc, view_src); if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite // the parent's data that it will need later (same layout requirement). the problem is that then @@ -428,170 +501,267 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); node->view_src = view_src; view_src_hn->n_views += 1; - init_view(alloc, node, false); + init_view(galloc, node, false); return; } } else { AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); node->view_src = parent; p_hn->n_views += 1; - init_view(alloc, node, false); + init_view(galloc, node, false); return; } } } } - ggml_allocr_alloc(alloc, node); + ggml_tallocr_alloc(alloc, node); } } } -size_t ggml_allocr_alloc_graph_n( - struct ggml_allocr * alloc, - struct ggml_cgraph ** graphs, int n_graphs, - struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) { +static void free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { + ggml_tallocr_t alloc = node_tallocr(galloc, node); - // reset hash table - struct hash_node * ht = alloc->hash_table; - memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE); + ggml_tallocr_free_tensor(alloc, node); +} + +static void ggml_tallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * gf) { + const int * parse_seq = galloc->parse_seq; + int parse_seq_len = galloc->parse_seq_len; // count number of children and views - for (int g = 0; g < n_graphs; g++) { - struct ggml_cgraph * gf = graphs[g]; - for (int i = 0; i < gf->n_nodes; i++) { + for (int i = 0; i < gf->n_nodes; i++) { + struct ggml_tensor * node = gf->nodes[i]; + + if (ggml_is_view(node)) { + struct ggml_tensor * view_src = node->view_src; + hash_get(galloc, view_src)->n_views += 1; + if (node->buffer == NULL && node->data != NULL) { + // view of a pre-allocated tensor, didn't call init_view() yet + init_view(galloc, node, true); + } + } + + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; + } + hash_get(galloc, parent)->n_children += 1; + if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { + init_view(galloc, parent, true); + } + } + } + + // allocate tensors + // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers + int last_barrier_pos = 0; + int n_nodes = parse_seq_len ? parse_seq_len : gf->n_nodes; + + for (int ind = 0; ind < n_nodes; ind++) { + // allocate a node if there is no parse_seq or this is not a barrier + if (parse_seq_len == 0 || parse_seq[ind] != -1) { + int i = parse_seq_len ? parse_seq[ind] : ind; struct ggml_tensor * node = gf->nodes[i]; - if (ggml_is_view(node)) { - struct ggml_tensor * view_src = node->view_src; - hash_get(ht, view_src)->n_views += 1; - if (node->buffer == NULL && node->data != NULL) { - // view of a pre-allocated tensor, didn't call init_view() yet - init_view(alloc, node, true); - } - } - + // allocate parents (leafs) for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { break; } - hash_get(ht, parent)->n_children += 1; - if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { - init_view(alloc, parent, true); + allocate_node(galloc, parent); + } + + // allocate node + allocate_node(galloc, node); + + AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; + } + AT_PRINTF("%s", parent->name); + if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { + AT_PRINTF(", "); } } + AT_PRINTF("\n"); } - } - // allocate tensors - for (int g = 0; g < n_graphs; g++) { - struct ggml_cgraph * gf = graphs[g]; - AT_PRINTF("####### graph %d/%d\n", g, n_graphs); - // graph inputs are allocated first to ensure that they are not overwritten by each other - if (inputs != NULL && inputs[g] != NULL) { - for (int i = 0; inputs[g][i] != NULL; i++) { - struct ggml_tensor * input = inputs[g][i]; - AT_PRINTF("input: %s\n", input->name); - allocate_node(alloc, input); - } - } - // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers - int last_barrier_pos = 0; - int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes; + // update parents + // update immediately if there is no parse_seq + // update only at barriers if there is parse_seq + if ((parse_seq_len == 0) || parse_seq[ind] == -1) { + int update_start = parse_seq_len ? last_barrier_pos : ind; + int update_end = parse_seq_len ? ind : ind + 1; + for (int i = update_start; i < update_end; i++) { + int node_i = parse_seq_len ? parse_seq[i] : i; + struct ggml_tensor * node = gf->nodes[node_i]; - for (int ind = 0; ind < n_nodes; ind++) { - // allocate a node if there is no parse_seq or this is not a barrier - if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) { - int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind; - struct ggml_tensor * node = gf->nodes[i]; - - // allocate parents (leafs) for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { break; } - allocate_node(alloc, parent); - } + struct hash_node * p_hn = hash_get(galloc, parent); + p_hn->n_children -= 1; - // allocate node - allocate_node(alloc, node); + //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views); - AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name); - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; - } - AT_PRINTF("%s", parent->name); - if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { - AT_PRINTF(", "); - } - } - AT_PRINTF("\n"); - } - - // update parents - // update immediately if there is no parse_seq - // update only at barriers if there is parse_seq - if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) { - int update_start = alloc->parse_seq_len ? last_barrier_pos : ind; - int update_end = alloc->parse_seq_len ? ind : ind + 1; - for (int i = update_start; i < update_end; i++) { - int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i; - struct ggml_tensor * node = gf->nodes[node_i]; - - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; + if (p_hn->n_children == 0 && p_hn->n_views == 0) { + if (ggml_is_view(parent)) { + struct ggml_tensor * view_src = parent->view_src; + struct hash_node * view_src_hn = hash_get(galloc, view_src); + view_src_hn->n_views -= 1; + AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); + if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0) { + free_node(galloc, view_src); + } } - struct hash_node * p_hn = hash_get(ht, parent); - p_hn->n_children -= 1; - - //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views); - - if (p_hn->n_children == 0 && p_hn->n_views == 0) { - if (ggml_is_view(parent)) { - struct ggml_tensor * view_src = parent->view_src; - struct hash_node * view_src_hn = hash_get(ht, view_src); - view_src_hn->n_views -= 1; - AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); - if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) { - ggml_allocr_free_tensor(alloc, view_src); - } - } - else { - if (parent->data != node->data) { - ggml_allocr_free_tensor(alloc, parent); - } - } + else { + free_node(galloc, parent); } } } - AT_PRINTF("\n"); - if (alloc->parse_seq_len) { - last_barrier_pos = ind + 1; - } } - } - // free graph outputs here that wouldn't be freed otherwise because they have no children - if (outputs != NULL && outputs[g] != NULL) { - for (int i = 0; outputs[g][i] != NULL; i++) { - struct ggml_tensor * output = outputs[g][i]; - AT_PRINTF("output: %s\n", output->name); - ggml_allocr_free_tensor(alloc, output); + AT_PRINTF("\n"); + if (parse_seq_len) { + last_barrier_pos = ind + 1; } } } - - return alloc->max_size; } -size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) { - return ggml_allocr_alloc_graph_n(alloc, &graph, 1, NULL, NULL); +size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph) { + size_t hash_size = graph->visited_hash_table.size; + + // check if the hash table is initialized and large enough + if (galloc->hash_set.size < hash_size) { + if (galloc->hash_set.keys != NULL) { + free(galloc->hash_set.keys); + } + if (galloc->hash_values != NULL) { + free(galloc->hash_values); + } + galloc->hash_set.keys = malloc(sizeof(struct ggml_tensor *) * hash_size); + galloc->hash_set.size = hash_size; + galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size); + } + + // reset hash table + memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * hash_size); + memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size); + + galloc->talloc = talloc; + ggml_tallocr_alloc_graph_impl(galloc, graph); + galloc->talloc = NULL; + + size_t max_size = ggml_tallocr_max_size(talloc); + + return max_size; } -size_t ggml_allocr_max_size(struct ggml_allocr * alloc) { - return alloc->max_size; +void ggml_gallocr_alloc_graph_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, struct ggml_hash_set hash_set, ggml_tallocr_t * hash_node_talloc) { + const size_t hash_size = hash_set.size; + + GGML_ASSERT(hash_size >= (size_t)(graph->n_nodes + graph->n_leafs)); + + galloc->talloc = NULL; + + // alloc hash_values if needed + if (galloc->hash_values == NULL || galloc->hash_values_size < hash_size) { + free(galloc->hash_values); + galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size); + galloc->hash_values_size = hash_size; + } + + // free hash_set.keys if needed + if (galloc->hash_set.keys != NULL) { + free(galloc->hash_set.keys); + } + galloc->hash_set = hash_set; + + // reset hash values + memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size); + + galloc->hash_allocs = hash_node_talloc; + + ggml_tallocr_alloc_graph_impl(galloc, graph); + + // remove unowned resources + galloc->hash_set.keys = NULL; + galloc->hash_allocs = NULL; +} + +// legacy API wrapper + +struct ggml_allocr { + ggml_tallocr_t talloc; + ggml_gallocr_t galloc; +}; + +static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) { + ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr)); + *alloc = (struct ggml_allocr) { + /*.talloc = */ talloc, + /*.galloc = */ ggml_gallocr_new(), + }; + return alloc; +} + +ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment) { + return ggml_allocr_new_impl(ggml_tallocr_new(data, size, alignment)); +} + +ggml_allocr_t ggml_allocr_new_measure(size_t alignment) { + return ggml_allocr_new_impl(ggml_tallocr_new_measure(alignment)); +} + +ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) { + return ggml_allocr_new_impl(ggml_tallocr_new_from_buffer(buffer)); +} + +ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size) { + return ggml_allocr_new_impl(ggml_tallocr_new_from_backend(backend, size)); +} + +ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend) { + return ggml_allocr_new_impl(ggml_tallocr_new_measure_from_backend(backend)); +} + +struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc) { + return ggml_tallocr_get_buffer(alloc->talloc); +} + +void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) { + ggml_gallocr_set_parse_seq(alloc->galloc, list, n); +} + +void ggml_allocr_free(ggml_allocr_t alloc) { + ggml_gallocr_free(alloc->galloc); + ggml_tallocr_free(alloc->talloc); + free(alloc); +} + +bool ggml_allocr_is_measure(ggml_allocr_t alloc) { + return ggml_tallocr_is_measure(alloc->talloc); +} + +void ggml_allocr_reset(ggml_allocr_t alloc) { + ggml_tallocr_reset(alloc->talloc); +} + +void ggml_allocr_alloc(ggml_allocr_t alloc, struct ggml_tensor * tensor) { + ggml_tallocr_alloc(alloc->talloc, tensor); +} + +size_t ggml_allocr_max_size(ggml_allocr_t alloc) { + return ggml_tallocr_max_size(alloc->talloc); +} + +size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) { + return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph); } diff --git a/ggml-alloc.h b/ggml-alloc.h index e38758878..dde2a06bf 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -6,27 +6,79 @@ extern "C" { #endif +struct ggml_backend; struct ggml_backend_buffer; -GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment); -GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment); -GGML_API struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer); +// +// Legacy API +// + +typedef struct ggml_allocr * ggml_allocr_t; + +// initialize allocator for use with CPU backend only +GGML_API ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment); +GGML_API ggml_allocr_t ggml_allocr_new_measure(size_t alignment); + +// initialize allocator for use with ggml-backend +GGML_API ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer +GGML_API ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend); + +GGML_API struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc); // tell the allocator to parse nodes following the order described in the list // you should call this if your graph are optimized to execute out-of-order -GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n); +GGML_API void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n); -GGML_API void ggml_allocr_free (struct ggml_allocr * alloc); -GGML_API bool ggml_allocr_is_measure (struct ggml_allocr * alloc); -GGML_API void ggml_allocr_reset (struct ggml_allocr * alloc); -GGML_API void ggml_allocr_alloc (struct ggml_allocr * alloc, struct ggml_tensor * tensor); -GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph); -GGML_API size_t ggml_allocr_max_size (struct ggml_allocr * alloc); +GGML_API void ggml_allocr_free (ggml_allocr_t alloc); +GGML_API bool ggml_allocr_is_measure (ggml_allocr_t alloc); +GGML_API void ggml_allocr_reset (ggml_allocr_t alloc); +GGML_API void ggml_allocr_alloc (ggml_allocr_t alloc, struct ggml_tensor * tensor); +GGML_API size_t ggml_allocr_max_size (ggml_allocr_t alloc); -GGML_API size_t ggml_allocr_alloc_graph_n( - struct ggml_allocr * alloc, - struct ggml_cgraph ** graphs, int n_graphs, - struct ggml_tensor *** inputs, struct ggml_tensor *** outputs); +GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph); + +// +// ggml-backend v2 API +// + +// Seperate tensor and graph allocator objects +// This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators +// The original API is kept as a wrapper around the new API + +// Tensor allocator +typedef struct ggml_tallocr * ggml_tallocr_t; + +GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment); +GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment); +GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer +GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend); + +GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc); + +GGML_API void ggml_tallocr_free (ggml_tallocr_t talloc); +GGML_API bool ggml_tallocr_is_measure (ggml_tallocr_t talloc); +GGML_API void ggml_tallocr_reset (ggml_tallocr_t talloc); +GGML_API void ggml_tallocr_alloc (ggml_tallocr_t talloc, struct ggml_tensor * tensor); +GGML_API size_t ggml_tallocr_max_size (ggml_tallocr_t talloc); + + +// Graph allocator +typedef struct ggml_gallocr * ggml_gallocr_t; + +GGML_API ggml_gallocr_t ggml_gallocr_new(void); +GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); + +GGML_API void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n); +GGML_API size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph); + +// Allocate tensors from the allocators given by the hash table +GGML_API void ggml_gallocr_alloc_graph_n( + ggml_gallocr_t galloc, + struct ggml_cgraph * graph, + struct ggml_hash_set hash_set, + ggml_tallocr_t * hash_node_talloc); #ifdef __cplusplus } diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h new file mode 100644 index 000000000..211e3d424 --- /dev/null +++ b/ggml-backend-impl.h @@ -0,0 +1,87 @@ +#pragma once + +// ggml-backend internal header + +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + // + // Backend buffer + // + + typedef void * ggml_backend_buffer_context_t; + + struct ggml_backend_buffer_i { + void (*free_buffer) (ggml_backend_buffer_t buffer); + void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer + size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback + void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback + void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback + }; + + struct ggml_backend_buffer { + struct ggml_backend_buffer_i iface; + + ggml_backend_t backend; + ggml_backend_buffer_context_t context; + + size_t size; + }; + + GGML_API ggml_backend_buffer_t ggml_backend_buffer_init( + struct ggml_backend * backend, + struct ggml_backend_buffer_i iface, + ggml_backend_buffer_context_t context, + size_t size); + + // + // Backend + // + + typedef void * ggml_backend_context_t; + + struct ggml_backend_i { + const char * (*get_name)(ggml_backend_t backend); + + void (*free)(ggml_backend_t backend); + + // buffer allocation + ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size); + + // get buffer alignment + size_t (*get_alignment)(ggml_backend_t backend); + + // tensor data access + // these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize + void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + void (*synchronize) (ggml_backend_t backend); + + // (optional) copy tensor between different backends, allow for single-copy tranfers + void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); + void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); + + // compute graph with a plan + ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); + void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + + // compute graph without a plan + void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + + // check if the backend supports an operation + bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + }; + + struct ggml_backend { + struct ggml_backend_i iface; + + ggml_backend_context_t context; + }; + +#ifdef __cplusplus +} +#endif diff --git a/ggml-backend.c b/ggml-backend.c index ca8d83daf..f6e5fceed 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1,7 +1,9 @@ -#include "ggml-backend.h" +#include "ggml-backend-impl.h" #include "ggml-alloc.h" +#include "ggml-impl.h" #include +#include #include #include #include @@ -33,6 +35,10 @@ ggml_backend_buffer_t ggml_backend_buffer_init( } void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { + if (buffer == NULL) { + return; + } + if (buffer->iface.free_buffer != NULL) { buffer->iface.free_buffer(buffer); } @@ -43,15 +49,20 @@ size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) { return ggml_backend_get_alignment(buffer->backend); } -void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { - return buffer->iface.get_base(buffer); -} - size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) { return buffer->size; } +void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { + void * base = buffer->iface.get_base(buffer); + + GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL"); + + return base; +} + size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // get_alloc_size is optional, defaults to ggml_nbytes if (buffer->iface.get_alloc_size) { return buffer->iface.get_alloc_size(buffer, tensor); } @@ -59,12 +70,14 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g } void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // init_tensor is optional if (buffer->iface.init_tensor) { buffer->iface.init_tensor(buffer, tensor); } } void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // free_tensor is optional if (buffer->iface.free_tensor) { buffer->iface.free_tensor(buffer, tensor); } @@ -73,14 +86,21 @@ void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_t // backend ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) { - return tensor->buffer->backend; + return tensor->buffer ? tensor->buffer->backend : NULL; } const char * ggml_backend_name(ggml_backend_t backend) { + if (backend == NULL) { + return "NULL"; + } return backend->iface.get_name(backend); } void ggml_backend_free(ggml_backend_t backend) { + if (backend == NULL) { + return; + } + backend->iface.free(backend); } @@ -101,13 +121,23 @@ void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * dat } void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size); - ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor)); + ggml_backend_t backend = ggml_get_backend(tensor); + + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(backend != NULL && "tensor backend not set"); + + backend->iface.set_tensor_async(backend, tensor, data, offset, size); + backend->iface.synchronize(backend); } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size); - ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor)); + ggml_backend_t backend = ggml_get_backend(tensor); + + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(backend != NULL && "tensor backend not set"); + + backend->iface.get_tensor_async(backend, tensor, data, offset, size); + backend->iface.synchronize(backend); } void ggml_backend_synchronize(ggml_backend_t backend) { @@ -156,7 +186,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]); GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); - // printf("cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); + // fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); if (src == dst) { return; @@ -234,6 +264,8 @@ static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backen size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? + GGML_ASSERT(data != NULL && "failed to allocate buffer"); + return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size); } @@ -271,8 +303,7 @@ static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml } static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { - // for a backend such as CUDA that can queue async calls, it is ok to do this asynchronously, but it may not be the case for other backends - ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src)); + ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); UNUSED(backend); } @@ -383,3 +414,537 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) { return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size); } + +// scheduler + +#define GGML_MAX_BACKENDS 4 +#define GGML_MAX_SPLITS 256 +#define GGML_MAX_SPLIT_INPUTS 16 + +struct ggml_backend_sched_split { + ggml_tallocr_t tallocr; + int i_start; + int i_end; + struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; + int n_inputs; + struct ggml_cgraph * graph; +}; + +struct ggml_backend_sched { + int n_backends; + ggml_backend_t backends[GGML_MAX_BACKENDS]; + ggml_tallocr_t tallocs[GGML_MAX_BACKENDS]; + + ggml_gallocr_t galloc; + + struct ggml_hash_set hash_set; + ggml_tallocr_t * node_talloc; // [hash_set.size] + struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS] + + struct ggml_cgraph * graph; + struct ggml_backend_sched_split splits[GGML_MAX_SPLITS]; + int n_splits; + + struct ggml_context * ctx; + + // align context_buffer to GGML_MEM_ALIGN + #ifdef _MSC_VER + __declspec(align(GGML_MEM_ALIGN)) + #else + __attribute__((aligned(GGML_MEM_ALIGN))) + #endif + char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + GGML_MAX_SPLITS*sizeof(struct ggml_cgraph)]; +}; + +#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node) +#define node_allocr(node) sched->node_talloc[hash_id(node)] + +static bool ggml_is_view_op(enum ggml_op op) { + return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE; +} + +// returns the priority of the backend, lower is better +static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) { + for (int i = 0; i < sched->n_backends; i++) { + if (sched->backends[i] == backend) { + return i; + } + } + return INT_MAX; +} + +static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) { + for (int i = 0; i < sched->n_backends; i++) { + if (sched->tallocs[i] == allocr) { + return i; + } + } + return INT_MAX; +} + +// returns the backend that should be used for the node based on the current locations +char causes[GGML_DEFAULT_GRAPH_SIZE*4 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove +static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { + // if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there + // ie. kv cache updates + // note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend. + // dst + ggml_backend_t cur_backend = ggml_get_backend(node); + if (cur_backend != NULL) { + sprintf(causes[hash_id(node)], "1.dst"); + return cur_backend; + } + + // view_src + if (node->view_src != NULL && ggml_get_backend(node->view_src) != NULL) { + sprintf(causes[hash_id(node)], "1.vsrc"); + return ggml_get_backend(node->view_src); + } + + // src + int cur_prio = INT_MAX; + size_t cur_size = 0; + + for (int i = 0; i < GGML_MAX_SRC; i++) { + const struct ggml_tensor * src = node->src[i]; + if (src == NULL) { + break; + } + ggml_backend_t src_backend = ggml_get_backend(src); + if (src_backend != NULL) { + int src_prio = sched_backend_prio(sched, src_backend); + size_t src_size = ggml_nbytes(src); + if (src_prio < cur_prio && src_size >= cur_size) { + cur_prio = src_prio; + cur_size = src_size; + cur_backend = src_backend; + sprintf(causes[hash_id(node)], "1.src%d", i); + } + } + } + return cur_backend; +} + +static char * fmt_size(size_t size) { + static char buffer[128]; + if (size >= 1024*1024) { + sprintf(buffer, "%zuM", size/1024/1024); + } else { + sprintf(buffer, "%zuK", size/1024); + } + return buffer; +} + +static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + int cur_split = 0; + for (int i = 0; i < graph->n_nodes; i++) { + if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) { + ggml_backend_t split_backend = ggml_tallocr_get_buffer(sched->splits[cur_split].tallocr)->backend; + fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend), sched->splits[cur_split].n_inputs); + for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) { + fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name, fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j]))); + } + fprintf(stderr, "\n"); + cur_split++; + } + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + ggml_backend_t node_backend = node_allocr ? ggml_tallocr_get_buffer(node_allocr)->backend : NULL; + fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", causes[hash_id(node)]); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + ggml_backend_t src_backend = src_allocr ? ggml_tallocr_get_buffer(src_allocr)->backend : NULL; + fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", causes[hash_id(src)]); + } + fprintf(stderr, "\n"); + } +} + +// creates a copy of the tensor with the same memory layout +static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) { + struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor); + for (int i = 0; i < GGML_MAX_DIMS; i++) { + dup->nb[i] = tensor->nb[i]; + } + return dup; +} + +// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend +// TODO: merge passes +static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + // reset state + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); + memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); + memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); + sched->n_splits = 0; + + struct ggml_init_params params = { + /*.mem_size = */ sizeof(sched->context_buffer), + /*.mem_buffer = */ sched->context_buffer, + /*.no_alloc = */ true + }; + + if (sched->ctx != NULL) { + ggml_free(sched->ctx); + } + + sched->ctx = ggml_init(params); + + // pass 1: assign backends to ops with allocated inputs + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + if (node_allocr(leaf) != NULL) { + // do not overwrite user assignments + continue; + } + ggml_backend_t leaf_backend = ggml_get_backend(leaf); + if (leaf_backend == NULL && leaf->view_src != NULL) { + leaf_backend = ggml_get_backend(leaf->view_src); + } + if (leaf_backend != NULL) { + node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend); + } + } + + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (node_allocr(node) != NULL) { + // do not overwrite user assignments + continue; + } + ggml_backend_t node_backend = sched_backend_from_cur(sched, node); + if (node_backend != NULL) { + node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend); + } + } + //printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + + // pass 2: assign backends to ops from current assignments + // TODO: + // - reuse sched_backend_from_cur + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr == NULL) { + int cur_prio = INT_MAX; + size_t cur_size = 0; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr != NULL) { + int src_prio = sched_allocr_prio(sched, src_allocr); + size_t src_size = ggml_nbytes(src); + if (src_prio < cur_prio && src_size >= cur_size) { + cur_prio = src_prio; + cur_size = src_size; + node_allocr = src_allocr; + sprintf(causes[hash_id(node)], "2.src%d", j); + } + } + } + if (node_allocr != NULL) { + node_allocr(node) = node_allocr; + } + } + } + //printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + + // pass 3: assign backends to remaining src from dst (should only be leafs) + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + ggml_tallocr_t node_allocr = node_allocr(node); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr == NULL) { + node_allocr(src) = node_allocr; + } + } + } + //printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + + // pass 4: split graph, find tensors that need to be copied + // TODO: + // - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost + // find first backend + int cur_split = 0; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (node->view_src == NULL) { + sched->splits[0].tallocr = node_allocr(node); + break; + } + } + sched->splits[0].i_start = 0; + sched->splits[0].n_inputs = 0; + memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK + ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; + size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + + if (ggml_is_view_op(node->op)) { + continue; + } + + ggml_tallocr_t node_allocr = node_allocr(node); + + if (node_allocr != cur_allocr) { + sched->splits[cur_split].i_end = i; + cur_split++; + GGML_ASSERT(cur_split < GGML_MAX_SPLITS); + sched->splits[cur_split].tallocr = node_allocr; + sched->splits[cur_split].i_start = i; + sched->splits[cur_split].n_inputs = 0; + memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK + cur_allocr = node_allocr; + cur_backend_id = sched_allocr_prio(sched, cur_allocr); + } + + // find inputs that are not on the same backend + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr != node_allocr) { + int n_inputs = sched->splits[cur_split].n_inputs++; + GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); + sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src; + + // create copies + size_t id = hash_id(src); + if (sched->node_copies[id][cur_backend_id] == NULL) { + struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + sched->node_copies[id][cur_backend_id] = tensor_copy; + node_allocr(tensor_copy) = cur_allocr; + ggml_backend_t backend = ggml_tallocr_get_buffer(cur_allocr)->backend; + ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + } + node->src[j] = sched->node_copies[id][cur_backend_id]; + } + } + } + sched->splits[cur_split].i_end = graph->n_nodes; + sched->n_splits = cur_split + 1; + + //fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout); + +#if 1 + // sanity check: all sources should have the same backend as the node + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr == NULL) { + fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); + } + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now + fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n", + node->name, node_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(node_allocr)->backend) : "NULL", + j, src->name, src_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(src_allocr)->backend) : "NULL"); + } + } + } +#endif + + // create copies of the graph for each split + // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way + struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false); + for (int i = 0; i < sched->n_splits; i++) { + struct ggml_backend_sched_split * split = &sched->splits[i]; + split->graph = ggml_graph_view(sched->ctx, graph, split->i_start, split->i_end); + + // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split + for (int j = 0; j < split->n_inputs; j++) { + struct ggml_tensor * input = split->inputs[j]; + struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)]; + input_cpy->src[0] = input; + graph_copy->nodes[graph_copy->n_nodes++] = input_cpy; + } + + for (int j = split->i_start; j < split->i_end; j++) { + graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j]; + } + } + sched->graph = graph_copy; +} + +static void sched_alloc_splits(ggml_backend_sched_t sched) { + ggml_gallocr_alloc_graph_n( + sched->galloc, + sched->graph, + sched->hash_set, + sched->node_talloc); +} + +static void sched_compute_splits(ggml_backend_sched_t sched) { + uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; + uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; + + struct ggml_backend_sched_split * splits = sched->splits; + + for (int i = 0; i < sched->n_splits; i++) { + struct ggml_backend_sched_split * split = &splits[i]; + ggml_backend_t split_backend = ggml_tallocr_get_buffer(split->tallocr)->backend; + int split_backend_id = sched_backend_prio(sched, split_backend); + + // copy the input tensors to the split backend + uint64_t copy_start_us = ggml_time_us(); + for (int j = 0; j < split->n_inputs; j++) { + struct ggml_tensor * input_cpy = sched->node_copies[hash_id(split->inputs[j])][sched_backend_prio(sched, split_backend)]; + if (split->inputs[j]->buffer == NULL) { + if (split->inputs[j]->view_src == NULL) { + fprintf(stderr, "input %s has no buffer and no view_src\n", split->inputs[j]->name); + exit(1); + } + struct ggml_tensor * view = split->inputs[j]; + view->backend = view->view_src->backend; + view->buffer = view->view_src->buffer; + view->data = (char *)view->view_src->data + view->view_offs; + ggml_backend_buffer_init_tensor(ggml_backend_sched_get_buffer(sched, view->buffer->backend), view); + } + if (input_cpy->buffer == NULL) { + fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name); + exit(1); + } + GGML_ASSERT(split->inputs[j]->buffer->backend != input_cpy->buffer->backend); + GGML_ASSERT(input_cpy->buffer->backend == split_backend); + ggml_backend_tensor_copy(split->inputs[j], input_cpy); + } + // ggml_backend_synchronize(split_backend); + int64_t copy_end_us = ggml_time_us(); + copy_us[split_backend_id] += copy_end_us - copy_start_us; + +#if 0 + char split_filename[GGML_MAX_NAME]; + snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend)); + ggml_graph_dump_dot(split->graph, NULL, split_filename); +#endif + + uint64_t compute_start_us = ggml_time_us(); + ggml_backend_graph_compute(split_backend, split->graph); + // ggml_backend_synchronize(split_backend); + uint64_t compute_end_us = ggml_time_us(); + compute_us[split_backend_id] += compute_end_us - compute_start_us; + } + +#if 0 + // per-backend timings + fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits); + for (int i = 0; i < sched->n_backends; i++) { + if (copy_us[i] > 0 || compute_us[i] > 0) { + fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]); + } + } +#endif +} + +static void sched_reset(ggml_backend_sched_t sched) { + for (int i = 0; i < sched->n_backends; i++) { + ggml_tallocr_reset(sched->tallocs[i]); + } +} + +ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) { + GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS); + + struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched)); + memset(sched, 0, sizeof(struct ggml_backend_sched)); + + fprintf(stderr, "ggml_backend_sched size: %lu KB\n", sizeof(struct ggml_backend_sched)/1024); + + sched->n_backends = n_backends; + for (int i = 0; i < n_backends; i++) { + sched->backends[i] = backends[i]; + } + + sched->galloc = ggml_gallocr_new(); + + // init measure allocs for each backend + for (int i = 0; i < n_backends; i++) { + sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]); + } + + return sched; +} + +void ggml_backend_sched_free(ggml_backend_sched_t sched) { + if (sched == NULL) { + return; + } + for (int i = 0; i < sched->n_backends; i++) { + ggml_tallocr_free(sched->tallocs[i]); + } + ggml_gallocr_free(sched->galloc); + free(sched->hash_set.keys); + free(sched->node_talloc); + free(sched->node_copies); + free(sched); +} + +void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { + // initialize hash tables + size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS; + sched->hash_set.size = hash_size; + sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size); + sched->node_talloc = malloc(sizeof(sched->node_talloc[0]) * hash_size); + sched->node_copies = malloc(sizeof(sched->node_copies[0]) * hash_size); + + sched_split_graph(sched, measure_graph); + sched_alloc_splits(sched); + + // allocate buffers and reset allocators + for (int i = 0; i < sched->n_backends; i++) { + size_t size = ggml_tallocr_max_size(sched->tallocs[i]); + ggml_tallocr_free(sched->tallocs[i]); + sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size); + } + + sched_reset(sched); +} + +void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + + sched_split_graph(sched, graph); + sched_alloc_splits(sched); + sched_compute_splits(sched); + sched_reset(sched); +} + +ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) { + int backend_index = sched_backend_prio(sched, backend); + return sched->tallocs[backend_index]; +} + +ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) { + int backend_index = sched_backend_prio(sched, backend); + return ggml_tallocr_get_buffer(sched->tallocs[backend_index]); +} + +void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { + int backend_index = sched_backend_prio(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); + node_allocr(node) = sched->tallocs[backend_index]; +} diff --git a/ggml-backend.h b/ggml-backend.h index da134b0db..966687320 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -1,51 +1,20 @@ #pragma once #include "ggml.h" +#include "ggml-alloc.h" #ifdef __cplusplus extern "C" { #endif - struct ggml_backend; + + // + // Backend buffer + // + struct ggml_backend_buffer; - - // type-erased backend-specific types / wrappers - typedef void * ggml_backend_context_t; - typedef void * ggml_backend_graph_plan_t; - typedef void * ggml_backend_buffer_context_t; - - // avoid accessing internals of these types - typedef struct ggml_backend * ggml_backend_t; typedef struct ggml_backend_buffer * ggml_backend_buffer_t; - // - // backend buffer - // - - struct ggml_backend_buffer_i { - void (*free_buffer) (ggml_backend_buffer_t buffer); - void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer - size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback - void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback - void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback - }; - - // TODO: hide behind API - struct ggml_backend_buffer { - struct ggml_backend_buffer_i iface; - - ggml_backend_t backend; - ggml_backend_buffer_context_t context; - - size_t size; - }; - // backend buffer functions - GGML_API ggml_backend_buffer_t ggml_backend_buffer_init( - struct ggml_backend * backend, - struct ggml_backend_buffer_i iface, - ggml_backend_buffer_context_t context, - size_t size); - GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); @@ -55,50 +24,13 @@ extern "C" { GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // - // backend + // Backend // - struct ggml_backend_i { - const char * (*get_name)(ggml_backend_t backend); + struct ggml_backend; + typedef struct ggml_backend * ggml_backend_t; + typedef void * ggml_backend_graph_plan_t; - void (*free)(ggml_backend_t backend); - - // buffer allocation - ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size); - - // get buffer alignment - size_t (*get_alignment)(ggml_backend_t backend); - - // tensor data access - // these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize - void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - void (*synchronize) (ggml_backend_t backend); - - // (optional) copy tensor between different backends, allow for single-copy tranfers - void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - - // compute graph with a plan - ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); - void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - - // compute graph without a plan - void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); - - // check if the backend supports an operation - bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); - }; - - // TODO: hide behind API - struct ggml_backend { - struct ggml_backend_i iface; - - ggml_backend_context_t context; - }; - - // backend helper functions GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor); GGML_API const char * ggml_backend_name(ggml_backend_t backend); @@ -133,11 +65,72 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_cpu_init(void); GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); + // Create a backend buffer from an existing pointer GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size); + + // + // Backend scheduler + // + + // The backend scheduler allows for multiple backends to be used together + // Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends + // The backends are selected based on: + // - the backend that supports the operation + // - the location of the pre-allocated tensors (e.g. the weights) + /* + Example usage: + + sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends); + // sched is initialized with measure allocators and cannot be used until allocated with a measure graph + + // initialize buffers from a measure graph + measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed + + // in build_graph: + build_graph(...) { + // allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer) + alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu); + ggml_allocr_alloc(alloc_cpu, tensor); + + // manually assigning nodes to a backend (optional, shouldn't be needed in most cases) + struct ggml_tensor * node = ggml_mul_mat(ctx, ...); + ggml_backend_sched_set_node_backend(sched, node, backend_gpu); + } + + // allocate backend buffers from measure graph + ggml_backend_sched_init_measure(sched, measure_graph); + + // the scheduler is now ready to compute graphs + + // compute + graph = build_graph(sched); + ggml_backend_sched_graph_compute(sched, graph); + */ + + struct ggml_backend_sched; + typedef struct ggml_backend_sched * ggml_backend_sched_t; + + // Initialize a backend scheduler + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends); + + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); + + // Initialize backend buffers from a measure graph + GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + + GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend); + GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend); + + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + + // Allocate a graph on the backend scheduler + GGML_API void ggml_backend_sched_graph_compute( + ggml_backend_sched_t sched, + struct ggml_cgraph * graph); + #ifdef __cplusplus } #endif diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 8d03ba664..163402446 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -81,6 +81,7 @@ #include "ggml-cuda.h" #include "ggml.h" +#include "ggml-backend-impl.h" #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 @@ -7751,11 +7752,11 @@ static size_t g_temp_tensor_extra_index = 0; static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { if (g_temp_tensor_extras == nullptr) { - g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES]; + g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE]; } size_t alloc_index = g_temp_tensor_extra_index; - g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_MAX_NODES; + g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE; ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; memset(extra, 0, sizeof(*extra)); @@ -8070,11 +8071,11 @@ struct ggml_backend_buffer_context_cuda { ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { if (temp_tensor_extras == nullptr) { - temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES]; + temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE]; } size_t alloc_index = temp_tensor_extra_index; - temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_MAX_NODES; + temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE; ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; memset(extra, 0, sizeof(*extra)); @@ -8160,7 +8161,12 @@ static ggml_backend_buffer_t ggml_backend_cuda_alloc_buffer(ggml_backend_t backe ggml_cuda_set_device(g_main_device); ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda; + + size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 + + ggml_cuda_set_device(g_main_device); CUDA_CHECK(cudaMalloc(&ctx->device, size)); + return ggml_backend_buffer_init(backend, cuda_backend_buffer_interface, ctx, size); } @@ -8227,6 +8233,8 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + continue; assert(node->backend == GGML_BACKEND_GPU); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { diff --git a/ggml-impl.h b/ggml-impl.h index 5ec18a50c..d88f26144 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -230,7 +230,19 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { #endif - // TODO: backend v2 PR +#define GGML_HASHTABLE_FULL ((size_t)-1) +#define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2) + +bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key); + +// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted +size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key); + +// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full +size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key); + +// return index, asserts if table is full +size_t ggml_hash_find_or_insert( struct ggml_hash_set hash_set, struct ggml_tensor * key); #ifdef __cplusplus } diff --git a/ggml-metal.m b/ggml-metal.m index 78ae4485d..c2cda0bf5 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1,5 +1,6 @@ #import "ggml-metal.h" +#import "ggml-backend-impl.h" #import "ggml.h" #import @@ -23,7 +24,7 @@ #define UNUSED(x) (void)(x) -#define GGML_MAX_CONCUR (2*GGML_MAX_NODES) +#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) struct ggml_metal_buffer { const char * name; @@ -744,6 +745,20 @@ void ggml_metal_graph_compute( struct ggml_tensor * src1 = gf->nodes[i]->src[1]; struct ggml_tensor * dst = gf->nodes[i]; + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + { + // noop -> next node + } continue; + default: + { + } break; + } + const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; const int64_t ne02 = src0 ? src0->ne[2] : 0; @@ -797,14 +812,6 @@ void ggml_metal_graph_compute( //} switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop - } break; case GGML_OP_CONCAT: { const int64_t nb = ne00; diff --git a/ggml.c b/ggml.c index 009d5b398..da78e6de9 100644 --- a/ggml.c +++ b/ggml.c @@ -100,6 +100,49 @@ typedef void * thread_ret_t; #include #endif +#if defined(__APPLE__) +#include +#endif + +#if (defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__)) && \ + (!defined(TARGET_OS_TV) && !defined(TARGET_OS_WATCH)) + +#include + +void ggml_print_backtrace(void) { + /* + #include + #include + + void * trace[100]; + + int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); + + backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); + */ + + // backtrack_symbols does not show line numbers, use gdb instead + char attach[32]; + snprintf(attach, sizeof(attach), "attach %d", getpid()); + int pid = fork(); + if (pid == 0) { + execlp("gdb", "gdb", "--batch", + "-ex", "set style enabled on", + "-ex", attach, + "-ex", "bt -frame-info source-and-location", + "-ex", "detach", + "-ex", "quit", + NULL); + } else { + waitpid(pid, NULL, 0); + } +} +#else +void ggml_print_backtrace(void) { + // platform not supported +} +#endif + /*#define GGML_PERF*/ #define GGML_DEBUG 0 #define GGML_GELU_FP16 @@ -1352,6 +1395,7 @@ inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } +inline static void ggml_vec_leaky_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.1f*x[i]; } static const float GELU_COEF_A = 0.044715f; static const float GELU_QUICK_COEF = -1.702f; @@ -3769,6 +3813,14 @@ struct ggml_tensor * ggml_relu_inplace( return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU); } +// ggml_leaky + +struct ggml_tensor * ggml_leaky( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_LEAKY); +} + // ggml_gelu struct ggml_tensor * ggml_gelu( @@ -5411,7 +5463,7 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0( // ggml_pool_* -static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { +static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, float p) { return (ins + 2 * p - ks) / s + 1; } @@ -5458,8 +5510,8 @@ struct ggml_tensor * ggml_pool_2d( int k1, int s0, int s1, - int p0, - int p1) { + float p0, + float p1) { bool is_node = false; @@ -8921,6 +8973,48 @@ static void ggml_compute_forward_silu( } } +// ggml_compute_forward_leaky + +static void ggml_compute_forward_leaky_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_leaky( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_leaky_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_silu_back static void ggml_compute_forward_silu_back_f32( @@ -12454,14 +12548,11 @@ static void ggml_compute_forward_pool_1d( ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst); } -// ggml_compute_forward_pool_2d_sk_p0 +// ggml_compute_forward_pool_2d -static void ggml_compute_forward_pool_2d_sk_p0( +static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, - const enum ggml_op_pool op, const struct ggml_tensor * src, - const int k0, - const int k1, struct ggml_tensor * dst) { assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); @@ -12470,6 +12561,14 @@ static void ggml_compute_forward_pool_2d_sk_p0( return; } + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; const char * cdata = (const char*)src->data; const char * const data_end = cdata + ggml_nbytes(src); @@ -12480,6 +12579,8 @@ static void ggml_compute_forward_pool_2d_sk_p0( float * dplane = (float *)dst->data; const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; while (cdata < data_end) { for (int oy = 0; oy < py; ++oy) { @@ -12492,13 +12593,15 @@ static void ggml_compute_forward_pool_2d_sk_p0( case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } - const int ix = ox * k0; - const int iy = oy * k1; + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; + if (j < 0 || j >= src->ne[0]) continue; switch (op) { case GGML_OP_POOL_AVG: *out += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > *out) *out = srow[j]; break; @@ -12519,29 +12622,6 @@ static void ggml_compute_forward_pool_2d_sk_p0( } } -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - GGML_ASSERT(p0 == 0); - GGML_ASSERT(p1 == 0); // padding not supported - GGML_ASSERT(k0 == s0); - GGML_ASSERT(k1 == s1); // only s = k supported - - ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst); -} - // ggml_compute_forward_upscale static void ggml_compute_forward_upscale_f32( @@ -13743,6 +13823,10 @@ static void ggml_compute_forward_unary( { ggml_compute_forward_silu(params, src0, dst); } break; + case GGML_UNARY_OP_LEAKY: + { + ggml_compute_forward_leaky(params, src0, dst); + } break; default: { GGML_ASSERT(false); @@ -14651,62 +14735,109 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm //////////////////////////////////////////////////////////////////////////////// -static_assert(GGML_GRAPH_HASHTABLE_SIZE > GGML_MAX_NODES * 2, "GGML_GRAPH_HT_SIZE is too small"); +static size_t ggml_hash_size(size_t min_sz) { + // next primes after powers of two + static const size_t primes[] = { + 2, 3, 5, 11, 17, 37, 67, 131, 257, 521, 1031, + 2053, 4099, 8209, 16411, 32771, 65537, 131101, + 262147, 524309, 1048583, 2097169, 4194319, 8388617, + 16777259, 33554467, 67108879, 134217757, 268435459, + 536870923, 1073741827, 2147483659 + }; + static const size_t n_primes = sizeof(primes)/sizeof(primes[0]); -static size_t hash(void * p) { - return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; + // find the smallest prime that is larger or equal to min_sz + size_t l = 0; + size_t r = n_primes; + while (l < r) { + size_t m = (l + r)/2; + if (primes[m] < min_sz) { + l = m + 1; + } else { + r = m; + } + } + size_t sz = l < n_primes ? primes[l] : min_sz | 1; + return sz; } -static size_t hash_find(void * hash_table[], void * p) { - size_t h = hash(p); +static size_t ggml_hash(const void * p) { + return (size_t)p; +} + +size_t ggml_hash_find(const struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t h = ggml_hash(key) % hash_set.size; // linear probing size_t i = h; - while (hash_table[i] != NULL && hash_table[i] != p) { - i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE; + while (hash_set.keys[i] != NULL && hash_set.keys[i] != key) { + i = (i + 1) % hash_set.size; if (i == h) { // visited all hash table entries -> not found - return GGML_GRAPH_HASHTABLE_SIZE; + return GGML_HASHTABLE_FULL; } } return i; } -static bool hash_insert(void * hash_table[], void * p) { - size_t i = hash_find(hash_table, p); +bool ggml_hash_contains(struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t i = ggml_hash_find(hash_set, key); + return i != GGML_HASHTABLE_FULL && hash_set.keys[i] == key; +} - GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full +size_t ggml_hash_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t i = ggml_hash_find(hash_set, key); - if (hash_table[i] == p) { - return true; + GGML_ASSERT(i != GGML_HASHTABLE_FULL); + + if (hash_set.keys[i] == key) { + return GGML_HASHTABLE_ALREADY_EXISTS; } // insert - GGML_ASSERT(hash_table[i] == NULL); - hash_table[i] = p; - return false; + GGML_ASSERT(hash_set.keys[i] == NULL); + hash_set.keys[i] = key; + return i; } -static bool hash_contains(void * hash_table[], void * p) { - size_t i = hash_find(hash_table, p); - return (i < GGML_GRAPH_HASHTABLE_SIZE) && (hash_table[i] == p); +size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t i = ggml_hash_find(hash_set, key); + + GGML_ASSERT(i != GGML_HASHTABLE_FULL); + + hash_set.keys[i] = key; + return i; } -struct hash_map { - void * keys[GGML_GRAPH_HASHTABLE_SIZE]; - void * vals[GGML_GRAPH_HASHTABLE_SIZE]; -}; - -static struct hash_map * new_hash_map(void) { - struct hash_map * result = malloc(sizeof(struct hash_map)); - for (int i=0; ikeys[i] = NULL; - result->vals[i] = NULL; - } +static struct ggml_hash_set ggml_hash_set_new(size_t size) { + size = ggml_hash_size(size); + struct ggml_hash_set result; + result.size = size; + result.keys = malloc(sizeof(struct ggml_tensor *) * size); + memset(result.keys, 0, sizeof(struct ggml_tensor *) * size); return result; } -static void free_hash_map(struct hash_map * map) { +static void ggml_hash_set_free(struct ggml_hash_set hash_set) { + free(hash_set.keys); +} + +struct hash_map { + struct ggml_hash_set set; + struct ggml_tensor ** vals; +}; + +static struct hash_map * ggml_new_hash_map(size_t size) { + struct hash_map * result = malloc(sizeof(struct hash_map)); + result->set = ggml_hash_set_new(size); + result->vals = malloc(sizeof(struct ggml_tensor *) * result->set.size); + memset(result->vals, 0, sizeof(struct ggml_tensor *) * result->set.size); + return result; +} + +static void ggml_hash_map_free(struct hash_map * map) { + ggml_hash_set_free(map->set); + free(map->vals); free(map); } @@ -14726,7 +14857,7 @@ static struct ggml_tensor * ggml_recompute_graph_node( return node; } - if (!hash_contains(graph->visited_hash_table, node)) { + if (!ggml_hash_contains(graph->visited_hash_table, node)) { return node; } @@ -14741,17 +14872,17 @@ static struct ggml_tensor * ggml_recompute_graph_node( return node; } - size_t i = hash_find(replacements->keys, node); - GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full - if (replacements->keys[i] == node) { - return (struct ggml_tensor *) replacements->vals[i]; + size_t i = ggml_hash_find(replacements->set, node); + GGML_ASSERT(i != GGML_HASHTABLE_FULL); // assert that not full + if (replacements->set.keys[i] == node) { + return replacements->vals[i]; } struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, node->n_dims, node->ne); // insert clone into replacements - GGML_ASSERT(replacements->keys[i] == NULL); // assert that we don't overwrite - replacements->keys[i] = node; + GGML_ASSERT(replacements->set.keys[i] == NULL); // assert that we don't overwrite + replacements->set.keys[i] = node; replacements->vals[i] = clone; clone->op = node->op; @@ -14788,26 +14919,26 @@ void ggml_build_backward_gradient_checkpointing( struct ggml_cgraph * gb_tmp, struct ggml_tensor * * checkpoints, int n_checkpoints) { - *gb_tmp = *gf; + ggml_graph_cpy(gf, gb_tmp); ggml_build_backward_expand(ctx, gf, gb_tmp, true); if (n_checkpoints <= 0) { - *gb = *gb_tmp; + ggml_graph_cpy(gb_tmp, gb); return; } - struct hash_map * replacements = new_hash_map(); + struct hash_map * replacements = ggml_new_hash_map(gf->n_nodes + gf->n_leafs + n_checkpoints); // insert checkpoints in replacements for (int i = 0; i < n_checkpoints; ++i) { - size_t k = hash_find(replacements->keys, checkpoints[i]); - GGML_ASSERT(k < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full - GGML_ASSERT(replacements->keys[k] == NULL); // assert that we don't overwrite - replacements->keys[k] = checkpoints[i]; - replacements->vals[k] = checkpoints[i]; + size_t k = ggml_hash_find(replacements->set, checkpoints[i]); + GGML_ASSERT(k != GGML_HASHTABLE_FULL); // assert that not full + GGML_ASSERT(replacements->set.keys[k] == NULL); // assert that we don't overwrite + replacements->set.keys[k] = checkpoints[i]; + replacements->vals[k] = checkpoints[i]; } - *gb = *gf; + ggml_graph_cpy(gf, gb); // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes], // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]), // by recomputing them from checkpoints @@ -14824,21 +14955,21 @@ void ggml_build_backward_gradient_checkpointing( ggml_build_forward_expand(gb, node); } - free_hash_map(replacements); + ggml_hash_map_free(replacements); } // functions to change gradients considering the case that input a might be initial gradient with zero value -static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { return b; } else { return ggml_add_impl(ctx, a, b, false); } } -static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { struct ggml_tensor * a_zero = ggml_scale(ctx, a, ggml_new_f32(ctx, 0)); return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false); } else { @@ -14846,23 +14977,23 @@ static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct gg } } -static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { return ggml_repeat(ctx, b, a); } else { return ggml_add1_impl(ctx, a, b, false); } } -static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { return ggml_neg(ctx, b); } else { return ggml_sub_impl(ctx, a, b, false); } } -static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, void * zero_table[]) { +static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; @@ -15695,7 +15826,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * } // check if already visited - if (hash_insert(cgraph->visited_hash_table, node)) { + if (ggml_hash_insert(cgraph->visited_hash_table, node) == GGML_HASHTABLE_ALREADY_EXISTS) { return; } @@ -15711,7 +15842,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * if (node->op == GGML_OP_NONE && node->grad == NULL) { // reached a leaf node, not part of the gradient graph (e.g. a constant) - GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES); + GGML_ASSERT(cgraph->n_leafs < cgraph->size); if (strlen(node->name) == 0) { ggml_format_name(node, "leaf_%d", cgraph->n_leafs); @@ -15720,22 +15851,24 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * cgraph->leafs[cgraph->n_leafs] = node; cgraph->n_leafs++; } else { - GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES); + GGML_ASSERT(cgraph->n_nodes < cgraph->size); if (strlen(node->name) == 0) { ggml_format_name(node, "node_%d", cgraph->n_nodes); } cgraph->nodes[cgraph->n_nodes] = node; - cgraph->grads[cgraph->n_nodes] = node->grad; + if (cgraph->grads) { + cgraph->grads[cgraph->n_nodes] = node->grad; + } cgraph->n_nodes++; } } static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) { if (!expand) { - cgraph->n_nodes = 0; - cgraph->n_leafs = 0; + // TODO: this branch isn't accessible anymore, maybe move this to ggml_build_forward_expand + ggml_graph_clear(cgraph); } const int n0 = cgraph->n_nodes; @@ -15756,25 +15889,6 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * ggml_build_forward_impl(cgraph, tensor, true); } -struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) { - struct ggml_cgraph result = { - /*.n_nodes =*/ 0, - /*.n_leafs =*/ 0, - /*.nodes =*/ { NULL }, - /*.grads =*/ { NULL }, - /*.leafs =*/ { NULL }, - /*.hash_table =*/ { NULL }, - /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, - /*.perf_runs =*/ 0, - /*.perf_cycles =*/ 0, - /*.perf_time_us =*/ 0, - }; - - ggml_build_forward_impl(&result, tensor, false); - - return result; -} - void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) { GGML_ASSERT(gf->n_nodes > 0); @@ -15791,11 +15905,10 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * } // remember original gradients which start with zero values - void ** zero_table = malloc(sizeof(void *) * GGML_GRAPH_HASHTABLE_SIZE); - memset(zero_table, 0, sizeof(void*) * GGML_GRAPH_HASHTABLE_SIZE); + struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size); for (int i = 0; i < gf->n_nodes; i++) { if (gf->grads[i]) { - hash_insert(zero_table, gf->grads[i]); + ggml_hash_insert(zero_table, gf->grads[i]); } } @@ -15818,26 +15931,54 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * } } - free(zero_table); + ggml_hash_set_free(zero_table); } -struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) { - struct ggml_cgraph result = *gf; - ggml_build_backward_expand(ctx, gf, &result, keep); - return result; +static size_t ggml_graph_nbytes(size_t size, bool grads) { + size_t nbytes = sizeof(struct ggml_cgraph); + nbytes += size * sizeof(struct ggml_tensor *) * 2; // leafs + nodes + if (grads) { + nbytes += size * sizeof(struct ggml_tensor *); // grads + } + nbytes += ggml_hash_size(size * 2) * sizeof(struct ggml_tensor *); // hash set + return nbytes; } -struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE); +size_t ggml_graph_overhead_custom(size_t size, bool grads) { + return GGML_OBJECT_SIZE + GGML_PAD(ggml_graph_nbytes(size, grads), GGML_MEM_ALIGN); +} + +size_t ggml_graph_overhead(void) { + return ggml_graph_overhead_custom(GGML_DEFAULT_GRAPH_SIZE, false); +} + +struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) { + const size_t obj_size = ggml_graph_nbytes(size, grads); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size); struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); + struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1); + + size_t hash_size = ggml_hash_size(size * 2); + struct ggml_tensor ** nodes_ptr = data_start; + struct ggml_tensor ** leafs_ptr = nodes_ptr + size; + struct ggml_tensor ** hash_keys_ptr = leafs_ptr + size; + struct ggml_tensor ** grads_ptr = grads ? hash_keys_ptr + hash_size : NULL; + + // check that we allocated the correct amount of memory + assert(obj_size == (size_t) ( + (grads ? (char *)(grads_ptr + size) : (char *)(hash_keys_ptr + hash_size)) - (char *)cgraph)); + + memset(hash_keys_ptr, 0, hash_size * sizeof(struct ggml_tensor *)); + *cgraph = (struct ggml_cgraph) { + /*.size =*/ size, /*.n_nodes =*/ 0, /*.n_leafs =*/ 0, - /*.nodes =*/ { NULL }, - /*.grads =*/ { NULL }, - /*.leafs =*/ { NULL }, - /*.hash_table =*/ { NULL }, + /*.nodes =*/ nodes_ptr, + /*.grads =*/ grads_ptr, + /*.leafs =*/ leafs_ptr, + /*.hash_table =*/ { hash_size, hash_keys_ptr }, /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, @@ -15847,14 +15988,85 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { return cgraph; } -struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) { - struct ggml_cgraph * cgraph = ggml_new_graph(ctx); - ggml_build_forward_impl(cgraph, tensor, false); +struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { + return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false); +} + +struct ggml_cgraph * ggml_graph_view(struct ggml_context * ctx, struct ggml_cgraph * cgraph0, int i0, int i1) { + const size_t obj_size = sizeof(struct ggml_cgraph); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size); + struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); + + *cgraph = (struct ggml_cgraph) { + /*.size =*/ 0, + /*.n_nodes =*/ i1 - i0, + /*.n_leafs =*/ 0, + /*.nodes =*/ cgraph0->nodes + i0, + /*.grads =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL, + /*.leafs =*/ NULL, + /*.hash_table =*/ { 0, NULL }, + /*.order =*/ cgraph0->order, + /*.perf_runs =*/ 0, + /*.perf_cycles =*/ 0, + /*.perf_time_us =*/ 0, + }; + return cgraph; } -size_t ggml_graph_overhead(void) { - return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN); +void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { + GGML_ASSERT(dst->size >= src->n_leafs); + GGML_ASSERT(dst->size >= src->n_nodes); + GGML_ASSERT(dst->visited_hash_table.size >= src->visited_hash_table.size); + + dst->n_leafs = src->n_leafs; + dst->n_nodes = src->n_nodes; + dst->order = src->order; + + for (int i = 0; i < src->n_leafs; ++i) { + dst->leafs[i] = src->leafs[i]; + } + + for (int i = 0; i < src->n_nodes; ++i) { + dst->nodes[i] = src->nodes[i]; + } + + if (src->grads) { + GGML_ASSERT(dst->grads != NULL); + for (int i = 0; i < src->n_nodes; ++i) { + dst->grads[i] = src->grads[i]; + } + } + + for (size_t i = 0; i < src->visited_hash_table.size; ++i) { + if (src->visited_hash_table.keys[i]) { + ggml_hash_insert(dst->visited_hash_table, src->visited_hash_table.keys[i]); + } + } +} + +struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { + struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL); + ggml_graph_cpy(cgraph, result); + return result; +} + +void ggml_graph_reset(struct ggml_cgraph * cgraph) { + GGML_ASSERT(cgraph->grads != NULL); + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * grad = cgraph->grads[i]; + + if (grad) { + ggml_set_zero(grad); + } + } +} + +void ggml_graph_clear(struct ggml_cgraph * cgraph) { + cgraph->n_leafs = 0; + cgraph->n_nodes = 0; + memset(cgraph->visited_hash_table.keys, 0, cgraph->visited_hash_table.size * sizeof(struct ggml_tensor *)); } // @@ -16007,13 +16219,252 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const node->perf_time_us += time_us_cur; } +static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { + int n_tasks = 0; + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_ACC: + { + n_tasks = n_threads; + } break; + case GGML_OP_SUB: + case GGML_OP_DIV: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_LOG: + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + case GGML_OP_ARGMAX: + case GGML_OP_REPEAT: + case GGML_OP_REPEAT_BACK: + { + n_tasks = 1; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_LEAKY: + { + n_tasks = 1; + } break; + + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + { + n_tasks = n_threads; + } break; + } + break; + case GGML_OP_SILU_BACK: + case GGML_OP_MUL: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_RMS_NORM_BACK: + case GGML_OP_GROUP_NORM: + case GGML_OP_CONCAT: + { + n_tasks = n_threads; + } break; + case GGML_OP_MUL_MAT: + { + n_tasks = n_threads; + + // TODO: use different scheduling for different matrix sizes + //const int nr0 = ggml_nrows(node->src[0]); + //const int nr1 = ggml_nrows(node->src[1]); + + //n_tasks = MIN(n_threads, MAX(1, nr0/128)); + //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); + +#if defined(GGML_USE_CUBLAS) + if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) { + n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + } +#elif defined(GGML_USE_CLBLAST) + if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { + n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + } +#endif +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) + if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { + n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + } +#endif + } break; + case GGML_OP_OUT_PROD: + { + n_tasks = n_threads; + } break; + case GGML_OP_SCALE: + case GGML_OP_SET: + case GGML_OP_CONT: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_GET_ROWS: + case GGML_OP_GET_ROWS_BACK: + case GGML_OP_DIAG: + { + n_tasks = 1; + } break; + case GGML_OP_DIAG_MASK_ZERO: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_SOFT_MAX_BACK: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + case GGML_OP_ADD_REL_POS: + { + n_tasks = n_threads; + } break; + case GGML_OP_ALIBI: + { + n_tasks = 1; //TODO + } break; + case GGML_OP_CLAMP: + { + n_tasks = 1; //TODO + } break; + case GGML_OP_CONV_1D: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_1D_STAGE_0: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_1D_STAGE_1: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_2D: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_2D_STAGE_0: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_2D_STAGE_1: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + n_tasks = n_threads; + } break; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + { + n_tasks = 1; + } break; + case GGML_OP_UPSCALE: + { + n_tasks = n_threads; + } break; + case GGML_OP_FLASH_ATTN: + { + n_tasks = n_threads; + } break; + case GGML_OP_FLASH_FF: + { + n_tasks = n_threads; + } break; + case GGML_OP_FLASH_ATTN_BACK: + { + n_tasks = n_threads; + } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: + case GGML_OP_GET_REL_POS: + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: + { + n_tasks = 1; + } break; + case GGML_OP_MAP_CUSTOM1: + { + struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM2: + { + struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM3: + { + struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: + { + n_tasks = n_threads; + } break; + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: + { + n_tasks = n_threads; + } break; + case GGML_OP_NONE: + { + n_tasks = 1; + } break; + case GGML_OP_COUNT: + { + GGML_ASSERT(false); + } break; + default: + { + GGML_ASSERT(false); + } break; + } + + assert(n_tasks > 0); + + return n_tasks; +} + static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; const struct ggml_cgraph * cgraph = state->shared->cgraph; const struct ggml_cplan * cplan = state->shared->cplan; - const int * n_tasks_arr = cplan->n_tasks; const int n_threads = state->shared->n_threads; set_numa_thread_affinity(state->ith, n_threads); @@ -16038,9 +16489,9 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (node_n != -1) { /* FINALIZE */ - struct ggml_tensor * node = state->shared->cgraph->nodes[node_n]; + struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { - params.nth = n_tasks_arr[node_n]; + params.nth = ggml_get_n_tasks(node, n_threads); ggml_compute_forward(¶ms, node); } ggml_graph_compute_perf_stats_node(node, state->shared); @@ -16051,7 +16502,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); struct ggml_tensor * node = cgraph->nodes[node_n]; - const int n_tasks = n_tasks_arr[node_n]; + const int n_tasks = ggml_get_n_tasks(node, n_threads); state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); @@ -16109,7 +16560,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* COMPUTE */ struct ggml_tensor * node = cgraph->nodes[node_n]; - const int n_tasks = n_tasks_arr[node_n]; + const int n_tasks = ggml_get_n_tasks(node, n_threads); struct ggml_compute_params params = { /*.type =*/ GGML_TASK_COMPUTE, @@ -16143,121 +16594,46 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { struct ggml_tensor * node = cgraph->nodes[i]; + size_t cur = 0; + switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: { n_tasks = n_threads; - size_t cur = 0; if (ggml_is_quantized(node->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); } break; case GGML_OP_ADD: case GGML_OP_ADD1: { n_tasks = n_threads; - size_t cur = 0; - if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); } break; case GGML_OP_ACC: { n_tasks = n_threads; - size_t cur = 0; - if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); } break; - case GGML_OP_SUB: - case GGML_OP_DIV: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_LOG: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - { - n_tasks = 1; - } break; - - case GGML_OP_UNARY: - { - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SGN: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_ELU: - case GGML_UNARY_OP_RELU: - { - n_tasks = 1; - } break; - - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - { - n_tasks = n_threads; - } break; - } - } break; - case GGML_OP_SILU_BACK: - case GGML_OP_MUL: - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_GROUP_NORM: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONCAT: case GGML_OP_MUL_MAT: { - n_tasks = n_threads; - - // TODO: use different scheduling for different matrix sizes - //const int nr0 = ggml_nrows(node->src[0]); - //const int nr1 = ggml_nrows(node->src[1]); - - //n_tasks = MIN(n_threads, MAX(1, nr0/128)); - //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); - - size_t cur = 0; const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; -#if defined(GGML_USE_CUBLAS) - if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning - } else -#elif defined(GGML_USE_CLBLAST) +#if defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node); } else #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning if (node->src[0]->type != GGML_TYPE_F32) { // here we need memory just for single 2D matrix from src0 cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]); @@ -16266,62 +16642,18 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { #endif if (node->src[1]->type != vec_dot_type) { cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type); - } else { - cur = 0; } - - work_size = MAX(work_size, cur); } break; case GGML_OP_OUT_PROD: { n_tasks = n_threads; - size_t cur = 0; - if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_SCALE: - { - n_tasks = 1; - } break; - case GGML_OP_SET: - case GGML_OP_CONT: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_GET_ROWS: - case GGML_OP_GET_ROWS_BACK: - case GGML_OP_DIAG: - { - n_tasks = 1; - } break; - case GGML_OP_DIAG_MASK_ZERO: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_ADD_REL_POS: - { - n_tasks = n_threads; - } break; - case GGML_OP_ALIBI: - { - n_tasks = 1; //TODO - } break; - case GGML_OP_CLAMP: - { - n_tasks = 1; //TODO } break; case GGML_OP_CONV_1D: { - n_tasks = n_threads; - GGML_ASSERT(node->src[0]->ne[3] == 1); GGML_ASSERT(node->src[1]->ne[2] == 1); GGML_ASSERT(node->src[1]->ne[3] == 1); @@ -16342,8 +16674,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { UNUSED(ne10); UNUSED(ne11); - size_t cur = 0; - if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0); @@ -16353,21 +16683,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else { GGML_ASSERT(false); } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_1D: { - n_tasks = n_threads; - GGML_ASSERT(node->src[0]->ne[3] == 1); GGML_ASSERT(node->src[1]->ne[2] == 1); GGML_ASSERT(node->src[1]->ne[3] == 1); @@ -16379,7 +16697,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { const int64_t ne10 = node->src[1]->ne[0]; // L const int64_t ne11 = node->src[1]->ne[1]; // Cin - size_t cur = 0; if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; @@ -16391,13 +16708,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else { GGML_ASSERT(false); } - - work_size = MAX(work_size, cur); } break; case GGML_OP_CONV_2D: { - n_tasks = n_threads; - const int64_t ne00 = node->src[0]->ne[0]; // W const int64_t ne01 = node->src[0]->ne[1]; // H const int64_t ne02 = node->src[0]->ne[2]; // C @@ -16417,8 +16730,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { UNUSED(ne03); UNUSED(ne2); - size_t cur = 0; - if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { // im2col: [N*OH*OW, IC*KH*KW] @@ -16429,21 +16740,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else { GGML_ASSERT(false); } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_2D_STAGE_1: - { - n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_2D: { - n_tasks = n_threads; - const int64_t ne00 = node->src[0]->ne[0]; // W const int64_t ne01 = node->src[0]->ne[1]; // H const int64_t ne02 = node->src[0]->ne[2]; // Channels Out @@ -16453,141 +16752,66 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { const int64_t ne11 = node->src[1]->ne[1]; // H const int64_t ne12 = node->src[1]->ne[2]; // Channels In - size_t cur = 0; cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: - { - n_tasks = 1; - } break; - case GGML_OP_UPSCALE: - { - n_tasks = n_threads; } break; case GGML_OP_FLASH_ATTN: { n_tasks = n_threads; - size_t cur = 0; - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } - - if (node->src[1]->type == GGML_TYPE_F16) { + } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 } - - work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_FF: { n_tasks = n_threads; - size_t cur = 0; - if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } - - if (node->src[1]->type == GGML_TYPE_F16) { + } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 } - - work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; - size_t cur = 0; - const int64_t D = node->src[0]->ne[0]; const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } - - if (node->src[1]->type == GGML_TYPE_F16) { + } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } + } break; - work_size = MAX(work_size, cur); - } break; - case GGML_OP_WIN_PART: - case GGML_OP_WIN_UNPART: - case GGML_OP_GET_REL_POS: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - { - n_tasks = 1; - } break; - case GGML_OP_MAP_CUSTOM1: - { - struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p->n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM2: - { - struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p->n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM3: - { - struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p->n_tasks, n_threads); - } - } break; case GGML_OP_CROSS_ENTROPY_LOSS: { n_tasks = n_threads; - size_t cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - n_tasks = n_threads; - } break; - case GGML_OP_NONE: - { - n_tasks = 1; + cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; + default: + break; } - cplan.n_tasks[i] = n_tasks; + work_size = MAX(work_size, cur); } if (work_size > 0) { @@ -16609,12 +16833,6 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { if (cplan->work_size > 0) { GGML_ASSERT(cplan->work_data); } - - for (int i = 0; i < cgraph->n_nodes; ++i) { - if (cgraph->nodes[i]->op != GGML_OP_NONE) { - GGML_ASSERT(cplan->n_tasks[i] > 0); - } - } } const int n_threads = cplan->n_threads; @@ -16687,16 +16905,6 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { return compute_status; } -void ggml_graph_reset(struct ggml_cgraph * cgraph) { - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * grad = cgraph->grads[i]; - - if (grad) { - ggml_set_zero(grad); - } - } -} - void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); @@ -16823,12 +17031,12 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { const uint32_t magic = GGML_FILE_MAGIC; const uint32_t version = GGML_FILE_VERSION; const uint32_t n_leafs = cgraph->n_leafs; - const uint32_t nodes = cgraph->n_nodes; + const uint32_t n_nodes = cgraph->n_nodes; fwrite(&magic, sizeof(uint32_t), 1, fout); fwrite(&version, sizeof(uint32_t), 1, fout); fwrite(&n_leafs, sizeof(uint32_t), 1, fout); - fwrite(&nodes, sizeof(uint32_t), 1, fout); + fwrite(&n_nodes, sizeof(uint32_t), 1, fout); fwrite(&size_eval, sizeof(uint64_t), 1, fout); } @@ -16916,7 +17124,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { if (idx == -1) { for (int k = 0; k < cgraph->n_nodes; ++k) { if (args[j] == cgraph->nodes[k]) { - idx = GGML_MAX_NODES + k; + idx = cgraph->n_leafs + k; break; } } @@ -16943,11 +17151,11 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { } } -struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { +struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { assert(*ctx_data == NULL); assert(*ctx_eval == NULL); - struct ggml_cgraph result = { 0 }; + struct ggml_cgraph * result = NULL; struct ggml_tensor * data = NULL; @@ -17019,13 +17227,11 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** const uint32_t n_leafs = *(const uint32_t *) ptr; ptr += sizeof(n_leafs); const uint32_t n_nodes = *(const uint32_t *) ptr; ptr += sizeof(n_nodes); const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval); - - result.n_leafs = n_leafs; - result.n_nodes = n_nodes; + const int graph_size = MAX(n_leafs, n_nodes); // create the data context { - const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead(); + const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph_size, false); struct ggml_init_params params = { .mem_size = size_eval + overhead, @@ -17041,6 +17247,12 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** } } + result = ggml_new_graph_custom(*ctx_eval, graph_size, false); + + result->n_leafs = n_leafs; + result->n_nodes = n_nodes; + + // leafs { uint32_t type; @@ -17079,7 +17291,7 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor->nb[j] = nb[j]; } - result.leafs[i] = tensor; + result->leafs[i] = tensor; ptr += ggml_nbytes(tensor); @@ -17131,10 +17343,10 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** continue; } - if (arg_idx < GGML_MAX_NODES) { - args[j] = result.leafs[arg_idx]; + if (arg_idx < result->n_leafs) { + args[j] = result->leafs[arg_idx]; } else { - args[j] = result.nodes[arg_idx - GGML_MAX_NODES]; + args[j] = result->nodes[arg_idx - result->n_leafs]; } } @@ -17186,7 +17398,7 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor->src[j] = args[j]; } - result.nodes[i] = tensor; + result->nodes[i] = tensor; fprintf(stderr, "%s: loaded node %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor)); } @@ -18091,10 +18303,11 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { case GGML_OPT_ADAM: { result = (struct ggml_opt_params) { - .type = GGML_OPT_ADAM, - .n_threads = 1, - .past = 0, - .delta = 1e-5f, + .type = GGML_OPT_ADAM, + .graph_size = GGML_DEFAULT_GRAPH_SIZE, + .n_threads = 1, // FIXME: GGML_DEFAULT_N_THREADS ? + .past = 0, + .delta = 1e-5f, .max_no_improvement = 100, @@ -18121,10 +18334,11 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { case GGML_OPT_LBFGS: { result = (struct ggml_opt_params) { - .type = GGML_OPT_LBFGS, - .n_threads = 1, - .past = 0, - .delta = 1e-5f, + .type = GGML_OPT_LBFGS, + .graph_size = GGML_DEFAULT_GRAPH_SIZE, + .n_threads = 1, + .past = 0, + .delta = 1e-5f, .max_no_improvement = 0, @@ -18266,14 +18480,11 @@ enum ggml_opt_result ggml_opt_resume( struct ggml_tensor * f) { // build forward + backward compute graphs - struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0)); - struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0)); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, opt->params.graph_size, true); + ggml_build_forward_expand(gf, f); - struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data; - struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data; - - *gf = ggml_build_forward (f); - *gb = ggml_build_backward(ctx, gf, true); + struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf); + ggml_build_backward_expand(ctx, gf, gb, true); return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); } diff --git a/ggml.h b/ggml.h index 26654fc8e..0118c99db 100644 --- a/ggml.h +++ b/ggml.h @@ -58,7 +58,8 @@ // { // ... // -// struct ggml_cgraph gf = ggml_build_forward(f); +// struct ggml_cgraph * gf = ggml_new_graph(ctx); +// ggml_build_forward_expand(gf, f); // // // set the input variable and parameter values // ggml_set_f32(x, 2.0f); @@ -213,15 +214,14 @@ #define GGML_QNT_VERSION 2 // bump this on quantization format changes #define GGML_QNT_VERSION_FACTOR 1000 // do not change this -#define GGML_MAX_DIMS 4 -#define GGML_MAX_NODES 16384 -#define GGML_MAX_PARAMS 1024 -#define GGML_MAX_CONTEXTS 64 -#define GGML_MAX_SRC 6 -#define GGML_MAX_NAME 64 -#define GGML_MAX_OP_PARAMS 64 -#define GGML_DEFAULT_N_THREADS 4 - +#define GGML_MAX_DIMS 4 +#define GGML_MAX_PARAMS 1024 +#define GGML_MAX_CONTEXTS 64 +#define GGML_MAX_SRC 6 +#define GGML_MAX_NAME 64 +#define GGML_MAX_OP_PARAMS 64 +#define GGML_DEFAULT_N_THREADS 4 +#define GGML_DEFAULT_GRAPH_SIZE 2048 #if UINTPTR_MAX == 0xFFFFFFFF #define GGML_MEM_ALIGN 4 #else @@ -245,7 +245,10 @@ do { \ if (!(x)) { \ fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \ - abort(); \ + fflush(stderr); \ + fflush(stdout); \ + ggml_print_backtrace(); \ + exit(1); \ } \ } while (0) @@ -451,6 +454,7 @@ extern "C" { GGML_UNARY_OP_GELU, GGML_UNARY_OP_GELU_QUICK, GGML_UNARY_OP_SILU, + GGML_UNARY_OP_LEAKY }; enum ggml_object_type { @@ -531,37 +535,33 @@ extern "C" { int n_threads; - // the `n_tasks` of nodes, 1:1 mapping to cgraph nodes - int n_tasks[GGML_MAX_NODES]; - // abort ggml_graph_compute when true bool (*abort_callback)(void * data); void * abort_callback_data; }; - // next prime after GGML_MAX_NODES - // #define GGML_GRAPH_HASHTABLE_SIZE 4099 - // next prime after GGML_MAX_NODES * 2 (nodes + leafs) - // #define GGML_GRAPH_HASHTABLE_SIZE 8273 - // #define GGML_GRAPH_HASHTABLE_SIZE 16411 - #define GGML_GRAPH_HASHTABLE_SIZE 32771 - enum ggml_cgraph_eval_order { GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0, GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT, GGML_CGRAPH_EVAL_ORDER_COUNT }; + struct ggml_hash_set { + size_t size; + struct ggml_tensor ** keys; + }; + // computation graph struct ggml_cgraph { + int size; int n_nodes; int n_leafs; - struct ggml_tensor * nodes[GGML_MAX_NODES]; - struct ggml_tensor * grads[GGML_MAX_NODES]; - struct ggml_tensor * leafs[GGML_MAX_NODES]; + struct ggml_tensor ** nodes; + struct ggml_tensor ** grads; + struct ggml_tensor ** leafs; - void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE]; + struct ggml_hash_set visited_hash_table; enum ggml_cgraph_eval_order order; @@ -571,8 +571,6 @@ extern "C" { int64_t perf_time_us; }; - static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph); - // scratch buffer struct ggml_scratch { size_t offs; @@ -617,6 +615,8 @@ extern "C" { GGML_API int64_t ggml_cycles(void); GGML_API int64_t ggml_cycles_per_ms(void); + GGML_API void ggml_print_backtrace(void); + GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node @@ -709,7 +709,7 @@ extern "C" { // Context tensor enumeration and lookup GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor); - GGML_API struct ggml_tensor * ggml_get_tensor (struct ggml_context * ctx, const char * name); + GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); @@ -943,6 +943,10 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_leaky( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); @@ -1482,6 +1486,8 @@ extern "C" { int s0, // stride int p0); // padding + // the result will have 2*p0 padding for the first dimension + // and 2*p1 padding for the second dimension GGML_API struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1490,8 +1496,8 @@ extern "C" { int k1, int s0, int s1, - int p0, - int p1); + float p0, + float p1); // nearest interpolate // used in stable-diffusion @@ -1732,19 +1738,22 @@ extern "C" { GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep); - GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); - GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); - // graph allocation in a context - GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); - GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor); + GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false + GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads); + GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph); + GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1); + GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst); + GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads + GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph); + GGML_API size_t ggml_graph_overhead(void); + GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads); // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); + GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data @@ -1752,8 +1761,8 @@ extern "C" { GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); - GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); - GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); + GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); + GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); // print info and performance information for the graph GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph); @@ -1816,6 +1825,8 @@ extern "C" { struct ggml_opt_params { enum ggml_opt_type type; + size_t graph_size; + int n_threads; // delta-based convergence test diff --git a/llama.cpp b/llama.cpp index a5f3876cc..76ee4ea23 100644 --- a/llama.cpp +++ b/llama.cpp @@ -91,6 +91,8 @@ #define LLAMA_ATTRIBUTE_FORMAT(...) #endif +#define LLAMA_MAX_NODES 4096 + // // logging // @@ -3618,7 +3620,7 @@ struct llm_build_context { } struct ggml_cgraph * build_llama() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); GGML_ASSERT(n_embd_head == hparams.n_rot); @@ -3730,7 +3732,7 @@ struct llm_build_context { } struct ggml_cgraph * build_baichuan() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -3850,7 +3852,7 @@ struct llm_build_context { } struct ggml_cgraph * build_falcon() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -3972,7 +3974,7 @@ struct llm_build_context { } struct ggml_cgraph * build_starcoder() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * pos; @@ -4071,7 +4073,7 @@ struct llm_build_context { } struct ggml_cgraph * build_persimmon() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_rot = n_embd_head / 2; @@ -4281,7 +4283,7 @@ struct llm_build_context { } struct ggml_cgraph * build_refact() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4372,7 +4374,7 @@ struct llm_build_context { } struct ggml_cgraph * build_bloom() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4466,7 +4468,7 @@ struct llm_build_context { } struct ggml_cgraph * build_mpt() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -8208,7 +8210,7 @@ struct llama_context * llama_new_context_with_model( { static const size_t tensor_alignment = 32; // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data - ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead()); + ctx->buf_compute.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); // create measure allocator ctx->alloc = ggml_allocr_new_measure(tensor_alignment); @@ -8597,8 +8599,8 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat if (kv_buf_size) { const size_t elt_size = ggml_element_size(kv_self.k); - ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); - ggml_cgraph gf{}; + ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); + ggml_cgraph * gf = ggml_new_graph(cpy_ctx); ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer); std::vector kout3d_data(ggml_nbytes(kout3d), 0); @@ -8616,9 +8618,9 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat kv_head, n_embd, n_layer, elt_size*n_ctx, elt_size*n_ctx*n_embd, 0); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d)); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d)); - ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k3d, kout3d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v3d, vout3d)); + ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); ggml_free(cpy_ctx); @@ -8725,8 +8727,8 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { const size_t elt_size = ggml_element_size(kv_self.k); - ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); - ggml_cgraph gf{}; + ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); + ggml_cgraph * gf = ggml_new_graph(cpy_ctx); ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer); kin3d->data = (void *) inp; @@ -8744,9 +8746,9 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { kv_head, n_embd, n_layer, elt_size*n_ctx, elt_size*n_ctx*n_embd, 0); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d)); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d)); - ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin3d, k3d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin3d, v3d)); + ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); ggml_free(cpy_ctx); } diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index 4311268bd..4024531b1 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -2,14 +2,20 @@ cp -rpv ../ggml/src/ggml.c ./ggml.c cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c +cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml-backend-impl.h cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c -cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu -cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h -cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp +cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h +cp -rpv ../ggml/src/ggml-impl.h ./ggml-impl.h cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal +cp -rpv ../ggml/src/ggml-mpi.h ./ggml-mpi.h +cp -rpv ../ggml/src/ggml-mpi.c ./ggml-mpi.c +cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp +cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h +cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c +cp -rpv ../ggml/src/ggml-quants.h ./ggml-quants.h cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 0a559b27a..7fe9154dd 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -231,9 +231,10 @@ static bool check_gradient( printf("GGML_N_THREADS = %d\n", n_threads); } - struct ggml_cgraph * gf = ggml_build_forward_ctx(ctx0, f); - struct ggml_cgraph * gb = ggml_new_graph(ctx0); - *gb = *gf; + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); + struct ggml_cgraph * gb = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); + ggml_build_forward_expand(gf, f); + ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx0, gf, gb, false); ggml_graph_compute_with_ctx(ctx0, gf, n_threads); diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index bb8af5962..2c9997fca 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -109,10 +109,11 @@ int main(void) { struct ggml_tensor * d = ggml_sub(ctx, c, ab); struct ggml_tensor * e = ggml_sum(ctx, ggml_sqr(ctx, d)); - struct ggml_cgraph ge = ggml_build_forward(e); - ggml_graph_reset(&ge); + struct ggml_cgraph * ge = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, true); + ggml_build_forward_expand(ge, e); + ggml_graph_reset(ge); - ggml_graph_compute_with_ctx(ctx, &ge, /*n_threads*/ 1); + ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); const float fe = ggml_get_f32_1d(e, 0); printf("%s: e = %.4f\n", __func__, fe); @@ -121,9 +122,9 @@ int main(void) { ggml_opt(ctx, opt_params, e); - ggml_graph_reset(&ge); + ggml_graph_reset(ge); - ggml_graph_compute_with_ctx(ctx, &ge, /*n_threads*/ 1); + ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); const float fe_opt = ggml_get_f32_1d(e, 0); printf("%s: original e = %.4f\n", __func__, fe); From c049b37d7baf558944501705b91ac89b26ee3e41 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 13 Nov 2023 14:18:08 +0200 Subject: [PATCH 064/664] readme : update hot topics --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index af39e8c0e..c7d232778 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ⚠️ **Upcoming change that might break functionality. Help with testing is needed:** https://github.com/ggerganov/llama.cpp/pull/3912 +- *No hot topics atm. Open to suggestions about what is hot today* ---- From 3d68f364f15778dc326f5024f2e5af1ad6dfddef Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 13 Nov 2023 16:55:52 +0200 Subject: [PATCH 065/664] ggml : sync (im2col, GPU conv, 32-bit arm compat) (#4060) ggml-ci --- ggml-cuda.cu | 106 ++++- ggml-impl.h | 6 - ggml-metal.h | 2 +- ggml-metal.m | 106 ++++- ggml-metal.metal | 108 ++++- ggml-quants.c | 241 +++++++---- ggml.c | 1073 +++++----------------------------------------- ggml.h | 19 +- 8 files changed, 586 insertions(+), 1075 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 163402446..7be63925f 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4489,6 +4489,13 @@ static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { *dsti = __float2half(*xi); } +static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { + const half * xi = (const half *) cxi; + half * dsti = (half *) cdsti; + + *dsti = *xi; +} + template static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, @@ -4742,6 +4749,25 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } +static __global__ void im2col_f32_f16( + const float * x, half * dst, + int ofs0, int ofs1, int IW, int IH, int CHW, + int s0, int s1, int p0, int p1, int d0, int d1) { + const int iiw = blockIdx.z * s0 + threadIdx.z * d0 - p0; + const int iih = blockIdx.y * s1 + threadIdx.y * d1 - p1; + + const int offset_dst = + (threadIdx.x * gridDim.y * gridDim.z + blockIdx.y * gridDim.z + blockIdx.z) * CHW + + (blockIdx.x * (blockDim.y * blockDim.z) + threadIdx.y * blockDim.z + threadIdx.z); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst[offset_dst] = __float2half(0.0f); + } else { + const int offset_src = threadIdx.x * ofs0 + blockIdx.x * ofs1; + dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]); + } +} + template static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) { const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); @@ -5642,6 +5668,16 @@ static void ggml_cpy_f32_f16_cuda( (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); } +static void ggml_cpy_f16_f16_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, + const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); +} + static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE; scale_f32<<>>(x, dst, scale, k); @@ -5725,6 +5761,15 @@ static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, c soft_max_f32<<>>(x, dst, ncols_x); } +static void im2col_f32_f16_cuda(const float * x, half * dst, + int OH, int IW, int IH, int OW, int IC, + int KH, int KW, int N, int ofs0, int ofs1, + int s0, int s1, int p0, int p1, int d0, int d1, cudaStream_t stream) { + dim3 block_nums(IC, OH, OW); + dim3 block_dims(N, KH, KW); + im2col_f32_f16<<>>(x, dst, ofs0, ofs1, IW, IH, (IC * KH * KW), s0, s1, p0, p1, d0, d1); +} + // buffer pool for cuda #define MAX_CUDA_BUFFERS 256 @@ -6522,8 +6567,7 @@ inline void ggml_cuda_op_mul_mat_cublas( src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); } - const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16; - + const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16; size_t dst_as = 0; half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); @@ -6698,6 +6742,45 @@ inline void ggml_cuda_op_alibi( (void) src1_dd; } +inline void ggml_cuda_op_im2col( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; + + const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; + + const int64_t N = src1->ne[is_2D ? 3 : 2]; + const int64_t IC = src1->ne[is_2D ? 2 : 1]; + const int64_t IH = is_2D ? src1->ne[1] : 1; + const int64_t IW = src1->ne[0]; + + const int64_t KH = is_2D ? src0->ne[1] : 1; + const int64_t KW = src0->ne[0]; + + const int64_t OH = is_2D ? dst->ne[2] : 1; + const int64_t OW = dst->ne[1]; + + const size_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 + const size_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + + im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, + OH, IW, IH, OW, IC, KH, KW, N, + ofs0, ofs1, s0, s1, p0, p1, d0, d1, main_stream); + + (void) src0; + (void) src0_dd; +} + inline void ggml_cuda_op_diag_mask_inf( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { @@ -7610,6 +7693,9 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { ggml_cpy_f32_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { + ggml_cpy_f16_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, + ne10, ne11, nb10, nb11, nb12, main_stream); } else { fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); @@ -7641,6 +7727,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); } +void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); +} + static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { (void) src0; (void) src1; @@ -7934,6 +8024,15 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ return false; } + if (tensor->op == GGML_OP_MUL_MAT) { + if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { +#ifndef NDEBUG + fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %d, src1->ne[3] = %d - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); +#endif + return false; + } + } + switch (tensor->op) { case GGML_OP_REPEAT: func = ggml_cuda_repeat; @@ -8012,6 +8111,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_ALIBI: func = ggml_cuda_alibi; break; + case GGML_OP_IM2COL: + func = ggml_cuda_im2col; + break; default: return false; } diff --git a/ggml-impl.h b/ggml-impl.h index d88f26144..06c07339e 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -39,12 +39,6 @@ extern "C" { #endif #endif -#undef MIN -#undef MAX - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t diff --git a/ggml-metal.h b/ggml-metal.h index 096b844e3..be2731f8b 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -26,7 +26,7 @@ #include // max memory buffers that can be mapped to the device -#define GGML_METAL_MAX_BUFFERS 16 +#define GGML_METAL_MAX_BUFFERS 64 #define GGML_METAL_MAX_COMMAND_BUFFERS 32 struct ggml_tensor; diff --git a/ggml-metal.m b/ggml-metal.m index c2cda0bf5..3d22b0b27 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -86,6 +86,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(norm); GGML_METAL_DECL_KERNEL(mul_mv_f32_f32); + GGML_METAL_DECL_KERNEL(mul_mv_f16_f16); GGML_METAL_DECL_KERNEL(mul_mv_f16_f32); GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row); GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4); @@ -114,6 +115,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(rope_f32); GGML_METAL_DECL_KERNEL(rope_f16); GGML_METAL_DECL_KERNEL(alibi_f32); + GGML_METAL_DECL_KERNEL(im2col_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f32); GGML_METAL_DECL_KERNEL(cpy_f16_f16); @@ -126,7 +128,7 @@ struct ggml_metal_context { // MSL code // TODO: move the contents here when ready // for now it is easier to work in a separate file -static NSString * const msl_library_source = @"see metal.metal"; +//static NSString * const msl_library_source = @"see metal.metal"; // Here to assist with NSBundle Path Hack @interface GGMLMetalClass : NSObject @@ -142,7 +144,8 @@ void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_dat ggml_metal_log_user_data = user_data; } -static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){ +GGML_ATTRIBUTE_FORMAT(2, 3) +static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ if (ggml_metal_log_callback != NULL) { va_list args; va_start(args, format); @@ -210,7 +213,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { } else { GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); - NSString * sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; + NSString * sourcePath; + NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; + if (ggmlMetalPathResources) { + sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"]; + } else { + sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; + } if (sourcePath == nil) { GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__); sourcePath = @"ggml-metal.metal"; @@ -281,6 +290,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(norm); GGML_METAL_ADD_KERNEL(mul_mv_f32_f32); + GGML_METAL_ADD_KERNEL(mul_mv_f16_f16); GGML_METAL_ADD_KERNEL(mul_mv_f16_f32); GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row); GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4); @@ -311,6 +321,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(rope_f32); GGML_METAL_ADD_KERNEL(rope_f16); GGML_METAL_ADD_KERNEL(alibi_f32); + GGML_METAL_ADD_KERNEL(im2col_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f32); GGML_METAL_ADD_KERNEL(cpy_f16_f16); @@ -329,7 +340,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { if ([ctx->device supportsFamily:i]) { - GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - MTLGPUFamilyApple1 + 1, i); + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); break; } } @@ -380,6 +391,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(norm); GGML_METAL_DEL_KERNEL(mul_mv_f32_f32); + GGML_METAL_DEL_KERNEL(mul_mv_f16_f16); GGML_METAL_DEL_KERNEL(mul_mv_f16_f32); GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row); GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4); @@ -410,6 +422,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(rope_f32); GGML_METAL_DEL_KERNEL(rope_f16); GGML_METAL_DEL_KERNEL(alibi_f32); + GGML_METAL_DEL_KERNEL(im2col_f16); GGML_METAL_DEL_KERNEL(cpy_f32_f16); GGML_METAL_DEL_KERNEL(cpy_f32_f32); GGML_METAL_DEL_KERNEL(cpy_f16_f16); @@ -467,6 +480,10 @@ static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru const int64_t tsize = ggml_nbytes(t); + if (t->buffer && t->buffer->backend && t->buffer->backend->context) { + ctx = t->buffer->backend->context; + } + // find the view that contains the tensor fully for (int i = 0; i < ctx->n_buffers; ++i) { const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data; @@ -567,7 +584,7 @@ bool ggml_metal_add_buffer( ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN(", warning: current allocated size is greater than the recommended max working set size\n", __func__); + GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); } else { GGML_METAL_LOG_INFO("\n"); } @@ -1024,7 +1041,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setThreadgroupMemoryLength:MAX(16, nth/32*sizeof(float)) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1133,6 +1150,7 @@ void ggml_metal_graph_compute( switch (src0t) { case GGML_TYPE_F32: { + GGML_ASSERT(src1t == GGML_TYPE_F32); [encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32]; nrows = 4; } break; @@ -1140,13 +1158,18 @@ void ggml_metal_graph_compute( { nth0 = 32; nth1 = 1; - if (ne11 * ne12 < 4) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row]; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4]; - nrows = ne11; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row]; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4]; + nrows = ne11; + } else { + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32]; + nrows = 4; + } } else { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f16]; nrows = 4; } } break; @@ -1336,7 +1359,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -1355,7 +1378,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:MAX(16, nth*sizeof(float)) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -1410,8 +1433,7 @@ void ggml_metal_graph_compute( const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[3]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); @@ -1459,6 +1481,58 @@ void ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_IM2COL: + { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int32_t N = src1->ne[is_2D ? 3 : 2]; + const int32_t IC = src1->ne[is_2D ? 2 : 1]; + const int32_t IH = is_2D ? src1->ne[1] : 1; + const int32_t IW = src1->ne[0]; + + const int32_t KH = is_2D ? src0->ne[1] : 1; + const int32_t KW = src0->ne[0]; + + const int32_t OH = is_2D ? dst->ne[2] : 1; + const int32_t OW = dst->ne[1]; + + const int32_t CHW = IC * KH * KW; + + const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; + const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; + + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_im2col_f16]; break; + default: GGML_ASSERT(false); + }; + + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } break; case GGML_OP_DUP: case GGML_OP_CPY: case GGML_OP_CONT: diff --git a/ggml-metal.metal b/ggml-metal.metal index 7c35f23a7..5d1357cd7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -792,7 +792,7 @@ kernel void kernel_mul_mv_f32_f32( constant int64_t & ne0, constant int64_t & ne1, uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; const int64_t rb = tgpig.y*N_F32_F32; @@ -844,6 +844,79 @@ kernel void kernel_mul_mv_f32_f32( } } +#define N_F16_F16 4 + +kernel void kernel_mul_mv_f16_f16( + device const char * src0, + device const char * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]]) { + + const int64_t r0 = tgpig.x; + const int64_t rb = tgpig.y*N_F16_F16; + const int64_t im = tgpig.z; + + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); + + if (ne00 < 128) { + for (int row = 0; row < N_F16_F16; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (half) x[i] * (half) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F16; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12); + device const half4 * y4 = (device const half4 *) y; + + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (half) x4[i][k] * y4[i][k]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (half) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + } +} + kernel void kernel_mul_mv_f16_f32_1row( device const char * src0, device const char * src1, @@ -1229,6 +1302,39 @@ kernel void kernel_rope( template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope; template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope; +kernel void kernel_im2col_f16( + device const float * x, + device half * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int32_t iiw = tgpig[2] * s0 + tpitg[2] * d0 - p0; + const int32_t iih = tgpig[1] * s1 + tpitg[1] * d1 - p1; + + const int32_t offset_dst = + (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + + (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst[offset_dst] = 0.0f; + } else { + const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; + dst[offset_dst] = x[offset_src + iih * IW + iiw]; + } +} + kernel void kernel_cpy_f16_f16( device const half * src0, device half * dst, diff --git a/ggml-quants.c b/ggml-quants.c index 740be6dc5..a48eda732 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,26 +14,6 @@ // #include -#if !defined(__aarch64__) -inline static int32_t vaddvq_s16(int16x8_t v) { - return - (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + - (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + - (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + - (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); -} - -inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { - int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); - int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); - return vcombine_s16(a0, b0); -} - -inline static int32_t vaddvq_s32(int32x4_t v) { - return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); -} -#endif - #else #ifdef __wasm_simd128__ @@ -47,13 +27,15 @@ inline static int32_t vaddvq_s32(int32x4_t v) { #if defined(_MSC_VER) || defined(__MINGW32__) #include #else -#if !defined(__riscv) && !defined(__s390__) +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) +#if !defined(__riscv) #include #endif #endif #endif #endif #endif +#endif #ifdef __riscv_v_intrinsic #include @@ -61,6 +43,7 @@ inline static int32_t vaddvq_s32(int32x4_t v) { #undef MIN #undef MAX + #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) @@ -283,9 +266,31 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) #if defined(__ARM_NEON) - #if !defined(__aarch64__) +// 64-bit compatibility + +// vaddvq_s16 +// vpaddq_s16 +// vaddvq_s32 +// vaddvq_f32 +// vmaxvq_f32 +// vcvtnq_s32_f32 + +inline static int32_t vaddvq_s16(int16x8_t v) { + return + (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + + (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + + (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + + (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); +} + +inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { + int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); + int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); + return vcombine_s16(a0, b0); +} + inline static int32_t vaddvq_s32(int32x4_t v) { return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); } @@ -311,6 +316,96 @@ inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { return res; } +// vld1q_s16_x2 +// vld1q_u8_x2 +// vld1q_u8_x4 +// vld1q_s8_x2 +// vld1q_s8_x4 +// TODO: double-check these work correctly + +typedef struct ggml_int16x8x2_t { + int16x8_t val[2]; +} ggml_int16x8x2_t; + +inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) { + ggml_int16x8x2_t res; + + res.val[0] = vld1q_s16(ptr + 0); + res.val[1] = vld1q_s16(ptr + 8); + + return res; +} + +typedef struct ggml_uint8x16x2_t { + uint8x16_t val[2]; +} ggml_uint8x16x2_t; + +inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) { + ggml_uint8x16x2_t res; + + res.val[0] = vld1q_u8(ptr + 0); + res.val[1] = vld1q_u8(ptr + 16); + + return res; +} + +typedef struct ggml_uint8x16x4_t { + uint8x16_t val[4]; +} ggml_uint8x16x4_t; + +inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) { + ggml_uint8x16x4_t res; + + res.val[0] = vld1q_u8(ptr + 0); + res.val[1] = vld1q_u8(ptr + 16); + res.val[2] = vld1q_u8(ptr + 32); + res.val[3] = vld1q_u8(ptr + 48); + + return res; +} + +typedef struct ggml_int8x16x2_t { + int8x16_t val[2]; +} ggml_int8x16x2_t; + +inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) { + ggml_int8x16x2_t res; + + res.val[0] = vld1q_s8(ptr + 0); + res.val[1] = vld1q_s8(ptr + 16); + + return res; +} + +typedef struct ggml_int8x16x4_t { + int8x16_t val[4]; +} ggml_int8x16x4_t; + +inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { + ggml_int8x16x4_t res; + + res.val[0] = vld1q_s8(ptr + 0); + res.val[1] = vld1q_s8(ptr + 16); + res.val[2] = vld1q_s8(ptr + 32); + res.val[3] = vld1q_s8(ptr + 48); + + return res; +} + +#else + +#define ggml_int16x8x2_t int16x8x2_t +#define ggml_uint8x16x2_t uint8x16x2_t +#define ggml_uint8x16x4_t uint8x16x4_t +#define ggml_int8x16x2_t int8x16x2_t +#define ggml_int8x16x4_t int8x16x4_t + +#define ggml_vld1q_s16_x2 vld1q_s16_x2 +#define ggml_vld1q_u8_x2 vld1q_u8_x2 +#define ggml_vld1q_u8_x4 vld1q_u8_x4 +#define ggml_vld1q_s8_x2 vld1q_s8_x2 +#define ggml_vld1q_s8_x4 vld1q_s8_x4 + #endif #endif @@ -3557,7 +3652,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t vzero = vdupq_n_s32(0); #endif - int8x16x2_t q2bytes; + ggml_int8x16x2_t q2bytes; uint8_t aux[16]; float sum = 0; @@ -3576,8 +3671,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri vst1q_u8(aux, scales); const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); - const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums); - const int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}; + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); + const ggml_int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}; const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), @@ -3605,7 +3700,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #endif #define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ - q8bytes = vld1q_s8_x2(q8); q8 += 32;\ + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\ q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ MULTIPLY_ACCUM_WITH_SCALE((index)); @@ -3613,9 +3708,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { - const uint8x16x2_t q2bits = vld1q_u8_x2(q2); q2 += 32; + const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; - int8x16x2_t q8bytes = vld1q_s8_x2(q8); q8 += 32; + ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); MULTIPLY_ACCUM_WITH_SCALE(0); @@ -3949,7 +4044,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t vzero = vdupq_n_s32(0); #endif - int8x16x4_t q2bytes; + ggml_int8x16x4_t q2bytes; uint32_t aux32[2]; const uint8_t * scales = (const uint8_t *)aux32; @@ -3974,7 +4069,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t q2bits = vld1q_u8(q2); - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3)); q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3)); @@ -4238,7 +4333,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t m3 = vshlq_n_u8(m0, 3); const int8_t m32 = 32; - int8x16x4_t q3bytes; + ggml_int8x16x4_t q3bytes; float sum = 0; @@ -4250,9 +4345,9 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict qh = x[i].hmask; const int8_t * restrict q8 = y[i].qs; - uint8x16x2_t qhbits = vld1q_u8_x2(qh); + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - uint8x16x4_t q3h; + ggml_uint8x16x4_t q3h; int32_t isum = 0; @@ -4268,9 +4363,9 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { - const uint8x16x2_t q3bits = vld1q_u8_x2(q3); q3 += 32; - const int8x16x4_t q8bytes_1 = vld1q_s8_x4(q8); q8 += 64; - const int8x16x4_t q8bytes_2 = vld1q_s8_x4(q8); q8 += 64; + const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; + const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; + const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); @@ -4772,7 +4867,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t m3b = vdupq_n_u8(0x3); const uint8x16_t mh = vdupq_n_u8(4); - int8x16x4_t q3bytes; + ggml_int8x16x4_t q3bytes; uint16_t aux16[2]; int8_t * scales = (int8_t *)aux16; @@ -4781,11 +4876,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - uint8x16x4_t q3h; + ggml_uint8x16x4_t q3h; const uint8x8_t hbits = vld1_u8(x[i].hmask); const uint8x16_t q3bits = vld1q_u8(x[i].qs); - const int8x16x4_t q8bytes = vld1q_s8_x4(y[i].qs); + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs); const uint16_t a = *(const uint16_t *)x[i].scales; aux16[0] = a & 0x0f0f; @@ -5134,8 +5229,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t mzero = vdupq_n_s32(0); #endif - int8x16x2_t q4bytes; - int8x16x2_t q8bytes; + ggml_int8x16x2_t q4bytes; + ggml_int8x16x2_t q8bytes; float sumf = 0; @@ -5170,17 +5265,17 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/64; ++j) { - const uint8x16x2_t q4bits = vld1q_u8_x2(q4); q4 += 32; + const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; #ifdef __ARM_FEATURE_DOTPROD - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi1 += vaddvq_s32(p1) * scales[2*j+0]; - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); @@ -5188,7 +5283,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri sumi2 += vaddvq_s32(p2) * scales[2*j+1]; #else - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), @@ -5197,7 +5292,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1]))); sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) * scales[2*j+0]; - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), @@ -5512,8 +5607,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri float sumf = 0; - int8x16x2_t q4bytes; - int8x16x4_t q8bytes; + ggml_int8x16x2_t q4bytes; + ggml_int8x16x4_t q8bytes; float sum_mins = 0.f; @@ -5534,10 +5629,10 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const float d = y[i].d * (float)x[i].d[0]; - const uint8x16x2_t q4bits = vld1q_u8_x2(q4); + const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); #ifdef __ARM_FEATURE_DOTPROD - q8bytes = vld1q_s8_x4(q8); + q8bytes = ggml_vld1q_s8_x4(q8); q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); @@ -5551,7 +5646,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; #else - q8bytes = vld1q_s8_x4(q8); + q8bytes = ggml_vld1q_s8_x4(q8); q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), @@ -5785,7 +5880,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t mzero = vdupq_n_s32(0); #endif - int8x16x4_t q5bytes; + ggml_int8x16x4_t q5bytes; float sumf = 0; @@ -5815,16 +5910,16 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict qh = x[i].qh; const int8_t * restrict q8 = y[i].qs; - uint8x16x2_t qhbits = vld1q_u8_x2(qh); + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - uint8x16x4_t q5h; + ggml_uint8x16x4_t q5h; int32_t sumi = 0; for (int j = 0; j < QK_K/64; ++j) { - const uint8x16x2_t q5bits = vld1q_u8_x2(q5); q5 += 32; - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; + const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32; + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -6218,8 +6313,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t mzero = vdupq_n_s32(0); #endif - int8x16x4_t q5bytes; - uint8x16x4_t q5h; + ggml_int8x16x4_t q5bytes; + ggml_uint8x16x4_t q5h; float sumf = 0; @@ -6234,8 +6329,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8x8_t qhbits = vld1_u8(qh); - const uint8x16x2_t q5bits = vld1q_u8_x2(q5); - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); + const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1)); q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4)); @@ -6511,8 +6606,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t mone = vdupq_n_u8(3); - int8x16x4_t q6bytes; - uint8x16x4_t q6h; + ggml_int8x16x4_t q6bytes; + ggml_uint8x16x4_t q6h; for (int i = 0; i < nb; ++i) { @@ -6524,9 +6619,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const int8_t * restrict scale = x[i].scales; - const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums); + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); const int8x16_t scales = vld1q_s8(scale); - const int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}; + const ggml_int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}; const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), @@ -6538,9 +6633,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { - uint8x16x2_t qhbits = vld1q_u8_x2(qh); qh += 32; - uint8x16x4_t q6bits = vld1q_u8_x4(q6); q6 += 64; - int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32; + ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64; + ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -6583,7 +6678,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri scale += 2; #endif - q8bytes = vld1q_s8_x4(q8); q8 += 64; + q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; shifted = vshrq_n_u8(qhbits.val[0], 4); q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); @@ -6987,8 +7082,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t mone = vdupq_n_u8(3); - int8x16x4_t q6bytes; - uint8x16x4_t q6h; + ggml_int8x16x4_t q6bytes; + ggml_uint8x16x4_t q6h; for (int i = 0; i < nb; ++i) { @@ -7002,9 +7097,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri int32_t isum = 0; - uint8x16_t qhbits = vld1q_u8(qh); - uint8x16x2_t q6bits = vld1q_u8_x2(q6); - int8x16x4_t q8bytes = vld1q_s8_x4(q8); + uint8x16_t qhbits = vld1q_u8(qh); + ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6); + ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4); uint8x16_t shifted = vshrq_n_u8(qhbits, 2); diff --git a/ggml.c b/ggml.c index da78e6de9..3202a517b 100644 --- a/ggml.c +++ b/ggml.c @@ -271,6 +271,12 @@ inline static void * ggml_aligned_malloc(size_t size) { // floating point type used to accumulate sums typedef double ggml_float; +#undef MIN +#undef MAX + +#define MIN(a, b) ((a) < (b) ? (a) : (b)) +#define MAX(a, b) ((a) > (b) ? (a) : (b)) + // // global data // @@ -604,6 +610,18 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { // simd mappings // +#if defined(__ARM_NEON) +#if !defined(__aarch64__) + +// 64-bit compatibility + +inline static float vaddvq_f32(float32x4_t v) { + return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); +} + +#endif +#endif + // we define a common set of C macros which map to specific intrinsics based on the current architecture // we then implement the fundamental computation operations below using only these macros // adding support for new architectures requires to define the corresponding SIMD macros @@ -1616,13 +1634,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ROPE_BACK", "ALIBI", "CLAMP", - "CONV_1D", - "CONV_1D_STAGE_0", - "CONV_1D_STAGE_1", "CONV_TRANSPOSE_1D", - "CONV_2D", - "CONV_2D_STAGE_0", - "CONV_2D_STAGE_1", + "IM2COL", "CONV_TRANSPOSE_2D", "POOL_1D", "POOL_2D", @@ -1653,7 +1666,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73"); +static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1703,13 +1716,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "rope_back(x)", "alibi(x)", "clamp(x)", - "conv_1d(x)", - "conv_1d_stage_0(x)", - "conv_1d_stage_1(x)", "conv_transpose_1d(x)", - "conv_2d(x)", - "conv_2d_stage_0(x)", - "conv_2d_stage_1(x)", + "im2col(x)", "conv_transpose_2d(x)", "pool_1d(x)", "pool_2d(x)", @@ -1740,7 +1748,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73"); +static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -1768,13 +1776,7 @@ static void ggml_setup_op_has_task_pass(void) { p[GGML_OP_GET_ROWS_BACK ] = true; p[GGML_OP_DIAG_MASK_INF ] = true; p[GGML_OP_DIAG_MASK_ZERO ] = true; - p[GGML_OP_CONV_1D ] = true; - p[GGML_OP_CONV_1D_STAGE_0 ] = true; - p[GGML_OP_CONV_1D_STAGE_1 ] = true; p[GGML_OP_CONV_TRANSPOSE_1D ] = true; - p[GGML_OP_CONV_2D ] = true; - p[GGML_OP_CONV_2D_STAGE_0 ] = true; - p[GGML_OP_CONV_2D_STAGE_1 ] = true; p[GGML_OP_CONV_TRANSPOSE_2D ] = true; p[GGML_OP_FLASH_ATTN_BACK ] = true; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; @@ -5128,82 +5130,6 @@ static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; } -// im2col: [N, IC, IL] => [N, OL, IC*K] -// a: [OC，IC, K] -// b: [N, IC, IL] -// result: [N, OL, IC*K] -static struct ggml_tensor * ggml_conv_1d_stage_0( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int s0, - int p0, - int d0) { - GGML_ASSERT(a->ne[1] == b->ne[1]); - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t OL = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); - - const int64_t ne[4] = { - a->ne[1] * a->ne[0], - OL, - b->ne[2], - 1, - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); - - int32_t params[] = { s0, p0, d0 }; - ggml_set_op_params(result, params, sizeof(params)); - - result->op = GGML_OP_CONV_1D_STAGE_0; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b; - - return result; -} - -// ggml_conv_1d_stage_1 - -// gemm: [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K] -// a: [OC, IC, K] -// b: [N, OL, IC * K] -// result: [N, OC, OL] -static struct ggml_tensor * ggml_conv_1d_stage_1( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t ne[4] = { - b->ne[1], - a->ne[2], - b->ne[2], - 1, - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - - result->op = GGML_OP_CONV_1D_STAGE_1; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b; - - return result; -} - -// ggml_conv_1d - GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, @@ -5211,44 +5137,18 @@ GGML_API struct ggml_tensor * ggml_conv_1d( int s0, int p0, int d0) { - struct ggml_tensor * result = ggml_conv_1d_stage_0(ctx, a, b, s0, p0, d0); - result = ggml_conv_1d_stage_1(ctx, a, result); + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K] + + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K] + + result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL] + return result; } -// GGML_API struct ggml_tensor * ggml_conv_1d( -// struct ggml_context * ctx, -// struct ggml_tensor * a, -// struct ggml_tensor * b, -// int s0, -// int p0, -// int d0) { -// GGML_ASSERT(ggml_is_matrix(b)); -// GGML_ASSERT(a->ne[1] == b->ne[1]); -// bool is_node = false; - -// if (a->grad || b->grad) { -// GGML_ASSERT(false); // TODO: implement backward -// is_node = true; -// } - -// const int64_t ne[4] = { -// ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), -// a->ne[2], 1, 1, -// }; -// struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - -// int32_t params[] = { s0, p0, d0 }; -// ggml_set_op_params(result, params, sizeof(params)); - -// result->op = GGML_OP_CONV_1D; -// result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; -// result->src[0] = a; -// result->src[1] = b; - -// return result; -// } - // ggml_conv_1d_ph struct ggml_tensor* ggml_conv_1d_ph( @@ -5310,7 +5210,7 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d( // a: [OC，IC, KH, KW] // b: [N, IC, IH, IW] // result: [N, OH, OW, IC*KH*KW] -static struct ggml_tensor * ggml_conv_2d_stage_0( +struct ggml_tensor * ggml_im2col( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -5319,9 +5219,14 @@ static struct ggml_tensor * ggml_conv_2d_stage_0( int p0, int p1, int d0, - int d1) { + int d1, + bool is_2D) { - GGML_ASSERT(a->ne[2] == b->ne[2]); + if(is_2D) { + GGML_ASSERT(a->ne[2] == b->ne[2]); + } else { + GGML_ASSERT(a->ne[1] == b->ne[1]); + } bool is_node = false; if (a->grad || b->grad) { @@ -5329,81 +5234,51 @@ static struct ggml_tensor * ggml_conv_2d_stage_0( is_node = true; } - const int64_t OH = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1); - const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); + const int64_t OH = is_2D ? ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1) : 0; + const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); const int64_t ne[4] = { - a->ne[2] * a->ne[1] * a->ne[0], + is_2D ? (a->ne[2] * a->ne[1] * a->ne[0]) : a->ne[1] * a->ne[0], OW, - OH, - b->ne[3], + is_2D ? OH : b->ne[2], + is_2D ? b->ne[3] : 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); - int32_t params[] = { s0, s1, p0, p1, d0, d1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); + int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_CONV_2D_STAGE_0; + result->op = GGML_OP_IM2COL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; - -} - -// gemm: [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] -// a: [OC, IC, KH, KW] -// b: [N, OH, OW, IC * KH * KW] -// result: [N, OC, OH, OW] -static struct ggml_tensor * ggml_conv_2d_stage_1( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t ne[4] = { - b->ne[1], - b->ne[2], - a->ne[3], - b->ne[3], - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - - result->op = GGML_OP_CONV_2D_STAGE_1; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b; - - return result; - } // a: [OC，IC, KH, KW] // b: [N, IC, IH, IW] // result: [N, OC, OH, OW] struct ggml_tensor * ggml_conv_2d( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int s0, - int s1, - int p0, - int p1, - int d0, - int d1) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW] - struct ggml_tensor * result = ggml_conv_2d_stage_0(ctx, a, b, s0, s1, p0, p1, d0, d1); // [N, OH, OW, IC * KH * KW] - result = ggml_conv_2d_stage_1(ctx, a, result); + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]), a->ne[3])); // [OC，IC, KH, KW] => [OC, IC * KH * KW] + + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], a->ne[3], im2col->ne[3]); // [N, OC, OH, OW] return result; - } // ggml_conv_2d_sk_p0 @@ -9498,6 +9373,8 @@ static bool ggml_compute_forward_mul_mat_use_blas( // TODO: find the optimal values for these if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && + src0->type == GGML_TYPE_F32 && + src1->type == GGML_TYPE_F32 && (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) { /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ @@ -9536,7 +9413,7 @@ static void ggml_compute_forward_mul_mat( // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == sizeof(float)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); @@ -11434,416 +11311,6 @@ static void ggml_compute_forward_rope_back( } } -// ggml_compute_forward_conv_1d - -static void ggml_compute_forward_conv_1d_f16_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00; - - // size of the convolution row - the kernel size unrolled across all input channels - const int ew0 = nk*ne01; - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(params->wdata, 0, params->wsize); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - ggml_fp16_t * dst_data = wdata; - - for (int64_t i0 = 0; i0 < ne0; i0++) { - for (int64_t ik = 0; ik < nk; ik++) { - const int idx0 = i0*s0 + ik*d0 - p0; - - if(!(idx0 < 0 || idx0 >= ne10)) { - dst_data[i0*ew0 + i11*nk + ik] = GGML_FP32_TO_FP16(src[idx0]); - } - } - } - } - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // total rows in dst - const int nr = ne2; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i2*nb2 + i1*nb1); - - for (int i0 = 0; i0 < ne0; i0++) { - ggml_vec_dot_f16(ew0, dst_data + i0, - (ggml_fp16_t *) ((char *) src0->data + i1*nb02), - (ggml_fp16_t *) wdata + i2*nb2 + i0*ew0); - } - } - } -} - -static void ggml_compute_forward_conv_1d_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00; - - const int ew0 = nk*ne01; - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(params->wdata, 0, params->wsize); - - float * const wdata = (float *) params->wdata + 0; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - float * dst_data = wdata; - - for (int64_t i0 = 0; i0 < ne0; i0++) { - for (int64_t ik = 0; ik < nk; ik++) { - const int idx0 = i0*s0 + ik*d0 - p0; - - if(!(idx0 < 0 || idx0 >= ne10)) { - dst_data[i0*ew0 + i11*nk + ik] = src[idx0]; - } - } - } - } - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // total rows in dst - const int nr = ne02; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * const wdata = (float *) params->wdata + 0; - - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i2*nb2 + i1*nb1); - - for (int i0 = 0; i0 < ne0; i0++) { - ggml_vec_dot_f32(ew0, dst_data + i0, - (float *) ((char *) src0->data + i1*nb02), - (float *) wdata + i2*nb2 + i0*ew0); - } - } - } -} - -// TODO: reuse ggml_mul_mat or implement ggml_im2col and remove stage_0 and stage_1 -static void gemm_f16_out_f32(int64_t m, int64_t n, int64_t k, - ggml_fp16_t * A, - ggml_fp16_t * B, - float * C, - const int ith, const int nth) { - // does not seem to make a difference - int64_t m0, m1, n0, n1; - // patches per thread - if (m > n) { - n0 = 0; - n1 = n; - - // total patches in dst - const int np = m; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - m0 = dp*ith; - m1 = MIN(m0 + dp, np); - } else { - m0 = 0; - m1 = m; - - // total patches in dst - const int np = n; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - n0 = dp*ith; - n1 = MIN(n0 + dp, np); - } - - // block-tiling attempt - int64_t blck_n = 16; - int64_t blck_m = 16; - - // int64_t CACHE_SIZE = 2 * 1024 * 1024; // 2MB - // int64_t blck_size = CACHE_SIZE / (sizeof(float) + 2 * sizeof(ggml_fp16_t) * K); - // if (blck_size > 0) { - // blck_0 = 4; - // blck_1 = blck_size / blck_0; - // if (blck_1 < 0) { - // blck_1 = 1; - // } - // // blck_0 = (int64_t)sqrt(blck_size); - // // blck_1 = blck_0; - // } - // // printf("%zd %zd %zd %zd\n", blck_size, K, blck_0, blck_1); - - for (int j = n0; j < n1; j+=blck_n) { - for (int i = m0; i < m1; i+=blck_m) { - // printf("i j k => %d %d %d\n", i, j, K); - for (int ii = i; ii < i + blck_m && ii < m1; ii++) { - for (int jj = j; jj < j + blck_n && jj < n1; jj++) { - ggml_vec_dot_f16(k, - C + ii*n + jj, - A + ii * k, - B + jj * k); - } - } - } - } -} - -// src0: kernel [OC, IC, K] -// src1: signal [N, IC, IL] -// dst: result [N, OL, IC*K] -static void ggml_compute_forward_conv_1d_stage_0_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int64_t N = ne12; - const int64_t IC = ne11; - const int64_t IL = ne10; - - const int64_t K = ne00; - - const int64_t OL = ne1; - - const int ith = params->ith; - const int nth = params->nth; - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(dst->data, 0, ggml_nbytes(dst)); - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // im2col: [N, IC, IL] => [N, OL, IC*K] - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t iol = 0; iol < OL; iol++) { - for (int64_t iic = ith; iic < IC; iic+=nth) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OL + iol)*(IC*K); // [IC, K] - const float * const src_data = (float *)((char *) src1->data + in*nb12 + iic*nb11); // [IL] - - for (int64_t ik = 0; ik < K; ik++) { - const int64_t iil = iol*s0 + ik*d0 - p0; - - if (!(iil < 0 || iil >= IL)) { - dst_data[iic*K + ik] = GGML_FP32_TO_FP16(src_data[iil]); - } - } - } - } - } - } -} - -// gemm: [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K] -// src0: [OC, IC, K] -// src1: [N, OL, IC * K] -// result: [N, OC, OL] -static void ggml_compute_forward_conv_1d_stage_1_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb0 == sizeof(float)); - - const int N = ne12; - const int OL = ne11; - - const int OC = ne02; - const int IC = ne01; - const int K = ne00; - - const int ith = params->ith; - const int nth = params->nth; - - int64_t m = OC; - int64_t n = OL; - int64_t k = IC * K; - - // [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K] - for (int i = 0; i < N; i++) { - ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k] - ggml_fp16_t * B = (ggml_fp16_t *)src1->data + i * m * k; // [n, k] - float * C = (float *)dst->data + i * m * n; // [m, n] - - gemm_f16_out_f32(m, n, k, A, B, C, ith, nth); - } -} - -static void ggml_compute_forward_conv_1d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch(src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_1d_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_1d_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_1d_stage_0( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch(src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_1d_stage_0_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_1d_stage_1( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch(src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_1d_stage_1_f16(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_conv_transpose_1d static void ggml_compute_forward_conv_transpose_1d_f16_f32( @@ -12055,12 +11522,10 @@ static void ggml_compute_forward_conv_transpose_1d( } } -// ggml_compute_forward_conv_2d - // src0: kernel [OC, IC, KH, KW] // src1: image [N, IC, IH, IW] // dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_conv_2d_stage_0_f32( +static void ggml_compute_forward_im2col_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12074,34 +11539,35 @@ static void ggml_compute_forward_conv_2d_stage_0_f32( GGML_TENSOR_BINARY_OP_LOCALS; - const int64_t N = ne13; - const int64_t IC = ne12; - const int64_t IH = ne11; - const int64_t IW = ne10; - - // const int64_t OC = ne03; - // const int64_t IC = ne02; - const int64_t KH = ne01; - const int64_t KW = ne00; - - const int64_t OH = ne2; - const int64_t OW = ne1; + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int ith = params->ith; const int nth = params->nth; - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { - memset(dst->data, 0, ggml_nbytes(dst)); return; } @@ -12114,20 +11580,22 @@ static void ggml_compute_forward_conv_2d_stage_0_f32( ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic+=nth) { + for (int64_t iic = ith; iic < IC; iic += nth) { // micro kernel ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*nb13 + iic*nb12); // [IH, IW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - for (int64_t ikh = 0; ikh < KH; ikh++) { + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 for (int64_t ikw = 0; ikw < KW; ikw++) { const int64_t iiw = iow*s0 + ikw*d0 - p0; const int64_t iih = ioh*s1 + ikh*d1 - p1; - if (!(iih < 0 || iih >= IH || iiw < 0 || iiw >= IW)) { + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); } } @@ -12139,180 +11607,7 @@ static void ggml_compute_forward_conv_2d_stage_0_f32( } } -// gemm: [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] -// src0: [OC, IC, KH, KW] -// src1: [N, OH, OW, IC * KH * KW] -// result: [N, OC, OH, OW] -static void ggml_compute_forward_conv_2d_stage_1_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb0 == sizeof(float)); - - const int N = ne13; - const int OH = ne12; - const int OW = ne11; - - const int OC = ne03; - const int IC = ne02; - const int KH = ne01; - const int KW = ne00; - - const int ith = params->ith; - const int nth = params->nth; - - int64_t m = OC; - int64_t n = OH * OW; - int64_t k = IC * KH * KW; - - // [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] - for (int i = 0; i < N; i++) { - ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k] - ggml_fp16_t * B = (ggml_fp16_t *)src1->data + i * m * k; // [n, k] - float * C = (float *)dst->data + i * m * n; // [m, n] - - gemm_f16_out_f32(m, n, k, A, B, C, ith, nth); - } -} - -static void ggml_compute_forward_conv_2d_f16_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS - - // src1: image [N, IC, IH, IW] - // src0: kernel [OC, IC, KH, KW] - // dst: result [N, OC, OH, OW] - // ne12: IC - // ne0: OW - // ne1: OH - // nk0: KW - // nk1: KH - // ne13: N - - const int N = ne13; - const int IC = ne12; - const int IH = ne11; - const int IW = ne10; - - const int OC = ne03; - // const int IC = ne02; - const int KH = ne01; - const int KW = ne00; - - const int OH = ne1; - const int OW = ne0; - - const int ith = params->ith; - const int nth = params->nth; - - // const int nk0 = ne00; - // const int nk1 = ne01; - - // size of the convolution row - the kernel size unrolled across all channels - // const int ew0 = nk0*nk1*ne02; - // ew0: IC*KH*KW - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(params->wdata, 0, params->wsize); - - // prepare source data (src1) - // im2col: [N, IC, IH, IW] => [N*OH*OW, IC*KH*KW] - - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int in = 0; in < N; in++) { - for (int iic = 0; iic < IC; iic++) { - for (int ioh = 0; ioh < OH; ioh++) { - for (int iow = 0; iow < OW; iow++) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*nb13 + iic*nb12); // [IH, IW] - - for (int ikh = 0; ikh < KH; ikh++) { - for (int ikw = 0; ikw < KW; ikw++) { - const int iiw = iow*s0 + ikw*d0 - p0; - const int iih = ioh*s1 + ikh*d1 - p1; - - if (!(iih < 0 || iih >= IH || iiw < 0 || iiw >= IW)) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - // wdata: [N*OH*OW, IC*KH*KW] - // dst: result [N, OC, OH, OW] - // src0: kernel [OC, IC, KH, KW] - - int64_t m = OC; - int64_t n = OH * OW; - int64_t k = IC * KH * KW; - - // [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] - for (int i = 0; i < N; i++) { - ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k] - ggml_fp16_t * B = (ggml_fp16_t *)wdata + i * m * k; // [n, k] - float * C = (float *)dst->data + i * m * n; // [m * k] - - gemm_f16_out_f32(m, n, k, A, B, C, ith, nth); - } -} - -static void ggml_compute_forward_conv_2d( +static void ggml_compute_forward_im2col( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12320,50 +11615,7 @@ static void ggml_compute_forward_conv_2d( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - //ggml_compute_forward_conv_2d_f32(params, src0, src1, dst); - GGML_ASSERT(false); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_2d_stage_0( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_2d_stage_0_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(false); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_2d_stage_1( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_2d_stage_1_f16(params, src0, src1, dst); + ggml_compute_forward_im2col_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { @@ -14580,33 +13832,13 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_clamp(params, tensor->src[0], tensor); } break; - case GGML_OP_CONV_1D: - { - ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - ggml_compute_forward_conv_1d_stage_0(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - ggml_compute_forward_conv_1d_stage_1(params, tensor->src[0], tensor->src[1], tensor); - } break; case GGML_OP_CONV_TRANSPOSE_1D: { ggml_compute_forward_conv_transpose_1d(params, tensor->src[0], tensor->src[1], tensor); } break; - case GGML_OP_CONV_2D: + case GGML_OP_IM2COL: { - ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - ggml_compute_forward_conv_2d_stage_0(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_2D_STAGE_1: - { - ggml_compute_forward_conv_2d_stage_1(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_im2col(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CONV_TRANSPOSE_2D: { @@ -15588,31 +14820,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_1D: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - GGML_ASSERT(false); // TODO: not implemented - } break; case GGML_OP_CONV_TRANSPOSE_1D: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_2D: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_2D_STAGE_1: + case GGML_OP_IM2COL: { GGML_ASSERT(false); // TODO: not implemented } break; @@ -16341,31 +15553,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = 1; //TODO } break; - case GGML_OP_CONV_1D: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - n_tasks = n_threads; - } break; case GGML_OP_CONV_TRANSPOSE_1D: { n_tasks = n_threads; } break; - case GGML_OP_CONV_2D: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_2D_STAGE_1: + case GGML_OP_IM2COL: { n_tasks = n_threads; } break; @@ -16450,6 +15642,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; default: { + printf("%s: op %s not implemented\n", __func__, ggml_op_name(node->op)); GGML_ASSERT(false); } break; } @@ -16652,38 +15845,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } } break; - case GGML_OP_CONV_1D: - { - GGML_ASSERT(node->src[0]->ne[3] == 1); - GGML_ASSERT(node->src[1]->ne[2] == 1); - GGML_ASSERT(node->src[1]->ne[3] == 1); - - const int64_t ne00 = node->src[0]->ne[0]; - const int64_t ne01 = node->src[0]->ne[1]; - const int64_t ne02 = node->src[0]->ne[2]; - - const int64_t ne10 = node->src[1]->ne[0]; - const int64_t ne11 = node->src[1]->ne[1]; - - const int64_t ne0 = node->ne[0]; - const int64_t ne1 = node->ne[1]; - const int64_t nk = ne00; - const int64_t ew0 = nk * ne01; - - UNUSED(ne02); - UNUSED(ne10); - UNUSED(ne11); - - if (node->src[0]->type == GGML_TYPE_F16 && - node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0); - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*(ne0*ne1*ew0); - } else { - GGML_ASSERT(false); - } - } break; case GGML_OP_CONV_TRANSPOSE_1D: { GGML_ASSERT(node->src[0]->ne[3] == 1); @@ -16709,37 +15870,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { GGML_ASSERT(false); } } break; - case GGML_OP_CONV_2D: + case GGML_OP_IM2COL: { - const int64_t ne00 = node->src[0]->ne[0]; // W - const int64_t ne01 = node->src[0]->ne[1]; // H - const int64_t ne02 = node->src[0]->ne[2]; // C - const int64_t ne03 = node->src[0]->ne[3]; // N - - const int64_t ne10 = node->src[1]->ne[0]; // W - const int64_t ne11 = node->src[1]->ne[1]; // H - const int64_t ne12 = node->src[1]->ne[2]; // C - - const int64_t ne0 = node->ne[0]; - const int64_t ne1 = node->ne[1]; - const int64_t ne2 = node->ne[2]; - const int64_t ne3 = node->ne[3]; - const int64_t nk = ne00*ne01; - const int64_t ew0 = nk * ne02; - - UNUSED(ne03); - UNUSED(ne2); - - if (node->src[0]->type == GGML_TYPE_F16 && - node->src[1]->type == GGML_TYPE_F32) { - // im2col: [N*OH*OW, IC*KH*KW] - cur = sizeof(ggml_fp16_t)*(ne3*ne0*ne1*ew0); - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)* (ne10*ne11*ne12); - } else { - GGML_ASSERT(false); - } + n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_2D: { diff --git a/ggml.h b/ggml.h index 0118c99db..8e6b64606 100644 --- a/ggml.h +++ b/ggml.h @@ -403,13 +403,8 @@ extern "C" { GGML_OP_ROPE_BACK, GGML_OP_ALIBI, GGML_OP_CLAMP, - GGML_OP_CONV_1D, - GGML_OP_CONV_1D_STAGE_0, // internal - GGML_OP_CONV_1D_STAGE_1, // internal GGML_OP_CONV_TRANSPOSE_1D, - GGML_OP_CONV_2D, - GGML_OP_CONV_2D_STAGE_0, // internal - GGML_OP_CONV_2D_STAGE_1, // internal + GGML_OP_IM2COL, GGML_OP_CONV_TRANSPOSE_2D, GGML_OP_POOL_1D, GGML_OP_POOL_2D, @@ -1403,6 +1398,18 @@ extern "C" { float min, float max); + GGML_API struct ggml_tensor * ggml_im2col( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1, + bool is_2D); + GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, From bd90eca237b498dd106d315dcb9ad3e6fae3906f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Mon, 13 Nov 2023 18:20:52 +0300 Subject: [PATCH 066/664] llava : fix regression for square images in #3613 (#4056) --- examples/llava/clip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index c26ee4957..fc0656c23 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -761,7 +761,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip temp->ny = img->ny; temp->size = img->size; temp->data = new uint8_t[temp->size](); - *temp->data = *img->data; // copy + memcpy(&temp->data[0], &img->data[0], temp->size); // copy } const int nx = temp->nx; From b46d12f86d56bef3dc8b596dfb3d22f3b08102be Mon Sep 17 00:00:00 2001 From: afrideva <95653597+afrideva@users.noreply.github.com> Date: Mon, 13 Nov 2023 17:03:40 -0800 Subject: [PATCH 067/664] convert.py: also look for plain model.safetensors (#4043) * add safetensors to convert.py help message * Check for single-file safetensors model * Update convert.py "model" option help message * revert convert.py help message change --- convert.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index a4b87e088..3d6216f1d 100755 --- a/convert.py +++ b/convert.py @@ -1036,7 +1036,8 @@ def load_some_model(path: Path) -> ModelPlus: # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first - files = list(path.glob("model-00001-of-*.safetensors")) + globs = ["model-00001-of-*.safetensors", "model.safetensors"] + files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"] @@ -1123,7 +1124,7 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)") parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY) From 36eed0c42c5b0bf74af81fb9243d262014f9382f Mon Sep 17 00:00:00 2001 From: Galunid Date: Tue, 14 Nov 2023 11:17:12 +0100 Subject: [PATCH 068/664] stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers --- README.md | 1 + convert-hf-to-gguf.py | 30 ++- gguf-py/gguf/constants.py | 17 ++ llama.cpp | 284 +++++++++++++++++++++++- models/ggml-vocab-stablelm-3b-4e1t.gguf | Bin 0 -> 1768581 bytes tests/CMakeLists.txt | 2 + 6 files changed, 322 insertions(+), 12 deletions(-) create mode 100644 models/ggml-vocab-stablelm-3b-4e1t.gguf diff --git a/README.md b/README.md index c7d232778..4de064765 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,7 @@ as the main playground for developing new features for the [ggml](https://github - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) +- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) **Bindings:** diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f7fe29fd4..e7db75912 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -150,8 +150,6 @@ class Model: @staticmethod def from_model_architecture(model_architecture): - if model_architecture == "StableLMEpochForCausalLM": - return StableLMModel if model_architecture == "GPTNeoXForCausalLM": return GPTNeoXModel if model_architecture == "BloomForCausalLM": @@ -168,6 +166,8 @@ class Model: return RefactModel if model_architecture == "PersimmonForCausalLM": return PersimmonModel + if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): + return StableLMModel return Model def _is_model_safetensors(self) -> bool: @@ -201,6 +201,8 @@ class Model: return gguf.MODEL_ARCH.REFACT if arch == "PersimmonForCausalLM": return gguf.MODEL_ARCH.PERSIMMON + if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): + return gguf.MODEL_ARCH.STABLELM raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -294,15 +296,6 @@ class Model: special_vocab.add_to_gguf(self.gguf_writer) -class StableLMModel(Model): - def set_gguf_parameters(self): - super().set_gguf_parameters() - self.gguf_writer.add_rope_dimension_count( - int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), - ) - self.gguf_writer.add_layer_norm_eps(1e-5) - - class GPTNeoXModel(Model): def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] @@ -824,6 +817,21 @@ class PersimmonModel(Model): self.gguf_writer.add_tensor(new_name, data) +class StableLMModel(Model): + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_name(dir_model.name) + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"]*(hparams["hidden_size"] // hparams["num_attention_heads"]))) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) + self.gguf_writer.add_layer_norm_eps(1e-5) + ###### CONVERSION LOGIC ###### def parse_args() -> argparse.Namespace: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index bf1ccf669..7f63361bd 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -90,6 +90,7 @@ class MODEL_ARCH(IntEnum): REFACT = auto() BERT = auto() BLOOM = auto() + STABLELM = auto() class MODEL_TENSOR(IntEnum): @@ -129,6 +130,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.BLOOM: "bloom", + MODEL_ARCH.STABLELM: "stablelm", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -299,6 +301,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.STABLELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.GPT2: [ # TODO ], diff --git a/llama.cpp b/llama.cpp index 76ee4ea23..01522fdb4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -192,6 +192,7 @@ enum llm_arch { LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, LLM_ARCH_BLOOM, + LLM_ARCH_STABLELM, LLM_ARCH_UNKNOWN, }; @@ -207,6 +208,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_PERSIMMON, "persimmon" }, { LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_BLOOM, "bloom" }, + { LLM_ARCH_STABLELM, "stablelm" }, }; enum llm_kv { @@ -495,6 +497,25 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, }, }, + { + LLM_ARCH_STABLELM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, + { LLM_ARCH_UNKNOWN, { @@ -2216,6 +2237,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_STABLELM: + { + GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS)); + + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + default: (void)0; } @@ -3087,6 +3118,81 @@ static void llm_load_tensors( } } } break; + case LLM_ARCH_STABLELM: + { + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + + // output + { + ggml_backend_type backend_norm; + ggml_backend_type backend_output; + + if (n_gpu_layers > int(n_layer)) { + // norm is not performance relevant on its own but keeping it in VRAM reduces data copying + // on Windows however this is detrimental unless everything is on the GPU +#ifndef _WIN32 + backend_norm = llama_backend_offload; +#else + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; +#endif // _WIN32 + + backend_output = llama_backend_offload_split; + } else { + backend_norm = GGML_BACKEND_CPU; + backend_output = GGML_BACKEND_CPU; + } + + model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); + model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); + model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + + if (backend_norm == GGML_BACKEND_GPU) { + vram_weights += ggml_nbytes(model.output_norm); + } + if (backend_output == GGML_BACKEND_GPU_SPLIT) { + vram_weights += ggml_nbytes(model.output); + } + } + + const uint32_t n_ff = hparams.n_ff; + + const int i_gpu_start = n_layer - n_gpu_layers; + + model.layers.resize(n_layer); + + for (uint32_t i = 0; i < n_layer; ++i) { + /* + llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ] + */ + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + + layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); + layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); + layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + + layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + + layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + + if (backend == GGML_BACKEND_GPU) { + vram_weights += + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + + ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); + } + } + } break; + default: throw std::runtime_error("unknown architecture"); } @@ -4565,6 +4671,177 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_stablelm() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(tmpq, "tmpq", il); + + struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(tmpk, "tmpk", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + // RoPE the first n_rot of q/k, pass the other half, and concat. + struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d( + ctx0, tmpq, hparams.n_rot, n_head, n_tokens, + ggml_element_size(tmpq) * n_embd_head, + ggml_element_size(tmpq) * n_embd_head * n_head, + 0 + )); + cb(qrot, "qrot", il); + + struct ggml_tensor * krot = ggml_cont(ctx0, ggml_view_3d( + ctx0, tmpk, hparams.n_rot, n_head, n_tokens, + ggml_element_size(tmpk) * n_embd_head, + ggml_element_size(tmpk) * n_embd_head * n_head_kv, + 0 + )); + cb(krot, "krot", il); + + // get the second half of tmpq, e.g tmpq[n_rot:, :, :] + struct ggml_tensor * qpass = ggml_view_3d( + ctx0, tmpq, (n_embd_head - hparams.n_rot), n_head, n_tokens, + ggml_element_size(tmpq) * n_embd_head, + ggml_element_size(tmpq) * n_embd_head * n_head, + ggml_element_size(tmpq) * hparams.n_rot + ); + cb(qpass, "qpass", il); + + struct ggml_tensor * kpass = ggml_view_3d( + ctx0, tmpk, (n_embd_head - hparams.n_rot), n_head_kv, n_tokens, + ggml_element_size(tmpk) * (n_embd_head), + ggml_element_size(tmpk) * (n_embd_head) * n_head_kv, + ggml_element_size(tmpk) * hparams.n_rot + ); + cb(kpass, "kpass", il); + + struct ggml_tensor * qrotated = ggml_rope_custom( + ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(qrotated, "qrotated", il); + + struct ggml_tensor * krotated = ggml_rope_custom( + ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(krotated, "krotated", il); + + // ggml currently only supports concatenation on dim=2 + // so we need to permute qrot, qpass, concat, then permute back. + qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); + cb(qrotated, "qrotated", il); + + krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); + cb(krotated, "krotated", il); + + qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); + cb(qpass, "qpass", il); + + kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); + cb(kpass, "kpass", il); + + struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); + cb(Q, "Q", il); + + Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, hparams, kv_self, + model.layers[il].wo, NULL, + Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; // @@ -5034,6 +5311,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_mpt(); } break; + case LLM_ARCH_STABLELM: + { + result = llm.build_stablelm(); + } break; default: GGML_ASSERT(false); } @@ -5209,7 +5490,8 @@ static int llama_decode_internal( model.arch == LLM_ARCH_FALCON || model.arch == LLM_ARCH_REFACT || model.arch == LLM_ARCH_MPT || - model.arch == LLM_ARCH_STARCODER; + model.arch == LLM_ARCH_STARCODER || + model.arch == LLM_ARCH_STABLELM; const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3; if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) { diff --git a/models/ggml-vocab-stablelm-3b-4e1t.gguf b/models/ggml-vocab-stablelm-3b-4e1t.gguf new file mode 100644 index 0000000000000000000000000000000000000000..ebb0cdb7d6a4ac313f45758010a6fda6ac530443 GIT binary patch literal 1768581 zcmd?S`IlVRapx(|zV`IYnYE{u+BB*FU1ae|XgVPmlh)TaW8$H9T8Q`+Lp2?#~y~`rqL<``7+v^Qt$jhok>s z_3}x__ixzGy!&50`&d2d z)q_DZ-pvpGdms6T&o}z(4cq>shy8Xjo?B->$-n>j+0J(Adhl?ko$goDLGJIr{>VrE 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` diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 39d1e83d1..5f93dcb66 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -36,6 +36,7 @@ using json = nlohmann::json; struct server_params { std::string hostname = "127.0.0.1"; + std::string api_key; std::string public_path = "examples/server/public"; int32_t port = 8080; int32_t read_timeout = 600; @@ -1953,6 +1954,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); + printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); @@ -2002,6 +2004,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } sparams.public_path = argv[i]; } + else if (arg == "--api-key") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + sparams.api_key = argv[i]; + } else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) @@ -2669,6 +2680,32 @@ int main(int argc, char **argv) httplib::Server svr; + // Middleware for API key validation + auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { + // If API key is not set, skip validation + if (sparams.api_key.empty()) { + return true; + } + + // Check for API key in the header + auto auth_header = req.get_header_value("Authorization"); + std::string prefix = "Bearer "; + if (auth_header.substr(0, prefix.size()) == prefix) { + std::string received_api_key = auth_header.substr(prefix.size()); + if (received_api_key == sparams.api_key) { + return true; // API key is valid + } + } + + // API key is invalid or not provided + res.set_content("Unauthorized: Invalid API Key", "text/plain"); + res.status = 401; // Unauthorized + + LOG_WARNING("Unauthorized: Invalid API Key", {}); + + return false; + }; + svr.set_default_headers({{"Server", "llama.cpp"}, {"Access-Control-Allow-Origin", "*"}, {"Access-Control-Allow-Headers", "content-type"}}); @@ -2711,8 +2748,11 @@ int main(int argc, char **argv) res.set_content(data.dump(), "application/json"); }); - svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res) + svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { + if (!validate_api_key(req, res)) { + return; + } json data = json::parse(req.body); const int task_id = llama.request_completion(data, false, false, -1); if (!json_value(data, "stream", false)) { @@ -2799,8 +2839,11 @@ int main(int argc, char **argv) }); // TODO: add mount point without "/v1" prefix -- how? - svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res) + svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { + if (!validate_api_key(req, res)) { + return; + } json data = oaicompat_completion_params_parse(json::parse(req.body)); const int task_id = llama.request_completion(data, false, false, -1); @@ -2869,8 +2912,11 @@ int main(int argc, char **argv) } }); - svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) + svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { + if (!validate_api_key(req, res)) { + return; + } json data = json::parse(req.body); const int task_id = llama.request_completion(data, true, false, -1); if (!json_value(data, "stream", false)) { @@ -3005,11 +3051,15 @@ int main(int argc, char **argv) svr.set_error_handler([](const httplib::Request &, httplib::Response &res) { + if (res.status == 401) + { + res.set_content("Unauthorized", "text/plain"); + } if (res.status == 400) { res.set_content("Invalid request", "text/plain"); } - else if (res.status != 500) + else if (res.status == 404) { res.set_content("File Not Found", "text/plain"); res.status = 404; @@ -3032,11 +3082,15 @@ int main(int argc, char **argv) // to make it ctrl+clickable: LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); - LOG_INFO("HTTP server listening", { - {"hostname", sparams.hostname}, - {"port", sparams.port}, - }); + std::unordered_map log_data; + log_data["hostname"] = sparams.hostname; + log_data["port"] = std::to_string(sparams.port); + if (!sparams.api_key.empty()) { + log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4); + } + + LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() { From 8a5be3bd5885d79ad84aadf32bb8c1a67bd43c19 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Fri, 15 Dec 2023 22:16:15 -0500 Subject: [PATCH 197/664] llama : sanity checks for access to logits (#4274) Co-authored-by: Georgi Gerganov --- llama.cpp | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/llama.cpp b/llama.cpp index eddb70859..58fe7492e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1505,6 +1505,10 @@ struct llama_context { // decode output (2-dimensional array: [n_tokens][n_vocab]) std::vector logits; +#ifndef NDEBUG + // guard against access to unset logits + std::vector logits_valid; +#endif bool logits_all = false; // input embedding (1-dimensional array: [n_embd]) @@ -6150,6 +6154,14 @@ static int llama_decode_internal( { auto & logits_out = lctx.logits; +#ifndef NDEBUG + auto & logits_valid = lctx.logits_valid; + logits_valid.clear(); + logits_valid.resize(n_tokens); + + logits_out.clear(); +#endif + if (batch.logits) { logits_out.resize(n_vocab * n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { @@ -6157,13 +6169,22 @@ static int llama_decode_internal( continue; } memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab); +#ifndef NDEBUG + logits_valid[i] = true; +#endif } } else if (lctx.logits_all) { logits_out.resize(n_vocab * n_tokens); memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens); +#ifndef NDEBUG + std::fill(logits_valid.begin(), logits_valid.end(), true); +#endif } else { logits_out.resize(n_vocab); memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab); +#ifndef NDEBUG + logits_valid[n_tokens - 1] = true; +#endif } } @@ -10052,6 +10073,7 @@ float * llama_get_logits(struct llama_context * ctx) { } float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { + assert(ctx->logits_valid.at(i)); return ctx->logits.data() + i*ctx->model.hparams.n_vocab; } From c6c4fc081c1df1c60a9bfe3e6a3fd086f1a29ec7 Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 16 Dec 2023 18:58:46 +0100 Subject: [PATCH 198/664] lora : add support for non-llama models (#3333) * lora : add support for non-llama models ggml-ci * avoid leaking ggml_context on failure cleanup ggml-ci * lora : allow 1d tensors * lora : include embd and output layers in size calculation * fix style --- convert-lora-to-ggml.py | 86 +++++++++++++------------- llama.cpp | 133 ++++++++++++++++++++-------------------- llama.h | 1 + 3 files changed, 114 insertions(+), 106 deletions(-) diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index a937410dd..53bb8a3d9 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -3,7 +3,6 @@ from __future__ import annotations import json import os -import re import struct import sys from typing import Any, BinaryIO, Sequence @@ -11,43 +10,15 @@ from typing import Any, BinaryIO, Sequence import numpy as np import torch +from pathlib import Path +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + + NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} -HF_SUBLAYER_TO_GGML = { - "self_attn.q_proj": "attn_q", - "self_attn.k_proj": "attn_k", - "self_attn.v_proj": "attn_v", - "self_attn.o_proj": "attn_output", - "mlp.gate_proj": "ffn_gate", - "mlp.down_proj": "ffn_down", - "mlp.up_proj": "ffn_up", - "input_layernorm": "attn_norm", - "post_attention_layernorm": "ffn_norm", -} - - -def translate_tensor_name(t: str) -> str: - match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t) - if match: - nn = match.group(1) - sub_layer = match.group(2) - lora_type = match.group(3) - - sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer) - if sub_layer_renamed is None: - print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}") - sys.exit(1) - - output_string = ( - f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}" - ) - return output_string - else: - print(f"Error: unrecognized tensor {t}") - sys.exit(1) - - def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: fout.write(b"ggla"[::-1]) # magic (ggml lora) fout.write(struct.pack("i", 1)) # file version @@ -61,9 +32,7 @@ def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: fout.write(struct.pack("i", int(params["lora_alpha"]))) -def write_tensor_header( - self, name: str, shape: Sequence[int], data_type: np.dtype[Any] -) -> None: +def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None: sname = name.encode("utf-8") fout.write( struct.pack( @@ -78,11 +47,12 @@ def write_tensor_header( fout.seek((fout.tell() + 31) & -32) -if len(sys.argv) != 2: - print(f"Usage: python {sys.argv[0]} ") +if len(sys.argv) < 2: + print(f"Usage: python {sys.argv[0]} [arch]") print( "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" ) + print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") sys.exit(1) input_json = os.path.join(sys.argv[1], "adapter_config.json") @@ -90,6 +60,14 @@ input_model = os.path.join(sys.argv[1], "adapter_model.bin") output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") model = torch.load(input_model, map_location="cpu") +arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" + +if arch_name not in gguf.MODEL_ARCH_NAMES.values(): + print(f"Error: unsupported architecture {arch_name}") + sys.exit(1) + +arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] +name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone with open(input_json, "r") as f: params = json.load(f) @@ -117,6 +95,7 @@ with open(output_path, "wb") as fout: write_file_header(fout, params) for k, v in model.items(): + orig_k = k if k.endswith(".default.weight"): k = k.replace(".default.weight", ".weight") if k in ["llama_proj.weight", "llama_proj.bias"]: @@ -129,7 +108,32 @@ with open(output_path, "wb") as fout: v = v.float() t = v.detach().numpy() - tname = translate_tensor_name(k) + + prefix = "base_model.model." + if k.startswith(prefix): + k = k[len(prefix) :] + + lora_suffixes = (".lora_A.weight", ".lora_B.weight") + if k.endswith(lora_suffixes): + suffix = k[-len(lora_suffixes[0]):] + k = k[: -len(lora_suffixes[0])] + else: + print(f"Error: unrecognized tensor name {orig_k}") + sys.exit(1) + + tname = name_map.get_name(k) + if tname is None: + print(f"Error: could not map tensor name {orig_k}") + print(" Note: the arch parameter must be specified if the model is not llama") + sys.exit(1) + + if suffix == ".lora_A.weight": + tname += ".weight.loraA" + elif suffix == ".lora_B.weight": + tname += ".weight.loraB" + else: + assert False + print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") write_tensor_header(fout, tname, t.shape, t.dtype) t.tofile(fout) diff --git a/llama.cpp b/llama.cpp index 58fe7492e..f49214c13 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8647,53 +8647,60 @@ static int llama_apply_lora_from_file_internal( const int64_t t_start_lora_us = ggml_time_us(); - auto fin = std::ifstream(path_lora, std::ios::binary); - if (!fin) { - LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora); - return 1; - } + llama_file fin(path_lora, "rb"); // verify magic and version { - uint32_t magic; - fin.read((char *) &magic, sizeof(magic)); - uint32_t format_version; - fin.read((char *) &format_version, sizeof(format_version)); + uint32_t magic = fin.read_u32(); + if (magic != LLAMA_FILE_MAGIC_GGLA) { + LLAMA_LOG_ERROR("%s: bad file magic\n", __func__); + return 1; + } + uint32_t format_version = fin.read_u32(); if (format_version != 1) { LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ ); return 1; } } - int32_t lora_r; - int32_t lora_alpha; - fin.read((char *) &lora_r, sizeof(lora_r)); - fin.read((char *) &lora_alpha, sizeof(lora_alpha)); + int32_t lora_r = fin.read_u32(); + int32_t lora_alpha = fin.read_u32(); float scaling = scale * (float)lora_alpha / (float)lora_r; LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling); + // create a name -> tensor map of the model to accelerate lookups + // find the max tensor size to estimate the required temporary buffer size + size_t max_tensor_size = 0; + std::unordered_map model_tensors; + for (const auto & kv : model.tensors_by_name) { + model_tensors.insert(kv); + size_t f32_size = ggml_nelements(kv.second) * sizeof(float); + max_tensor_size = std::max(max_tensor_size, f32_size); + } + // create a temporary ggml context to store the lora tensors - // todo: calculate size from biggest possible tensor - std::vector lora_buf(1024ull * 1024ull * 1024ull); + // TODO: use ggml-alloc + size_t lora_ctx_size = max_tensor_size * 3; + LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0); + std::vector lora_buf(lora_ctx_size); + struct ggml_init_params params; params.mem_size = lora_buf.size(); params.mem_buffer = lora_buf.data(); params.no_alloc = false; - ggml_context * lora_ctx = ggml_init(params); - std::unordered_map lora_tensors; + using unique_context = std::unique_ptr; - // create a name -> tensor map of the model to accelerate lookups - std::unordered_map model_tensors; - for (const auto & kv : model.tensors_by_name) { - model_tensors.insert(kv); - } + unique_context lora_ctx(nullptr, ggml_free); + lora_ctx.reset(ggml_init(params)); + std::unordered_map lora_tensors; // load base model std::unique_ptr ml; - ggml_context * base_ctx = NULL; + + unique_context base_ctx(nullptr, ggml_free); std::vector base_buf; if (path_base_model) { LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); @@ -8702,6 +8709,7 @@ static int llama_apply_lora_from_file_internal( size_t ctx_size; size_t mmapped_size; ml->calc_sizes(ctx_size, mmapped_size); + base_buf.resize(ctx_size); ggml_init_params base_params; @@ -8709,9 +8717,9 @@ static int llama_apply_lora_from_file_internal( base_params.mem_buffer = base_buf.data(); base_params.no_alloc = ml->use_mmap; - base_ctx = ggml_init(base_params); + base_ctx.reset(ggml_init(base_params)); - // maybe this should in llama_model_loader + // maybe this should be in llama_model_loader if (ml->use_mmap) { ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa())); } @@ -8724,27 +8732,35 @@ static int llama_apply_lora_from_file_internal( std::vector work_buffer; while (true) { + if (fin.tell() == fin.size) { + // eof + break; + } + int32_t n_dims; - int32_t length; + int32_t name_len; int32_t ftype; - fin.read(reinterpret_cast(&n_dims), sizeof(n_dims)); - fin.read(reinterpret_cast(&length), sizeof(length)); - fin.read(reinterpret_cast(&ftype), sizeof(ftype)); - if (fin.eof()) { - break; + fin.read_raw(&n_dims, sizeof(n_dims)); + fin.read_raw(&name_len, sizeof(name_len)); + fin.read_raw(&ftype, sizeof(ftype)); + + if (n_dims != 1 && n_dims != 2) { + LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims); + return 1; } int32_t ne[2] = { 1, 1 }; for (int i = 0; i < n_dims; ++i) { - fin.read(reinterpret_cast(&ne[i]), sizeof(ne[i])); + fin.read_raw(&ne[i], sizeof(ne[i])); } std::string name; { + GGML_ASSERT(name_len <= 1024); char buf[1024]; - fin.read(buf, length); - name = std::string(buf, length); + fin.read_raw(buf, name_len); + name = std::string(buf, name_len); } // check for lora suffix and get the type of tensor @@ -8758,7 +8774,7 @@ static int llama_apply_lora_from_file_internal( std::string lora_type = name.substr(pos + lora_suffix.length()); std::string base_name = name; base_name.erase(pos); - // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str()); + // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str()); if (model_tensors.find(base_name) == model_tensors.end()) { LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data()); @@ -8777,22 +8793,15 @@ static int llama_apply_lora_from_file_internal( return false; } } - ggml_tensor * lora_tensor; - if (n_dims == 2) { - lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]); - } - else { - LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims); - return 1; - } - ggml_set_name(lora_tensor, "lora_tensor"); + ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]); + ggml_set_name(lora_tensor, name.c_str()); // load tensor data - size_t offset = fin.tellg(); + size_t offset = fin.tell(); size_t tensor_data_size = ggml_nbytes(lora_tensor); offset = (offset + 31) & -32; - fin.seekg(offset); - fin.read((char*)lora_tensor->data, tensor_data_size); + fin.seek(offset, SEEK_SET); + fin.read_raw(lora_tensor->data, tensor_data_size); lora_tensors[name] = lora_tensor; @@ -8822,13 +8831,11 @@ static int llama_apply_lora_from_file_internal( // load from base model if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) { - // TODO: throw LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); return 1; } - // TODO: not tested!! maybe not working! - base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU); + base_t = ml->create_tensor(base_ctx.get(), base_name, { dest_t->ne[0], dest_t->ne[1] }, GGML_BACKEND_CPU); ml->load_data_for(base_t); } else { base_t = dest_t; @@ -8857,43 +8864,45 @@ static int llama_apply_lora_from_file_internal( } // w = w + BA*s - ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB); + ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB); offload_func(BA); ggml_set_name(BA, "BA"); if (scaling != 1.0f) { - ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling); + ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx.get(), scaling); ggml_set_name(scale_tensor, "scale_tensor"); - BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor); + BA = ggml_scale_inplace(lora_ctx.get(), BA, scale_tensor); offload_func(BA); ggml_set_name(BA, "BA_scaled"); } ggml_tensor * r; if (base_t == dest_t) { - r = ggml_add_inplace(lora_ctx, dest_t, BA); + r = ggml_add_inplace(lora_ctx.get(), dest_t, BA); offload_func_force_inplace(r); ggml_set_name(r, "r_add_inplace"); } else { - r = ggml_add(lora_ctx, base_t, BA); + r = ggml_add(lora_ctx.get(), base_t, BA); offload_func(r); ggml_set_name(r, "r_add"); - r = ggml_cpy(lora_ctx, r, dest_t); + r = ggml_cpy(lora_ctx.get(), r, dest_t); offload_func(r); ggml_set_name(r, "r_cpy"); } - struct ggml_cgraph * gf = ggml_new_graph(lora_ctx); + struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get()); ggml_build_forward_expand(gf, r); ggml_graph_compute_helper(work_buffer, gf, n_threads); + // the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other + GGML_ASSERT(lora_tensors.size() == 2); + // we won't need these tensors again, reset the context to save memory - ggml_free(lora_ctx); - lora_ctx = ggml_init(params); + lora_ctx.reset(ggml_init(params)); lora_tensors.clear(); n_tensors++; @@ -8903,12 +8912,6 @@ static int llama_apply_lora_from_file_internal( } } - // TODO: this should be in a destructor, it will leak on failure - ggml_free(lora_ctx); - if (base_ctx) { - ggml_free(base_ctx); - } - const int64_t t_lora_us = ggml_time_us() - t_start_lora_us; LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0); diff --git a/llama.h b/llama.h index 45a65cacb..15ab4f80e 100644 --- a/llama.h +++ b/llama.h @@ -39,6 +39,7 @@ #define LLAMA_MAX_RNG_STATE (64*1024) +#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN From 5daa5f54fdcd2b5228add1a4c43a1897b2168f35 Mon Sep 17 00:00:00 2001 From: Bach Le Date: Sun, 17 Dec 2023 18:57:33 +0800 Subject: [PATCH 199/664] Link to cublas dynamically on Windows even with LLAMA_STATIC (#4506) --- CMakeLists.txt | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 57b43c136..e3cd43ab3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -291,7 +291,12 @@ if (LLAMA_CUBLAS) add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE}) if (LLAMA_STATIC) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) + if (WIN32) + # As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt) + else () + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) + endif() else() set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt) endif() From 62bd52b7bf90819e75f427a95a484cd5eee0b3c7 Mon Sep 17 00:00:00 2001 From: mzcu Date: Sun, 17 Dec 2023 15:54:37 +0100 Subject: [PATCH 200/664] server : allow requests larger than 8K (#4500) --- examples/server/server.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5f93dcb66..a9f8b3747 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -10,7 +10,8 @@ // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 #endif - +// increase max payload length to allow use of larger context size +#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576 #include "httplib.h" #include "json.hpp" From eb16dae7e70ca97396190698b29c0f9ee3388e88 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sun, 17 Dec 2023 14:56:09 +0000 Subject: [PATCH 201/664] server : fix possible ambiguity in content type charset (#4501) --- examples/server/server.cpp | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a9f8b3747..be7b5b95e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2699,7 +2699,7 @@ int main(int argc, char **argv) } // API key is invalid or not provided - res.set_content("Unauthorized: Invalid API Key", "text/plain"); + res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); res.status = 401; // Unauthorized LOG_WARNING("Unauthorized: Invalid API Key", {}); @@ -2714,28 +2714,28 @@ int main(int argc, char **argv) // this is only called if no index.html is found in the public --path svr.Get("/", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); return false; }); // this is only called if no index.js is found in the public --path svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); + res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); return false; }); // this is only called if no index.html is found in the public --path svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); return false; }); // this is only called if no index.html is found in the public --path svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); + res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); return false; }); @@ -2746,7 +2746,7 @@ int main(int argc, char **argv) { "user_name", llama.name_user.c_str() }, { "assistant_name", llama.name_assistant.c_str() } }; - res.set_content(data.dump(), "application/json"); + res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) @@ -2760,12 +2760,12 @@ int main(int argc, char **argv) std::string completion_text; task_result result = llama.next_result(task_id); if (!result.error && result.stop) { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); + res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 404; - res.set_content(result.result_json["content"], "text/plain"); + res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); return; } } else { @@ -2836,7 +2836,7 @@ int main(int argc, char **argv) }} }; - res.set_content(models.dump(), "application/json"); + res.set_content(models.dump(), "application/json; charset=utf-8"); }); // TODO: add mount point without "/v1" prefix -- how? @@ -2858,10 +2858,10 @@ int main(int argc, char **argv) res.set_content(oaicompat_result.dump(-1, ' ', false, json::error_handler_t::replace), - "application/json"); + "application/json; charset=utf-8"); } else { res.status = 500; - res.set_content(result.result_json["content"], "text/plain"); + res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); return; } } else { @@ -2925,12 +2925,12 @@ int main(int argc, char **argv) task_result result = llama.next_result(task_id); if (!result.error && result.stop) { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); + res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 404; - res.set_content(result.result_json["content"], "text/plain"); + res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); return; } } else { @@ -2979,11 +2979,11 @@ int main(int argc, char **argv) svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) { const json data = llama.get_model_props(); - return res.set_content(data.dump(), "application/json"); + return res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) - { return res.set_content("", "application/json"); }); + { return res.set_content("", "application/json; charset=utf-8"); }); svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) { @@ -2994,7 +2994,7 @@ int main(int argc, char **argv) tokens = llama.tokenize(body["content"], false); } const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json"); + return res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) @@ -3008,7 +3008,7 @@ int main(int argc, char **argv) } const json data = format_detokenized_response(content); - return res.set_content(data.dump(), "application/json"); + return res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) @@ -3025,7 +3025,7 @@ int main(int argc, char **argv) } const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1); task_result result = llama.next_result(task_id); - return res.set_content(result.result_json.dump(), "application/json"); + return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); svr.set_logger(log_server_request); @@ -3046,7 +3046,7 @@ int main(int argc, char **argv) { snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); } - res.set_content(buf, "text/plain"); + res.set_content(buf, "text/plain; charset=utf-8"); res.status = 500; }); @@ -3054,15 +3054,15 @@ int main(int argc, char **argv) { if (res.status == 401) { - res.set_content("Unauthorized", "text/plain"); + res.set_content("Unauthorized", "text/plain; charset=utf-8"); } if (res.status == 400) { - res.set_content("Invalid request", "text/plain"); + res.set_content("Invalid request", "text/plain; charset=utf-8"); } else if (res.status == 404) { - res.set_content("File Not Found", "text/plain"); + res.set_content("File Not Found", "text/plain; charset=utf-8"); res.status = 404; } }); From 8edd2b40fdbcafbf630f2cf29306b29d5cb48c42 Mon Sep 17 00:00:00 2001 From: AdithyanI Date: Sun, 17 Dec 2023 15:57:56 +0100 Subject: [PATCH 202/664] server : fix grammar being ignored (#4494) Fix bug in identifying the grammar. --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index be7b5b95e..c97efe97d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2414,7 +2414,7 @@ json oaicompat_completion_params_parse( llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0); - if (llama_params.count("grammar") != 0) { + if (body.count("grammar") != 0) { llama_params["grammar"] = json_value(body, "grammar", json::object()); } From 0ffc92d2d23a789625f018840469af045be1e3c0 Mon Sep 17 00:00:00 2001 From: olexiyb Date: Sun, 17 Dec 2023 17:02:16 +0200 Subject: [PATCH 203/664] server : disable llm logs if SERVER_VERBOSE is off (#3792) --- examples/server/server.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c97efe97d..04038530f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2645,6 +2645,9 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con int main(int argc, char **argv) { +#if SERVER_VERBOSE != 1 + log_disable(); +#endif // own arguments required by this example gpt_params params; server_params sparams; From 45668633fdb522a925c3dafc1ecf426f539efb27 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 17 Dec 2023 16:05:56 +0100 Subject: [PATCH 204/664] finetune : keep allocs alive until all allocations are done (#4486) --- examples/finetune/finetune.cpp | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index b9849e8c9..6a668d764 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1620,8 +1620,6 @@ int main(int argc, char ** argv) { opt->params.adam.gclip = params.common.adam_gclip; opt->params.adam.eps_f = params.common.adam_eps_f; - ggml_allocr * alloc = NULL; - printf("%s: init model\n", __func__); bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train); @@ -1725,10 +1723,9 @@ int main(int argc, char ** argv) { // allocate input tensors mem_input_data.resize(max_input_size); - alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment); - ggml_allocr_alloc(alloc, tokens_input); - ggml_allocr_alloc(alloc, target_probs); - ggml_allocr_free(alloc); + ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment); + ggml_allocr_alloc(alloc_inps, tokens_input); + ggml_allocr_alloc(alloc_inps, target_probs); // context for compute tensors without their data const size_t estimated_compute_size_wo_data = ( @@ -1755,7 +1752,7 @@ int main(int argc, char ** argv) { // find best evaluation order for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); - alloc = ggml_allocr_new_measure(tensor_alignment); + ggml_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); @@ -1788,7 +1785,7 @@ int main(int argc, char ** argv) { // allocate compute tensors mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); - alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); + ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); @@ -1804,6 +1801,8 @@ int main(int argc, char ** argv) { params.common.use_checkpointing ); ggml_allocr_free(alloc); + ggml_allocr_free(alloc_inps); + // tokenize data std::vector train_tokens; From 919c40660fd27157b391b5832d2a577d5afef4cb Mon Sep 17 00:00:00 2001 From: Matheus Gabriel Alves Silva Date: Sun, 17 Dec 2023 12:23:33 -0300 Subject: [PATCH 205/664] build : Check the ROCm installation location (#4485) * build : Check the ROCm installation location * more generic approach * fixup! It was returning the path instead of the command output * fixup! Trailing whitespace --- Makefile | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index fb775ae5b..8273f8400 100644 --- a/Makefile +++ b/Makefile @@ -439,9 +439,15 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h endif # LLAMA_CLBLAST ifdef LLAMA_HIPBLAS - ROCM_PATH ?= /opt/rocm - HIPCC ?= $(ROCM_PATH)/bin/hipcc - GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) + + ifeq ($(wildcard /opt/rocm),) + ROCM_PATH ?= /usr + GPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) + else + ROCM_PATH ?= /opt/rocm + GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) + endif + HIPCC ?= $(ROCM_PATH)/bin/hipcc LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 From f7f468a97dceec2f8fe8b1ed7a2091083446ebc7 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sun, 17 Dec 2023 10:45:46 -0500 Subject: [PATCH 206/664] gguf-py : fail fast on nonsensical special token IDs (#4489) --- gguf-py/gguf/vocab.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index de3e5edb5..76924d8f2 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -109,8 +109,10 @@ class SpecialVocab: return True def _set_special_token(self, typ: str, tid: Any) -> None: - if not isinstance(tid, int) or tid < 0: + if not isinstance(tid, int): return + if tid < 0: + raise ValueError(f'invalid value for special token type {typ}: {tid}') if self.n_vocab is None or tid < self.n_vocab: if typ in self.special_token_ids: return From 800a489e4a8be199122259a995b1ee9dd7fae320 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 17 Dec 2023 19:38:41 +0200 Subject: [PATCH 207/664] llama.swiftui : add bench functionality (#4483) * llama.swiftui : add bench button * llama.swiftui : initial bench functionality * force to use n_gpu_layers on simulator * add download buttons & expose llamaState.loadModel * update project.pbxproj * comment #Preview & fix editorconfig check * gitignore : xcode stuff * llama.swiftui : UX improvements * llama.swiftui : avoid data copy via "downloadTask" * llama.swiftui : remove model from project * llama : remove "mostly" from model infos * llama.swiftui : improve bench --------- Co-authored-by: jhen --- .editorconfig | 3 + examples/llama.swiftui/.gitignore | 1 + .../llama.cpp.swift/LibLlama.swift | 182 +++- .../llama.swiftui.xcodeproj/project.pbxproj | 898 +++++++++--------- .../llama.swiftui/Models/LlamaState.swift | 52 +- .../llama.swiftui/UI/ContentView.swift | 114 ++- .../llama.swiftui/UI/DownloadButton.swift | 122 +++ llama.cpp | 33 +- 8 files changed, 895 insertions(+), 510 deletions(-) create mode 100644 examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift diff --git a/.editorconfig b/.editorconfig index a56e9ccc8..16d16b3b5 100644 --- a/.editorconfig +++ b/.editorconfig @@ -23,3 +23,6 @@ insert_final_newline = unset [examples/server/public/*] indent_size = 2 + +[examples/llama.swiftui/llama.swiftui.xcodeproj/*] +indent_style = tab diff --git a/examples/llama.swiftui/.gitignore b/examples/llama.swiftui/.gitignore index 9bce6af39..e585a2a4f 100644 --- a/examples/llama.swiftui/.gitignore +++ b/examples/llama.swiftui/.gitignore @@ -1 +1,2 @@ xcuserdata +xcshareddata diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 3754f0551..272e1fd8a 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -6,16 +6,34 @@ enum LlamaError: Error { case couldNotInitializeContext } +func llama_batch_clear(_ batch: inout llama_batch) { + batch.n_tokens = 0 +} + +func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama_pos, _ seq_ids: [llama_seq_id], _ logits: Bool) { + batch.token [Int(batch.n_tokens)] = id + batch.pos [Int(batch.n_tokens)] = pos + batch.n_seq_id[Int(batch.n_tokens)] = Int32(seq_ids.count) + for i in 0.. LlamaContext { + static func create_context(path: String) throws -> LlamaContext { llama_backend_init(false) - let model_params = llama_model_default_params() + var model_params = llama_model_default_params() +#if targetEnvironment(simulator) + model_params.n_gpu_layers = 0 + print("Running on simulator, force use n_gpu_layers = 0") +#endif let model = llama_load_model_from_file(path, model_params) guard let model else { print("Could not load model at \(path)") throw LlamaError.couldNotInitializeContext } + + let n_threads = max(1, min(8, ProcessInfo.processInfo.processorCount - 2)) + print("Using \(n_threads) threads") + var ctx_params = llama_context_default_params() - ctx_params.seed = 1234 + ctx_params.seed = 1234 ctx_params.n_ctx = 2048 - ctx_params.n_threads = 8 - ctx_params.n_threads_batch = 8 + ctx_params.n_threads = UInt32(n_threads) + ctx_params.n_threads_batch = UInt32(n_threads) let context = llama_new_context_with_model(model, ctx_params) guard let context else { @@ -56,6 +83,26 @@ actor LlamaContext { return LlamaContext(model: model, context: context) } + func model_info() -> String { + let result = UnsafeMutablePointer.allocate(capacity: 256) + result.initialize(repeating: Int8(0), count: 256) + defer { + result.deallocate() + } + + // TODO: this is probably very stupid way to get the string from C + + let nChars = llama_model_desc(model, result, 256) + let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nChars)) + + var SwiftString = "" + for char in bufferPointer { + SwiftString.append(Character(UnicodeScalar(UInt8(char)))) + } + + return SwiftString + } + func get_n_tokens() -> Int32 { return batch.n_tokens; } @@ -79,16 +126,11 @@ actor LlamaContext { print(String(cString: token_to_piece(token: id) + [0])) } - // batch = llama_batch_init(512, 0) // done in init() - batch.n_tokens = Int32(tokens_list.count) + llama_batch_clear(&batch) - for i1 in 0.. String { + var pp_avg: Double = 0 + var tg_avg: Double = 0 + + var pp_std: Double = 0 + var tg_std: Double = 0 + + for r in 0.. 1 { + pp_std = sqrt(pp_std / Double(nr - 1) - pp_avg * pp_avg * Double(nr) / Double(nr - 1)) + tg_std = sqrt(tg_std / Double(nr - 1) - tg_avg * tg_avg * Double(nr) / Double(nr - 1)) + } else { + pp_std = 0 + tg_std = 0 + } + + let model_desc = model_info(); + let model_size = String(format: "%.2f GiB", Double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0); + let model_n_params = String(format: "%.2f B", Double(llama_model_n_params(model)) / 1e9); + let backend = "Metal"; + let pp_avg_str = String(format: "%.2f", pp_avg); + let tg_avg_str = String(format: "%.2f", tg_avg); + let pp_std_str = String(format: "%.2f", pp_std); + let tg_std_str = String(format: "%.2f", tg_std); + + var result = "" + + result += String("| model | size | params | backend | test | t/s |\n") + result += String("| --- | --- | --- | --- | --- | --- |\n") + result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | pp \(pp) | \(pp_avg_str) ± \(pp_std_str) |\n") + result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | tg \(tg) | \(tg_avg_str) ± \(tg_std_str) |\n") + + return result; + } + func clear() { tokens_list.removeAll() temporary_invalid_cchars.removeAll() + llama_kv_cache_clear(context) } private func tokenize(text: String, add_bos: Bool) -> [llama_token] { let utf8Count = text.utf8.count - let n_tokens = utf8Count + (add_bos ? 1 : 0) + let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1 let tokens = UnsafeMutablePointer.allocate(capacity: n_tokens) let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false) diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index bc1fd15ce..2e6159928 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ 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a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift index babc60cdc..3393eb242 100644 --- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift +++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift @@ -3,24 +3,26 @@ import Foundation @MainActor class LlamaState: ObservableObject { @Published var messageLog = "" + @Published var cacheCleared = false private var llamaContext: LlamaContext? - private var modelUrl: URL? { - Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models") + private var defaultModelUrl: URL? { + Bundle.main.url(forResource: "ggml-model", withExtension: "gguf", subdirectory: "models") // Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models") } + init() { do { - try loadModel() + try loadModel(modelUrl: defaultModelUrl) } catch { messageLog += "Error!\n" } } - private func loadModel() throws { + func loadModel(modelUrl: URL?) throws { messageLog += "Loading model...\n" if let modelUrl { - llamaContext = try LlamaContext.createContext(path: modelUrl.path()) + llamaContext = try LlamaContext.create_context(path: modelUrl.path()) messageLog += "Loaded model \(modelUrl.lastPathComponent)\n" } else { messageLog += "Could not locate model\n" @@ -31,7 +33,7 @@ class LlamaState: ObservableObject { guard let llamaContext else { return } - messageLog += "Attempting to complete text...\n" + await llamaContext.completion_init(text: text) messageLog += "\(text)" @@ -42,4 +44,42 @@ class LlamaState: ObservableObject { await llamaContext.clear() messageLog += "\n\ndone\n" } + + func bench() async { + guard let llamaContext else { + return + } + + messageLog += "\n" + messageLog += "Running benchmark...\n" + messageLog += "Model info: " + messageLog += await llamaContext.model_info() + "\n" + + let t_start = DispatchTime.now().uptimeNanoseconds + await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up + let t_end = DispatchTime.now().uptimeNanoseconds + + let t_heat = Double(t_end - t_start) / 1_000_000_000.0 + messageLog += "Heat up time: \(t_heat) seconds, please wait...\n" + + // if more than 5 seconds, then we're probably running on a slow device + if t_heat > 5.0 { + messageLog += "Heat up time is too long, aborting benchmark\n" + return + } + + let result = await llamaContext.bench(pp: 512, tg: 128, pl: 1, nr: 3) + + messageLog += "\(result)" + messageLog += "\n" + } + + func clear() async { + guard let llamaContext else { + return + } + + await llamaContext.clear() + messageLog = "" + } } diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index 0bd16a806..219bf4dc1 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -5,24 +5,97 @@ struct ContentView: View { @State private var multiLineText = "" + private static func cleanupModelCaches() { + // Delete all models (*.gguf) + let fileManager = FileManager.default + let documentsUrl = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0] + do { + let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil) + for fileURL in fileURLs { + if fileURL.pathExtension == "gguf" { + try fileManager.removeItem(at: fileURL) + } + } + } catch { + print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)") + } + } + var body: some View { VStack { - ScrollView(.vertical) { + ScrollView(.vertical, showsIndicators: true) { Text(llamaState.messageLog) + .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) + .padding() + .onTapGesture { + UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil) + } } TextEditor(text: $multiLineText) - .frame(height: 200) + .frame(height: 80) .padding() .border(Color.gray, width: 0.5) - Button(action: { - sendText() - }) { - Text("Send") - .padding() - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) + + HStack { + Button("Send") { + sendText() + } + .padding(8) + .background(Color.blue) + .foregroundColor(.white) + .cornerRadius(8) + + Button("Bench") { + bench() + } + .padding(8) + .background(Color.blue) + .foregroundColor(.white) + .cornerRadius(8) + + Button("Clear") { + clear() + } + .padding(8) + .background(Color.blue) + .foregroundColor(.white) + .cornerRadius(8) + + Button("Copy") { + UIPasteboard.general.string = llamaState.messageLog + } + .padding(8) + .background(Color.blue) + .foregroundColor(.white) + .cornerRadius(8) + } + + VStack { + DownloadButton( + llamaState: llamaState, + modelName: "TinyLlama-1.1B (Q4_0)", + modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true", + filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf" + ) + .font(.system(size: 12)) + .padding(.top, 4) + + DownloadButton( + llamaState: llamaState, + modelName: "TinyLlama-1.1B (Q8_0)", + modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true", + filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf" + ) + .font(.system(size: 12)) + + Button("Clear downloaded models") { + ContentView.cleanupModelCaches() + llamaState.cacheCleared = true + } + .padding(8) + .font(.system(size: 12)) } } .padding() @@ -34,9 +107,20 @@ struct ContentView: View { multiLineText = "" } } + + func bench() { + Task { + await llamaState.bench() + } + } + + func clear() { + Task { + await llamaState.clear() + } + } } -/* -#Preview { - ContentView() -} -*/ + +//#Preview { +// ContentView() +//} diff --git a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift new file mode 100644 index 000000000..4bd75cb69 --- /dev/null +++ b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift @@ -0,0 +1,122 @@ +import SwiftUI + +struct DownloadButton: View { + @ObservedObject private var llamaState: LlamaState + private var modelName: String + private var modelUrl: String + private var filename: String + + @State private var status: String + + @State private var downloadTask: URLSessionDownloadTask? + @State private var progress = 0.0 + @State private var observation: NSKeyValueObservation? + + private static func getFileURL(filename: String) -> URL { + FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename) + } + + private func checkFileExistenceAndUpdateStatus() { + } + + init(llamaState: LlamaState, modelName: String, modelUrl: String, filename: String) { + self.llamaState = llamaState + self.modelName = modelName + self.modelUrl = modelUrl + self.filename = filename + + let fileURL = DownloadButton.getFileURL(filename: filename) + status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download" + } + + private func download() { + status = "downloading" + print("Downloading model \(modelName) from \(modelUrl)") + guard let url = URL(string: modelUrl) else { return } + let fileURL = DownloadButton.getFileURL(filename: filename) + + downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in + if let error = error { + print("Error: \(error.localizedDescription)") + return + } + + guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else { + print("Server error!") + return + } + + do { + if let temporaryURL = temporaryURL { + try FileManager.default.copyItem(at: temporaryURL, to: fileURL) + print("Writing to \(filename) completed") + + llamaState.cacheCleared = false + + status = "downloaded" + } + } catch let err { + print("Error: \(err.localizedDescription)") + } + } + + observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in + self.progress = progress.fractionCompleted + } + + downloadTask?.resume() + } + + var body: some View { + VStack { + if status == "download" { + Button(action: download) { + Text("Download " + modelName) + } + } else if status == "downloading" { + Button(action: { + downloadTask?.cancel() + status = "download" + }) { + Text("\(modelName) (Downloading \(Int(progress * 100))%)") + } + } else if status == "downloaded" { + Button(action: { + let fileURL = DownloadButton.getFileURL(filename: filename) + if !FileManager.default.fileExists(atPath: fileURL.path) { + download() + return + } + do { + try llamaState.loadModel(modelUrl: fileURL) + } catch let err { + print("Error: \(err.localizedDescription)") + } + }) { + Text("\(modelName) (Downloaded)") + } + } else { + Text("Unknown status") + } + } + .onDisappear() { + downloadTask?.cancel() + } + .onChange(of: llamaState.cacheCleared) { newValue in + if newValue { + downloadTask?.cancel() + let fileURL = DownloadButton.getFileURL(filename: filename) + status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download" + } + } + } +} + +// #Preview { +// DownloadButton( +// llamaState: LlamaState(), +// modelName: "TheBloke / TinyLlama-1.1B-1T-OpenOrca-GGUF (Q4_0)", +// modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true", +// filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf" +// ) +// } diff --git a/llama.cpp b/llama.cpp index f49214c13..fd9fd6ed9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2397,25 +2397,25 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { switch (ftype) { case LLAMA_FTYPE_ALL_F32: return "all F32"; - case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16"; - case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0"; - case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1"; + case LLAMA_FTYPE_MOSTLY_F16: return "F16"; + case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0"; + case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1"; case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16: - return "mostly Q4_1, some F16"; - case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0"; - case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1"; - case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0"; + return "Q4_1, some F16"; + case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0"; + case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1"; + case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0"; // K-quants - case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K"; - case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small"; - case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium"; - case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large"; - case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small"; - case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium"; - case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small"; - case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium"; - case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K"; + case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K"; + case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small"; + case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large"; + case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small"; + case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small"; + case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; default: return "unknown, may not work"; } @@ -2533,6 +2533,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { + case 22: model.type = e_model::MODEL_1B; break; case 26: model.type = e_model::MODEL_3B; break; case 32: model.type = e_model::MODEL_7B; break; case 40: model.type = e_model::MODEL_13B; break; From b1306c439490c7fa4ec33594500d980d1e9e15e6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 17 Dec 2023 20:16:23 +0200 Subject: [PATCH 208/664] readme : update hot topics --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index edbe6ba57..01aef2afc 100644 --- a/README.md +++ b/README.md @@ -10,11 +10,11 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- Collecting Apple Silicon performance stats: + - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 + - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508 - Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406 -- **llama.h API change for handling KV cache offloading and data type: https://github.com/ggerganov/llama.cpp/pull/4309** -- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225 - Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 -- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167 ---- From 2994f0c5a2e8c96955b422dedc93ec2595d16b82 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sun, 17 Dec 2023 19:39:02 -0500 Subject: [PATCH 209/664] decode : fix logits_valid for legacy API (#4516) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index fd9fd6ed9..d6d575f9e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6184,7 +6184,7 @@ static int llama_decode_internal( logits_out.resize(n_vocab); memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab); #ifndef NDEBUG - logits_valid[n_tokens - 1] = true; + logits_valid[0] = true; #endif } } From 3c04bf6da89eaf4c7d317e0518f0687dfcbf2de7 Mon Sep 17 00:00:00 2001 From: hankcs Date: Mon, 18 Dec 2023 05:14:58 -0800 Subject: [PATCH 210/664] llama : fix try_override for bool_value which always return true (#4519) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index d6d575f9e..99facbf77 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1937,7 +1937,7 @@ namespace GGUFMeta { target = override->bool_value; return true; } - return true; + return false; } template From b9e74f9bca5fdf7d0a22ed25e7a9626335fdfa48 Mon Sep 17 00:00:00 2001 From: Ebey Abraham Date: Mon, 18 Dec 2023 17:27:47 +0000 Subject: [PATCH 211/664] llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren * Update ggml-cuda.cu Co-authored-by: slaren * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham Co-authored-by: Georgi Gerganov Co-authored-by: slaren --- convert-hf-to-gguf.py | 22 +++ ggml-cuda.cu | 117 +++++++++---- ggml-metal.metal | 13 +- ggml.c | 46 ++++- ggml.h | 12 ++ gguf-py/gguf/constants.py | 13 ++ gguf-py/gguf/tensor_mapping.py | 8 + llama.cpp | 307 +++++++++++++++++++++++++++++---- tests/test-backend-ops.cpp | 1 + 9 files changed, 463 insertions(+), 76 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index e46a7813a..e71a96c48 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -182,6 +182,8 @@ class Model: return QwenModel if model_architecture == "MixtralForCausalLM": return MixtralModel + if model_architecture == "PhiForCausalLM": + return Phi2Model return Model def _is_model_safetensors(self) -> bool: @@ -221,6 +223,8 @@ class Model: return gguf.MODEL_ARCH.QWEN if arch == "MixtralForCausalLM": return gguf.MODEL_ARCH.LLAMA + if arch == "PhiForCausalLM": + return gguf.MODEL_ARCH.PHI2 raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -980,6 +984,24 @@ class QwenModel(Model): print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) + +class Phi2Model(Model): + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_name("Phi2") + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_add_bos_token(False) + + ###### CONVERSION LOGIC ###### diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0a63c1ecf..d0f3d8034 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4998,7 +4998,16 @@ static __global__ void rope_neox( const int ib = col / n_dims; const int ic = col % n_dims; - const int i = row*ncols + ib*n_dims + ic/2; + if (ib > 0) { + const int i = row*ncols + ib*n_dims + ic; + + dst[i + 0] = x[i + 0]; + dst[i + 1] = x[i + 1]; + + return; + } + + const int i = row*ncols + ib*n_dims + ic/2; const int i2 = row/p_delta_rows; float cur_rot = inv_ndims * ic - ib; @@ -7057,6 +7066,7 @@ inline void ggml_cuda_op_upscale( (void) src1; (void) dst; + (void) src1_dd; } inline void ggml_cuda_op_pad( @@ -7073,6 +7083,7 @@ inline void ggml_cuda_op_pad( (void) src1; (void) dst; + (void) src1_dd; } inline void ggml_cuda_op_rms_norm( @@ -7376,7 +7387,7 @@ inline void ggml_cuda_op_mul_mat_cublas( const int compute_capability = g_compute_capabilities[id]; - if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) { + if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 half * src0_as_f16 = nullptr; size_t src0_as = 0; @@ -8300,27 +8311,27 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor } static __global__ void k_compute_batched_ptrs( - const half * src0_as_f16, const half * src1_as_f16, half * dst_f16, + const half * src0_as_f16, const half * src1_as_f16, char * dst, const void ** ptrs_src, void ** ptrs_dst, - int ne12, int ne13, - int ne23, - int nb02, int nb03, - int nb12, int nb13, - int nb2, int nb3, - int r2, int r3) { - int i13 = blockIdx.x * blockDim.x + threadIdx.x; - int i12 = blockIdx.y * blockDim.y + threadIdx.y; + int64_t ne12, int64_t ne13, + int64_t ne23, + size_t nb02, size_t nb03, + size_t nb12, size_t nb13, + size_t nbd2, size_t nbd3, + int64_t r2, int64_t r3) { + int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x; + int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y; if (i13 >= ne13 || i12 >= ne12) { return; } - int i03 = i13 / r3; - int i02 = i12 / r2; + int64_t i03 = i13 / r3; + int64_t i02 = i12 / r2; ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -8376,7 +8387,41 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); + + half * dst_f16 = nullptr; + char * dst_t = nullptr; + + cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; + cudaDataType_t cu_data_type = CUDA_R_16F; + + // dst strides + size_t nbd2 = dst->nb[2]; + size_t nbd3 = dst->nb[3]; + + const half alpha_f16 = 1.0f; + const half beta_f16 = 0.0f; + + const float alpha_f32 = 1.0f; + const float beta_f32 = 0.0f; + + const void * alpha = &alpha_f16; + const void * beta = &beta_f16; + + if (dst->op_params[0] == GGML_PREC_DEFAULT) { + dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); + dst_t = (char *) dst_f16; + + nbd2 /= sizeof(float) / sizeof(half); + nbd3 /= sizeof(float) / sizeof(half); + } else { + dst_t = (char *) dst_ddf; + + cu_compute_type = CUBLAS_COMPUTE_32F; + cu_data_type = CUDA_R_32F; + + alpha = &alpha_f32; + beta = &beta_f32; + } GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -8385,9 +8430,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; - #if 0 // use cublasGemmEx { @@ -8397,12 +8439,12 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const int i02 = i12 / r2; CUBLAS_CHECK( - cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, + cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half), - (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float), - &beta_f16, ( char *) dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2, CUDA_R_16F, ne01, - CUBLAS_COMPUTE_16F, + alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half), + (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float), + beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, + cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } } @@ -8414,11 +8456,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA - (const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB - &beta_f16, ( char *) dst_f16, CUDA_R_16F, ne01, dst->nb[2]/sizeof(float), // strideC + alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA + (const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB + beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC ne12*ne13, - CUBLAS_COMPUTE_16F, + cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } else { // use cublasGemmBatchedEx @@ -8435,24 +8477,24 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( - src0_as_f16, src1_as_f16, dst_f16, + src0_as_f16, src1_as_f16, dst_t, ptrs_src, ptrs_dst, ne12, ne13, ne23, nb02, nb03, nb12, nb13, - dst->nb[2], dst->nb[3], + nbd2, nbd3, r2, r3); CUDA_CHECK(cudaGetLastError()); CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float), - &beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01, + alpha, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half), + (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + beta, ( void **) (ptrs_dst + 0*ne23), cu_data_type, ne01, ne23, - CUBLAS_COMPUTE_16F, + cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); if (ptrs_src_s != 0) { @@ -8464,11 +8506,14 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const } #endif - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); + if (dst->op_params[0] == GGML_PREC_DEFAULT) { + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); + to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); + + ggml_cuda_pool_free(dst_f16, dst_as); + } ggml_cuda_pool_free(src1_as_f16, src1_as); - ggml_cuda_pool_free(dst_f16, dst_as); } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { diff --git a/ggml-metal.metal b/ggml-metal.metal index fe0ada445..d5b54e112 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1702,8 +1702,9 @@ kernel void kernel_rope( dst_data[1] = x0*sin_theta + x1*cos_theta; } } else { - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) { + for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) { + if (ic < n_dims) { + const int64_t ib = 0; // simplified from `(ib * n_dims + ic) * inv_ndims` const float cur_rot = inv_ndims*ic - ib; @@ -1722,6 +1723,14 @@ kernel void kernel_rope( dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } else { + const int64_t i0 = ic; + + device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; } } } diff --git a/ggml.c b/ggml.c index ad546a731..6da65bd92 100644 --- a/ggml.c +++ b/ggml.c @@ -4098,6 +4098,14 @@ struct ggml_tensor * ggml_mul_mat( return result; } +void ggml_mul_mat_set_prec( + struct ggml_tensor * a, + enum ggml_prec prec) { + const int32_t prec_i32 = (int32_t) prec; + + ggml_set_op_params_i32(a, 0, prec_i32); +} + // ggml_mul_mat_id struct ggml_tensor * ggml_mul_mat_id( @@ -9168,6 +9176,8 @@ static void ggml_compute_forward_norm_f32( float eps; memcpy(&eps, dst->op_params, sizeof(float)); + GGML_ASSERT(eps > 0.0f); + // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { @@ -9237,6 +9247,8 @@ static void ggml_compute_forward_rms_norm_f32( float eps; memcpy(&eps, dst->op_params, sizeof(float)); + GGML_ASSERT(eps > 0.0f); + // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { @@ -11562,10 +11574,13 @@ static void ggml_compute_forward_rope_f32( } } else { // TODO: this might be wrong for ne0 != n_dims - need double check - // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 + // it seems we have to rope just the first n_dims elements and do nothing with the rest + // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 theta_base *= freq_scale; - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 0; ic < n_dims; ic += 2) { + for (int64_t ic = 0; ic < ne0; ic += 2) { + if (ic < n_dims) { + const int64_t ib = 0; + // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; @@ -11588,6 +11603,14 @@ static void ggml_compute_forward_rope_f32( dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } else { + const int64_t i0 = ic; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; } } } @@ -11715,10 +11738,13 @@ static void ggml_compute_forward_rope_f16( } } else { // TODO: this might be wrong for ne0 != n_dims - need double check - // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 + // it seems we have to rope just the first n_dims elements and do nothing with the rest + // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 theta_base *= freq_scale; - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 0; ic < n_dims; ic += 2) { + for (int64_t ic = 0; ic < ne0; ic += 2) { + if (ic < n_dims) { + const int64_t ib = 0; + // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; @@ -11741,6 +11767,14 @@ static void ggml_compute_forward_rope_f16( dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } else { + const int64_t i0 = ic; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; } } } diff --git a/ggml.h b/ggml.h index 68f7833b6..f1003984f 100644 --- a/ggml.h +++ b/ggml.h @@ -343,6 +343,12 @@ extern "C" { GGML_TYPE_COUNT, }; + // precision + enum ggml_prec { + GGML_PREC_DEFAULT, + GGML_PREC_F32, + }; + enum ggml_backend_type { GGML_BACKEND_CPU = 0, GGML_BACKEND_GPU = 10, @@ -1057,6 +1063,12 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); + // change the precision of a matrix multiplication + // set to GGML_PREC_F32 for higher precision (useful for phi-2) + GGML_API void ggml_mul_mat_set_prec( + struct ggml_tensor * a, + enum ggml_prec prec); + // indirect matrix multiplication // ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b) GGML_API struct ggml_tensor * ggml_mul_mat_id( diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 12133882b..390dca049 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -95,6 +95,7 @@ class MODEL_ARCH(IntEnum): BLOOM = auto() STABLELM = auto() QWEN = auto() + PHI2 = auto() class MODEL_TENSOR(IntEnum): @@ -140,6 +141,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", + MODEL_ARCH.PHI2: "phi2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -350,6 +352,17 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_ARCH.GPT2: [ # TODO ], + MODEL_ARCH.PHI2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ] # TODO } diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 0115ea1c6..6fcbdbc1c 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -17,6 +17,7 @@ class TensorNameMap: "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert "language_model.embedding.word_embeddings", # persimmon + "transformer.embd.wte", # phi2 ), # Token type embeddings @@ -41,6 +42,7 @@ class TensorNameMap: "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen "output", # llama-pth bloom "word_embeddings_for_head", # persimmon + "lm_head.linear", # phi2 ), # Output norm @@ -53,6 +55,7 @@ class TensorNameMap: "transformer.norm_f", # mpt "ln_f", # refact bloom qwen "language_model.encoder.final_layernorm", # persimmon + "lm_head.ln", # phi2 ), # Rope frequencies @@ -75,6 +78,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi + "transformer.h.{bid}.ln", # phi2 ), # Attention norm 2 @@ -90,6 +94,7 @@ class TensorNameMap: "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + "transformer.h.{bid}.mixer.Wqkv", # phi2 ), # Attention query @@ -128,6 +133,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "transformer.h.{bid}.mixer.out_proj", # phi2 ), # Rotary embeddings @@ -167,6 +173,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc_in", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen + "transformer.h.{bid}.mlp.fc1", # phi2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -198,6 +205,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + "transformer.h.{bid}.mlp.fc2", # phi2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 99facbf77..edd2910b3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -195,6 +195,7 @@ enum llm_arch { LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, + LLM_ARCH_PHI2, LLM_ARCH_UNKNOWN, }; @@ -212,6 +213,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, + { LLM_ARCH_PHI2, "phi2" }, }; enum llm_kv { @@ -550,6 +552,19 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_PHI2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, @@ -1420,6 +1435,7 @@ struct llama_model { struct ggml_tensor * output_norm; struct ggml_tensor * output_norm_b; struct ggml_tensor * output; + struct ggml_tensor * output_b; std::vector layers; @@ -2635,6 +2651,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_PHI2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -2987,7 +3012,7 @@ static void llm_load_tensors( (void) main_gpu; - enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; + enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; #ifdef GGML_USE_CUBLAS @@ -3630,7 +3655,73 @@ static void llm_load_tensors( } } } break; + case LLM_ARCH_PHI2: + { + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + // output + { + ggml_backend_type backend_norm; + ggml_backend_type backend_output; + + if (n_gpu_layers > int(n_layer)) { + backend_norm = llama_backend_offload; + backend_output = llama_backend_offload; + } else { + backend_norm = GGML_BACKEND_CPU; + backend_output = GGML_BACKEND_CPU; + } + + model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); + model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); + model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output); + + if (backend_norm == GGML_BACKEND_GPU) { + vram_weights += ggml_nbytes(model.output_norm); + vram_weights += ggml_nbytes(model.output_norm_b); + vram_weights += ggml_nbytes(model.output); + vram_weights += ggml_nbytes(model.output_b); + } + } + + const uint32_t n_ff = hparams.n_ff; + + const int i_gpu_start = n_layer - n_gpu_layers; + + model.layers.resize(n_layer); + + for (uint32_t i = 0; i < n_layer; ++i) { + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + + layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + + if (backend == GGML_BACKEND_GPU) { + vram_weights += + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + + ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + + ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b) + + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b); + } + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -3991,6 +4082,7 @@ static struct ggml_tensor * llm_build_ffn( // if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, + const llama_model & model, const llama_hparams & hparams, const llama_kv_cache & kv, struct ggml_tensor * wo, @@ -4002,6 +4094,7 @@ static struct ggml_tensor * llm_build_kqv( int32_t n_tokens, int32_t n_kv, float max_alibi_bias, + float scale, const llm_build_cb & cb, int il) { const int64_t n_embd = hparams.n_embd; @@ -4024,6 +4117,12 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); + if (model.arch == LLM_ARCH_PHI2) { + // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs + // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 + ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + } + if (max_alibi_bias > 0.0f) { // temporary branch until we figure out how to handle ggml_alibi through ggml_add kq = ggml_scale(ctx, kq, kq_scale); @@ -4043,7 +4142,7 @@ static struct ggml_tensor * llm_build_kqv( kq = ggml_soft_max(ctx, kq); cb(kq, "kq_soft_max", il); } else { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, 1.0f/sqrtf(float(n_embd_head))); + kq = ggml_soft_max_ext(ctx, kq, kq_mask, scale); cb(kq, "kq_soft_max_ext", il); } @@ -4250,9 +4349,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4433,9 +4532,9 @@ struct llm_build_context { // apply ALiBi for 13B model const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f; - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4557,9 +4656,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4657,9 +4756,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4866,9 +4965,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); // TODO: not tested, could be broken - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4957,9 +5056,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5054,9 +5153,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5148,9 +5247,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5261,9 +5360,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5320,15 +5419,15 @@ struct llm_build_context { cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos= ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); // KQ_scale - struct ggml_tensor * KQ_scale= ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask= ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed @@ -5378,9 +5477,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5422,6 +5521,122 @@ struct llm_build_context { ggml_build_forward_expand(gf, cur); + return gf; + } + struct ggml_cgraph * build_phi2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + struct ggml_tensor * cur; + struct ggml_tensor * attn_norm_output; + struct ggml_tensor * ffn_output; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // Q_scale + struct ggml_tensor * Q_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(Q_scale, "Q_scale", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + attn_norm_output = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(attn_norm_output, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_custom( + ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Qcur = ggml_scale(ctx0, Qcur, Q_scale); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, model, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + // FF + { + ffn_output = llm_build_ffn(ctx0, attn_norm_output, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(ffn_output, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_output); + cb(cur, "l_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output_no_bias", -1); + + cur = ggml_add(ctx0, cur, model.output_b); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + return gf; } }; @@ -5437,7 +5652,7 @@ enum llm_offload_func_e { OFFLOAD_FUNC_FRC, // force offload OFFLOAD_FUNC_KQV, OFFLOAD_FUNC_NR, - OFFLOAD_FUNC_EMB, + OFFLOAD_FUNC_EMB, // embeddings OFFLOAD_FUNC_OUT, }; @@ -5522,6 +5737,7 @@ static const std::unordered_map k_offload_map { "pos_embd", OFFLOAD_FUNC_NR }, { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope) + { "Q_scale", OFFLOAD_FUNC_FRC }, { "KQ_scale", OFFLOAD_FUNC_FRC }, { "KQ_mask", OFFLOAD_FUNC_FRC }, { "K_shift", OFFLOAD_FUNC_FRC }, @@ -5606,6 +5822,7 @@ static const std::unordered_map k_offload_map { "l_out", OFFLOAD_FUNC }, { "result_norm", OFFLOAD_FUNC_EMB }, + { "result_output_no_bias", OFFLOAD_FUNC_EMB }, { "result_output", OFFLOAD_FUNC_OUT }, }; @@ -5623,6 +5840,7 @@ static struct ggml_cgraph * llama_build_graph( bool alloc_inp_tokens = false; bool alloc_inp_embd = false; bool alloc_inp_pos = false; + bool alloc_inp_Q_scale = false; bool alloc_inp_KQ_scale = false; bool alloc_inp_KQ_mask = false; bool alloc_inp_K_shift = false; @@ -5690,7 +5908,7 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_pos = true; } - if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) { + if (!alloc_inp_Q_scale && strcmp(name, "Q_scale") == 0) { ggml_allocr_alloc(lctx.alloc, cur); if (!ggml_allocr_is_measure(lctx.alloc)) { @@ -5698,6 +5916,23 @@ static struct ggml_cgraph * llama_build_graph( ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); } + alloc_inp_Q_scale = true; + } + + if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc)) { + const int64_t n_embd_head = model.hparams.n_embd_head(); + if (model.arch == LLM_ARCH_PHI2) { + // with phi2, we scale the Q to avoid precision issues + // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 + ggml_set_f32(cur, 1.0f); + } else { + ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); + } + } + alloc_inp_KQ_scale = true; } @@ -5922,6 +6157,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_qwen(); } break; + case LLM_ARCH_PHI2: + { + result = llm.build_phi2(); + } break; default: GGML_ASSERT(false); } @@ -6055,12 +6294,16 @@ static int llama_decode_internal( ggml_allocr_alloc_graph(lctx.alloc, gf); - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + // the output is always the last tensor in the graph + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + GGML_ASSERT(strcmp(res->name, "result_output") == 0); + + // the embeddings could be the second to last tensor, or the third to last tensor struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; - - GGML_ASSERT(strcmp(res->name, "result_output") == 0); - GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); - + if (strcmp(embeddings->name, "result_norm") != 0) { + embeddings = gf->nodes[gf->n_nodes - 3]; + GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); + } #ifdef GGML_USE_CUBLAS for (int i = 0; i < gf->n_leafs; i++) { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index df2c3fb6e..f04b9438a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1555,6 +1555,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512)); // neox (falcon 40B) test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512)); // neox (falcon 40B) test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512)); // neox (stablelm) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2) } test_cases.emplace_back(new test_alibi()); From 6ff39b129d0281d045f83d515e51b7197b44b253 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 18 Dec 2023 20:05:12 +0200 Subject: [PATCH 212/664] llama.swiftui : add more models --- .../llama.cpp.swift/LibLlama.swift | 2 +- .../llama.swiftui/UI/ContentView.swift | 31 +++++++++++++++++-- 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 272e1fd8a..464fb3277 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -203,7 +203,7 @@ actor LlamaContext { var pp_std: Double = 0 var tg_std: Double = 0 - for r in 0.. Date: Mon, 18 Dec 2023 20:17:43 +0200 Subject: [PATCH 213/664] llama.swiftui : add tinyllama 1.1B F16 --- .../llama.swiftui/llama.swiftui/UI/ContentView.swift | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index 9cbe8efd6..c78f107b3 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -91,6 +91,15 @@ struct ContentView: View { ) .font(.system(size: 12)) + DownloadButton( + llamaState: llamaState, + modelName: "TinyLlama-1.1B (F16, 2.2 GiB)", + modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", + filename: "tinyllama-1.1b-f16.gguf" + ) + .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) + DownloadButton( llamaState: llamaState, modelName: "Phi-2.7B (Q4_0, 1.6 GiB)", @@ -98,7 +107,6 @@ struct ContentView: View { filename: "phi-2-q4_0.gguf" ) .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -107,6 +115,7 @@ struct ContentView: View { filename: "phi-2-q8_0.gguf" ) .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -115,7 +124,6 @@ struct ContentView: View { filename: "mistral-7b-v0.1.Q4_0.gguf" ) .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) Button("Clear downloaded models") { ContentView.cleanupModelCaches() From a7aee47b98e45539d491071b25778b833b77e387 Mon Sep 17 00:00:00 2001 From: arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com> Date: Mon, 18 Dec 2023 22:33:45 +0100 Subject: [PATCH 214/664] ggml-cuda: Fix HIP build (#4528) regression of #4490 Adds defines for two new datatypes cublasComputeType_t, cudaDataType_t. Currently using deprecated hipblasDatatype_t since newer ones very recent. --- ggml-cuda.cu | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d0f3d8034..f20846fef 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -31,6 +31,7 @@ #define CUDA_R_16F HIPBLAS_R_16F #define CUDA_R_32F HIPBLAS_R_32F #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width) +#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6 #define cublasCreate hipblasCreate #define cublasGemmEx hipblasGemmEx #define cublasGemmBatchedEx hipblasGemmBatchedEx @@ -40,6 +41,7 @@ #define cublasSetStream hipblasSetStream #define cublasSgemm hipblasSgemm #define cublasStatus_t hipblasStatus_t +#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6 #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess From 328b83de23b33240e28f4e74900d1d06726f5eb1 Mon Sep 17 00:00:00 2001 From: Eric Sommerlade Date: Tue, 19 Dec 2023 16:17:01 +0000 Subject: [PATCH 215/664] ggml : fixed check for _MSC_VER (#4535) Co-authored-by: Eric Sommerlade --- ggml.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.h b/ggml.h index f1003984f..beacdc8be 100644 --- a/ggml.h +++ b/ggml.h @@ -303,7 +303,7 @@ extern "C" { #if defined(__ARM_NEON) && defined(__CUDACC__) typedef half ggml_fp16_t; -#elif defined(__ARM_NEON) +#elif defined(__ARM_NEON) && !defined(_MSC_VER) typedef __fp16 ggml_fp16_t; #else typedef uint16_t ggml_fp16_t; From 799fc2268989482054944c902874cca76337580f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 20 Dec 2023 15:41:22 +0100 Subject: [PATCH 216/664] CUDA: Faster Mixtral prompt processing (#4538) * CUDA: make MoE tensors contiguous for batch size>1 * Update ggml-cuda.cu Co-authored-by: slaren --------- Co-authored-by: slaren --- ggml-cuda.cu | 118 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 93 insertions(+), 25 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f20846fef..9f4b188cb 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7830,6 +7830,11 @@ static void ggml_cuda_set_peer_access(const int n_tokens) { } #ifdef NDEBUG + for (int id = 0; id < g_device_count; ++id) { + CUDA_CHECK(ggml_cuda_set_device(id)); + CUDA_CHECK(cudaDeviceSynchronize()); + } + for (int id = 0; id < g_device_count; ++id) { CUDA_CHECK(ggml_cuda_set_device(id)); @@ -7881,8 +7886,6 @@ static void ggml_cuda_op_mul_mat( const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; - ggml_cuda_set_peer_access(ne11); - GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT); @@ -8781,16 +8784,21 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + const int64_t nb11 = src1->nb[1]; + const int64_t nb1 = dst->nb[1]; + const struct ggml_tensor * ids = src0; const int32_t id = ((int32_t *) dst->op_params)[0]; const int32_t n_as = ((int32_t *) dst->op_params)[1]; std::vector ids_host(ggml_nbytes(ids)); + const cudaStream_t stream = g_cudaStreams[g_main_device][0]; + if (ids->backend == GGML_BACKEND_GPU) { const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; - CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0])); - CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0])); + CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); + CUDA_CHECK(cudaStreamSynchronize(stream)); } else { memcpy(ids_host.data(), ids->data, ggml_nbytes(ids)); } @@ -8804,37 +8812,93 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s ggml_tensor src1_row = *src1; ggml_tensor dst_row = *dst; - src1_row.ne[1] = 1; - dst_row.ne[1] = 1; - - src1_row.nb[2] = src1_row.nb[1]; - dst_row.nb[2] = dst_row.nb[1]; - - src1_row.nb[3] = src1_row.nb[1]; - dst_row.nb[3] = dst_row.nb[1]; - src1_row.extra = &src1_row_extra; dst_row.extra = &dst_row_extra; + char * src1_original = (char *) src1_extra->data_device[g_main_device]; + char * dst_original = (char *) dst_extra->data_device[g_main_device]; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - //int32_t row_id; - //CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0])); - //CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0])); + if (src1->ne[1] == 1) { + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + //int32_t row_id; + //CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0])); + //CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0])); - const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); - GGML_ASSERT(row_id >= 0 && row_id < n_as); + GGML_ASSERT(row_id >= 0 && row_id < n_as); - const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + const struct ggml_tensor * src0_row = dst->src[row_id + 2]; - src1_row_extra.data_device[g_main_device] = (char *) src1_extra->data_device[g_main_device] + i01*src1->nb[1]; - src1_row.data = (char *) src1->data + i01*src1->nb[1]; + src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1]; + src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? - dst_row_extra.data_device[g_main_device] = (char *) dst_extra->data_device[g_main_device] + i01*dst->nb[1]; - dst_row.data = (char *) dst->data + i01*dst->nb[1]; + dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1]; + dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? - ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row); + ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row); + } + } else { + size_t as_src1, as_dst; + char * src1_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(src1), &as_src1); + char * dst_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(dst), &as_dst); + + src1_row_extra.data_device[g_main_device] = src1_contiguous; + dst_row_extra.data_device[g_main_device] = dst_contiguous; + + for (int32_t row_id = 0; row_id < n_as; ++row_id) { + const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + + int64_t num_src1_rows = 0; + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + + if (row_id_i != row_id) { + continue; + } + + GGML_ASSERT(row_id >= 0 && row_id < n_as); + + CUDA_CHECK(cudaMemcpyAsync(src1_contiguous + num_src1_rows*nb11, src1_original + i01*nb11, + nb11, cudaMemcpyDeviceToDevice, stream)); + num_src1_rows++; + } + + if (num_src1_rows == 0) { + continue; + } + + src1_row.ne[1] = num_src1_rows; + dst_row.ne[1] = num_src1_rows; + + src1_row.nb[1] = nb11; + src1_row.nb[2] = num_src1_rows*nb11; + src1_row.nb[3] = num_src1_rows*nb11; + + dst_row.nb[1] = nb1; + dst_row.nb[2] = num_src1_rows*nb1; + dst_row.nb[3] = num_src1_rows*nb1; + + ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row); + + num_src1_rows = 0; + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + + if (row_id_i != row_id) { + continue; + } + + GGML_ASSERT(row_id >= 0 && row_id < n_as); + + CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous + num_src1_rows*nb1, + nb1, cudaMemcpyDeviceToDevice, stream)); + num_src1_rows++; + } + } + + ggml_cuda_pool_free(src1_contiguous, as_src1); + ggml_cuda_pool_free(dst_contiguous, as_dst); } } @@ -9370,6 +9434,10 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ return false; } + if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) { + ggml_cuda_set_peer_access(tensor->src[1]->ne[1]); + } + if (params->ith != 0) { return true; } From 1d7a1912cea2227f9a1a449758ed622c560542f9 Mon Sep 17 00:00:00 2001 From: LoganDark Date: Thu, 21 Dec 2023 01:59:27 -0800 Subject: [PATCH 217/664] Fix access violation in ggml_cuda_free_data if tensor->extra is NULL (#4554) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9f4b188cb..28d378784 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -9091,7 +9091,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { } void ggml_cuda_free_data(struct ggml_tensor * tensor) { - if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { + if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { return; } From d3223afdad0ed2821a8ddf739c291cd410c92a11 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 21 Dec 2023 17:34:17 +0100 Subject: [PATCH 218/664] llama : disable per-tensor info prints on model load (#4562) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index edd2910b3..90d860eb9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2083,7 +2083,7 @@ struct llama_model_loader { type_max = meta->type; } - LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str()); + // LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str()); } switch (type_max) { From 139882392258671ffe5acdfcadc0bc08572d6eef Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 21 Dec 2023 18:02:30 +0100 Subject: [PATCH 219/664] cuda : replace asserts in wrong architecture checks with __trap (#4556) * cuda : replace asserts in wrong architecture checks with __trap * make bad_arch noreturn, remove returns --- ggml-cuda.cu | 82 +++++++++++++++++++++++----------------------------- 1 file changed, 36 insertions(+), 46 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 28d378784..e7c9dee45 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -512,6 +512,14 @@ static size_t g_scratch_offset = 0; static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; +[[noreturn]] +static __device__ void bad_arch() { + printf("ERROR: ggml-cuda was compiled without support for the current GPU architecture.\n"); + __trap(); + + (void) bad_arch; // suppress unused function warning +} + static __device__ __forceinline__ float warp_reduce_sum(float x) { #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -1972,8 +1980,7 @@ template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp // second part effectively subtracts 8 from each quant value return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y); #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2010,8 +2017,7 @@ template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1)); #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2046,8 +2052,7 @@ template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp // second part effectively subtracts 16 from each quant value return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y); #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2092,8 +2097,7 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp return sumi*d5d8 + m5s8 / (QI5_1 / vdr); #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2114,8 +2118,7 @@ template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_imp return d8_0*d8_1 * sumi; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2145,8 +2148,7 @@ template static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it return sumi*d8d8 + m8s8 / (QI8_1 / vdr); #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2181,8 +2183,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( return dm2f.x*sumf_d - dm2f.y*sumf_m; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2219,8 +2220,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m); #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2260,8 +2260,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq( return d3 * sumf; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2286,8 +2285,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( return d3*d8 * sumi; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2320,8 +2318,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( return dm4f.x*sumf_d - dm4f.y*sumf_m; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2354,8 +2351,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( return dm4f.x*sumf_d - dm4f.y*sumf_m; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2395,8 +2391,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( return dm5f.x*sumf_d - dm5f.y*sumf_m; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2429,8 +2424,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( return dm4f.x*sumf_d - dm4f.y*sumf_m; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2460,8 +2454,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq( return d*sumf; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2492,8 +2485,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( return d6 * sumf_d; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -3359,8 +3351,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( return dall * sumf_d - dmin * sumf_m; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A #endif @@ -3543,8 +3534,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1( return d * sumf_d; #else - assert(false); - return 0.0f; // only to satisfy the compiler + bad_arch(); #endif // __CUDA_ARCH__ >= MIN_CC_DP4A #endif @@ -3954,7 +3944,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q4_0_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4023,7 +4013,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q4_1_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4090,7 +4080,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q5_0_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4157,7 +4147,7 @@ mul_mat_q5_1( (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q5_1_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4224,7 +4214,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q8_0_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4291,7 +4281,7 @@ mul_mat_q2_K( (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q2_K_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4360,7 +4350,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q3_K_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4429,7 +4419,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q4_K_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4496,7 +4486,7 @@ mul_mat_q5_K( (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q5_K_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4565,7 +4555,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q6_K_q8_1_mul_mat; - assert(false); + bad_arch(); #endif // __CUDA_ARCH__ >= CC_VOLTA } From 66f35a2f48e1965a13835a523e677223dbf148be Mon Sep 17 00:00:00 2001 From: bobqianic <129547291+bobqianic@users.noreply.github.com> Date: Thu, 21 Dec 2023 17:06:44 +0000 Subject: [PATCH 220/664] cuda : better error message for ggml_get_rows (#4561) * Update ggml-cuda.cu * Update ggml-cuda.cu * Update ggml-cuda.cu --------- Co-authored-by: Georgi Gerganov --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e7c9dee45..1ca071d90 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6815,6 +6815,7 @@ static void ggml_cuda_op_get_rows( break; default: // TODO: k-quants + fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); GGML_ASSERT(false); break; } From 880e352277fc017df4d5794f0c21c44e1eae2b84 Mon Sep 17 00:00:00 2001 From: howlger Date: Thu, 21 Dec 2023 18:07:34 +0100 Subject: [PATCH 221/664] py : open merges file as 'utf-8' (#4566) Otherwise, on Windows converting bling-phi-2-v0 () via convert-hf-to-gguf.py will fail with the following error: ``` Traceback (most recent call last): File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 1061, in model_instance.set_vocab() File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 52, in set_vocab self._set_vocab_gpt2() File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 264, in _set_vocab_gpt2 special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) File "C:\Users\User\git\gguf\gguf\vocab.py", line 33, in __init__ self._load(Path(path)) File "C:\Users\User\git\gguf\gguf\vocab.py", line 81, in _load self._try_load_merges_txt(path) File "C:\Users\User\git\gguf\gguf\vocab.py", line 95, in _try_load_merges_txt for line in fp: File "C:\Users\User\miniconda3\envs\gguf\lib\encodings\cp1252.py", line 23, in decode return codecs.charmap_decode(input,self.errors,decoding_table)[0] UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 1415: character maps to ``` --- gguf-py/gguf/vocab.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 76924d8f2..cd1942975 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -84,7 +84,7 @@ class SpecialVocab: merges_file = path / 'merges.txt' if not merges_file.is_file(): return False - with open(merges_file, 'r') as fp: + with open(merges_file, 'r', encoding = 'utf-8') as fp: first_line = next(fp, '').strip() if not first_line.startswith('#'): fp.seek(0) From c083718c895b7c8c7fb2a4660643fb78d0c64dfd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 21 Dec 2023 19:27:14 +0200 Subject: [PATCH 222/664] readme : update coding guidelines --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 01aef2afc..80ce194ca 100644 --- a/README.md +++ b/README.md @@ -982,6 +982,8 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m / - There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions +- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices +- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT` ### Docs From 9154494808dc865475c59022c29060b4947a803b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 21 Dec 2023 18:42:59 +0100 Subject: [PATCH 223/664] CUDA: mul_mat_id always on GPU for batches >= 32 (#4553) --- ggml-cuda.cu | 29 ++++++++++++++++++++++------- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 1ca071d90..036668bfd 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8773,8 +8773,6 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s // TODO: mmq/mmv support #endif - GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); - const int64_t nb11 = src1->nb[1]; const int64_t nb1 = dst->nb[1]; @@ -8803,13 +8801,21 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s ggml_tensor src1_row = *src1; ggml_tensor dst_row = *dst; + src1_row.backend = GGML_BACKEND_GPU; + dst_row.backend = GGML_BACKEND_GPU; + src1_row.extra = &src1_row_extra; dst_row.extra = &dst_row_extra; - char * src1_original = (char *) src1_extra->data_device[g_main_device]; - char * dst_original = (char *) dst_extra->data_device[g_main_device]; + char * src1_original = src1->backend == GGML_BACKEND_CPU ? + (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; + char * dst_original = dst->backend == GGML_BACKEND_CPU ? + (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; if (src1->ne[1] == 1) { + GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { //int32_t row_id; //CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0])); @@ -8837,6 +8843,11 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s src1_row_extra.data_device[g_main_device] = src1_contiguous; dst_row_extra.data_device[g_main_device] = dst_contiguous; + const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ? + cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; + const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_CPU ? + cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; + for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; @@ -8851,7 +8862,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(row_id >= 0 && row_id < n_as); CUDA_CHECK(cudaMemcpyAsync(src1_contiguous + num_src1_rows*nb11, src1_original + i01*nb11, - nb11, cudaMemcpyDeviceToDevice, stream)); + nb11, src1_kind, stream)); num_src1_rows++; } @@ -8883,7 +8894,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(row_id >= 0 && row_id < n_as); CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous + num_src1_rows*nb1, - nb1, cudaMemcpyDeviceToDevice, stream)); + nb1, dst_kind, stream)); num_src1_rows++; } } @@ -8891,6 +8902,10 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s ggml_cuda_pool_free(src1_contiguous, as_src1); ggml_cuda_pool_free(dst_contiguous, as_dst); } + + if (dst->backend == GGML_BACKEND_CPU) { + CUDA_CHECK(cudaStreamSynchronize(stream)); + } } static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -9289,7 +9304,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); - if (!any_on_device && tensor->op != GGML_OP_MUL_MAT) { + if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) { return false; } From 8fe03ffddaaa0ab5d48feaafe398151c9f22d4f6 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Thu, 21 Dec 2023 12:55:34 -0500 Subject: [PATCH 224/664] common : remove incorrect --model-draft default (#4568) --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 93d5483e4..b3425ab09 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -920,7 +920,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -md FNAME, --model-draft FNAME\n"); - printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str()); + printf(" draft model for speculative decoding\n"); printf(" -ld LOGDIR, --logdir LOGDIR\n"); printf(" path under which to save YAML logs (no logging if unset)\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); From 562cf222b5129e40b312877e928eac3a02e4ec33 Mon Sep 17 00:00:00 2001 From: arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com> Date: Thu, 21 Dec 2023 20:13:25 +0100 Subject: [PATCH 225/664] ggml-cuda: Fix HIP build by adding define for __trap (#4569) Regression of 139882392258671ffe5acdfcadc0bc08572d6eef HIP doesn't have trap, only abort --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 036668bfd..61d92d7ef 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -80,6 +80,7 @@ #define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags) #define cudaStream_t hipStream_t #define cudaSuccess hipSuccess +#define __trap abort #else #include #include From 0f630fbc924aaabeea6eaf466bb4b47d13015c3e Mon Sep 17 00:00:00 2001 From: Erik Garrison Date: Thu, 21 Dec 2023 13:45:32 -0600 Subject: [PATCH 226/664] cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON --- CMakeLists.txt | 4 ++++ README.md | 16 +++++++++------- ggml-cuda.cu | 5 +++++ 3 files changed, 18 insertions(+), 7 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e3cd43ab3..6fc6508c5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -91,6 +91,7 @@ set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "llama: max. batch size for using peer access") option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) +option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) @@ -377,6 +378,9 @@ if (LLAMA_HIPBLAS) if (${hipblas_FOUND} AND ${hip_FOUND}) message(STATUS "HIP and hipBLAS found") add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) + if (LLAMA_HIP_UMA) + add_compile_definitions(GGML_HIP_UMA) + endif() add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h) if (BUILD_SHARED_LIBS) set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON) diff --git a/README.md b/README.md index 80ce194ca..73fe59bb4 100644 --- a/README.md +++ b/README.md @@ -432,14 +432,15 @@ Building the program with BLAS support may lead to some performance improvements ```bash make LLAMA_HIPBLAS=1 ``` - - Using `CMake` for Linux: + - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash - mkdir build - cd build - CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON - cmake --build . + CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \ + cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build -- -j 16 ``` - - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS): + On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`. + However, this hurts performance for non-integrated GPUs. + - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): ```bash set PATH=%HIP_PATH%\bin;%PATH% mkdir build @@ -448,10 +449,11 @@ Building the program with BLAS support may lead to some performance improvements cmake --build . ``` Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) + Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. - If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. + If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): | Option | Legal values | Default | Description | diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 61d92d7ef..32603a8d1 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -60,8 +60,13 @@ #define cudaGetDeviceProperties hipGetDeviceProperties #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError +#ifdef GGML_HIP_UMA +#define cudaMalloc hipMallocManaged +#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size) +#else #define cudaMalloc hipMalloc #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) +#endif #define cudaMemcpy hipMemcpy #define cudaMemcpy2DAsync hipMemcpy2DAsync #define cudaMemcpyAsync hipMemcpyAsync From 56fa50819f7a3ca2128f63b81c17c08a4454479e Mon Sep 17 00:00:00 2001 From: Finn Voorhees Date: Thu, 21 Dec 2023 14:55:02 -0500 Subject: [PATCH 227/664] metal : fix `ggml_metal_log` vargs (#4373) From 31f27758faf4a4bd08101a57c7ec3a473f771f86 Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Thu, 21 Dec 2023 11:57:48 -0800 Subject: [PATCH 228/664] llama : allow getting n_batch from llama_context in c api (#4540) * allowed getting n_batch from llama_context in c api * changed to use `uint32_t` instead of `int` * changed to use `uint32_t` instead of `int` in `llama_n_ctx` * Update llama.h --------- Co-authored-by: Georgi Gerganov --- llama.cpp | 6 +++++- llama.h | 4 +++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 90d860eb9..63ebe581b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9532,10 +9532,14 @@ const llama_model * llama_get_model(const struct llama_context * ctx) { return &ctx->model; } -int llama_n_ctx(const struct llama_context * ctx) { +uint32_t llama_n_ctx(const struct llama_context * ctx) { return ctx->cparams.n_ctx; } +uint32_t llama_n_batch(const struct llama_context * ctx) { + return ctx->cparams.n_batch; +} + enum llama_vocab_type llama_vocab_type(const struct llama_model * model) { return model->vocab.type; } diff --git a/llama.h b/llama.h index 15ab4f80e..0be4b1337 100644 --- a/llama.h +++ b/llama.h @@ -314,7 +314,9 @@ extern "C" { LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); - LLAMA_API int llama_n_ctx (const struct llama_context * ctx); + // TODO: become more consistent with returned int types across the API + LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); From d232aca5a73b290e218a2e48b91023d5e994203f Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 21 Dec 2023 21:07:46 +0100 Subject: [PATCH 229/664] llama : initial ggml-backend integration (#4520) * llama : initial ggml-backend integration * add ggml-metal * cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST access all tensor data with ggml_backend_tensor_get/set * add ggml_backend_buffer_clear zero-init KV cache buffer * add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data * disable gpu backends with ngl 0 * more accurate mlock * unmap offloaded part of the model * use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap * update quantize and lora * update session copy/set to use ggml-backend ggml-ci * use posix_fadvise instead of posix_fadvise64 * ggml_backend_alloc_ctx_tensors_from_buft : remove old print * llama_mmap::align_offset : use pointers instead of references for out parameters * restore progress_callback behavior * move final progress_callback call to load_all_data * cuda : fix fprintf format string (minor) * do not offload scales * llama_mmap : avoid unmapping the same fragments again in the destructor * remove unnecessary unmap * metal : add default log function that prints to stderr, cleanup code ggml-ci --------- Co-authored-by: Georgi Gerganov --- Makefile | 2 +- ggml-alloc.c | 16 +- ggml-backend-impl.h | 20 +- ggml-backend.c | 80 ++- ggml-backend.h | 7 + ggml-cuda.cu | 89 ++-- ggml-metal.h | 3 + ggml-metal.m | 228 +++++++-- ggml.c | 24 +- ggml.h | 13 +- llama.cpp | 1196 ++++++++++++++++++++----------------------- 11 files changed, 926 insertions(+), 752 deletions(-) diff --git a/Makefile b/Makefile index 8273f8400..512407a1d 100644 --- a/Makefile +++ b/Makefile @@ -65,7 +65,7 @@ test: $(TEST_TARGETS) ./$$test_target; \ fi; \ if [ $$? -ne 0 ]; then \ - printf 'Test $$test_target FAILED!\n\n' $$test_target; \ + printf 'Test %s FAILED!\n\n' $$test_target; \ failures=$$(( failures + 1 )); \ else \ printf 'Test %s passed.\n\n' $$test_target; \ diff --git a/ggml-alloc.c b/ggml-alloc.c index d3049efb4..a97436b17 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -449,11 +449,10 @@ static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool upd if (update_backend) { view->backend = view->view_src->backend; } - view->buffer = view->view_src->buffer; + // views are initialized in the alloc buffer rather than the view_src buffer + view->buffer = alloc->buffer; view->data = (char *)view->view_src->data + view->view_offs; - // FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend - // due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft); if (!alloc->measure) { @@ -736,6 +735,10 @@ void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) { } void ggml_allocr_free(ggml_allocr_t alloc) { + if (alloc == NULL) { + return; + } + ggml_gallocr_free(alloc->galloc); ggml_tallocr_free(alloc->talloc); free(alloc); @@ -775,7 +778,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte } if (nbytes == 0) { - fprintf(stderr, "%s: no tensors to allocate\n", __func__); + // all the tensors in the context are already allocated return NULL; } @@ -789,6 +792,11 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte } else { ggml_backend_view_init(buffer, t); } + } else { + if (t->view_src != NULL) { + // view of a pre-allocated tensor + ggml_backend_view_init(buffer, t); + } } } diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index f588af602..05859935a 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -20,6 +20,9 @@ extern "C" { size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend + // check if tensor data is in host memory + // should be equivalent to supports_backend(buft, ggml_backend_cpu_init()) + bool (*is_host) (ggml_backend_buffer_type_t buft); }; struct ggml_backend_buffer_type { @@ -31,15 +34,16 @@ extern "C" { typedef void * ggml_backend_buffer_context_t; struct ggml_backend_buffer_i { - void (*free_buffer)(ggml_backend_buffer_t buffer); + void (*free_buffer) (ggml_backend_buffer_t buffer); //void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras - void * (*get_base) (ggml_backend_buffer_t buffer); - void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + void * (*get_base) (ggml_backend_buffer_t buffer); + void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); // (optional) copy tensor between different buffer-type, allow for single-copy tranfers - void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); + void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); + void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); + void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); }; struct ggml_backend_buffer { @@ -78,7 +82,7 @@ extern "C" { void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*synchronize) (ggml_backend_t backend); + void (*synchronize)(ggml_backend_t backend); // compute graph with a plan ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); diff --git a/ggml-backend.c b/ggml-backend.c index 3a22cd085..0c8c9ec43 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -35,6 +35,13 @@ bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_ba return buft->iface.supports_backend(buft, backend); } +bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) { + if (buft->iface.is_host) { + return buft->iface.is_host(buft); + } + return false; +} + // backend buffer ggml_backend_buffer_t ggml_backend_buffer_init( @@ -94,6 +101,14 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type(buffer), tensor); } +void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + buffer->iface.clear(buffer, value); +} + +bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { + return ggml_backend_buft_is_host(ggml_backend_buffer_type(buffer)); +} + ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer) { return buffer->buft; } @@ -378,7 +393,6 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { free(buffer->context); - GGML_UNUSED(buffer); } static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { @@ -411,6 +425,10 @@ static void ggml_backend_cpu_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, GGML_UNUSED(buffer); } +static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + static struct ggml_backend_buffer_i cpu_backend_buffer_i = { /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, /* .get_base = */ ggml_backend_cpu_buffer_get_base, @@ -419,6 +437,7 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i = { /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from, /* .cpy_tensor_to = */ ggml_backend_cpu_buffer_cpy_tensor_to, + /* .clear = */ ggml_backend_cpu_buffer_clear, }; // for buffers from ptr, free is not called @@ -430,6 +449,7 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from, /* .cpy_tensor_to = */ ggml_backend_cpu_buffer_cpy_tensor_to, + /* .clear = */ ggml_backend_cpu_buffer_clear, }; static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 @@ -455,20 +475,70 @@ static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_ty GGML_UNUSED(buft); } +static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return true; + + GGML_UNUSED(buft); +} + ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_cpu = { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { /* .iface = */ { /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, }, /* .context = */ NULL, }; - return &ggml_backend_buffer_type_cpu; + return &ggml_backend_cpu_buffer_type; } +#ifdef GGML_USE_CPU_HBM + +// buffer type HBM + +#include + +static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { + hbw_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + //void * ptr = hbw_malloc(size); + void * ptr; + int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); + if (result != 0) { + fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size); + return NULL; + } + + // FIXME: this is a hack to avoid having to implement a new buffer type + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { + /* .iface = */ { + /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type_hbm; +} +#endif + struct ggml_backend_cpu_context { int n_threads; void * work_data; @@ -505,7 +575,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); - cpu_plan->cgraph = *cgraph; + cpu_plan->cgraph = *cgraph; // FIXME: deep copy if (cpu_plan->cplan.work_size > 0) { cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size); @@ -1180,7 +1250,7 @@ void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml // utils void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { GGML_ASSERT(tensor->buffer == NULL); - GGML_ASSERT(tensor->data == NULL); + //GGML_ASSERT(tensor->data == NULL); // views of pre-allocted tensors may have the data set, but still need to be initialized GGML_ASSERT(tensor->view_src != NULL); GGML_ASSERT(tensor->view_src->buffer != NULL); GGML_ASSERT(tensor->view_src->data != NULL); diff --git a/ggml-backend.h b/ggml-backend.h index 58d5ccae6..a9d2fddd7 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -21,6 +21,7 @@ extern "C" { GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); + GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); // buffer GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); @@ -29,6 +30,8 @@ extern "C" { GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); + GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer); // @@ -76,6 +79,10 @@ extern "C" { GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); +#ifdef GGML_USE_CPU_HBM + GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); +#endif + // // Backend registry // diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 32603a8d1..f5e060d32 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -9081,7 +9081,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { char * buf; CUDA_CHECK(cudaMalloc(&buf, size)); - char * buf_host = (char*)data + offset_split; + char * buf_host = (char *)data + offset_split; // set padding to 0 to avoid possible NaN values if (size > original_size) { @@ -9226,11 +9226,10 @@ void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra(); - const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) || - tensor->op == GGML_OP_VIEW; + const bool inplace = tensor->view_src != nullptr; - if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; + if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) { + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; size_t view_offset = 0; if (tensor->op == GGML_OP_VIEW) { @@ -9317,7 +9316,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ if (tensor->op == GGML_OP_MUL_MAT) { if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { #ifndef NDEBUG - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = " PRId64 ", src1->ne[3] = " PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); + fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); #endif return false; } @@ -9523,7 +9522,7 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { - assert(tensor->view_src->buffer->buft == buffer->buft); // TODO + assert(tensor->view_src->buffer->buft == buffer->buft); tensor->backend = tensor->view_src->backend; tensor->extra = tensor->view_src->extra; return; @@ -9554,23 +9553,34 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g } static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice)); + ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; - UNUSED(buffer); + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + + CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice)); } static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost)); + ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; - UNUSED(buffer); + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + + CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost)); +} + +static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + + CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size)); } static struct ggml_backend_buffer_i cuda_backend_buffer_interface = { @@ -9581,6 +9591,7 @@ static struct ggml_backend_buffer_i cuda_backend_buffer_interface = { /* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor, /* .cpy_tensor_from = */ NULL, /* .cpy_tensor_to = */ NULL, + /* .clear = */ ggml_backend_cuda_buffer_clear, }; // cuda buffer type @@ -9632,35 +9643,36 @@ static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_t UNUSED(buft); } -static ggml_backend_buffer_type_i cuda_backend_buffer_type_interface = { +static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, + /* .is_host = */ nullptr, }; ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_cuda[GGML_CUDA_MAX_DEVICES]; - static bool ggml_backend_buffer_type_cuda_initialized = false; - if (!ggml_backend_buffer_type_cuda_initialized) { + static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; + + static bool ggml_backend_cuda_buffer_type_initialized = false; + + if (!ggml_backend_cuda_buffer_type_initialized) { for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) { - ggml_backend_buffer_type_cuda[i] = { - /* .iface = */ cuda_backend_buffer_type_interface, + ggml_backend_cuda_buffer_types[i] = { + /* .iface = */ ggml_backend_cuda_buffer_type_interface, /* .context = */ (ggml_backend_buffer_type_context_t) (intptr_t) i, }; } - ggml_backend_buffer_type_cuda_initialized = true; + ggml_backend_cuda_buffer_type_initialized = true; } - return &ggml_backend_buffer_type_cuda[device]; + return &ggml_backend_cuda_buffer_types[device]; } // host buffer type static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; - CUDA_CHECK(cudaFreeHost(ctx->dev_ptr)); - delete ctx; + CUDA_CHECK(cudaFreeHost(buffer->context)); } static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { @@ -9673,24 +9685,21 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; return buffer; - - UNUSED(buft); } -struct ggml_backend_buffer_type_i cuda_backend_host_buffer_type_interface = { - /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, -}; - ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_cuda_host = { - /* .iface = */ cuda_backend_host_buffer_type_interface, + static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { + /* .iface = */ { + /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, /* .context = */ nullptr, }; - return &ggml_backend_buffer_type_cuda_host; + return &ggml_backend_cuda_buffer_type_host; } // backend @@ -9722,8 +9731,6 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0])); @@ -9733,8 +9740,6 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); diff --git a/ggml-metal.h b/ggml-metal.h index bf52d9cd3..b5e02b668 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -98,7 +98,10 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void); GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); +GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size); + GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb); + GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); // helper to check if the device supports a specific family diff --git a/ggml-metal.m b/ggml-metal.m index 465679a6b..e60b93b36 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -180,7 +180,15 @@ struct ggml_metal_context { @implementation GGMLMetalClass @end -ggml_log_callback ggml_metal_log_callback = NULL; + +static void ggml_metal_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { + fprintf(stderr, "%s", msg); + + UNUSED(level); + UNUSED(user_data); +} + +ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback; void * ggml_metal_log_user_data = NULL; void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { @@ -607,12 +615,24 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) { } // temporarily defined here for compatibility between ggml-backend and the old API -struct ggml_backend_metal_buffer_context { - void * data; + +struct ggml_backend_metal_buffer { + void * data; + size_t size; id metal; }; +struct ggml_backend_metal_buffer_context { + void * all_data; + size_t all_size; + bool owned; + + // multiple buffers are used only to avoid the maximum buffer size limitation when using mmap + int n_buffers; + struct ggml_backend_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; +}; + // finds the Metal buffer that contains the tensor data on the GPU device // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the // Metal buffer based on the host memory pointer @@ -622,17 +642,29 @@ static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru const int64_t tsize = ggml_nbytes(t); + ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer; + // compatibility with ggml-backend - if (t->buffer && t->buffer->buft == ggml_backend_metal_buffer_type()) { - struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) t->buffer->context; + if (buffer && buffer->buft == ggml_backend_metal_buffer_type()) { + struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context; - const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->data; + // find the view that contains the tensor fully + for (int i = 0; i < buf_ctx->n_buffers; ++i) { + const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data; - GGML_ASSERT(ioffs >= 0 && ioffs + tsize <= (int64_t) t->buffer->size); + //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size); + if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) { + *offs = (size_t) ioffs; - *offs = (size_t) ioffs; + //GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs); - return buf_ctx->metal; + return buf_ctx->buffers[i].metal; + } + } + + GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name); + + return nil; } // find the view that contains the tensor fully @@ -2361,6 +2393,7 @@ void ggml_metal_graph_compute( // backend interface +// default buffer static id g_backend_device = nil; static int g_backend_device_ref_count = 0; @@ -2388,34 +2421,31 @@ static void ggml_backend_metal_free_device(void) { static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; - return ctx->data; + return ctx->all_data; } static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; - [ctx->metal release]; + for (int i = 0; i < ctx->n_buffers; i++) { + [ctx->buffers[i].metal release]; + } ggml_backend_metal_free_device(); - free(ctx->data); - free(ctx); + if (ctx->owned) { + free(ctx->all_data); + } - UNUSED(buffer); + free(ctx); } static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - memcpy((char *)tensor->data + offset, data, size); UNUSED(buffer); } static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - memcpy(data, (const char *)tensor->data + offset, size); UNUSED(buffer); @@ -2433,7 +2463,13 @@ static void ggml_backend_metal_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer UNUSED(buffer); } -static struct ggml_backend_buffer_i metal_backend_buffer_i = { +static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; + + memset(ctx->all_data, value, ctx->all_size); +} + +static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_tensor = */ NULL, @@ -2441,8 +2477,11 @@ static struct ggml_backend_buffer_i metal_backend_buffer_i = { /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, /* .cpy_tensor_from = */ ggml_backend_metal_buffer_cpy_tensor_from, /* .cpy_tensor_to = */ ggml_backend_metal_buffer_cpy_tensor_to, + /* .clear = */ ggml_backend_metal_buffer_clear, }; +// default buffer type + static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); @@ -2453,13 +2492,46 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba size_aligned += (size_page - (size_aligned % size_page)); } - ctx->data = ggml_metal_host_malloc(size); - ctx->metal = [ggml_backend_metal_get_device() newBufferWithBytesNoCopy:ctx->data + id device = ggml_backend_metal_get_device(); + + ctx->all_data = ggml_metal_host_malloc(size_aligned); + ctx->all_size = size_aligned; + ctx->owned = true; + ctx->n_buffers = 1; + + ctx->buffers[0].data = ctx->all_data; + ctx->buffers[0].size = size; + ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; - return ggml_backend_buffer_init(buft, metal_backend_buffer_i, ctx, size); + if (ctx->buffers[0].metal == nil) { + GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); + free(ctx); + ggml_backend_metal_free_device(); + return NULL; + } + + GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); + + +#if TARGET_OS_OSX + GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", + device.currentAllocatedSize / 1024.0 / 1024.0, + device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + + if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { + GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); + } else { + GGML_METAL_LOG_INFO("\n"); + } +#else + GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); +#endif + + + return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); } static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { @@ -2470,7 +2542,13 @@ static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_t static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend); - GGML_UNUSED(buft); + UNUSED(buft); +} + +static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return true; + + UNUSED(buft); } ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { @@ -2480,6 +2558,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_metal_buffer_type_is_host, }, /* .context = */ NULL, }; @@ -2487,6 +2566,87 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { return &ggml_backend_buffer_type_metal; } +// buffer from ptr + +ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { + struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); + + ctx->all_data = data; + ctx->all_size = size; + ctx->owned = false; + ctx->n_buffers = 0; + + const size_t size_page = sysconf(_SC_PAGESIZE); + size_t size_aligned = size; + if ((size_aligned % size_page) != 0) { + size_aligned += (size_page - (size_aligned % size_page)); + } + + id device = ggml_backend_metal_get_device(); + + // the buffer fits into the max buffer size allowed by the device + if (size_aligned <= device.maxBufferLength) { + ctx->buffers[ctx->n_buffers].data = data; + ctx->buffers[ctx->n_buffers].size = size; + + ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; + + if (ctx->buffers[ctx->n_buffers].metal == nil) { + GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); + return false; + } + + GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); + + ++ctx->n_buffers; + } else { + // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into + // one of the views + const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case + const size_t size_step = device.maxBufferLength - size_ovlp; + const size_t size_view = device.maxBufferLength; + + for (size_t i = 0; i < size; i += size_step) { + const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); + + ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i); + ctx->buffers[ctx->n_buffers].size = size_step_aligned; + + ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; + + if (ctx->buffers[ctx->n_buffers].metal == nil) { + GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0); + return false; + } + + GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i); + if (i + size_step < size) { + GGML_METAL_LOG_INFO("\n"); + } + + ++ctx->n_buffers; + } + } + +#if TARGET_OS_OSX + GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", + device.currentAllocatedSize / 1024.0 / 1024.0, + device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + + if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { + GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); + } else { + GGML_METAL_LOG_INFO("\n"); + } +#else + GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); +#endif + + return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); +} + +// backend + static const char * ggml_backend_metal_name(ggml_backend_t backend) { return "Metal"; @@ -2499,10 +2659,6 @@ static void ggml_backend_metal_free(ggml_backend_t backend) { free(backend); } -static void ggml_backend_metal_synchronize(ggml_backend_t backend) { - UNUSED(backend); -} - static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { return ggml_backend_metal_buffer_type(); @@ -2529,25 +2685,15 @@ static struct ggml_backend_i metal_backend_i = { /* .get_tensor_async = */ NULL, /* .cpy_tensor_from_async = */ NULL, /* .cpy_tensor_to_async = */ NULL, - /* .synchronize = */ ggml_backend_metal_synchronize, - /* .graph_plan_create = */ NULL, // the metal implementation does not require creating graph plans atm + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, /* .supports_op = */ ggml_backend_metal_supports_op, }; -// TODO: make a common log callback for all backends in ggml-backend -static void ggml_backend_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { - fprintf(stderr, "%s", msg); - - UNUSED(level); - UNUSED(user_data); -} - ggml_backend_t ggml_backend_metal_init(void) { - ggml_metal_log_set_callback(ggml_backend_log_callback, NULL); - struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); if (ctx == NULL) { diff --git a/ggml.c b/ggml.c index 6da65bd92..236148514 100644 --- a/ggml.c +++ b/ggml.c @@ -2383,20 +2383,8 @@ size_t ggml_get_mem_size(const struct ggml_context * ctx) { size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { size_t max_size = 0; - struct ggml_object * obj = ctx->objects_begin; - - while (obj != NULL) { - if (obj->type == GGML_OBJECT_TENSOR) { - struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs); - - const size_t size = ggml_nbytes(tensor); - - if (max_size < size) { - max_size = size; - } - } - - obj = obj->next; + for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) { + max_size = MAX(max_size, ggml_nbytes(tensor)); } return max_size; @@ -3093,7 +3081,7 @@ struct ggml_tensor * ggml_view_tensor( return result; } -struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) { +struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) { struct ggml_object * obj = ctx->objects_begin; char * const mem_buffer = ctx->mem_buffer; @@ -3109,7 +3097,7 @@ struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) { return NULL; } -struct ggml_tensor * ggml_get_next_tensor(struct ggml_context * ctx, struct ggml_tensor * tensor) { +struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struct ggml_tensor * tensor) { struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE); obj = obj->next; @@ -19213,6 +19201,10 @@ char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) { return ctx->infos[i].name.data; } +enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int i) { + return ctx->infos[i].type; +} + // returns the index static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) { const int idx = gguf_find_key(ctx, key); diff --git a/ggml.h b/ggml.h index beacdc8be..b17314897 100644 --- a/ggml.h +++ b/ggml.h @@ -735,8 +735,8 @@ extern "C" { GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src); // Context tensor enumeration and lookup - GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx); - GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor); + GGML_API struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx); + GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); @@ -2135,10 +2135,11 @@ extern "C" { GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id); GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i); - GGML_API int gguf_get_n_tensors (const struct gguf_context * ctx); - GGML_API int gguf_find_tensor (const struct gguf_context * ctx, const char * name); - GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i); - GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i); + GGML_API int gguf_get_n_tensors (const struct gguf_context * ctx); + GGML_API int gguf_find_tensor (const struct gguf_context * ctx, const char * name); + GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i); + GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i); + GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i); // overrides existing values or adds a new one GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val); diff --git a/llama.cpp b/llama.cpp index 63ebe581b..ba970ce8d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1,11 +1,12 @@ #define LLAMA_API_INTERNAL +//#define LLAMA_GGML_BACKEND_CUDA_TEST // for testing only - enables ggml-cuda through ggml-backend, disables partial offloading #include "llama.h" #include "unicode.h" #include "ggml.h" - #include "ggml-alloc.h" +#include "ggml-backend.h" #ifdef GGML_USE_CUBLAS # include "ggml-cuda.h" @@ -32,6 +33,7 @@ #include #if defined(_POSIX_MAPPED_FILES) #include + #include #endif #if defined(_POSIX_MEMLOCK_RANGE) #include @@ -712,38 +714,6 @@ static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * // llama helpers // -inline void * llama_host_malloc(size_t n) { -#ifdef GGML_USE_CUBLAS - if (ggml_cublas_loaded()) { - return ggml_cuda_host_malloc(n); - } else { - return malloc(n); - } -#elif GGML_USE_METAL - return ggml_metal_host_malloc(n); -#elif GGML_USE_CPU_HBM - return hbw_malloc(n); -#else - return malloc(n); -#endif -} - -inline void llama_host_free(void * ptr) { -#ifdef GGML_USE_CUBLAS - if (ggml_cublas_loaded()) { - return ggml_cuda_host_free(ptr); - } else { - return free(ptr); - } -#elif GGML_USE_METAL - return ggml_metal_host_free(ptr); -#elif GGML_USE_CPU_HBM - return hbw_free(ptr); -#else - return free(ptr); -#endif -} - #if defined(_WIN32) static std::string llama_format_win_err(DWORD err) { LPSTR buf; @@ -758,40 +728,10 @@ static std::string llama_format_win_err(DWORD err) { } #endif -struct llama_buffer { - void * data = NULL; - size_t size = 0; - - // fallback to malloc / free - // useful in cases where CUDA can try to allocate PINNED memory - bool fallback = false; - - void resize(size_t n) { - llama_host_free(data); - - data = llama_host_malloc(n); - if (!data) { - fallback = true; - data = malloc(n); - } else { - fallback = false; - } - - GGML_ASSERT(data); - size = n; - } - - ~llama_buffer() { - if (data) { - if (fallback) { // NOLINT - free(data); - } else { - llama_host_free(data); - } - } - - data = NULL; - } +template +struct no_init { + T value; + no_init() { /* do nothing */ } }; struct llama_file { @@ -879,6 +819,9 @@ struct llama_mmap { #ifdef _POSIX_MAPPED_FILES static constexpr bool SUPPORTED = true; + // list of mapped fragments (first_offset, last_offset) + std::vector> mapped_fragments; + llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) { size = file->size; int fd = fileno(file->fp); @@ -886,17 +829,22 @@ struct llama_mmap { // prefetch/readahead impairs performance on NUMA systems if (numa) { prefetch = 0; } #ifdef __linux__ + // advise the kernel to read the file sequentially (increases readahead) + if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) { + LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n", + strerror(errno)); + } if (prefetch) { flags |= MAP_POPULATE; } #endif addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0); - if (addr == MAP_FAILED) { + if (addr == MAP_FAILED) { // NOLINT throw std::runtime_error(format("mmap failed: %s", strerror(errno))); } if (prefetch > 0) { - // Advise the kernel to preload the mapped memory + // advise the kernel to preload the mapped memory if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) { - fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n", + LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n", strerror(errno)); } } @@ -904,14 +852,81 @@ struct llama_mmap { // advise the kernel not to use readahead // (because the next page might not belong on the same node) if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) { - fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n", + LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n", strerror(errno)); } } + + // initialize list of mapped_fragments + mapped_fragments.emplace_back(0, file->size); + } + + static void align_range(size_t * first, size_t * last, size_t page_size) { + // align first to the next page + size_t offset_in_page = *first & (page_size - 1); + size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page; + *first += offset_to_page; + + // align last to the previous page + *last = *last & ~(page_size - 1); + + if (*last <= *first) { + *last = *first; + } + } + + // partially unmap the file in the range [first, last) + void unmap_fragment(size_t first, size_t last) { + // note: this function must not be called multiple times with overlapping ranges + // otherwise, there is a risk of invalidating addresses that have been repurposed for other mappings + int page_size = sysconf(_SC_PAGESIZE); + align_range(&first, &last, page_size); + size_t len = last - first; + + if (len == 0) { + return; + } + + GGML_ASSERT(first % page_size == 0); + GGML_ASSERT(last % page_size == 0); + GGML_ASSERT(last > first); + + void * next_page_start = (uint8_t *) addr + first; + + // unmap the range + if (munmap(next_page_start, len)) { + LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno)); + } + + // update the list of mapped fragments to avoid unmapping the same range again in the destructor + std::vector> new_mapped_fragments; + for (const auto & frag : mapped_fragments) { + if (frag.first < first && frag.second > last) { + // the range is in the middle of the fragment, split it + new_mapped_fragments.emplace_back(frag.first, first); + new_mapped_fragments.emplace_back(last, frag.second); + } else if (frag.first < first && frag.second > first) { + // the range starts in the middle of the fragment + new_mapped_fragments.emplace_back(frag.first, first); + } else if (frag.first < last && frag.second > last) { + // the range ends in the middle of the fragment + new_mapped_fragments.emplace_back(last, frag.second); + } else if (frag.first >= first && frag.second <= last) { + // the range covers the entire fragment + } else { + // the range is outside the fragment + new_mapped_fragments.push_back(frag); + } + } + mapped_fragments = std::move(new_mapped_fragments); } ~llama_mmap() { - munmap(addr, size); + for (const auto & frag : mapped_fragments) { + if (munmap((char *) addr + frag.first, frag.second - frag.first)) { + LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno)); + } + } } #elif defined(_WIN32) static constexpr bool SUPPORTED = true; @@ -959,6 +974,12 @@ struct llama_mmap { } } + void unmap_fragment(size_t first, size_t last) { + // not supported + GGML_UNUSED(first); + GGML_UNUSED(last); + } + ~llama_mmap() { if (!UnmapViewOfFile(addr)) { fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n", @@ -975,6 +996,13 @@ struct llama_mmap { throw std::runtime_error(std::string("mmap not supported")); } + + void unmap(size_t offset, size_t len) { + (void) offset; + (void) len; + + throw std::runtime_error(std::string("mmap not supported")); + } #endif }; @@ -1148,6 +1176,26 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_ return std::string(result.data(), result.size()); } +static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) { +#ifdef GGML_USE_METAL + if (n_gpu_layers > 0) { + return ggml_backend_metal_buffer_type(); + } +#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) + if (n_gpu_layers > 0) { + return ggml_backend_cuda_buffer_type(0); + } +#elif defined(GGML_USE_CUBLAS) + return ggml_backend_cuda_host_buffer_type(); +#elif defined(GGML_USE_CPU_HBM) + return ggml_backend_cpu_hbm_buffer_type(); +#endif + + return ggml_backend_cpu_buffer_type(); + + GGML_UNUSED(n_gpu_layers); +} + // // globals // @@ -1348,14 +1396,10 @@ struct llama_kv_cache { struct ggml_context * ctx = NULL; - llama_buffer buf; + ggml_backend_buffer_t buf = NULL; ~llama_kv_cache() { - if (ctx) { - ggml_free(ctx); - } - -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (ggml_cublas_loaded()) { for (size_t i = 0; i < k_l.size(); ++i) { ggml_cuda_free_data(k_l[i]); @@ -1363,6 +1407,11 @@ struct llama_kv_cache { } } #endif + if (ctx) { + ggml_free(ctx); + } + + ggml_backend_buffer_free(buf); } }; @@ -1402,11 +1451,11 @@ struct llama_vocab { id special_suffix_id = 32008; id special_eot_id = 32010; - int find_bpe_rank(std::string token_left, std::string token_right) const { - GGML_ASSERT(token_left.find(" ") == std::string::npos); - GGML_ASSERT(token_left.find("\n") == std::string::npos); - GGML_ASSERT(token_right.find(" ") == std::string::npos); - GGML_ASSERT(token_right.find("\n") == std::string::npos); + int find_bpe_rank(const std::string & token_left, const std::string & token_right) const { + GGML_ASSERT(token_left.find(' ') == std::string::npos); + GGML_ASSERT(token_left.find('\n') == std::string::npos); + GGML_ASSERT(token_right.find(' ') == std::string::npos); + GGML_ASSERT(token_right.find('\n') == std::string::npos); auto it = bpe_ranks.find(std::make_pair(token_left, token_right)); if (it == bpe_ranks.end()) { @@ -1448,7 +1497,7 @@ struct llama_model { struct ggml_context * ctx = NULL; // the model memory buffer - llama_buffer buf; + ggml_backend_buffer_t buf = NULL; // model memory mapped file std::unique_ptr mapping; @@ -1464,11 +1513,7 @@ struct llama_model { int64_t t_start_us = 0; ~llama_model() { - if (ctx) { - ggml_free(ctx); - } - -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (ggml_cublas_loaded()) { for (size_t i = 0; i < tensors_by_name.size(); ++i) { ggml_cuda_free_data(tensors_by_name[i].second); @@ -1482,24 +1527,26 @@ struct llama_model { ggml_cl_free_data(tensors_by_name[i].second); } #endif + if (ctx) { + ggml_free(ctx); + } + + ggml_backend_buffer_free(buf); } }; struct llama_context { llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {} ~llama_context() { -#ifdef GGML_USE_METAL - if (ctx_metal) { - ggml_metal_free(ctx_metal); - } -#endif - if (alloc) { - ggml_allocr_free(alloc); - } + ggml_allocr_free(alloc); + ggml_backend_buffer_free(buf_alloc); + ggml_backend_free(backend); } llama_cparams cparams; + ggml_backend_t backend = nullptr; + const llama_model & model; // key + value cache for the self attention @@ -1530,18 +1577,13 @@ struct llama_context { // input embedding (1-dimensional array: [n_embd]) std::vector embedding; - // reusable buffer for `struct ggml_graph_plan.work_data` - std::vector work_buffer; - // memory buffers used to evaluate the model - llama_buffer buf_compute; - - llama_buffer buf_alloc; + std::vector buf_compute_meta; + ggml_backend_buffer_t buf_alloc = NULL; ggml_allocr * alloc = NULL; -#ifdef GGML_USE_METAL - ggml_metal_context * ctx_metal = NULL; -#endif + // temporary buffer for copying data to/from the backend + std::vector> buf_copy; #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -1563,9 +1605,6 @@ static bool llama_kv_cache_init( const uint32_t n_embd = hparams.n_embd_gqa(); const uint32_t n_layer = hparams.n_layer; - const int64_t n_mem = n_layer*n_ctx; - const int64_t n_elements = n_embd*n_mem; - cache.has_shift = false; cache.head = 0; @@ -1575,13 +1614,10 @@ static bool llama_kv_cache_init( cache.cells.clear(); cache.cells.resize(n_ctx); - cache.buf.resize(ggml_row_size(ktype, n_elements) + ggml_row_size(vtype, n_elements) + 2u*n_layer*ggml_tensor_overhead()); - memset(cache.buf.data, 0, cache.buf.size); - struct ggml_init_params params; - params.mem_size = cache.buf.size; - params.mem_buffer = cache.buf.data; - params.no_alloc = false; + params.mem_size = 2u*n_layer*ggml_tensor_overhead(); + params.mem_buffer = NULL; + params.no_alloc = true; cache.ctx = ggml_init(params); @@ -1595,9 +1631,7 @@ static bool llama_kv_cache_init( cache.k_l.reserve(n_layer); cache.v_l.reserve(n_layer); - const int i_gpu_start = (int) n_layer - n_gpu_layers; GGML_UNUSED(i_gpu_start); - - GGML_UNUSED(offload); + const int i_gpu_start = (int) n_layer - n_gpu_layers; for (int i = 0; i < (int) n_layer; i++) { ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd*n_ctx); @@ -1606,23 +1640,35 @@ static bool llama_kv_cache_init( ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); cache.v_l.push_back(v); -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (i >= i_gpu_start) { if (offload) { ggml_cuda_assign_buffers_no_scratch(k); - vram_kv_cache += ggml_nbytes(k); ggml_cuda_assign_buffers_no_scratch(v); + vram_kv_cache += ggml_nbytes(k); vram_kv_cache += ggml_nbytes(v); + // HACK: mark tensor as allocated + k->data = v->data = (void *)(uintptr_t)1; } } #endif // GGML_USE_CUBLAS } + // allocate tensors + cache.buf = ggml_backend_alloc_ctx_tensors_from_buft(cache.ctx, llama_default_buffer_type(n_gpu_layers)); + + // buf may be NULL with full offload + if (cache.buf) { + // initialize the buffer to avoid NaNs in the padding + ggml_backend_buffer_clear(cache.buf, 0); + } + if (vram_kv_cache > 0) { LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); } - GGML_UNUSED(n_gpu_layers); + GGML_UNUSED(i_gpu_start); + GGML_UNUSED(offload); return true; } @@ -2073,14 +2119,13 @@ struct llama_model_loader { enum ggml_type type_max = GGML_TYPE_F32; for (int i = 0; i < n_tensors; i++) { - const char * name = gguf_get_tensor_name(ctx_gguf, i); - struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name); + enum ggml_type type = gguf_get_tensor_type(ctx_gguf, i); - n_type[meta->type]++; + n_type[type]++; - if (n_type_max < n_type[meta->type]) { - n_type_max = n_type[meta->type]; - type_max = meta->type; + if (n_type_max < n_type[type]) { + n_type_max = n_type[type]; + type_max = type; } // LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str()); @@ -2221,34 +2266,19 @@ struct llama_model_loader { return gguf_get_tensor_name(ctx_gguf, i); } - struct ggml_tensor * get_tensor_meta(int i) const { - return ggml_get_tensor(ctx_meta, get_tensor_name(i)); + struct ggml_tensor * get_tensor_meta(const char * name) const { + return ggml_get_tensor(ctx_meta, name); } - void calc_sizes(size_t & ctx_size_p, size_t & mmapped_size_p) const { - ctx_size_p = 0; - mmapped_size_p = 0; - - for (int i = 0; i < n_tensors; i++) { - struct ggml_tensor * meta = get_tensor_meta(i); - ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE; - (use_mmap ? mmapped_size_p : ctx_size_p) += ggml_nbytes_pad(meta); - } + struct ggml_tensor * get_tensor_meta(int i) const { + return get_tensor_meta(get_tensor_name(i)); } struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) { - if (backend != GGML_BACKEND_CPU) { - ggml_set_no_alloc(ctx, true); - } - struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta); tensor->backend = backend; // TODO: ggml_set_backend ggml_set_name(tensor, ggml_get_name(meta)); - if (backend != GGML_BACKEND_CPU) { - ggml_set_no_alloc(ctx, use_mmap); - } - n_created++; return tensor; @@ -2306,90 +2336,137 @@ struct llama_model_loader { return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx); } + void init_mapping(bool prefetch = true) { + /* + // prefetch only CPU tensors + if (use_mmap) { + size_t size_pref = 0; // prefetch + + for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { + struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); + if (cur->backend == GGML_BACKEND_CPU) { + size_t tensor_end = gguf_get_tensor_offset(ctx_gguf, i) + ggml_nbytes(cur); + size_pref = std::max(size_pref, tensor_end); + } + } + mapping.reset(new llama_mmap(&file, gguf_get_data_offset(ctx_gguf) + size_pref, ggml_is_numa())); + } + */ + // prefetch the whole file - all the data is needed anyway + if (use_mmap) { + mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa())); + } + } + + // for backwards compatibility, does not support ggml-backend void load_data_for(struct ggml_tensor * cur) const { const size_t offs = file_offset(ggml_get_name(cur)); - if (use_mmap) { - cur->data = (uint8_t *) mapping->addr + offs; + if (use_mmap && mapping) { + GGML_ASSERT(cur->data == nullptr); + cur->data = (uint8_t *)mapping->addr + offs; } else { + GGML_ASSERT(cur->data != nullptr); file.seek(offs, SEEK_SET); file.read_raw(cur->data, ggml_nbytes(cur)); } } - void load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) { + void load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const { size_t size_data = 0; - size_t size_lock = 0; - size_t size_pref = 0; // prefetch for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); size_data += ggml_nbytes(cur); - if (cur->backend == GGML_BACKEND_CPU) { - size_pref += ggml_nbytes(cur); - } } - if (use_mmap) { - mapping.reset(new llama_mmap(&file, size_pref, ggml_is_numa())); + if (use_mmap && buf_mmap) { if (lmlock) { lmlock->init(mapping->addr); } } - size_t done_size = 0; +#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST) + const bool legacy_offload = true; +#else + const bool legacy_offload = false; +#endif + + std::vector> read_buf; + + size_t size_done = 0; + + size_t mmap_first = -1; + size_t mmap_last = 0; + for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); GGML_ASSERT(cur); // unused tensors should have been caught by load_data already if (progress_callback) { - progress_callback((float) done_size / size_data, progress_callback_user_data); + progress_callback((float) size_done / size_data, progress_callback_user_data); } - // allocate temp buffer if not using mmap - if (!use_mmap && cur->data == NULL) { - GGML_ASSERT(cur->backend != GGML_BACKEND_CPU); - #ifdef GGML_USE_CPU_HBM - cur->data = (uint8_t*)hbw_malloc(ggml_nbytes(cur)); - #else - cur->data = (uint8_t*)malloc(ggml_nbytes(cur)); - #endif - } + const size_t offs = file_offset(ggml_get_name(cur)); - load_data_for(cur); - - switch (cur->backend) { - case GGML_BACKEND_CPU: - if (use_mmap && lmlock) { - size_lock += ggml_nbytes(cur); - lmlock->grow_to(size_lock); + if (!legacy_offload || cur->backend == GGML_BACKEND_CPU) { + if (use_mmap && mapping) { + if (buf_mmap) { + ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs); + if (lmlock) { + lmlock->grow_to(offs + ggml_nbytes(cur)); + } + mmap_first = std::min(mmap_first, offs); + mmap_last = std::max(mmap_last, offs + ggml_nbytes(cur)); + } else { + ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur)); } - break; -#ifdef GGML_USE_CUBLAS - case GGML_BACKEND_GPU: - case GGML_BACKEND_GPU_SPLIT: - // old code: - //ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor); - - // TODO: test if this works !! - ggml_cuda_transform_tensor(cur->data, cur); - if (!use_mmap) { - free(cur->data); + } else { + if (ggml_backend_buffer_is_host(cur->buffer)) { + file.seek(offs, SEEK_SET); + file.read_raw(cur->data, ggml_nbytes(cur)); + } else { + read_buf.resize(ggml_nbytes(cur)); + file.seek(offs, SEEK_SET); + file.read_raw(read_buf.data(), ggml_nbytes(cur)); + ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur)); } - break; + } + } else { + // HACK: mark tensor as allocated + cur->data = (void *)(uintptr_t)1; + void * data; + if (use_mmap && mapping) { + data = (uint8_t *) mapping->addr + offs; + } else { + read_buf.resize(ggml_nbytes(cur)); + file.seek(offs, SEEK_SET); + file.read_raw(read_buf.data(), ggml_nbytes(cur)); + data = read_buf.data(); + } + +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) + ggml_cuda_transform_tensor(data, cur); #elif defined(GGML_USE_CLBLAST) - case GGML_BACKEND_GPU: - ggml_cl_transform_tensor(cur->data, cur); - if (!use_mmap) { - free(cur->data); - } - break; + GGML_ASSERT(cur->backend == GGML_BACKEND_GPU); + ggml_cl_transform_tensor(data, cur); +#else + GGML_ASSERT(!"GPU tensor without a GPU backend"); + GGML_UNUSED(data); #endif - default: - continue; } - done_size += ggml_nbytes(cur); + size_done += ggml_nbytes(cur); + } + + // unmap offloaded tensors and metadata + if (use_mmap && mapping) { + mapping->unmap_fragment(0, mmap_first); + mapping->unmap_fragment(mmap_last, mapping->size); + } + + if (progress_callback) { + progress_callback(1.0f, progress_callback_user_data); } } }; @@ -2983,25 +3060,16 @@ static void llm_load_tensors( model.n_gpu_layers = n_gpu_layers; - size_t ctx_size; - size_t mmapped_size; + size_t ctx_size = ggml_tensor_overhead() * ml.n_tensors; - ml.calc_sizes(ctx_size, mmapped_size); - - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0); + LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0); // create the ggml context { - model.buf.resize(ctx_size); - if (use_mlock) { - model.mlock_buf.init (model.buf.data); - model.mlock_buf.grow_to(model.buf.size); - } - struct ggml_init_params params = { - /*.mem_size =*/ model.buf.size, - /*.mem_buffer =*/ model.buf.data, - /*.no_alloc =*/ ml.use_mmap, + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, }; model.ctx = ggml_init(params); @@ -3015,22 +3083,21 @@ static void llm_load_tensors( enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (ggml_cublas_loaded()) { LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); ggml_cuda_set_main_device(main_gpu); - llama_backend_offload = GGML_BACKEND_GPU; + llama_backend_offload = GGML_BACKEND_GPU; llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT; } #elif defined(GGML_USE_CLBLAST) LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); - llama_backend_offload = GGML_BACKEND_GPU; + llama_backend_offload = GGML_BACKEND_GPU; llama_backend_offload_split = GGML_BACKEND_GPU; #endif - // prepare memory for the weights - size_t vram_weights = 0; + // create tensors for the weights { const int64_t n_embd = hparams.n_embd; const int64_t n_embd_gqa = hparams.n_embd_gqa(); @@ -3059,13 +3126,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3115,28 +3175,6 @@ static void llm_load_tensors( layer.ffn_up_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}, backend_split); } } - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + - ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + - (layer.bq ? ggml_nbytes(layer.bq) : 0) + - (layer.bk ? ggml_nbytes(layer.bk) : 0) + - (layer.bv ? ggml_nbytes(layer.bv) : 0) + - (layer.bo ? ggml_nbytes(layer.bo) : 0) + - ggml_nbytes(layer.ffn_norm); - - if (layer.ffn_gate_inp == nullptr) { - vram_weights += - ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); - } else { - vram_weights += ggml_nbytes(layer.ffn_gate_inp); - for (uint32_t x = 0; x < hparams.n_expert; ++x) { - vram_weights += - ggml_nbytes(layer.ffn_gate_exp[x]) + ggml_nbytes(layer.ffn_down_exp[x]) + ggml_nbytes(layer.ffn_up_exp[x]); - } - } - } } } break; case LLM_ARCH_BAICHUAN: @@ -3156,13 +3194,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3189,19 +3220,10 @@ static void llm_load_tensors( layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + - ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + - ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); - } } } break; case LLM_ARCH_FALCON: { - // TODO: CPU-only for now - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); // output @@ -3220,14 +3242,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - vram_weights += ggml_nbytes(model.output_norm_b); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3248,11 +3262,6 @@ static void llm_load_tensors( if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) { layer.attn_norm_2 = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend); layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, backend); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(layer.attn_norm_2); - vram_weights += ggml_nbytes(layer.attn_norm_2_b); - } } layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); @@ -3260,13 +3269,6 @@ static void llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + - ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.wo) + - ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); - } } } break; case LLM_ARCH_STARCODER: @@ -3290,14 +3292,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - vram_weights += ggml_nbytes(model.output_norm_b); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3329,16 +3323,6 @@ static void llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + - ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + - ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + - ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) + - ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b) + - ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b); - } } } break; case LLM_ARCH_PERSIMMON: @@ -3360,14 +3344,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - vram_weights += ggml_nbytes(model.output_norm_b); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3397,8 +3373,6 @@ static void llm_load_tensors( } break; case LLM_ARCH_BLOOM: { - // TODO: CPU-only for now - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); model.tok_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU); model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, GGML_BACKEND_CPU); @@ -3419,14 +3393,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - vram_weights += ggml_nbytes(model.output_norm_b); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3458,16 +3424,6 @@ static void llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + - ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + - ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + - ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) + - ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b) + - ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b); - } } } break; case LLM_ARCH_MPT: @@ -3489,13 +3445,6 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3518,16 +3467,6 @@ static void llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + - ggml_nbytes(layer.wqkv) + - ggml_nbytes(layer.wo) + - ggml_nbytes(layer.ffn_norm) + - ggml_nbytes(layer.ffn_down) + - ggml_nbytes(layer.ffn_up); - } } } break; case LLM_ARCH_STABLELM: @@ -3550,13 +3489,6 @@ static void llm_load_tensors( model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } } const uint32_t n_ff = hparams.n_ff; @@ -3588,13 +3520,6 @@ static void llm_load_tensors( layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + - ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + - ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); - } } } break; case LLM_ARCH_QWEN: @@ -3614,14 +3539,7 @@ static void llm_load_tensors( model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - } - if (backend_output == GGML_BACKEND_GPU_SPLIT) { - vram_weights += ggml_nbytes(model.output); - } - } + } const uint32_t n_ff = hparams.n_ff / 2; @@ -3646,13 +3564,6 @@ static void llm_load_tensors( layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + - ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) + - ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); - } } } break; case LLM_ARCH_PHI2: @@ -3676,13 +3587,6 @@ static void llm_load_tensors( model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output); - - if (backend_norm == GGML_BACKEND_GPU) { - vram_weights += ggml_nbytes(model.output_norm); - vram_weights += ggml_nbytes(model.output_norm_b); - vram_weights += ggml_nbytes(model.output); - vram_weights += ggml_nbytes(model.output_b); - } } const uint32_t n_ff = hparams.n_ff; @@ -3711,15 +3615,6 @@ static void llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); - - if (backend == GGML_BACKEND_GPU) { - vram_weights += - ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + - ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + - ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + - ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b) + - ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b); - } } } break; default: @@ -3729,16 +3624,78 @@ static void llm_load_tensors( ml.done_getting_tensors(); + ml.init_mapping(); + + // allocate tensors + size_t vram_weights = 0; + size_t buf_size = 0; + + ggml_backend_buffer_type_t buft = llama_default_buffer_type(n_gpu_layers); + + for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + // GGML_BACKEND_GPU tensors are for CUDA and OpenCL only, which are handled separately without ggml-backend + if (t->backend == GGML_BACKEND_CPU) { + buf_size += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), ggml_backend_buft_get_alignment(buft)); + } else { + vram_weights += ggml_nbytes(t); + } + } + + // create backend buffer + ggml_backend_buffer_t buf_mmap = nullptr; + +#ifdef GGML_USE_METAL + if (n_gpu_layers > 0) { + if (ml.use_mmap) { + const size_t max_size = ggml_get_max_tensor_size(ctx); + model.buf = ggml_backend_metal_buffer_from_ptr(ml.mapping->addr, ml.mapping->size, max_size); + buf_mmap = model.buf; + } else { + model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_metal_buffer_type()); + } + } +#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) + // for testing only + if (n_gpu_layers > 0) { + model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cuda_buffer_type(0)); + } +#endif + + if (model.buf == nullptr) { + // CPU backend, and indirectly CUDA and OpenCL + if (ml.use_mmap) { + model.buf = ggml_backend_cpu_buffer_from_ptr(ml.mapping->addr, ml.mapping->size); + buf_mmap = model.buf; + } else { + // allocate only CPU tensors + model.buf = ggml_backend_buft_alloc_buffer(buft, buf_size); + ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(model.buf); + for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + if (t->backend == GGML_BACKEND_CPU) { + ggml_tallocr_alloc(alloc, t); + } + } + ggml_tallocr_free(alloc); + } + } + + if (use_mlock && ggml_backend_buffer_is_host(model.buf)) { + model.mlock_buf.init (ggml_backend_buffer_get_base(model.buf)); + model.mlock_buf.grow_to(ggml_backend_buffer_get_size(model.buf)); + } + // print memory requirements { - // this is the total memory required to run the inference - size_t mem_required = - ctx_size + - mmapped_size - vram_weights; // weights in VRAM not in memory + size_t sys_mem_required = ctx_size + buf_size; - LLAMA_LOG_INFO("%s: mem required = %7.2f MiB\n", __func__, mem_required / 1024.0 / 1024.0); + if (sys_mem_required > 0) { + LLAMA_LOG_INFO("%s: system memory used = %7.2f MiB\n", __func__, sys_mem_required / 1024.0 / 1024.0); + } + if (vram_weights > 0) { + LLAMA_LOG_INFO("%s: VRAM used = %7.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0); + } -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) +#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); @@ -3746,39 +3703,26 @@ static void llm_load_tensors( LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__); } -#ifdef GGML_USE_CUBLAS const int max_backend_supported_layers = hparams.n_layer + 1; const int max_offloadable_layers = hparams.n_layer + 1; -#elif GGML_USE_CLBLAST - const int max_backend_supported_layers = hparams.n_layer + 1; - const int max_offloadable_layers = hparams.n_layer + 1; -#endif // GGML_USE_CUBLAS LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); - LLAMA_LOG_INFO("%s: VRAM used: %.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0); -#else - (void) n_gpu_layers; #endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) } - // populate `tensors_by_name` +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) + ggml_cuda_set_tensor_split(tensor_split); +#else + GGML_UNUSED(tensor_split); +#endif // GGML_USE_CUBLAS + + // populate tensors_by_name for (int i = 0; i < ml.n_tensors; ++i) { struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i)); model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); } - (void) tensor_split; -#ifdef GGML_USE_CUBLAS - { - ggml_cuda_set_tensor_split(tensor_split); - } -#endif - - ml.load_all_data(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL); - - if (progress_callback) { - progress_callback(1.0f, progress_callback_user_data); - } + ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL); model.mapping = std::move(ml.mapping); @@ -4211,7 +4155,7 @@ struct llm_build_context { const llm_build_cb & cb; - llama_buffer & buf_compute; + std::vector & buf_compute_meta; struct ggml_context * ctx0 = nullptr; @@ -4221,35 +4165,35 @@ struct llm_build_context { const llama_batch & batch, const llm_build_cb & cb, bool worst_case) : - model (lctx.model), - hparams (model.hparams), - cparams (lctx.cparams), - batch (batch), - kv_self (lctx.kv_self), - n_embd (hparams.n_embd), - n_layer (hparams.n_layer), - n_ctx (cparams.n_ctx), - n_head (hparams.n_head), - n_head_kv (hparams.n_head_kv), - n_embd_head (hparams.n_embd_head()), - n_embd_gqa (hparams.n_embd_gqa()), - n_expert (hparams.n_expert), - n_expert_used (hparams.n_expert_used), - freq_base (cparams.rope_freq_base), - freq_scale (cparams.rope_freq_scale), - ext_factor (cparams.yarn_ext_factor), - attn_factor (cparams.yarn_attn_factor), - beta_fast (cparams.yarn_beta_fast), - beta_slow (cparams.yarn_beta_slow), - norm_eps (hparams.f_norm_eps), - norm_rms_eps (hparams.f_norm_rms_eps), - n_tokens (batch.n_tokens), - n_kv (worst_case ? n_ctx : kv_self.n), - kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), - n_orig_ctx (cparams.n_yarn_orig_ctx), - do_rope_shift (worst_case || kv_self.has_shift), - cb (cb), - buf_compute (lctx.buf_compute) { + model (lctx.model), + hparams (model.hparams), + cparams (lctx.cparams), + batch (batch), + kv_self (lctx.kv_self), + n_embd (hparams.n_embd), + n_layer (hparams.n_layer), + n_ctx (cparams.n_ctx), + n_head (hparams.n_head), + n_head_kv (hparams.n_head_kv), + n_embd_head (hparams.n_embd_head()), + n_embd_gqa (hparams.n_embd_gqa()), + n_expert (hparams.n_expert), + n_expert_used (hparams.n_expert_used), + freq_base (cparams.rope_freq_base), + freq_scale (cparams.rope_freq_scale), + ext_factor (cparams.yarn_ext_factor), + attn_factor (cparams.yarn_attn_factor), + beta_fast (cparams.yarn_beta_fast), + beta_slow (cparams.yarn_beta_slow), + norm_eps (hparams.f_norm_eps), + norm_rms_eps (hparams.f_norm_rms_eps), + n_tokens (batch.n_tokens), + n_kv (worst_case ? n_ctx : kv_self.n), + kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), + n_orig_ctx (cparams.n_yarn_orig_ctx), + do_rope_shift (worst_case || kv_self.has_shift), + cb (cb), + buf_compute_meta (lctx.buf_compute_meta) { GGML_ASSERT(!!kv_self.ctx); // all initializations should be done in init() @@ -4257,8 +4201,8 @@ struct llm_build_context { void init() { struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, + /*.mem_size =*/ buf_compute_meta.size(), + /*.mem_buffer =*/ buf_compute_meta.data(), /*.no_alloc =*/ true, }; @@ -5737,8 +5681,8 @@ static const std::unordered_map k_offload_map { "pos_embd", OFFLOAD_FUNC_NR }, { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope) - { "Q_scale", OFFLOAD_FUNC_FRC }, - { "KQ_scale", OFFLOAD_FUNC_FRC }, + { "Q_scale", OFFLOAD_FUNC_NOP }, + { "KQ_scale", OFFLOAD_FUNC_NOP }, { "KQ_mask", OFFLOAD_FUNC_FRC }, { "K_shift", OFFLOAD_FUNC_FRC }, @@ -5845,7 +5789,7 @@ static struct ggml_cgraph * llama_build_graph( bool alloc_inp_KQ_mask = false; bool alloc_inp_K_shift = false; -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) const bool do_offload = true; #else const bool do_offload = true; // TODO: set to false after finishing refactoring @@ -5873,7 +5817,7 @@ static struct ggml_cgraph * llama_build_graph( if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) { const int64_t n_tokens = cur->ne[0]; - memcpy(cur->data, batch.token, n_tokens*ggml_element_size(cur)); + ggml_backend_tensor_set(cur, batch.token, 0, n_tokens*ggml_element_size(cur)); } alloc_inp_tokens = true; @@ -5886,7 +5830,7 @@ static struct ggml_cgraph * llama_build_graph( const int64_t n_embd = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; - memcpy(cur->data, batch.embd, n_tokens*n_embd*ggml_element_size(cur)); + ggml_backend_tensor_set(cur, batch.embd, 0, n_tokens*n_embd*ggml_element_size(cur)); } alloc_inp_embd = true; @@ -5898,11 +5842,8 @@ static struct ggml_cgraph * llama_build_graph( if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) { const int64_t n_tokens = cur->ne[0]; - int32_t * data = (int32_t *) cur->data; - - for (int i = 0; i < n_tokens; ++i) { - data[i] = batch.pos[i]; - } + static_assert(std::is_same::value, "llama_pos must be int32_t"); + ggml_backend_tensor_set(cur, batch.pos, 0, n_tokens*ggml_element_size(cur)); } alloc_inp_pos = true; @@ -5913,7 +5854,8 @@ static struct ggml_cgraph * llama_build_graph( if (!ggml_allocr_is_measure(lctx.alloc)) { const int64_t n_embd_head = model.hparams.n_embd_head(); - ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); + float f = 1.0f/sqrtf(float(n_embd_head)); + ggml_backend_tensor_set(cur, &f, 0, sizeof(f)); } alloc_inp_Q_scale = true; @@ -5924,13 +5866,15 @@ static struct ggml_cgraph * llama_build_graph( if (!ggml_allocr_is_measure(lctx.alloc)) { const int64_t n_embd_head = model.hparams.n_embd_head(); + float f; if (model.arch == LLM_ARCH_PHI2) { // with phi2, we scale the Q to avoid precision issues // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 - ggml_set_f32(cur, 1.0f); + f = 1.0f; } else { - ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); + f = 1.0f/sqrtf(float(n_embd_head)); } + ggml_backend_tensor_set(cur, &f, 0, sizeof(f)); } alloc_inp_KQ_scale = true; @@ -5943,8 +5887,13 @@ static struct ggml_cgraph * llama_build_graph( const int64_t n_kv = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; - float * data = (float *) cur->data; - memset(data, 0, ggml_nbytes(cur)); + float * data; + if (ggml_backend_buffer_is_host(cur->buffer)) { + data = (float *) cur->data; + } else { + lctx.buf_copy.resize(ggml_nbytes(cur)); + data = (float *) lctx.buf_copy.data(); + } for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { @@ -5952,12 +5901,20 @@ static struct ggml_cgraph * llama_build_graph( const llama_seq_id seq_id = batch.seq_id[j][0]; for (int i = 0; i < n_kv; ++i) { + float f; if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; + f = -INFINITY; + } else { + f = 0; } + data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } } + + if (data != cur->data) { + ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur)); + } } alloc_inp_KQ_mask = true; @@ -5969,11 +5926,21 @@ static struct ggml_cgraph * llama_build_graph( if (!ggml_allocr_is_measure(lctx.alloc)) { const int64_t n_ctx = cur->ne[0]; - int32_t * data = (int32_t *) cur->data; + int32_t * data; + if (ggml_backend_buffer_is_host(cur->buffer)) { + data = (int32_t *) cur->data; + } else { + lctx.buf_copy.resize(ggml_nbytes(cur)); + data = (int32_t *) lctx.buf_copy.data(); + } for (int i = 0; i < n_ctx; ++i) { data[i] = lctx.kv_self.cells[i].delta; } + + if (data != cur->data) { + ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur)); + } } alloc_inp_K_shift = true; @@ -6010,7 +5977,7 @@ static struct ggml_cgraph * llama_build_graph( static const std::unordered_map> k_offload_func_name = { { OFFLOAD_FUNC_NOP, "CPU" }, { OFFLOAD_FUNC_OUT, "CPU" }, -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) { OFFLOAD_FUNC, "GPU (CUDA)" }, { OFFLOAD_FUNC_FRC, "GPU (CUDA) FRC" }, { OFFLOAD_FUNC_KQV, "GPU (CUDA) KQV" }, @@ -6083,7 +6050,7 @@ static struct ggml_cgraph * llama_build_graph( offload_func_t func = ggml_offload_nop; // this is needed for compatibility with Metal for example -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc; #else static offload_func_t ggml_offload_gpu = ggml_offload_nop; @@ -6305,11 +6272,12 @@ static int llama_decode_internal( GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); } -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) + char * buf_alloc_base = (char *)ggml_backend_buffer_get_base(lctx.buf_alloc); for (int i = 0; i < gf->n_leafs; i++) { ggml_tensor * node = gf->leafs[i]; if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) { - ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data); + ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base); ggml_cuda_copy_to_device(node); } } @@ -6317,7 +6285,7 @@ static int llama_decode_internal( for (int i = 0; i < gf->n_nodes; i++) { ggml_tensor * node = gf->nodes[i]; if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) { - ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data); + ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base); } } @@ -6344,23 +6312,23 @@ static int llama_decode_internal( n_threads = 1; } -#if GGML_USE_MPI +#ifdef GGML_USE_MPI const int64_t n_layer = hparams.n_layer; ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); #endif #ifdef GGML_USE_METAL - if (lctx.ctx_metal) { - ggml_metal_set_n_cb (lctx.ctx_metal, n_threads); - ggml_metal_graph_compute(lctx.ctx_metal, gf); - } else { - ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads); + if (ggml_backend_is_metal(lctx.backend)) { + ggml_backend_metal_set_n_cb(lctx.backend, n_threads); } -#else - ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads); #endif -#if GGML_USE_MPI + if (ggml_backend_is_cpu(lctx.backend)) { + ggml_backend_cpu_set_n_threads(lctx.backend, n_threads); + } + ggml_backend_graph_compute(lctx.backend, gf); + +#ifdef GGML_USE_MPI ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); #endif @@ -6412,20 +6380,20 @@ static int llama_decode_internal( if (batch.logits[i] == 0) { continue; } - memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab); + ggml_backend_tensor_get(res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[i] = true; #endif } } else if (lctx.logits_all) { logits_out.resize(n_vocab * n_tokens); - memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens); + ggml_backend_tensor_get(res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); #endif } else { logits_out.resize(n_vocab); - memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab); + ggml_backend_tensor_get(res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[0] = true; #endif @@ -6437,7 +6405,7 @@ static int llama_decode_internal( auto & embedding_out = lctx.embedding; embedding_out.resize(n_embd); - memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(n_tokens - 1)), sizeof(float)*n_embd); + ggml_backend_tensor_get(embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float)); } // measure the performance only for the single-token evals @@ -8395,12 +8363,6 @@ void llama_beam_search(llama_context * ctx, // quantization // -template -struct no_init { - T value; - no_init() { /* do nothing */ } -}; - struct quantize_state_internal { const llama_model & model; const llama_model_quantize_params * params; @@ -8643,9 +8605,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s #endif llama_model_loader ml(fname_inp, use_mmap, NULL); - if (ml.use_mmap) { - ml.mapping.reset(new llama_mmap(&ml.file, /* prefetch */ 0, ggml_is_numa())); - } + ml.init_mapping(false); // no prefetching? llama_model model; llm_load_arch(ml, model); @@ -8944,29 +8904,10 @@ static int llama_apply_lora_from_file_internal( // load base model std::unique_ptr ml; - unique_context base_ctx(nullptr, ggml_free); - std::vector base_buf; - if (path_base_model) { + if (path_base_model) { LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); - ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ NULL)); - - size_t ctx_size; - size_t mmapped_size; - ml->calc_sizes(ctx_size, mmapped_size); - - base_buf.resize(ctx_size); - - ggml_init_params base_params; - base_params.mem_size = base_buf.size(); - base_params.mem_buffer = base_buf.data(); - base_params.no_alloc = ml->use_mmap; - - base_ctx.reset(ggml_init(base_params)); - - // maybe this should be in llama_model_loader - if (ml->use_mmap) { - ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa())); - } + ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr)); + ml->init_mapping(false); // no prefetching } // read tensors and apply @@ -9058,7 +8999,7 @@ static int llama_apply_lora_from_file_internal( offload_func_t offload_func = ggml_offload_nop; offload_func_t offload_func_force_inplace = ggml_offload_nop; -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) { if (dest_t->type != GGML_TYPE_F16) { throw std::runtime_error(format( @@ -9079,7 +9020,7 @@ static int llama_apply_lora_from_file_internal( return 1; } - base_t = ml->create_tensor(base_ctx.get(), base_name, { dest_t->ne[0], dest_t->ne[1] }, GGML_BACKEND_CPU); + base_t = ml->get_tensor_meta(base_name.c_str()); ml->load_data_for(base_t); } else { base_t = dest_t; @@ -9364,7 +9305,39 @@ struct llama_context * llama_new_context_with_model( // reserve memory for context buffers if (!hparams.vocab_only) { - if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, type_k, type_v, cparams.n_ctx, model->n_gpu_layers, cparams.offload_kqv)) { + // initialize backend +#ifdef GGML_USE_METAL + if (model->n_gpu_layers > 0) { + ctx->backend = ggml_backend_metal_init(); + if (ctx->backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__); + } + } +#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) + // for testing only + if (model->n_gpu_layers > 0) { + ctx->backend = ggml_backend_cuda_init(0); + if (ctx->backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CUDA backend\n", __func__); + } + } +#endif + + if (ctx->backend == nullptr && ggml_backend_buffer_is_host(model->buf)) { + ctx->backend = ggml_backend_cpu_init(); + if (ctx->backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); + } + } + + if (ctx->backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize a backend\n", __func__); + delete ctx; + return nullptr; + } + + if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, type_k, type_v, + cparams.n_ctx, model->n_gpu_layers, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -9400,12 +9373,11 @@ struct llama_context * llama_new_context_with_model( } { - static const size_t tensor_alignment = 32; // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data - ctx->buf_compute.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); + ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); // create measure allocator - ctx->alloc = ggml_allocr_new_measure(tensor_alignment); + ctx->alloc = ggml_allocr_new_measure_from_backend(ctx->backend); // build worst-case graph int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch); @@ -9413,98 +9385,50 @@ struct llama_context * llama_new_context_with_model( llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0)); -#ifdef GGML_USE_METAL - if (model->n_gpu_layers > 0) { - ctx->ctx_metal = ggml_metal_init(1); - if (!ctx->ctx_metal) { - LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__); - llama_free(ctx); - return NULL; - } - //ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false); - //ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal)); - } -#endif // measure memory requirements for the graph - size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment; + size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf); - LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute_meta.size() + alloc_size) / 1024.0 / 1024.0); - // recreate allocator with exact memory requirements + // create allocator again with exact memory requirements ggml_allocr_free(ctx->alloc); - ctx->buf_alloc.resize(alloc_size); - ctx->alloc = ggml_allocr_new(ctx->buf_alloc.data, ctx->buf_alloc.size, tensor_alignment); -#ifdef GGML_USE_METAL - if (ctx->ctx_metal) { - //ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal)); - } -#endif -#ifdef GGML_USE_CUBLAS - ggml_cuda_set_scratch_size(alloc_size); - LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0); + ctx->buf_alloc = ggml_backend_alloc_buffer(ctx->backend, alloc_size); + ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc); +#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) + if (model->n_gpu_layers > 0) { + ggml_cuda_set_scratch_size(alloc_size); + LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0); - // calculate total VRAM usage - auto add_tensor = [](const ggml_tensor * t, size_t & size) { - if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) { - size += ggml_nbytes(t); + // calculate total VRAM usage + auto add_tensor = [](const ggml_tensor * t, size_t & size) { + if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) { + size += ggml_nbytes(t); + } + }; + size_t model_vram_size = 0; + for (const auto & kv : model->tensors_by_name) { + add_tensor(kv.second, model_vram_size); } - }; - size_t model_vram_size = 0; - for (const auto & kv : model->tensors_by_name) { - add_tensor(kv.second, model_vram_size); - } - size_t kv_vram_size = 0; - for (auto & k : ctx->kv_self.k_l) { - add_tensor(k, kv_vram_size); - } - for (auto & v : ctx->kv_self.v_l) { - add_tensor(v, kv_vram_size); - } + size_t kv_vram_size = 0; + for (auto & k : ctx->kv_self.k_l) { + add_tensor(k, kv_vram_size); + } + for (auto & v : ctx->kv_self.v_l) { + add_tensor(v, kv_vram_size); + } - size_t ctx_vram_size = alloc_size + kv_vram_size; - size_t total_vram_size = model_vram_size + ctx_vram_size; + size_t ctx_vram_size = alloc_size + kv_vram_size; + size_t total_vram_size = model_vram_size + ctx_vram_size; - LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__, - total_vram_size / 1024.0 / 1024.0, - model_vram_size / 1024.0 / 1024.0, - ctx_vram_size / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__, + total_vram_size / 1024.0 / 1024.0, + model_vram_size / 1024.0 / 1024.0, + ctx_vram_size / 1024.0 / 1024.0); + } #endif } - -#ifdef GGML_USE_METAL - if (model->n_gpu_layers > 0) { - // this allocates all Metal resources and memory buffers - - void * data_ptr = NULL; - size_t data_size = 0; - - if (ctx->model.mapping) { - data_ptr = ctx->model.mapping->addr; - data_size = ctx->model.mapping->size; - } else { - data_ptr = ggml_get_mem_buffer(ctx->model.ctx); - data_size = ggml_get_mem_size (ctx->model.ctx); - } - - const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx); - - LLAMA_LOG_INFO("%s: max tensor size = %8.2f MiB\n", __func__, max_size/1024.0/1024.0); - -#define LLAMA_METAL_CHECK_BUF(result) \ - if (!(result)) { \ - LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \ - llama_free(ctx); \ - return NULL; \ - } - - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0)); -#undef LLAMA_METAL_CHECK_BUF - } -#endif } #ifdef GGML_USE_MPI @@ -9796,7 +9720,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_embedding = ctx->embedding.size() * sizeof(float); const size_t s_kv_size = sizeof(size_t); const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = ctx->kv_self.buf.size; + const size_t s_kv = ggml_backend_buffer_get_size(ctx->kv_self.buf); const size_t s_total = ( + s_rng_size @@ -9924,7 +9848,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto n_embd = hparams.n_embd_gqa(); const auto n_ctx = cparams.n_ctx; - const size_t kv_buf_size = kv_self.buf.size; + const size_t kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf); const uint32_t kv_head = kv_self.head; const uint32_t kv_size = kv_self.size; const uint32_t kv_used = kv_self.used; @@ -9940,17 +9864,12 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); ggml_cgraph * gf = ggml_new_graph(cpy_ctx); - std::vector> kout2d_data(n_layer); - std::vector> vout2d_data(n_layer); + std::vector kout2d(n_layer); + std::vector vout2d(n_layer); for (int il = 0; il < (int) n_layer; ++il) { - ggml_tensor * kout2d = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head); - kout2d_data[il].resize(ggml_nbytes(kout2d)); - kout2d->data = kout2d_data[il].data(); - - ggml_tensor * vout2d = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd); - vout2d_data[il].resize(ggml_nbytes(vout2d)); - vout2d->data = vout2d_data[il].data(); + kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head); + vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd); ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], n_embd, kv_head, @@ -9960,20 +9879,28 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat kv_head, n_embd, elt_size*n_ctx, 0); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d)); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v2d, vout2d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il])); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v2d, vout2d[il])); } - ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend); + + ggml_backend_graph_compute(ctx->backend, gf); + + std::vector tmp_buf; + for (int il = 0; il < (int) n_layer; ++il) { + tmp_buf.resize(ggml_nbytes(kout2d[il])); + ggml_backend_tensor_get(kout2d[il], tmp_buf.data(), 0, tmp_buf.size()); + data_ctx->write(tmp_buf.data(), tmp_buf.size()); + + tmp_buf.resize(ggml_nbytes(vout2d[il])); + ggml_backend_tensor_get(vout2d[il], tmp_buf.data(), 0, tmp_buf.size()); + data_ctx->write(tmp_buf.data(), tmp_buf.size()); + } ggml_free(cpy_ctx); - // our data is now in the kout2d_data and vout2d_data buffers - // write them to file - for (uint32_t il = 0; il < n_layer; ++il) { - data_ctx->write(kout2d_data[il].data(), kout2d_data[il].size()); - data_ctx->write(vout2d_data[il].data(), vout2d_data[il].size()); - } + ggml_backend_buffer_free(buf); } for (uint32_t i = 0; i < kv_size; ++i) { @@ -10071,21 +9998,19 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used); if (kv_buf_size) { - GGML_ASSERT(kv_self.buf.size == kv_buf_size); + GGML_ASSERT(ggml_backend_buffer_get_size(kv_self.buf) == kv_buf_size); const size_t elt_size = ggml_element_size(kv_self.k_l[0]); ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); ggml_cgraph * gf = ggml_new_graph(cpy_ctx); - for (int il = 0; il < n_layer; ++il) { - ggml_tensor * kin2d = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head); - kin2d->data = (void *) inp; - inp += ggml_nbytes(kin2d); + std::vector kin2d(n_layer); + std::vector vin2d(n_layer); - ggml_tensor * vin2d = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd); - vin2d->data = (void *) inp; - inp += ggml_nbytes(vin2d); + for (int il = 0; il < n_layer; ++il) { + kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head); + vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd); ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], n_embd, kv_head, @@ -10095,13 +10020,26 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { kv_head, n_embd, elt_size*n_ctx, 0); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d, k2d)); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin2d, v2d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin2d[il], v2d)); } - ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend); + + // load data into the tensors + for (int il = 0; il < n_layer; ++il) { + ggml_backend_tensor_set(kin2d[il], inp, 0, ggml_nbytes(kin2d[il])); + inp += ggml_nbytes(kin2d[il]); + + ggml_backend_tensor_set(vin2d[il], inp, 0, ggml_nbytes(vin2d[il])); + inp += ggml_nbytes(vin2d[il]); + } + + ggml_backend_graph_compute(ctx->backend, gf); ggml_free(cpy_ctx); + + ggml_backend_buffer_free(buf); } ctx->kv_self.head = kv_head; From 4a5f9d629ecfd0a53afdddbaf54a4fa02d9a9ce9 Mon Sep 17 00:00:00 2001 From: Samuel Maynard Date: Thu, 21 Dec 2023 22:36:26 +0200 Subject: [PATCH 230/664] ci : add `jlumbroso/free-disk-space` to docker workflow (#4150) * [github][workflows][docker]: removes hardcoded `ggerganov` from `ghcr` repo * [github][workflows][docker]: adds `jlumbroso/free-disk-space` --- .github/workflows/docker.yml | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 9c90c77ac..a7165a38f 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -52,6 +52,23 @@ jobs: username: ${{ github.repository_owner }} password: ${{ secrets.GITHUB_TOKEN }} + # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example + - name: Free Disk Space (Ubuntu) + uses: jlumbroso/free-disk-space@main + with: + # this might remove tools that are actually needed, + # if set to "true" but frees about 6 GB + tool-cache: false + + # all of these default to true, but feel free to set to + # "false" if necessary for your workflow + android: true + dotnet: true + haskell: true + large-packages: true + docker-images: true + swap-storage: true + - name: Build and push Docker image (versioned) if: github.event_name == 'push' uses: docker/build-push-action@v4 @@ -59,7 +76,7 @@ jobs: context: . push: true platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" + tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" file: ${{ matrix.config.dockerfile }} - name: Build and push Docker image (tagged) @@ -68,5 +85,5 @@ jobs: context: . push: ${{ github.event_name == 'push' }} platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}" + tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}" file: ${{ matrix.config.dockerfile }} From 32259b2dade6f6856739bf7ba0a4ff7b474dc760 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 21 Dec 2023 23:07:58 +0200 Subject: [PATCH 231/664] gguf : simplify example dependencies --- Makefile | 2 +- examples/gguf/CMakeLists.txt | 2 +- examples/gguf/gguf.cpp | 1 - 3 files changed, 2 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 512407a1d..68df7702a 100644 --- a/Makefile +++ b/Makefile @@ -606,7 +606,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual -gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) +gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt index 7d1806af3..6481f087b 100644 --- a/examples/gguf/CMakeLists.txt +++ b/examples/gguf/CMakeLists.txt @@ -1,5 +1,5 @@ set(TARGET gguf) add_executable(${TARGET} gguf.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index 9e24bf24c..e67be4fb2 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -1,5 +1,4 @@ #include "ggml.h" -#include "llama.h" #include #include From 769a7bc85eaa44e3d7eadf39abfeff7bb0b9cc2f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 21 Dec 2023 23:20:36 +0200 Subject: [PATCH 232/664] gguf-py : fix broken link --- gguf-py/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gguf-py/README.md b/gguf-py/README.md index a27d2fc0e..22d7ffa52 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -3,7 +3,7 @@ This is a Python package for writing binary files in the [GGUF](https://github.com/ggerganov/ggml/pull/302) (GGML Universal File) format. -See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-llama-hf-to-gguf.py) +See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-hf-to-gguf.py) as an example for its usage. ## Installation From afefa319f1f59b002dfa0d1ef407a2c74bd9770b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 21 Dec 2023 23:20:49 +0200 Subject: [PATCH 233/664] ggml : change ggml_scale to take a float instead of tensor (#4573) * ggml : change ggml_scale to take a float instead of tensor * ggml : fix CPU implementation * tests : fix test-grad0 ggml-ci --- examples/baby-llama/baby-llama.cpp | 15 +-- examples/export-lora/export-lora.cpp | 2 +- examples/finetune/finetune.cpp | 42 +++---- examples/llava/clip.cpp | 8 +- .../train-text-from-scratch.cpp | 14 +-- ggml-cuda.cu | 14 +-- ggml-metal.m | 6 +- ggml.c | 42 +++---- ggml.h | 4 +- llama.cpp | 119 +++--------------- tests/test-backend-ops.cpp | 9 +- tests/test-grad0.cpp | 12 +- 12 files changed, 82 insertions(+), 205 deletions(-) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 2dc2988d3..e7d2ad592 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -575,10 +575,7 @@ static struct ggml_tensor * forward( // KQ_scaled = KQ / sqrt(n_embd/n_head) // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = - ggml_scale(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); + struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); // KQ_masked = mask_past(KQ_scaled) // KQ_masked shape [n_past + N, N, n_head, 1] @@ -844,10 +841,7 @@ static struct ggml_tensor * forward_batch( // KQ_scaled = KQ / sqrt(n_embd/n_head) // KQ_scaled shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_scaled = - ggml_scale(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); + struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch); // KQ_masked = mask_past(KQ_scaled) @@ -1131,10 +1125,7 @@ static struct ggml_tensor * forward_lora( // KQ_scaled = KQ / sqrt(n_embd/n_head) // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = - ggml_scale(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); + struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); // KQ_masked = mask_past(KQ_scaled) // KQ_masked shape [n_past + N, N, n_head, 1] diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index c8754ce70..58fbe204d 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -309,7 +309,7 @@ static struct ggml_cgraph * build_graph_lora( ) { struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b); if (scaling != 1.0f) { - ab = ggml_scale(ctx, ab, ggml_new_f32(ctx, scaling)); + ab = ggml_scale(ctx, ab, scaling); } struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab); diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 6a668d764..7b1333a9d 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -269,7 +269,7 @@ static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_h float rope_freq_scale = 1.0f; GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS)); GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); - GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); + GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); if (rope_freq_scale != 1.0f) { hparams->rope_freq_scale = 1.0f / rope_freq_scale; } @@ -612,6 +612,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( const int n_rot = hparams.n_embd_head(); const int n_embd_head = hparams.n_embd_head(); const int n_embd_gqa = hparams.n_embd_gqa(); + const float rms_norm_eps = hparams.f_norm_rms_eps; const float rope_freq_base = hparams.rope_freq_base; const float rope_freq_scale = hparams.rope_freq_scale; @@ -680,10 +681,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( checkpoints.push_back(t01); } - struct ggml_tensor * kv_scale = NULL; - if (!enable_flash_attn) { - kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head)); - } + const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head); for (int il = 0; il < n_layer; ++il) { struct my_llama_layer & layer = model->layers[il]; @@ -781,32 +779,32 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // make sure some tensors are not reallocated by inserting new temporary nodes depending on them int n_leafs_before = gb->n_leafs; int n_nodes_before = gb->n_nodes; - struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f); + // output tensors - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f)); // input gradient - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f)); GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL); ggml_allocr_alloc(alloc, t36->grad); // KQ_pos - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f)); // make sure base model tensors data cannot be used in viewable operations - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f)); for (int il = 0; il < n_layer; ++il) { struct my_llama_layer & layer = model->layers[il]; - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f)); } // allocating checkpoints in one block to reduce memory fragmentation diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 112465968..f06ec400d 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -330,12 +330,6 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima ggml_repeat(ctx0, model.pre_ln_b, embeddings)); } - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - ggml_allocr_alloc(ctx->alloc, KQ_scale); - if (!ggml_allocr_is_measure(ctx->alloc)) { - ggml_set_f32(KQ_scale, 1.0f / sqrt((float)d_head)); - } - // loop over layers for (int il = 0; il < n_layer - 1; il++) { struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states @@ -356,7 +350,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima struct ggml_tensor * Q = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur)); - Q = ggml_scale_inplace(ctx0, Q, KQ_scale); + Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index f7ed63365..4a9a2340b 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -369,10 +369,7 @@ static struct ggml_tensor * llama_build_train_graphs( checkpoints.push_back(t00); checkpoints.push_back(t01); - struct ggml_tensor * kv_scale = NULL; - if (!enable_flash_attn) { - kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head)); - } + const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head); for (int il = 0; il < n_layer; ++il) { struct my_llama_layer & layer = model->layers[il]; @@ -444,14 +441,13 @@ static struct ggml_tensor * llama_build_train_graphs( // make sure some tensors are not reallocated by inserting new temporary nodes depending on them int n_leafs_before = gb->n_leafs; int n_nodes_before = gb->n_nodes; - struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f); // output tensors - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f)); // input gradient - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f)); // KQ_pos - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f)); GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL); ggml_allocr_alloc(alloc, t36->grad); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f5e060d32..ac91ee12e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7700,17 +7700,9 @@ inline void ggml_cuda_op_scale( const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - float scale; - // HACK: support for ggml backend interface - if (src1->backend == GGML_BACKEND_CPU) { - scale = ((float *) src1->data)[0]; - } else { - // TODO: pass pointer to kernel instead of copying to host - CUDA_CHECK(cudaMemcpy(&scale, src1->data, sizeof(float), cudaMemcpyDeviceToHost)); - } + const float scale = ((float *) dst->op_params)[0]; scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream); CUDA_CHECK(cudaGetLastError()); @@ -7757,8 +7749,6 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU; const bool dst_on_device = dst->backend == GGML_BACKEND_GPU; - const bool src1_stays_on_host = use_src1 && dst->op == GGML_OP_SCALE; - // dd = data device float * src0_ddf = nullptr; float * src1_ddf = nullptr; @@ -7779,7 +7769,7 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream)); } - if (use_src1 && !src1_stays_on_host) { + if (use_src1) { if (src1_on_device) { src1_ddf = (float *) src1_extra->data_device[g_main_device]; } else { diff --git a/ggml-metal.m b/ggml-metal.m index e60b93b36..51a72ae33 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1293,7 +1293,7 @@ void ggml_metal_graph_compute( { GGML_ASSERT(ggml_is_contiguous(src0)); - const float scale = *(const float *) src1->data; + const float scale = *(const float *) dst->op_params; int64_t n = ggml_nelements(dst); @@ -1304,8 +1304,8 @@ void ggml_metal_graph_compute( [encoder setComputePipelineState:ctx->pipeline_scale]; } - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; diff --git a/ggml.c b/ggml.c index 236148514..f27920a2d 100644 --- a/ggml.c +++ b/ggml.c @@ -4171,23 +4171,23 @@ struct ggml_tensor * ggml_out_prod( static struct ggml_tensor * ggml_scale_impl( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b, + float s, bool inplace) { - GGML_ASSERT(ggml_is_scalar(b)); GGML_ASSERT(ggml_is_padded_1d(a)); bool is_node = false; - if (a->grad || b->grad) { + if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + ggml_set_op_params(result, &s, sizeof(s)); + result->op = GGML_OP_SCALE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -4195,15 +4195,15 @@ static struct ggml_tensor * ggml_scale_impl( struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b) { - return ggml_scale_impl(ctx, a, b, false); + float s) { + return ggml_scale_impl(ctx, a, s, false); } struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b) { - return ggml_scale_impl(ctx, a, b, true); + float s) { + return ggml_scale_impl(ctx, a, s, true); } // ggml_set @@ -10325,19 +10325,17 @@ static void ggml_compute_forward_out_prod( static void ggml_compute_forward_scale_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scale factor - const float v = *(float *) src1->data; + const float v = *(float *) dst->op_params; const int ith = params->ith; const int nth = params->nth; @@ -10368,12 +10366,11 @@ static void ggml_compute_forward_scale_f32( static void ggml_compute_forward_scale( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_scale_f32(params, src0, src1, dst); + ggml_compute_forward_scale_f32(params, src0, dst); } break; default: { @@ -14383,7 +14380,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_SCALE: { - ggml_compute_forward_scale(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_scale(params, tensor->src[0], tensor); } break; case GGML_OP_SET: { @@ -14839,7 +14836,7 @@ static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct gg static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set zero_table) { if (ggml_hash_contains(zero_table, a)) { - struct ggml_tensor * a_zero = ggml_scale(ctx, a, ggml_new_f32(ctx, 0)); + struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f); return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false); } else { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); @@ -14975,7 +14972,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src0->grad, ggml_scale(ctx, ggml_mul(ctx, src0, tensor->grad), - ggml_new_f32(ctx, 2.0f)), + 2.0f), zero_table); } } break; @@ -14989,7 +14986,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_div(ctx, tensor->grad, tensor), - ggml_new_f32(ctx, 0.5f)), + 0.5f), zero_table); } } break; @@ -15155,17 +15152,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { + const float s = ((float *) tensor->op_params)[0]; + src0->grad = ggml_add_or_set(ctx, src0->grad, - ggml_scale_impl(ctx, tensor->grad, src1, false), - zero_table); - } - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_sum(ctx, ggml_mul_impl(ctx, tensor->grad, src0, false)), + ggml_scale_impl(ctx, tensor->grad, s, false), zero_table); } } break; diff --git a/ggml.h b/ggml.h index b17314897..75918502b 100644 --- a/ggml.h +++ b/ggml.h @@ -1094,13 +1094,13 @@ extern "C" { GGML_API struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + float s); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + float s); // b -> view(a,offset,nb1,nb2,3), return modified a GGML_API struct ggml_tensor * ggml_set( diff --git a/llama.cpp b/llama.cpp index ba970ce8d..d6c192441 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4032,13 +4032,12 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * wo, struct ggml_tensor * wo_b, struct ggml_tensor * q_cur, - struct ggml_tensor * kq_scale, struct ggml_tensor * kq_mask, int64_t n_ctx, int32_t n_tokens, int32_t n_kv, float max_alibi_bias, - float scale, + float kq_scale, const llm_build_cb & cb, int il) { const int64_t n_embd = hparams.n_embd; @@ -4086,7 +4085,7 @@ static struct ggml_tensor * llm_build_kqv( kq = ggml_soft_max(ctx, kq); cb(kq, "kq_soft_max", il); } else { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, scale); + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale); cb(kq, "kq_soft_max_ext", il); } @@ -4231,10 +4230,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -4295,7 +4290,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4416,10 +4411,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -4478,7 +4469,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4536,10 +4527,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -4602,7 +4589,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4659,10 +4646,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -4702,7 +4685,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4759,10 +4742,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -4911,7 +4890,7 @@ struct llm_build_context { // TODO: not tested, could be broken cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Q, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4965,10 +4944,6 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); cb(inpL, "inp_embd", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -5002,7 +4977,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5056,10 +5031,6 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); cb(inpL, "inp_embd", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -5099,7 +5070,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5150,10 +5121,6 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); cb(inpL, "inp_embd", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -5193,7 +5160,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5253,10 +5220,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -5306,7 +5269,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5366,10 +5329,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -5423,7 +5382,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5482,14 +5441,6 @@ struct llm_build_context { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); - // Q_scale - struct ggml_tensor * Q_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(Q_scale, "Q_scale", -1); - - // KQ_scale - struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); - cb(KQ_scale, "KQ_scale", -1); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); @@ -5531,7 +5482,9 @@ struct llm_build_context { ); cb(Qcur, "Qcur", il); - Qcur = ggml_scale(ctx0, Qcur, Q_scale); + // with phi2, we scale the Q to avoid precision issues + // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 + Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( @@ -5544,7 +5497,7 @@ struct llm_build_context { cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il); + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il); cb(cur, "kqv_out", il); } @@ -5681,8 +5634,6 @@ static const std::unordered_map k_offload_map { "pos_embd", OFFLOAD_FUNC_NR }, { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope) - { "Q_scale", OFFLOAD_FUNC_NOP }, - { "KQ_scale", OFFLOAD_FUNC_NOP }, { "KQ_mask", OFFLOAD_FUNC_FRC }, { "K_shift", OFFLOAD_FUNC_FRC }, @@ -5784,8 +5735,6 @@ static struct ggml_cgraph * llama_build_graph( bool alloc_inp_tokens = false; bool alloc_inp_embd = false; bool alloc_inp_pos = false; - bool alloc_inp_Q_scale = false; - bool alloc_inp_KQ_scale = false; bool alloc_inp_KQ_mask = false; bool alloc_inp_K_shift = false; @@ -5849,37 +5798,6 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_pos = true; } - if (!alloc_inp_Q_scale && strcmp(name, "Q_scale") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); - - if (!ggml_allocr_is_measure(lctx.alloc)) { - const int64_t n_embd_head = model.hparams.n_embd_head(); - float f = 1.0f/sqrtf(float(n_embd_head)); - ggml_backend_tensor_set(cur, &f, 0, sizeof(f)); - } - - alloc_inp_Q_scale = true; - } - - if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); - - if (!ggml_allocr_is_measure(lctx.alloc)) { - const int64_t n_embd_head = model.hparams.n_embd_head(); - float f; - if (model.arch == LLM_ARCH_PHI2) { - // with phi2, we scale the Q to avoid precision issues - // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 - f = 1.0f; - } else { - f = 1.0f/sqrtf(float(n_embd_head)); - } - ggml_backend_tensor_set(cur, &f, 0, sizeof(f)); - } - - alloc_inp_KQ_scale = true; - } - if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) { ggml_allocr_alloc(lctx.alloc, cur); @@ -9054,10 +8972,7 @@ static int llama_apply_lora_from_file_internal( ggml_set_name(BA, "BA"); if (scaling != 1.0f) { - ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx.get(), scaling); - ggml_set_name(scale_tensor, "scale_tensor"); - - BA = ggml_scale_inplace(lora_ctx.get(), BA, scale_tensor); + BA = ggml_scale_inplace(lora_ctx.get(), BA, scaling); offload_func(BA); ggml_set_name(BA, "BA_scaled"); } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f04b9438a..f3df8a8c6 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -766,18 +766,19 @@ struct test_bin_bcast : public test_case { struct test_scale : public test_case { const ggml_type type; const std::array ne; + float scale; std::string vars() override { - return VARS_TO_STR2(type, ne); + return VARS_TO_STR3(type, ne, scale); } test_scale(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 10}) - : type(type), ne(ne) {} + std::array ne = {10, 10, 10, 10}, + float scale = 2.0f) + : type(type), ne(ne), scale(scale) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * scale = ggml_new_tensor_1d(ctx, type, 1); ggml_tensor * out = ggml_scale(ctx, a, scale); return out; } diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 81c20a89c..14914def5 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -881,19 +881,19 @@ int main(int argc, const char ** argv) { // scale { srand(seed); - const int nargs = 2; + const int nargs = 1; int64_t ne2[4]; ne2[0] = 1; for (int ndims = 1; ndims <= 2; ++ndims) { - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); + const float s = -1.0f + 2.0f*frand(); - struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], x[1])); + ggml_set_param(ctx0, x[0]); + + struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], s)); check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY); } @@ -1395,7 +1395,7 @@ int main(int argc, const char ** argv) { ggml_add1(ctx0, ggml_scale(ctx0, ggml_soft_max(ctx0, x[0]), - ggml_new_f32(ctx0, 1.0f - eps)), + 1.0f - eps), ggml_new_f32(ctx0, eps)))); check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY); From c7e9701f86564088350209d2f9d71c96ea00527f Mon Sep 17 00:00:00 2001 From: crasm Date: Fri, 22 Dec 2023 01:19:36 -0500 Subject: [PATCH 234/664] llama : add ability to cancel model loading (#4462) * llama : Add ability to cancel model load Updated llama_progress_callback so that if it returns false, the model loading is aborted. * llama : Add test for model load cancellation * Fix bool return in llama_model_load, remove std::ignore use * Update llama.cpp Co-authored-by: Jared Van Bortel * Fail test if model file is missing * Revert "Fail test if model file is missing" This reverts commit 32ebd525bf7e5a87ee8a3dbaab3d92ce79fbf23d. * Add test-model-load-cancel to Makefile * Revert "Revert "Fail test if model file is missing"" This reverts commit 2796953257ee5383fa7c8fe8fa8fc888c048fb0b. * Simplify .gitignore for tests, clang-tidy fixes * Label all ctest tests * ci : ctest uses -L main * Attempt at writing ctest_with_model * ci : get ci/run.sh working with test-model-load-cancel * ci : restrict .github/workflows/build.yml ctest to -L main * update requirements.txt * Disable test-model-load-cancel in make * Remove venv before creation * Restructure requirements.txt Top-level now imports the specific additional requirements for each python file. Using `pip install -r requirements.txt` will fail if versions become mismatched in the per-file requirements. * Make per-python-script requirements work alone This doesn't break the main requirements.txt. * Add comment * Add convert-persimmon-to-gguf.py to new requirements.txt scheme * Add check-requirements.sh script and GitHub workflow * Remove shellcheck installation step from workflow * Add nocleanup special arg * Fix merge see: https://github.com/ggerganov/llama.cpp/pull/4462#discussion_r1434593573 * reset to upstream/master * Redo changes for cancelling model load --------- Co-authored-by: Georgi Gerganov Co-authored-by: Jared Van Bortel --- llama.cpp | 46 +++++++++++++++++++++++++++++++++------------- llama.h | 6 ++++-- 2 files changed, 37 insertions(+), 15 deletions(-) diff --git a/llama.cpp b/llama.cpp index d6c192441..cb0546c95 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2372,7 +2372,8 @@ struct llama_model_loader { } } - void load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const { + // Returns false if cancelled by progress_callback + bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const { size_t size_data = 0; for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { @@ -2404,7 +2405,9 @@ struct llama_model_loader { GGML_ASSERT(cur); // unused tensors should have been caught by load_data already if (progress_callback) { - progress_callback((float) size_done / size_data, progress_callback_user_data); + if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) { + return false; + } } const size_t offs = file_offset(ggml_get_name(cur)); @@ -2466,8 +2469,11 @@ struct llama_model_loader { } if (progress_callback) { - progress_callback(1.0f, progress_callback_user_data); + // Even though the model is done loading, we still honor + // cancellation since we need to free allocations. + return progress_callback(1.0f, progress_callback_user_data); } + return true; } }; @@ -3044,7 +3050,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); } } -static void llm_load_tensors( +// Returns false if cancelled by progress_callback +static bool llm_load_tensors( llama_model_loader & ml, llama_model & model, int n_gpu_layers, @@ -3722,16 +3729,20 @@ static void llm_load_tensors( model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); } - ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL); + if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL)) { + return false; + } model.mapping = std::move(ml.mapping); // loading time will be recalculate after the first eval, so // we take page faults deferred by mmap() into consideration model.t_load_us = ggml_time_us() - model.t_start_us; + return true; } -static bool llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) { +// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback +static int llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) { try { llama_model_loader ml(fname, params.use_mmap, params.kv_overrides); @@ -3749,19 +3760,21 @@ static bool llama_model_load(const std::string & fname, llama_model & model, con if (params.vocab_only) { LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__); - return true; + return 0; } - llm_load_tensors( + if (!llm_load_tensors( ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data - ); + )) { + return -2; + } } catch (const std::exception & err) { LLAMA_LOG_ERROR("error loading model: %s\n", err.what()); - return false; + return -1; } - return true; + return 0; } // @@ -9141,11 +9154,18 @@ struct llama_model * llama_load_model_from_file( LLAMA_LOG_INFO("\n"); } } + return true; }; } - if (!llama_model_load(path_model, *model, params)) { - LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); + int status = llama_model_load(path_model, *model, params); + GGML_ASSERT(status <= 0); + if (status < 0) { + if (status == -1) { + LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); + } else if (status == -2) { + LLAMA_LOG_INFO("%s: cancelled model load\n", __func__); + } delete model; return nullptr; } diff --git a/llama.h b/llama.h index 0be4b1337..af76bae2d 100644 --- a/llama.h +++ b/llama.h @@ -127,7 +127,7 @@ extern "C" { bool sorted; } llama_token_data_array; - typedef void (*llama_progress_callback)(float progress, void *ctx); + typedef bool (*llama_progress_callback)(float progress, void *ctx); // Input data for llama_decode // A llama_batch object can contain input about one or many sequences @@ -180,7 +180,9 @@ extern "C" { int32_t main_gpu; // the GPU that is used for scratch and small tensors const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES) - // called with a progress value between 0 and 1, pass NULL to disable + // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. + // If the provided progress_callback returns true, model loading continues. + // If it returns false, model loading is immediately aborted. llama_progress_callback progress_callback; // context pointer passed to the progress callback From 0137ef88ea9f8fd837a065700814329d24adeec3 Mon Sep 17 00:00:00 2001 From: bobqianic <129547291+bobqianic@users.noreply.github.com> Date: Fri, 22 Dec 2023 06:47:01 +0000 Subject: [PATCH 235/664] ggml : extend `enum ggml_log_level` with `GGML_LOG_LEVEL_DEBUG` (#4579) --- ggml.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml.h b/ggml.h index 75918502b..338f355a4 100644 --- a/ggml.h +++ b/ggml.h @@ -484,7 +484,8 @@ extern "C" { enum ggml_log_level { GGML_LOG_LEVEL_ERROR = 2, GGML_LOG_LEVEL_WARN = 3, - GGML_LOG_LEVEL_INFO = 4 + GGML_LOG_LEVEL_INFO = 4, + GGML_LOG_LEVEL_DEBUG = 5 }; // ggml object From 2bb98279c5a087d62949972b35cf63ff974ffe6a Mon Sep 17 00:00:00 2001 From: Deins Date: Fri, 22 Dec 2023 08:49:54 +0200 Subject: [PATCH 236/664] readme : add zig bindings (#4581) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 73fe59bb4..8e17d5ba4 100644 --- a/README.md +++ b/README.md @@ -123,6 +123,7 @@ as the main playground for developing new features for the [ggml](https://github - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp) +- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig) **UI:** From f31b98489824a86c937fa62ccf5dfd4bb0327b86 Mon Sep 17 00:00:00 2001 From: rhuddleston Date: Thu, 21 Dec 2023 23:56:34 -0700 Subject: [PATCH 237/664] ci : tag docker image with build number (#4584) --- .github/workflows/docker.yml | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index a7165a38f..7f4de50ea 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -69,6 +69,19 @@ jobs: docker-images: true swap-storage: true + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + - name: Build and push Docker image (versioned) if: github.event_name == 'push' uses: docker/build-push-action@v4 @@ -85,5 +98,5 @@ jobs: context: . push: ${{ github.event_name == 'push' }} platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}" + tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}" , "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" file: ${{ matrix.config.dockerfile }} From 28cb35a0ecb9852adc3494aa51dde60141939d64 Mon Sep 17 00:00:00 2001 From: Michael Kesper Date: Fri, 22 Dec 2023 09:03:25 +0100 Subject: [PATCH 238/664] make : add LLAMA_HIP_UMA option (#4587) NB: LLAMA_HIP_UMA=1 (or any value) adds MK_CPPFLAG -DGGML_HIP_UMA --- Makefile | 3 +++ README.md | 8 +++++++- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 68df7702a..42686ce71 100644 --- a/Makefile +++ b/Makefile @@ -452,6 +452,9 @@ ifdef LLAMA_HIPBLAS LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS +ifdef LLAMA_HIP_UMA + MK_CPPFLAGS += -DGGML_HIP_UMA +endif # LLAMA_HIP_UMA MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) diff --git a/README.md b/README.md index 8e17d5ba4..377d3928b 100644 --- a/README.md +++ b/README.md @@ -440,7 +440,13 @@ Building the program with BLAS support may lead to some performance improvements && cmake --build build -- -j 16 ``` On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`. - However, this hurts performance for non-integrated GPUs. + However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). + + - Using `make` (example for target gfx1030, build with 16 CPU threads): + ```bash + make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030 + ``` + - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): ```bash set PATH=%HIP_PATH%\bin;%PATH% From 48b24b170e3b4f9dc28200306840cb07d1c123df Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Fri, 22 Dec 2023 09:26:49 +0000 Subject: [PATCH 239/664] ggml : add comment about backward GGML_OP_DIAG_MASK_INF (#4203) --- ggml.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml.c b/ggml.c index f27920a2d..15e1984d1 100644 --- a/ggml.c +++ b/ggml.c @@ -15335,6 +15335,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_or_set(ctx, src0->grad, + /* ggml_diag_mask_inf_impl() shouldn't be here */ + /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), zero_table); } From 48b7ff193e64c97ab174280ba0eb8d14b47c49ba Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 22 Dec 2023 12:12:53 +0100 Subject: [PATCH 240/664] llama : fix platforms without mmap (#4578) * llama : fix platforms without mmap * win32 : limit prefetch size to the file size * fix win32 error clobber, unnecessary std::string in std::runtime_error --- ggml-cuda.cu | 3 ++- ggml.c | 6 ++++-- llama.cpp | 36 ++++++++++++++++++------------------ 3 files changed, 24 insertions(+), 21 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index ac91ee12e..37d7f2792 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7702,7 +7702,8 @@ inline void ggml_cuda_op_scale( GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const float scale = ((float *) dst->op_params)[0]; + float scale; + memcpy(&scale, dst->op_params, sizeof(float)); scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream); CUDA_CHECK(cudaGetLastError()); diff --git a/ggml.c b/ggml.c index 15e1984d1..3656422d7 100644 --- a/ggml.c +++ b/ggml.c @@ -10335,7 +10335,8 @@ static void ggml_compute_forward_scale_f32( } // scale factor - const float v = *(float *) dst->op_params; + float v; + memcpy(&v, dst->op_params, sizeof(float)); const int ith = params->ith; const int nth = params->nth; @@ -15152,7 +15153,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { - const float s = ((float *) tensor->op_params)[0]; + float s; + memcpy(&s, tensor->op_params, sizeof(float)); src0->grad = ggml_add_or_set(ctx, diff --git a/llama.cpp b/llama.cpp index cb0546c95..4e4495739 100644 --- a/llama.cpp +++ b/llama.cpp @@ -778,7 +778,7 @@ struct llama_file { throw std::runtime_error(format("read error: %s", strerror(errno))); } if (ret != 1) { - throw std::runtime_error(std::string("unexpectedly reached end of file")); + throw std::runtime_error("unexpectedly reached end of file"); } } @@ -931,29 +931,29 @@ struct llama_mmap { #elif defined(_WIN32) static constexpr bool SUPPORTED = true; - llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) { - (void) numa; + llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) { + GGML_UNUSED(numa); size = file->size; HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp)); HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL); - DWORD error = GetLastError(); if (hMapping == NULL) { + DWORD error = GetLastError(); throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str())); } addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0); - error = GetLastError(); + DWORD error = GetLastError(); CloseHandle(hMapping); if (addr == NULL) { throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str())); } - if (prefetch) { + if (prefetch > 0) { // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG); HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll"); @@ -965,9 +965,9 @@ struct llama_mmap { // advise the kernel to preload the mapped memory WIN32_MEMORY_RANGE_ENTRY range; range.VirtualAddress = addr; - range.NumberOfBytes = (SIZE_T)size; + range.NumberOfBytes = (SIZE_T) std::min(size, prefetch); if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) { - fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n", + LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n", llama_format_win_err(GetLastError()).c_str()); } } @@ -982,26 +982,26 @@ struct llama_mmap { ~llama_mmap() { if (!UnmapViewOfFile(addr)) { - fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n", + LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n", llama_format_win_err(GetLastError()).c_str()); } } #else static constexpr bool SUPPORTED = false; - llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) { - (void) file; - (void) prefetch; - (void) numa; + llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) { + GGML_UNUSED(file); + GGML_UNUSED(prefetch); + GGML_UNUSED(numa); - throw std::runtime_error(std::string("mmap not supported")); + throw std::runtime_error("mmap not supported"); } - void unmap(size_t offset, size_t len) { - (void) offset; - (void) len; + void unmap_fragment(size_t first, size_t last) { + GGML_UNUSED(first); + GGML_UNUSED(last); - throw std::runtime_error(std::string("mmap not supported")); + throw std::runtime_error("mmap not supported"); } #endif }; From 6724ef16573ec7ecce620be56cbbff145856b2fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrik=20Forst=C3=A9n?= Date: Fri, 22 Dec 2023 15:34:05 +0200 Subject: [PATCH 241/664] Fix CudaMemcpy direction (#4599) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 37d7f2792..da8fd1e09 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8843,7 +8843,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_CPU ? - cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; + cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice; for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; From a55876955b1a83464171de8d578d3ab062a7b62d Mon Sep 17 00:00:00 2001 From: FantasyGmm <16450052+FantasyGmm@users.noreply.github.com> Date: Fri, 22 Dec 2023 23:11:12 +0800 Subject: [PATCH 242/664] cuda : fix jetson compile error (#4560) * fix old jetson compile error * Update Makefile * update jetson detect and cuda version detect * update cuda marco define * update makefile and cuda,fix some issue * Update README.md Co-authored-by: Georgi Gerganov * Update Makefile * Update README.md --------- Co-authored-by: Georgi Gerganov --- Makefile | 22 +++++++++++++++++++--- README.md | 3 +++ ggml-cuda.cu | 7 +++++++ ggml-quants.c | 4 ++-- 4 files changed, 31 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index 42686ce71..6a998091b 100644 --- a/Makefile +++ b/Makefile @@ -282,8 +282,17 @@ endif ifneq ($(filter aarch64%,$(UNAME_M)),) # Apple M1, M2, etc. # Raspberry Pi 3, 4, Zero 2 (64-bit) + # Nvidia Jetson MK_CFLAGS += -mcpu=native MK_CXXFLAGS += -mcpu=native + JETSON_RELEASE_INFO = $(shell jetson_release) + ifdef JETSON_RELEASE_INFO + ifneq ($(filter TX2%,$(JETSON_RELEASE_INFO)),) + JETSON_EOL_MODULE_DETECT = 1 + CC = aarch64-unknown-linux-gnu-gcc + cxx = aarch64-unknown-linux-gnu-g++ + endif + endif endif ifneq ($(filter armv6%,$(UNAME_M)),) @@ -357,10 +366,13 @@ ifdef LLAMA_BLIS endif # LLAMA_BLIS ifdef LLAMA_CUBLAS - MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib + MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include + MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib OBJS += ggml-cuda.o - MK_NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math + MK_NVCCFLAGS = -use_fast_math +ifndef JETSON_EOL_MODULE_DETECT + MK_NVCCFLAGS += --forward-unknown-to-host-compiler +endif # JETSON_EOL_MODULE_DETECT ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo @@ -417,7 +429,11 @@ ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) endif ggml-cuda.o: ggml-cuda.cu ggml-cuda.h +ifdef JETSON_EOL_MODULE_DETECT + $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ +else $(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ +endif # JETSON_EOL_MODULE_DETECT endif # LLAMA_CUBLAS ifdef LLAMA_CLBLAST diff --git a/README.md b/README.md index 377d3928b..649c3b333 100644 --- a/README.md +++ b/README.md @@ -396,6 +396,9 @@ Building the program with BLAS support may lead to some performance improvements - #### cuBLAS This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). + + For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling. + - Using `make`: ```bash make LLAMA_CUBLAS=1 diff --git a/ggml-cuda.cu b/ggml-cuda.cu index da8fd1e09..b124774a9 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -90,6 +90,13 @@ #include #include #include +// CUDA 10.2 does not have these macro definitions. +#ifndef CUBLAS_TF32_TENSOR_OP_MATH +#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH +#define CUBLAS_COMPUTE_16F CUDA_R_16F +#define CUBLAS_COMPUTE_32F CUDA_R_32F +#define cublasComputeType_t cudaDataType_t +#endif #endif // defined(GGML_USE_HIPBLAS) #include "ggml-cuda.h" diff --git a/ggml-quants.c b/ggml-quants.c index 0e8163a16..a15a24048 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3677,7 +3677,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); - const ggml_int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}; + const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}}; const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), @@ -6626,7 +6626,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); const int8x16_t scales = vld1q_s8(scale); - const ggml_int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}; + const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}}; const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), From ba661751322a7c201fd3bef71af077c5aebfaa2a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 22 Dec 2023 17:53:43 +0200 Subject: [PATCH 243/664] sync : ggml (fix im2col) (#4591) * cuda : fix im2col_f32_f16 (ggml/#658) ggml-ci * ggml-alloc : fix ggml_tallocr_is_own --------- Co-authored-by: leejet --- ggml-alloc.c | 2 +- ggml-cuda.cu | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index a97436b17..a27dd54b0 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -72,7 +72,7 @@ static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * t // check if a tensor is allocated by this buffer static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) { - return tensor->buffer == alloc->buffer; + return tensor->buffer == alloc->buffer && (!tensor->view_src || tensor->view_src->buffer == alloc->buffer); } static bool ggml_is_view(struct ggml_tensor * t) { diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b124774a9..7c2a834e3 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5273,17 +5273,17 @@ static __global__ void im2col_f32_f16( const int ky = (i - kd) / OW; const int ix = i % OW; - const int iiw = ix * s0 + kx * d0 - p0; - const int iih = blockIdx.y * s1 + ky * d1 - p1; + const int64_t iiw = ix * s0 + kx * d0 - p0; + const int64_t iih = blockIdx.y * s1 + ky * d1 - p1; - const int offset_dst = + const int64_t offset_dst = (blockIdx.y * OW + ix) * CHW + (blockIdx.z * (KW * KH) + ky * KW + kx); if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { dst[offset_dst] = __float2half(0.0f); } else { - const int offset_src = blockIdx.z * offset_delta; + const int64_t offset_src = blockIdx.z * offset_delta; dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]); } } From 7082d24cec35e9ce9147535a2224dfc67ee0a78c Mon Sep 17 00:00:00 2001 From: LeonEricsson <70749762+LeonEricsson@users.noreply.github.com> Date: Fri, 22 Dec 2023 17:05:56 +0100 Subject: [PATCH 244/664] lookup : add prompt lookup decoding example (#4484) * initial commit, going through initializations * main loop finished, starting to debug * BUG: generates gibberish/repeating tokens after a while * kv_cache management * Added colors to distinguish drafted tokens (--color). Updated README * lookup : fix token positions in the draft batch * lookup : use n_draft from CLI params * lookup : final touches --------- Co-authored-by: Leon Ericsson Co-authored-by: Georgi Gerganov --- .gitignore | 1 + Makefile | 5 +- common/common.h | 3 +- examples/CMakeLists.txt | 1 + examples/lookup/CMakeLists.txt | 5 + examples/lookup/README.md | 13 ++ examples/lookup/lookup.cpp | 230 +++++++++++++++++++++++++++++++++ 7 files changed, 256 insertions(+), 2 deletions(-) create mode 100644 examples/lookup/CMakeLists.txt create mode 100644 examples/lookup/README.md create mode 100644 examples/lookup/lookup.cpp diff --git a/.gitignore b/.gitignore index 76b3d2861..def74a1e9 100644 --- a/.gitignore +++ b/.gitignore @@ -48,6 +48,7 @@ models-mnt /llama-bench /llava-cli /lookahead +/lookup /main /metal /perplexity diff --git a/Makefile b/Makefile index 6a998091b..cb5a4e948 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ BUILD_TARGETS = \ main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ - speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o + speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o # Binaries only useful for tests TEST_TARGETS = \ @@ -664,6 +664,9 @@ parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) +lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + ifdef LLAMA_METAL metal: examples/metal/metal.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) diff --git a/common/common.h b/common/common.h index e87ce1133..9659aa045 100644 --- a/common/common.h +++ b/common/common.h @@ -51,7 +51,7 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 16; // number of tokens to draft during speculative decoding + int32_t n_draft = 8; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode @@ -240,3 +240,4 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); + diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 6744944fd..4cc13d6e9 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -33,6 +33,7 @@ else() add_subdirectory(simple) add_subdirectory(speculative) add_subdirectory(lookahead) + add_subdirectory(lookup) add_subdirectory(train-text-from-scratch) if (LLAMA_METAL) add_subdirectory(metal) diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt new file mode 100644 index 000000000..c060b8f56 --- /dev/null +++ b/examples/lookup/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET lookup) +add_executable(${TARGET} lookup.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/lookup/README.md b/examples/lookup/README.md new file mode 100644 index 000000000..5bfb0de93 --- /dev/null +++ b/examples/lookup/README.md @@ -0,0 +1,13 @@ +# llama.cpp/examples/lookup + +Demonstration of Prompt Lookup Decoding + +https://github.com/apoorvumang/prompt-lookup-decoding + +The key parameters for lookup decoding are `ngram_min`, `ngram_max` and `n_draft`. The first two determine the size of the ngrams to search for in the prompt for a match. The latter specifies how many subsequent tokens to draft if a match is found. + +More info: + +https://github.com/ggerganov/llama.cpp/pull/4484 +https://github.com/ggerganov/llama.cpp/issues/4226 + diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp new file mode 100644 index 000000000..d8de7dd38 --- /dev/null +++ b/examples/lookup/lookup.cpp @@ -0,0 +1,230 @@ +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv){ + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + return 1; + } + + // max/min n-grams size to search for in prompt + const int ngram_max = 4; + const int ngram_min = 1; + + // length of the candidate / draft sequence, if match is found + const int n_draft = params.n_draft; + + const bool dump_kv_cache = params.dump_kv_cache; + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("lookup", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); +#endif // LOG_DISABLE_LOGS + + // init llama.cpp + llama_backend_init(params.numa); + + llama_model * model = NULL; + llama_context * ctx = NULL; + + // load the model + std::tie(model, ctx) = llama_init_from_gpt_params(params); + + // tokenize the prompt + const bool add_bos = llama_should_add_bos_token(model); + LOG("add_bos tgt: %d\n", add_bos); + + std::vector inp; + inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + + const int max_context_size = llama_n_ctx(ctx); + const int max_tokens_list_size = max_context_size - 4; + + if ((int) inp.size() > max_tokens_list_size) { + fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); + return 1; + } + + fprintf(stderr, "\n\n"); + + for (auto id : inp) { + fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str()); + } + + fflush(stderr); + + const int n_input = inp.size(); + + const auto t_enc_start = ggml_time_us(); + + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + + const auto t_enc_end = ggml_time_us(); + + int n_predict = 0; + int n_drafted = 0; + int n_accept = 0; + + int n_past = inp.size(); + + bool has_eos = false; + + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams); + + std::vector draft; + + llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1); + + // debug + struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1); + + const auto t_dec_start = ggml_time_us(); + + while (true) { + // debug + if (dump_kv_cache) { + llama_kv_cache_view_update(ctx, &kvc_view); + dump_kv_cache_view_seqs(kvc_view, 40); + } + + // print current draft sequence + LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str()); + + int i_dft = 0; + while (true) { + // sample from the target model + llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft); + + llama_sampling_accept(ctx_sampling, ctx, id, true); + + const std::string token_str = llama_token_to_piece(ctx, id); + + if (!params.use_color) { + printf("%s", token_str.c_str()); + } + + if (id == llama_token_eos(model)) { + has_eos = true; + } + + ++n_predict; + + // check if the target token matches the draft + if (i_dft < (int) draft.size() && id == draft[i_dft]) { + LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str()); + ++n_accept; + ++n_past; + ++i_dft; + inp.push_back(id); + + if (params.use_color) { + // color accepted draft token + printf("\033[34m%s\033[0m", token_str.c_str()); + fflush(stdout); + } + continue; + } + + if (params.use_color) { + printf("%s", token_str.c_str()); + } + fflush(stdout); + + + LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); + + draft.clear(); + draft.push_back(id); + inp.push_back(id); + break; + } + + if ((params.n_predict > 0 && n_predict > params.n_predict) || has_eos) { + break; + } + + // KV cache management + // clean the cache of draft tokens that weren't accepted + llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + + llama_batch_clear(batch_tgt); + llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true); + + // generate n_pred tokens through prompt lookup + auto prompt_lookup = [&]() -> void { + int inp_size = inp.size(); + for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){ + const llama_token * ngram = &inp[inp_size - ngram_size]; + + for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) { + bool match = true; + for (int j = 0; j < ngram_size; ++j) { + if (inp[i + j] != ngram[j]) { + match = false; + break; + } + } + + if (match) { + const int startIdx = i + ngram_size; + const int endIdx = startIdx + n_draft; + if (endIdx < inp_size) { + for (int j = startIdx; j < endIdx; ++j) { + LOG(" - draft candidate %d: %d\n", j, inp[j]); + draft.push_back(inp[j]); + llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true); + ++n_drafted; + } + return; + } + } + } + } + return; + }; + + prompt_lookup(); + + llama_decode(ctx, batch_tgt); + ++n_past; + + draft.erase(draft.begin()); + } + + auto t_dec_end = ggml_time_us(); + + LOG_TEE("\n\n"); + + LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f)); + LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); + + LOG_TEE("\n"); + LOG_TEE("n_draft = %d\n", n_draft); + LOG_TEE("n_predict = %d\n", n_predict); + LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("n_accept = %d\n", n_accept); + LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + LOG_TEE("\ntarget:\n"); + llama_print_timings(ctx); + + llama_sampling_free(ctx_sampling); + llama_batch_free(batch_tgt); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + fprintf(stderr, "\n\n"); + + return 0; +} From e0a4002273907b2c414b6b5442d99e08bfe2df35 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 23 Dec 2023 09:16:33 +0100 Subject: [PATCH 245/664] CUDA: fixed row rounding for 0 tensor splits (#4594) --- ggml-cuda.cu | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7c2a834e3..490081cac 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7937,12 +7937,16 @@ static void ggml_cuda_op_mul_mat( if (id != 0) { row_low[id] = ne01*g_tensor_split[id]; - row_low[id] -= row_low[id] % rounding; + if (row_low[id] < ne01) { + row_low[id] -= row_low[id] % rounding; + } } if (id != g_device_count - 1) { row_high[id] = ne01*g_tensor_split[id + 1]; - row_high[id] -= row_high[id] % rounding; + if (row_high[id] < ne01) { + row_high[id] -= row_high[id] % rounding; + } } } } From b9ec82d262cb20d7f0a8a1157bfa9aace40e2625 Mon Sep 17 00:00:00 2001 From: kalomaze <66376113+kalomaze@users.noreply.github.com> Date: Sat, 23 Dec 2023 03:27:07 -0600 Subject: [PATCH 246/664] grammar : check the full vocab only if necessary (opt) (#4306) * Check the full vocab for grammar only if necessary * Fix missing logit restoration step (?) Does this matter, actually? * Fix whitespace / formatting * Adjust comment * Didn't mean to push test gbnf * Split sampling into the helper function (?) And also revert the changes made to the header * common : fix final newline --------- Co-authored-by: Georgi Gerganov --- common/sampling.cpp | 48 ++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 45 insertions(+), 3 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index f4e76df31..5b15204be 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -149,11 +149,12 @@ static void sampler_queue( } } -llama_token llama_sampling_sample( +static llama_token llama_sampling_sample_impl( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, struct llama_context * ctx_cfg, - const int idx) { + const int idx, + bool is_resampling) { // Add a parameter to indicate if we are resampling const llama_sampling_params & params = ctx_sampling->params; const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); @@ -173,8 +174,17 @@ llama_token llama_sampling_sample( llama_token id = 0; + // Get a pointer to the logits float * logits = llama_get_logits_ith(ctx_main, idx); + // Declare original_logits at the beginning of the function scope + std::vector original_logits; + + if (!is_resampling) { + // Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this. + original_logits = std::vector(logits, logits + llama_n_vocab(llama_get_model(ctx_main))); + } + // apply params.logit_bias map for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { logits[it->first] += it->second; @@ -210,7 +220,8 @@ llama_token llama_sampling_sample( } } - if (ctx_sampling->grammar != NULL) { + // If we are in the resampling phase, apply grammar checks before sampling logic + if (is_resampling && ctx_sampling->grammar != NULL) { llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); } @@ -252,9 +263,40 @@ llama_token llama_sampling_sample( } } + if (ctx_sampling->grammar != NULL && !is_resampling) { + // Create an array with a single token data element for the sampled id + llama_token_data single_token_data = {id, logits[id], 0.0f}; + llama_token_data_array single_token_data_array = { &single_token_data, 1, false }; + + // Apply grammar constraints to the single token + llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar); + + // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY + bool is_valid = single_token_data_array.data[0].logit != -INFINITY; + + // If the token is not valid according to the grammar, perform resampling + if (!is_valid) { + LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str()); + + // Restore logits from the copy + std::copy(original_logits.begin(), original_logits.end(), logits); + + return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling + } + } + return id; } +llama_token llama_sampling_sample( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + // Call the implementation function with is_resampling set to false by default + return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); +} + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, From 6123979952385847d8348e295d77d6e01da8aa84 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 23 Dec 2023 09:31:49 +0000 Subject: [PATCH 247/664] server : allow to specify custom prompt for penalty calculation (#3727) --- common/sampling.cpp | 8 ++++--- common/sampling.h | 3 +++ examples/server/README.md | 2 ++ examples/server/server.cpp | 44 ++++++++++++++++++++++++++++++++++++++ 4 files changed, 54 insertions(+), 3 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 5b15204be..8e45909f1 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -203,12 +203,14 @@ static llama_token llama_sampling_sample_impl( } // apply penalties - if (!prev.empty()) { + const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; + const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); + if (penalty_tokens_used_size) { const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))]; llama_sample_repetition_penalties(ctx_main, &cur_p, - prev.data() + prev.size() - penalty_last_n, - penalty_last_n, penalty_repeat, penalty_freq, penalty_present); + penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size, + penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present); if (!penalize_nl) { for (size_t idx = 0; idx < cur_p.size; idx++) { diff --git a/common/sampling.h b/common/sampling.h index fdfa9eed1..f16ef97e3 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -36,6 +36,9 @@ typedef struct llama_sampling_params { float cfg_scale = 1.f; // how strong is guidance std::unordered_map logit_bias; // logit bias for specific tokens + + std::vector penalty_prompt_tokens; + bool use_penalty_prompt_tokens = false; } llama_sampling_params; // general sampler context diff --git a/examples/server/README.md b/examples/server/README.md index 0751b9612..f1e586a1c 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -148,6 +148,8 @@ node index.js `frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled); + `penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`). + `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0). `mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0). diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 04038530f..72dfe452c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -761,6 +761,42 @@ struct llama_server_context slot->prompt = ""; } + slot->sparams.penalty_prompt_tokens.clear(); + slot->sparams.use_penalty_prompt_tokens = false; + const auto &penalty_prompt = data.find("penalty_prompt"); + if (penalty_prompt != data.end()) + { + if (penalty_prompt->is_string()) + { + const auto penalty_prompt_string = penalty_prompt->get(); + auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false); + slot->sparams.penalty_prompt_tokens.swap(penalty_tokens); + if (slot->params.n_predict > 0) + { + slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict); + } + slot->sparams.use_penalty_prompt_tokens = true; + } + else if (penalty_prompt->is_array()) + { + const auto n_tokens = penalty_prompt->size(); + slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict)); + const int n_vocab = llama_n_vocab(model); + for (const auto &penalty_token : *penalty_prompt) + { + if (penalty_token.is_number_integer()) + { + const auto tok = penalty_token.get(); + if (tok >= 0 && tok < n_vocab) + { + slot->sparams.penalty_prompt_tokens.push_back(tok); + } + } + } + slot->sparams.use_penalty_prompt_tokens = true; + } + } + slot->sparams.logit_bias.clear(); if (json_value(data, "ignore_eos", false)) @@ -992,6 +1028,12 @@ struct llama_server_context slot.generated_text += token_str; slot.has_next_token = true; + if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) + { + // we can change penalty_prompt_tokens because it is always created from scratch each request + slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok); + } + // check if there is incomplete UTF-8 character at the end bool incomplete = false; for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) @@ -1183,6 +1225,8 @@ struct llama_server_context {"repeat_penalty", slot.sparams.penalty_repeat}, {"presence_penalty", slot.sparams.penalty_present}, {"frequency_penalty", slot.sparams.penalty_freq}, + {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, + {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens}, {"mirostat", slot.sparams.mirostat}, {"mirostat_tau", slot.sparams.mirostat_tau}, {"mirostat_eta", slot.sparams.mirostat_eta}, From 925e5584a058afb612f9c20bc472c130f5d0f891 Mon Sep 17 00:00:00 2001 From: Samuel Maynard Date: Sat, 23 Dec 2023 11:35:55 +0200 Subject: [PATCH 248/664] ci(docker): fix tags in "Build and push docker image (tagged)" (#4603) --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 7f4de50ea..87904b75e 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -98,5 +98,5 @@ jobs: context: . push: ${{ github.event_name == 'push' }} platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}" , "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" + tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" file: ${{ matrix.config.dockerfile }} From 708e179e8562c2604240df95a2241dea17fd808b Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 23 Dec 2023 16:10:51 +0100 Subject: [PATCH 249/664] fallback to CPU buffer if host buffer alloc fails (#4610) --- ggml-cuda.cu | 11 ++++++----- llama.cpp | 16 +++++++++++----- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 490081cac..f9830328b 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6729,8 +6729,7 @@ void * ggml_cuda_host_malloc(size_t size) { void * ptr = nullptr; cudaError_t err = cudaMallocHost((void **) &ptr, size); if (err != cudaSuccess) { - // The allocation error can be bypassed. A null ptr will assigned out of this function. - // This can fixed the OOM error in WSL. + // clear the error cudaGetLastError(); fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", size/1024.0/1024.0, cudaGetErrorString(err)); @@ -9674,12 +9673,14 @@ ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { // host buffer type static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - CUDA_CHECK(cudaFreeHost(buffer->context)); + ggml_cuda_host_free(buffer->context); } static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr; - CUDA_CHECK(cudaMallocHost(&ptr, size)); + void * ptr = ggml_cuda_host_malloc(size); + if (ptr == nullptr) { + return nullptr; + } // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); diff --git a/llama.cpp b/llama.cpp index 4e4495739..5699a0fcf 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1177,21 +1177,27 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_ } static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) { + ggml_backend_buffer_type_t buft = nullptr; + #ifdef GGML_USE_METAL if (n_gpu_layers > 0) { - return ggml_backend_metal_buffer_type(); + buft = ggml_backend_metal_buffer_type(); } #elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (n_gpu_layers > 0) { - return ggml_backend_cuda_buffer_type(0); + buft = ggml_backend_cuda_buffer_type(0); } #elif defined(GGML_USE_CUBLAS) - return ggml_backend_cuda_host_buffer_type(); + buft = ggml_backend_cuda_host_buffer_type(); #elif defined(GGML_USE_CPU_HBM) - return ggml_backend_cpu_hbm_buffer_type(); + buft = ggml_backend_cpu_hbm_buffer_type(); #endif - return ggml_backend_cpu_buffer_type(); + if (buft == nullptr) { + buft = ggml_backend_cpu_buffer_type(); + } + + return buft; GGML_UNUSED(n_gpu_layers); } From 5bf3953d7e9831ea22b0bc017ce97409b801ccf1 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 24 Dec 2023 14:34:22 +0100 Subject: [PATCH 250/664] cuda : improve cuda pool efficiency using virtual memory (#4606) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * cuda : improve cuda pool efficiency using virtual memory * fix mixtral * fix cmake build * check for vmm support, disable for hip ggml-ci * fix hip build * clarify granularity * move all caps to g_device_caps * refactor error checking * add cuda_pool_alloc, refactor most pool allocations ggml-ci * fix hip build * CUBLAS_TF32_TENSOR_OP_MATH is not a macro * more hip crap * llama : fix msvc warnings * ggml : fix msvc warnings * minor * minor * cuda : fallback to CPU on host buffer alloc fail * Update ggml-cuda.cu Co-authored-by: Johannes Gäßler * Update ggml-cuda.cu Co-authored-by: Johannes Gäßler * ensure allocations are always aligned * act_size -> actual_size --------- Co-authored-by: Johannes Gäßler --- CMakeLists.txt | 2 + Makefile | 6 +- ggml-backend.c | 16 +- ggml-cuda.cu | 499 +++++++++++++++++++++++++++---------------- ggml.c | 2 +- ggml.h | 2 + llama.cpp | 6 +- tests/test-grad0.cpp | 3 - 8 files changed, 328 insertions(+), 208 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 6fc6508c5..545aab267 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -302,6 +302,8 @@ if (LLAMA_CUBLAS) set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt) endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) # 52 == lowest CUDA 12 standard # 60 == f16 CUDA intrinsics diff --git a/Makefile b/Makefile index cb5a4e948..28c6d79bc 100644 --- a/Makefile +++ b/Makefile @@ -367,17 +367,15 @@ endif # LLAMA_BLIS ifdef LLAMA_CUBLAS MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include - MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib + MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o MK_NVCCFLAGS = -use_fast_math ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT - ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo -endif - +endif # LLAMA_DEBUG ifdef LLAMA_CUDA_NVCC NVCC = $(LLAMA_CUDA_NVCC) else diff --git a/ggml-backend.c b/ggml-backend.c index 0c8c9ec43..526ce732b 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -297,7 +297,7 @@ static void ggml_backend_registry_init(void) { void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG); - int id = ggml_backend_registry_count; + size_t id = ggml_backend_registry_count; ggml_backend_registry[id] = (struct ggml_backend_reg) { /* .name = */ {0}, @@ -330,6 +330,8 @@ size_t ggml_backend_reg_find_by_name(const char * name) { return i; } } + + // not found return SIZE_MAX; } @@ -340,15 +342,15 @@ ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str) const char * params = strchr(backend_str, ':'); char backend_name[128]; if (params == NULL) { - strcpy(backend_name, backend_str); + snprintf(backend_name, sizeof(backend_name), "%s", backend_str); params = ""; } else { - strncpy(backend_name, backend_str, params - backend_str); - backend_name[params - backend_str] = '\0'; + snprintf(backend_name, sizeof(backend_name), "%.*s", (int)(params - backend_str), backend_str); params++; } size_t backend_i = ggml_backend_reg_find_by_name(backend_name); + if (backend_i == SIZE_MAX) { fprintf(stderr, "%s: backend %s not found\n", __func__, backend_name); return NULL; @@ -396,18 +398,12 @@ static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { } static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - memcpy((char *)tensor->data + offset, data, size); GGML_UNUSED(buffer); } static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - memcpy(data, (const char *)tensor->data + offset, size); GGML_UNUSED(buffer); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f9830328b..ac3b3c14d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -86,17 +86,28 @@ #define cudaStream_t hipStream_t #define cudaSuccess hipSuccess #define __trap abort +#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS +#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED +#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED +#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE +#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH +#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR +#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED +#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR +#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED #else #include +#include #include #include -// CUDA 10.2 does not have these macro definitions. -#ifndef CUBLAS_TF32_TENSOR_OP_MATH + +#if CUDART_VERSION < 11020 #define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH #define CUBLAS_COMPUTE_16F CUDA_R_16F #define CUBLAS_COMPUTE_32F CUDA_R_32F #define cublasComputeType_t cudaDataType_t -#endif +#endif // CUDART_VERSION < 11020 + #endif // defined(GGML_USE_HIPBLAS) #include "ggml-cuda.h" @@ -200,45 +211,45 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); -#define CUDA_CHECK(err) \ - do { \ - cudaError_t err_ = (err); \ - if (err_ != cudaSuccess) { \ - int id; \ - cudaGetDevice(&id); \ - fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ - cudaGetErrorString(err_)); \ - fprintf(stderr, "current device: %d\n", id); \ - GGML_ASSERT(!"CUDA error"); \ - } \ - } while (0) - #if CUDART_VERSION >= 12000 -#define CUBLAS_CHECK(err) \ - do { \ - cublasStatus_t err_ = (err); \ - if (err_ != CUBLAS_STATUS_SUCCESS) { \ - int id; \ - cudaGetDevice(&id); \ - fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \ - err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \ - fprintf(stderr, "current device: %d\n", id); \ - GGML_ASSERT(!"cuBLAS error"); \ - } \ - } while (0) + static const char * cublas_get_error_str(const cublasStatus_t err) { + return cublasGetStatusString(err); + } #else -#define CUBLAS_CHECK(err) \ - do { \ - cublasStatus_t err_ = (err); \ - if (err_ != CUBLAS_STATUS_SUCCESS) { \ - int id; \ - cudaGetDevice(&id); \ - fprintf(stderr, "\ncuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \ - fprintf(stderr, "current device: %d\n", id); \ - GGML_ASSERT(!"cuBLAS error"); \ - } \ - } while (0) -#endif // CUDART_VERSION >= 11 + static const char * cublas_get_error_str(const cublasStatus_t err) { + switch (err) { + case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS"; + case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED"; + case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED"; + case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE"; + case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH"; + case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR"; + case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED"; + case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR"; + case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED"; + default: return "unknown error"; + } + } +#endif // CUDART_VERSION >= 12000 + +[[noreturn]] +static void ggml_cuda_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) { + fprintf(stderr, "CUDA error: %s: %s\n", stmt, msg); + fprintf(stderr, " in function %s at %s:%d\n", func, file, line); + GGML_ASSERT(!"CUDA error"); +} + +#define CUDA_CHECK(err) do { auto err_ = (err); if (err_ != cudaSuccess) ggml_cuda_error(#err, __func__, __FILE__, __LINE__, cudaGetErrorString(err_)); } while (0) +#define CUBLAS_CHECK(err) do { auto err_ = (err); if (err_ != CUBLAS_STATUS_SUCCESS) ggml_cuda_error(#err, __func__, __FILE__, __LINE__, cublas_get_error_str(err_)); } while (0) + +#if !defined(GGML_USE_HIPBLAS) +static const char * cu_get_error_str(CUresult err) { + const char * err_str; + cuGetErrorString(err, &err_str); + return err_str; +} +#define CU_CHECK(err) do { auto err_ = (err); if (err_ != CUDA_SUCCESS) ggml_cuda_error(#err, __func__, __FILE__, __LINE__, cu_get_error_str(err_)); } while (0) +#endif #if CUDART_VERSION >= 11100 #define GGML_CUDA_ASSUME(x) __builtin_assume(x) @@ -516,9 +527,17 @@ inline cudaError_t ggml_cuda_set_device(const int device) { static int g_device_count = -1; static int g_main_device = 0; -static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES]; static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0}; +struct cuda_device_capabilities { + int cc; // compute capability + bool vmm; // virtual memory support + size_t vmm_granularity; // granularity of virtual memory +}; + +static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, false, 0} }; + + static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default static size_t g_scratch_offset = 0; @@ -5875,7 +5894,7 @@ static void ggml_mul_mat_q4_0_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -5920,7 +5939,7 @@ static void ggml_mul_mat_q4_1_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -5965,7 +5984,7 @@ static void ggml_mul_mat_q5_0_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6010,7 +6029,7 @@ static void ggml_mul_mat_q5_1_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6055,7 +6074,7 @@ static void ggml_mul_mat_q8_0_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6100,7 +6119,7 @@ static void ggml_mul_mat_q2_K_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6147,7 +6166,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6193,7 +6212,7 @@ static void ggml_mul_mat_q4_K_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6238,7 +6257,7 @@ static void ggml_mul_mat_q5_K_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6283,7 +6302,7 @@ static void ggml_mul_mat_q6_K_q8_1_cuda( int id; CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= CC_RDNA2) { @@ -6543,21 +6562,24 @@ struct scoped_spin_lock { scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; }; +static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT; + +// #define DEBUG_CUDA_MALLOC struct cuda_buffer { void * ptr = nullptr; size_t size = 0; }; static cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS]; -static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT; +static size_t g_cuda_pool_size[GGML_CUDA_MAX_DEVICES] = {0}; -static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { +static void * ggml_cuda_pool_malloc_leg(size_t size, size_t * actual_size) { scoped_spin_lock lock(g_cuda_pool_lock); int id; CUDA_CHECK(cudaGetDevice(&id)); #ifdef DEBUG_CUDA_MALLOC int nnz = 0; - size_t max_size = 0, tot_size = 0; + size_t max_size = 0; #endif size_t best_diff = 1ull << 36; int ibest = -1; @@ -6566,7 +6588,6 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { if (b.ptr != nullptr) { #ifdef DEBUG_CUDA_MALLOC ++nnz; - tot_size += b.size; if (b.size > max_size) max_size = b.size; #endif if (b.size >= size) { @@ -6593,19 +6614,20 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { b.size = 0; return ptr; } -#ifdef DEBUG_CUDA_MALLOC - fprintf(stderr, "%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024)); -#endif void * ptr; size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); *actual_size = look_ahead_size; + g_cuda_pool_size[id] += look_ahead_size; +#ifdef DEBUG_CUDA_MALLOC + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); +#endif return ptr; } -static void ggml_cuda_pool_free(void * ptr, size_t size) { +static void ggml_cuda_pool_free_leg(void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); int id; CUDA_CHECK(cudaGetDevice(&id)); @@ -6620,8 +6642,152 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { } fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); CUDA_CHECK(cudaFree(ptr)); + g_cuda_pool_size[id] -= size; } +#if !defined(GGML_USE_HIPBLAS) +// pool with virtual memory +static std::vector g_cuda_pool_handles[GGML_CUDA_MAX_DEVICES]; +static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0}; +static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0}; +static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 36; // 64 GB + +static void * ggml_cuda_pool_malloc_vmm(size_t size, size_t * actual_size) { + scoped_spin_lock lock(g_cuda_pool_lock); + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types + const size_t alignment = 128; + size = alignment * ((size + alignment - 1) / alignment); + + size_t avail = g_cuda_pool_size[id] - g_cuda_pool_used[id]; + + if (size > avail) { + // round up to the next multiple of the granularity + size_t reserve_size = size - avail; + const size_t granularity = g_device_caps[id].vmm_granularity; + reserve_size = granularity * ((reserve_size + granularity - 1) / granularity); + + GGML_ASSERT(g_cuda_pool_size[id] + reserve_size <= CUDA_POOL_VMM_MAX_SIZE); + + // allocate more physical memory + CUmemAllocationProp prop = {}; + prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; + prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = id; + CUmemGenericAllocationHandle handle; + CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); + + // reserve virtual address space (if not already reserved) + if (g_cuda_pool_addr[id] == 0) { + CU_CHECK(cuMemAddressReserve(&g_cuda_pool_addr[id], CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)); + } + + // map at the end of the pool + CU_CHECK(cuMemMap(g_cuda_pool_addr[id] + g_cuda_pool_size[id], reserve_size, 0, handle, 0)); + + // set access + CUmemAccessDesc access = {}; + access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; + access.location.id = id; + access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; + CU_CHECK(cuMemSetAccess(g_cuda_pool_addr[id] + g_cuda_pool_size[id], reserve_size, &access, 1)); + + // add to the pool + g_cuda_pool_handles[id].push_back(handle); + g_cuda_pool_size[id] += reserve_size; + + //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n", + // id, (unsigned long long) (g_cuda_pool_size[id]/1024/1024), + // (unsigned long long) (reserve_size/1024/1024)); + } + + GGML_ASSERT(g_cuda_pool_addr[id] != 0); + + void * ptr = (void *) (g_cuda_pool_addr[id] + g_cuda_pool_used[id]); + *actual_size = size; + g_cuda_pool_used[id] += size; + +#ifdef DEBUG_CUDA_MALLOC + printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr); +#endif + + return ptr; +} + +static void ggml_cuda_pool_free_vmm(void * ptr, size_t size) { + scoped_spin_lock lock(g_cuda_pool_lock); + int id; + CUDA_CHECK(cudaGetDevice(&id)); + +#ifdef DEBUG_CUDA_MALLOC + printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); +#endif + + g_cuda_pool_used[id] -= size; + + // all deallocations must be in reverse order of the allocations + GGML_ASSERT(ptr == (void *) (g_cuda_pool_addr[id] + g_cuda_pool_used[id])); +} + +static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { + int id; + CUDA_CHECK(cudaGetDevice(&id)); + if (g_device_caps[id].vmm) { + return ggml_cuda_pool_malloc_vmm(size, actual_size); + } else { + return ggml_cuda_pool_malloc_leg(size, actual_size); + } +} + +static void ggml_cuda_pool_free(void * ptr, size_t size) { + int id; + CUDA_CHECK(cudaGetDevice(&id)); + if (g_device_caps[id].vmm) { + ggml_cuda_pool_free_vmm(ptr, size); + } else { + ggml_cuda_pool_free_leg(ptr, size); + } +} +#else +#define ggml_cuda_pool_malloc ggml_cuda_pool_malloc_leg +#define ggml_cuda_pool_free ggml_cuda_pool_free_leg +#endif // !defined(GGML_USE_HIPBLAS) + +template +struct cuda_pool_alloc { + T * ptr = nullptr; + size_t actual_size = 0; + + // size is in number of elements + T * alloc(size_t size) { + GGML_ASSERT(ptr == nullptr); + ptr = (T *) ggml_cuda_pool_malloc(size * sizeof(T), &this->actual_size); + return ptr; + } + + cuda_pool_alloc(size_t size) { + alloc(size); + } + + ~cuda_pool_alloc() { + if (ptr != nullptr) { + ggml_cuda_pool_free(ptr, actual_size); + } + } + + T * get() { + return ptr; + } + + cuda_pool_alloc() = default; + cuda_pool_alloc(const cuda_pool_alloc &) = delete; + cuda_pool_alloc(cuda_pool_alloc &&) = delete; + cuda_pool_alloc& operator=(const cuda_pool_alloc &) = delete; + cuda_pool_alloc& operator=(cuda_pool_alloc &&) = delete; +}; + static bool g_cublas_loaded = false; bool ggml_cublas_loaded(void) { @@ -6660,16 +6826,33 @@ void ggml_init_cublas() { #endif fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count); for (int id = 0; id < g_device_count; ++id) { + int device_vmm = 0; + +#if !defined(GGML_USE_HIPBLAS) + CUdevice device; + CU_CHECK(cuDeviceGet(&device, id)); + CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); + + if (device_vmm) { + CUmemAllocationProp alloc_prop = {}; + alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; + alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; + alloc_prop.location.id = id; + CU_CHECK(cuMemGetAllocationGranularity(&g_device_caps[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM)); + } +#endif // !defined(GGML_USE_HIPBLAS) + g_device_caps[id].vmm = !!device_vmm; + cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor); + fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); g_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - g_compute_capabilities[id] = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; + g_device_caps[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; #else - g_compute_capabilities[id] = 100*prop.major + 10*prop.minor; + g_device_caps[id].cc = 100*prop.major + 10*prop.minor; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) } for (int id = 0; id < g_device_count; ++id) { @@ -7178,11 +7361,11 @@ static int64_t get_row_rounding(ggml_type type) { int64_t max_compute_capability = INT_MIN; for (int64_t id = 0; id < g_device_count; ++id) { if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - if (min_compute_capability > g_compute_capabilities[id]) { - min_compute_capability = g_compute_capabilities[id]; + if (min_compute_capability > g_device_caps[id].cc) { + min_compute_capability = g_device_caps[id].cc; } - if (max_compute_capability < g_compute_capabilities[id]) { - max_compute_capability = g_compute_capabilities[id]; + if (max_compute_capability < g_device_caps[id].cc) { + max_compute_capability = g_device_caps[id].cc; } } } @@ -7297,8 +7480,8 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_CUDA_F16 - size_t ash; - dfloat * src1_dfloat = nullptr; // dfloat == half + cuda_pool_alloc src1_dfloat_a; + half * src1_dfloat = nullptr; // dfloat == half bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || @@ -7306,7 +7489,7 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; if (src1_convert_f16) { - src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash); + src1_dfloat = src1_dfloat_a.alloc(ne00); ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00, ne00, 1, sizeof(float), 0, 0, ne00, 1, sizeof(half), 0, 0, stream); @@ -7354,12 +7537,6 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( break; } -#ifdef GGML_CUDA_F16 - if (src1_convert_f16) { - ggml_cuda_pool_free(src1_dfloat, ash); - } -#endif // GGML_CUDA_F16 - (void) src1; (void) dst; (void) src1_ddq_i; @@ -7390,33 +7567,30 @@ inline void ggml_cuda_op_mul_mat_cublas( // ldc == nrows of the matrix that cuBLAS writes into int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; - const int compute_capability = g_compute_capabilities[id]; + const int compute_capability = g_device_caps[id].cc; if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 - half * src0_as_f16 = nullptr; - size_t src0_as = 0; + cuda_pool_alloc src0_as_f16; if (src0->type != GGML_TYPE_F16) { const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = row_diff*ne00; - src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as); - to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream); + src0_as_f16.alloc(ne); + to_fp16_cuda(src0_dd_i, src0_as_f16.get(), ne, stream); } - const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16; + const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get(); - half * src1_as_f16 = nullptr; - size_t src1_as = 0; + cuda_pool_alloc src1_as_f16; if (src1->type != GGML_TYPE_F16) { const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = src1_ncols*ne10; - src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); - to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); + src1_as_f16.alloc(ne); + to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream); } - const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16; - size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); + const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get(); + cuda_pool_alloc dst_f16(row_diff*src1_ncols); const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; @@ -7425,36 +7599,25 @@ inline void ggml_cuda_op_mul_mat_cublas( CUBLAS_CHECK( cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, row_diff, src1_ncols, ne10, - &alpha_f16, src0_ptr, CUDA_R_16F, ne00, - src1_ptr, CUDA_R_16F, ne10, - &beta_f16, dst_f16, CUDA_R_16F, ldc, + &alpha_f16, src0_ptr, CUDA_R_16F, ne00, + src1_ptr, CUDA_R_16F, ne10, + &beta_f16, dst_f16.get(), CUDA_R_16F, ldc, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream); - - ggml_cuda_pool_free(dst_f16, dst_as); - - if (src0_as != 0) { - ggml_cuda_pool_free(src0_as_f16, src0_as); - } - - if (src1_as != 0) { - ggml_cuda_pool_free(src1_as_f16, src1_as); - } + to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); } else { - float * src0_ddq_as_f32 = nullptr; - size_t src0_as = 0; + cuda_pool_alloc src0_ddq_as_f32; if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT - to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream); + src0_ddq_as_f32.alloc(row_diff*ne00); + to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); } - const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32; + const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); const float alpha = 1.0f; const float beta = 0.0f; @@ -7466,10 +7629,6 @@ inline void ggml_cuda_op_mul_mat_cublas( &alpha, src0_ddf_i, ne00, src1_ddf_i, ne10, &beta, dst_dd_i, ldc)); - - if (src0_as != 0) { - ggml_cuda_pool_free(src0_ddq_as_f32, src0_as); - } } (void) dst; @@ -7761,18 +7920,17 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s float * src1_ddf = nullptr; float * dst_ddf = nullptr; - // as = actual size - size_t src0_asf = 0; - size_t src1_asf = 0; - size_t dst_asf = 0; + cuda_pool_alloc src0_f; + cuda_pool_alloc src1_f; + cuda_pool_alloc dst_f; ggml_cuda_set_device(g_main_device); - const cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; if (src0_on_device) { src0_ddf = (float *) src0_extra->data_device[g_main_device]; } else { - src0_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_asf); + src0_ddf = src0_f.alloc(ggml_nelements(src0)); CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream)); } @@ -7780,14 +7938,14 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s if (src1_on_device) { src1_ddf = (float *) src1_extra->data_device[g_main_device]; } else { - src1_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf); + src1_ddf = src1_f.alloc(ggml_nelements(src1)); CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); } } if (dst_on_device) { dst_ddf = (float *) dst_extra->data_device[g_main_device]; } else { - dst_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(dst), &dst_asf); + dst_ddf = dst_f.alloc(ggml_nelements(dst)); } // do the computation @@ -7799,16 +7957,6 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); } - if (src0_asf > 0) { - ggml_cuda_pool_free(src0_ddf, src0_asf); - } - if (src1_asf > 0) { - ggml_cuda_pool_free(src1_ddf, src1_asf); - } - if (dst_asf > 0) { - ggml_cuda_pool_free(dst_ddf, dst_asf); - } - if (dst->backend == GGML_BACKEND_CPU) { CUDA_CHECK(cudaDeviceSynchronize()); } @@ -8122,17 +8270,17 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(id)); // free buffers again when done - if (src0_as[id] > 0) { - ggml_cuda_pool_free(src0_dd[id], src0_as[id]); - } - if (src1_asf[id] > 0) { - ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); + if (dst_as[id] > 0) { + ggml_cuda_pool_free(dst_dd[id], dst_as[id]); } if (src1_asq[id] > 0) { ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]); } - if (dst_as[id] > 0) { - ggml_cuda_pool_free(dst_dd[id], dst_as[id]); + if (src1_asf[id] > 0) { + ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); + } + if (src0_as[id] > 0) { + ggml_cuda_pool_free(src0_dd[id], src0_as[id]); } } @@ -8385,14 +8533,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); - size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); - to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); + cuda_pool_alloc src1_as_f16(ne1); + to_fp16_cuda(src1_ddf, src1_as_f16.get(), ne1, main_stream); - size_t dst_as = 0; - - half * dst_f16 = nullptr; - char * dst_t = nullptr; + cuda_pool_alloc dst_f16; + char * dst_t; cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; cudaDataType_t cu_data_type = CUDA_R_16F; @@ -8411,8 +8556,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const const void * beta = &beta_f16; if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); - dst_t = (char *) dst_f16; + dst_t = (char *) dst_f16.alloc(ne); nbd2 /= sizeof(float) / sizeof(half); nbd3 /= sizeof(float) / sizeof(half); @@ -8459,9 +8603,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA - (const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB - beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC + alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA + (const char *) src1_as_f16.get(), CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB + beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC ne12*ne13, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); @@ -8469,19 +8613,13 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const // use cublasGemmBatchedEx const int ne23 = ne12*ne13; - const void ** ptrs_src = nullptr; - void ** ptrs_dst = nullptr; - - size_t ptrs_src_s = 0; - size_t ptrs_dst_s = 0; - - ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); - ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); + cuda_pool_alloc ptrs_src(2*ne23); + cuda_pool_alloc< void *> ptrs_dst(1*ne23); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( - src0_as_f16, src1_as_f16, dst_t, - ptrs_src, ptrs_dst, + src0_as_f16, src1_as_f16.get(), dst_t, + ptrs_src.get(), ptrs_dst.get(), ne12, ne13, ne23, nb02, nb03, @@ -8493,30 +8631,19 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float), - beta, ( void **) (ptrs_dst + 0*ne23), cu_data_type, ne01, + alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/sizeof(half), + (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, ne23, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - - if (ptrs_src_s != 0) { - ggml_cuda_pool_free(ptrs_src, ptrs_src_s); - } - if (ptrs_dst_s != 0) { - ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s); - } } #endif if (dst->op_params[0] == GGML_PREC_DEFAULT) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); - - ggml_cuda_pool_free(dst_f16, dst_as); + to_fp32_cuda(dst_f16.get(), dst_ddf, ne, main_stream); } - - ggml_cuda_pool_free(src1_as_f16, src1_as); } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -8529,8 +8656,8 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 int64_t min_compute_capability = INT_MAX; for (int64_t id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - min_compute_capability = g_compute_capabilities[id]; + if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { + min_compute_capability = g_device_caps[id].cc; } } @@ -8843,12 +8970,11 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row); } } else { - size_t as_src1, as_dst; - char * src1_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(src1), &as_src1); - char * dst_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(dst), &as_dst); + cuda_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); + cuda_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); - src1_row_extra.data_device[g_main_device] = src1_contiguous; - dst_row_extra.data_device[g_main_device] = dst_contiguous; + src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); + dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; @@ -8868,7 +8994,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(row_id >= 0 && row_id < n_as); - CUDA_CHECK(cudaMemcpyAsync(src1_contiguous + num_src1_rows*nb11, src1_original + i01*nb11, + CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11, nb11, src1_kind, stream)); num_src1_rows++; } @@ -8900,14 +9026,11 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(row_id >= 0 && row_id < n_as); - CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous + num_src1_rows*nb1, + CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1, nb1, dst_kind, stream)); num_src1_rows++; } } - - ggml_cuda_pool_free(src1_contiguous, as_src1); - ggml_cuda_pool_free(dst_contiguous, as_dst); } if (dst->backend == GGML_BACKEND_CPU) { @@ -9678,8 +9801,10 @@ static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buff static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { void * ptr = ggml_cuda_host_malloc(size); + if (ptr == nullptr) { - return nullptr; + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); } // FIXME: this is a hack to avoid having to implement a new buffer type diff --git a/ggml.c b/ggml.c index 3656422d7..73600ab05 100644 --- a/ggml.c +++ b/ggml.c @@ -19351,7 +19351,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) { data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data; } gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n); - free(data); + free((void *)data); } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) { GGML_ASSERT(false && "nested arrays not supported"); } else { diff --git a/ggml.h b/ggml.h index 338f355a4..67d6bc4f1 100644 --- a/ggml.h +++ b/ggml.h @@ -255,6 +255,8 @@ #define GGML_UNREACHABLE() GGML_ASSERT(!"statement should not be reached") #elif defined(__GNUC__) #define GGML_UNREACHABLE() __builtin_unreachable() +#elif defined(_MSC_VER) +#define GGML_UNREACHABLE() __assume(0) #else #define GGML_UNREACHABLE() ((void) 0) #endif diff --git a/llama.cpp b/llama.cpp index 5699a0fcf..a24621539 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1281,7 +1281,7 @@ struct llama_hparams { if (this->rope_finetuned != other.rope_finetuned) return true; if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; - const float EPSILON = 1e-9; + const float EPSILON = 1e-9f; if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true; @@ -10300,7 +10300,7 @@ int llama_token_to_piece(const struct llama_model * model, llama_token token, ch std::string result = model->vocab.id_to_token[token].text; llama_unescape_whitespace(result); if (length < (int) result.length()) { - return -result.length(); + return -(int) result.length(); } memcpy(buf, result.c_str(), result.length()); return result.length(); @@ -10330,7 +10330,7 @@ int llama_token_to_piece(const struct llama_model * model, llama_token token, ch std::string result = model->vocab.id_to_token[token].text; result = llama_decode_text(result); if (length < (int) result.length()) { - return -result.length(); + return -(int) result.length(); } memcpy(buf, result.c_str(), result.length()); return result.length(); diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 14914def5..8ff76c891 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -883,9 +883,6 @@ int main(int argc, const char ** argv) { srand(seed); const int nargs = 1; - int64_t ne2[4]; - ne2[0] = 1; - for (int ndims = 1; ndims <= 2; ++ndims) { x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); From 753be377b69bda2d65a7e089f2b7f0c53ef3495e Mon Sep 17 00:00:00 2001 From: Shintarou Okada Date: Sun, 24 Dec 2023 22:35:49 +0900 Subject: [PATCH 251/664] llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov --- README.md | 1 + convert-hf-to-gguf.py | 86 +++++++++++++++- gguf-py/gguf/constants.py | 17 ++++ gguf-py/gguf/tensor_mapping.py | 37 ++++--- llama.cpp | 181 +++++++++++++++++++++++++++++++++ 5 files changed, 307 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 649c3b333..09338d226 100644 --- a/README.md +++ b/README.md @@ -102,6 +102,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) +- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) **Multimodal models:** diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index e71a96c48..303d08170 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -184,6 +184,8 @@ class Model: return MixtralModel if model_architecture == "PhiForCausalLM": return Phi2Model + if model_architecture == "PlamoForCausalLM": + return PlamoModel return Model def _is_model_safetensors(self) -> bool: @@ -225,6 +227,8 @@ class Model: return gguf.MODEL_ARCH.LLAMA if arch == "PhiForCausalLM": return gguf.MODEL_ARCH.PHI2 + if arch == "PlamoForCausalLM": + return gguf.MODEL_ARCH.PLAMO raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1002,11 +1006,91 @@ class Phi2Model(Model): self.gguf_writer.add_add_bos_token(False) +class PlamoModel(Model): + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_name("PLaMo") + self.gguf_writer.add_context_length(4096) # not in config.json + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong + self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + + def shuffle_attn_q_weight(self, data_torch): + assert data_torch.size() == (5120, 5120) + data_torch = data_torch.reshape(8, 5, 128, 5120) + data_torch = torch.permute(data_torch, (1, 0, 2, 3)) + data_torch = torch.reshape(data_torch, (5120, 5120)) + return data_torch + + def shuffle_attn_output_weight(self, data_torch): + assert data_torch.size() == (5120, 5120) + data_torch = data_torch.reshape(5120, 8, 5, 128) + data_torch = torch.permute(data_torch, (0, 2, 1, 3)) + data_torch = torch.reshape(data_torch, (5120, 5120)) + return data_torch + + def write_tensors(self): + block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + if "self_attn.rotary_emb.inv_freq" in name: + continue + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + # shuffle for broadcasting of gqa in ggml_mul_mat + if new_name.endswith("attn_q.weight"): + data_torch = self.shuffle_attn_q_weight(data_torch) + elif new_name.endswith("attn_output.weight"): + data_torch = self.shuffle_attn_output_weight(data_torch) + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + ###### CONVERSION LOGIC ###### def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file") + parser = argparse.ArgumentParser( + description="Convert a huggingface model to a GGML compatible file") parser.add_argument( "--vocab-only", action="store_true", help="extract only the vocab", diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 390dca049..4cd87cdda 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -96,6 +96,7 @@ class MODEL_ARCH(IntEnum): STABLELM = auto() QWEN = auto() PHI2 = auto() + PLAMO = auto() class MODEL_TENSOR(IntEnum): @@ -142,6 +143,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", MODEL_ARCH.PHI2: "phi2", + MODEL_ARCH.PLAMO: "plamo", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -349,6 +351,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.PLAMO: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.GPT2: [ # TODO ], diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 6fcbdbc1c..446c6b688 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -79,6 +79,7 @@ class TensorNameMap: "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi "transformer.h.{bid}.ln", # phi2 + "model.layers.layers.{bid}.norm", # plamo ), # Attention norm 2 @@ -99,26 +100,29 @@ class TensorNameMap: # Attention query MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf - "layers.{bid}.attention.wq", # llama-pth - "encoder.layer.{bid}.attention.self.query", # bert - "transformer.h.{bid}.attn.q_proj", # gpt-j + "model.layers.{bid}.self_attn.q_proj", # llama-hf + "layers.{bid}.attention.wq", # llama-pth + "encoder.layer.{bid}.attention.self.query", # bert + "transformer.h.{bid}.attn.q_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.q_proj", # plamo ), # Attention key MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf - "layers.{bid}.attention.wk", # llama-pth - "encoder.layer.{bid}.attention.self.key", # bert - "transformer.h.{bid}.attn.k_proj", # gpt-j + "model.layers.{bid}.self_attn.k_proj", # llama-hf + "layers.{bid}.attention.wk", # llama-pth + "encoder.layer.{bid}.attention.self.key", # bert + "transformer.h.{bid}.attn.k_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.k_proj", # plamo ), # Attention value MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf - "layers.{bid}.attention.wv", # llama-pth - "encoder.layer.{bid}.attention.self.value", # bert - "transformer.h.{bid}.attn.v_proj", # gpt-j + "model.layers.{bid}.self_attn.v_proj", # llama-hf + "layers.{bid}.attention.wv", # llama-pth + "encoder.layer.{bid}.attention.self.value", # bert + "transformer.h.{bid}.attn.v_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.v_proj", # plamo ), # Attention output @@ -134,12 +138,14 @@ class TensorNameMap: "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon "transformer.h.{bid}.mixer.out_proj", # phi2 + "model.layers.layers.{bid}.self_attn.o_proj", # plamo ), # Rotary embeddings MODEL_TENSOR.ATTN_ROT_EMBD: ( - "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf - "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf + "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo ), # Feed-forward norm @@ -174,6 +180,7 @@ class TensorNameMap: "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen "transformer.h.{bid}.mlp.fc1", # phi2 + "model.layers.layers.{bid}.mlp.up_proj", # plamo ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -186,6 +193,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.gate_proj", # llama-hf refact "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen + "model.layers.layers.{bid}.mlp.gate_proj", # plamo ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -206,6 +214,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon "transformer.h.{bid}.mlp.fc2", # phi2 + "model.layers.layers.{bid}.mlp.down_proj", # plamo ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index a24621539..0b99f1e03 100644 --- a/llama.cpp +++ b/llama.cpp @@ -198,6 +198,7 @@ enum llm_arch { LLM_ARCH_STABLELM, LLM_ARCH_QWEN, LLM_ARCH_PHI2, + LLM_ARCH_PLAMO, LLM_ARCH_UNKNOWN, }; @@ -216,6 +217,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, { LLM_ARCH_PHI2, "phi2" }, + { LLM_ARCH_PLAMO, "plamo" }, }; enum llm_kv { @@ -567,6 +569,24 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_PLAMO, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, @@ -2749,6 +2769,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_PLAMO: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_13B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3630,6 +3659,51 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); } } break; + case LLM_ARCH_PLAMO: + { + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + + // output + { + ggml_backend_type backend_norm; + ggml_backend_type backend_output; + + if (n_gpu_layers > int(n_layer)) { + backend_norm = llama_backend_offload; + backend_output = llama_backend_offload_split; + } else { + backend_norm = GGML_BACKEND_CPU; + backend_output = GGML_BACKEND_CPU; + } + + model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); + model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + } + + const uint32_t n_ff = hparams.n_ff; + + const int i_gpu_start = n_layer - n_gpu_layers; + + model.layers.resize(n_layer); + + for (uint32_t i = 0; i < n_layer; ++i) { + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + + layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); + layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); + layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + + layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -5555,6 +5629,109 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_plamo() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + struct ggml_tensor * attention_norm = cur; + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, model, hparams, kv_self, + model.layers[il].wo, NULL, + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + struct ggml_tensor * sa_out = cur; + + cur = attention_norm; + + // feed-forward network + { + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, sa_out); + cb(cur, "l_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; // @@ -6065,6 +6242,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_phi2(); } break; + case LLM_ARCH_PLAMO: + { + result = llm.build_plamo(); + } break; default: GGML_ASSERT(false); } From b9f47952ffae4e0d3420905526003c23333f6c98 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 24 Dec 2023 21:01:12 +0100 Subject: [PATCH 252/664] simplify bug issue template (#4623) --- .github/ISSUE_TEMPLATE/bug.md | 177 +--------------------------------- 1 file changed, 1 insertion(+), 176 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md index c003fe7c1..ce69e6395 100644 --- a/.github/ISSUE_TEMPLATE/bug.md +++ b/.github/ISSUE_TEMPLATE/bug.md @@ -6,179 +6,4 @@ assignees: '' --- -# Prerequisites - -Please answer the following questions for yourself before submitting an issue. - -- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now. -- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). -- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed). -- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share. - -# Expected Behavior - -Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do. - -# Current Behavior - -Please provide a detailed written description of what `llama.cpp` did, instead. - -# Environment and Context - -Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions. - -* Physical (or virtual) hardware you are using, e.g. for Linux: - -`$ lscpu` - -* Operating System, e.g. for Linux: - -`$ uname -a` - -* SDK version, e.g. for Linux: - -``` -$ python3 --version -$ make --version -$ g++ --version -``` - -# Failure Information (for bugs) - -Please help provide information about the failure / bug. - -# Steps to Reproduce - -Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better. - -1. step 1 -2. step 2 -3. step 3 -4. etc. - -# Failure Logs - -Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes. - -Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability. - -Example environment info: -``` -llama.cpp$ git log | head -1 -commit 2af23d30434a677c6416812eea52ccc0af65119c - -llama.cpp$ lscpu | egrep "AMD|Flags" -Vendor ID: AuthenticAMD -Model name: AMD Ryzen Threadripper 1950X 16-Core Processor -Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev -Virtualization: AMD-V - -llama.cpp$ python3 --version -Python 3.10.9 - -llama.cpp$ pip list | egrep "torch|numpy|sentencepiece" -numpy 1.24.2 -numpydoc 1.5.0 -sentencepiece 0.1.97 -torch 1.13.1 -torchvision 0.14.1 - -llama.cpp$ make --version | head -1 -GNU Make 4.3 - -$ md5sum ./models/65B/ggml-model-q4_0.bin -dbdd682cce80e2d6e93cefc7449df487 ./models/65B/ggml-model-q4_0.bin -``` - -Example run with the Linux command [perf](https://www.brendangregg.com/perf.html) -``` -llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered." -main: seed = 1679149377 -llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ... -llama_model_load: n_vocab = 32000 -llama_model_load: n_ctx = 512 -llama_model_load: n_embd = 8192 -llama_model_load: n_mult = 256 -llama_model_load: n_head = 64 -llama_model_load: n_layer = 80 -llama_model_load: n_rot = 128 -llama_model_load: f16 = 2 -llama_model_load: n_ff = 22016 -llama_model_load: n_parts = 8 -llama_model_load: ggml ctx size = 41477.73 MB -llama_model_load: memory_size = 2560.00 MB, n_mem = 40960 -llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 -llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7' -llama_model_load: .......................................................................................... done -llama_model_load: model size = 4869.09 MB / num tensors = 723 - -system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | - -main: prompt: 'Please close your issue when it has been answered.' -main: number of tokens in prompt = 11 - 1 -> '' - 12148 -> 'Please' - 3802 -> ' close' - 596 -> ' your' - 2228 -> ' issue' - 746 -> ' when' - 372 -> ' it' - 756 -> ' has' - 1063 -> ' been' - 7699 -> ' answered' - 29889 -> '.' - -sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000 - - -Please close your issue when it has been answered. -@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine?? -I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!! -@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text] - - -main: mem per token = 71159620 bytes -main: load time = 19309.95 ms -main: sample time = 168.62 ms -main: predict time = 223895.61 ms / 888.47 ms per token -main: total time = 246406.42 ms - - Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.': - - 3636882.89 msec task-clock # 14.677 CPUs utilized - 13509 context-switches # 3.714 /sec - 2436 cpu-migrations # 0.670 /sec - 10476679 page-faults # 2.881 K/sec - 13133115082869 cycles # 3.611 GHz (16.77%) - 29314462753 stalled-cycles-frontend # 0.22% frontend cycles idle (16.76%) - 10294402631459 stalled-cycles-backend # 78.39% backend cycles idle (16.74%) - 23479217109614 instructions # 1.79 insn per cycle - # 0.44 stalled cycles per insn (16.76%) - 2353072268027 branches # 647.002 M/sec (16.77%) - 1998682780 branch-misses # 0.08% of all branches (16.76%) - - 247.802177522 seconds time elapsed - - 3618.573072000 seconds user - 18.491698000 seconds sys -``` +Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug. From a206137f927daef1752753cf5e281220b449a468 Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Mon, 25 Dec 2023 16:09:53 +0000 Subject: [PATCH 253/664] Adding Emeltal reference to UI list (#4629) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 09338d226..3b202a336 100644 --- a/README.md +++ b/README.md @@ -133,6 +133,7 @@ as the main playground for developing new features for the [ggml](https://github - [withcatai/catai](https://github.com/withcatai/catai) - [semperai/amica](https://github.com/semperai/amica) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) +- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) --- From 77465dad48d7c945c367ab46b6f2ea98ae9b7b15 Mon Sep 17 00:00:00 2001 From: FantasyGmm <16450052+FantasyGmm@users.noreply.github.com> Date: Tue, 26 Dec 2023 18:38:36 +0800 Subject: [PATCH 254/664] Fix new CUDA10 compilation errors (#4635) --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index ac3b3c14d..f32e83ab6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -102,6 +102,7 @@ #include #if CUDART_VERSION < 11020 +#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED #define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH #define CUBLAS_COMPUTE_16F CUDA_R_16F #define CUBLAS_COMPUTE_32F CUDA_R_32F From de8e496437c59e7d1cc84109e3e49a3478aee25a Mon Sep 17 00:00:00 2001 From: WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com> Date: Tue, 26 Dec 2023 05:42:08 -0500 Subject: [PATCH 255/664] Update comment for AdamW implementation reference. (#4604) Co-authored-by: Will Findley --- ggml.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml.c b/ggml.c index 73600ab05..d24560480 100644 --- a/ggml.c +++ b/ggml.c @@ -17456,9 +17456,9 @@ static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g } // -// ADAM +// Using AdamW - ref: https://arxiv.org/pdf/1711.05101v3.pdf // -// ref: https://arxiv.org/pdf/1412.6980.pdf +// (Original Adam - ref: https://arxiv.org/pdf/1412.6980.pdf) // static enum ggml_opt_result ggml_opt_adam( From dc68f0054cd279cddddb0cae0c9ef4f9cbaa512a Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 26 Dec 2023 21:23:59 +0100 Subject: [PATCH 256/664] cuda : fix vmm pool with multi GPU (#4620) * cuda : fix vmm pool with multi GPU * hip * use recommended granularity instead of minimum * better error checking * fix mixtral * use cudaMemcpy3DPeerAsync * use cuda_pool_alloc in ggml_cuda_op_mul_mat * consolidate error checking in ggml_cuda_set_device * remove unnecessary inlines ggml-ci * style fixes * only use vmm for the main device * fix scratch buffer size, re-enable vmm pool for all devices * remove unnecessary check id != g_main_device --- ggml-cuda.cu | 483 +++++++++++++++++++++++++-------------------------- ggml.c | 3 - llama.cpp | 3 +- 3 files changed, 243 insertions(+), 246 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f32e83ab6..abad9cc39 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -68,8 +68,9 @@ #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) #endif #define cudaMemcpy hipMemcpy -#define cudaMemcpy2DAsync hipMemcpy2DAsync #define cudaMemcpyAsync hipMemcpyAsync +#define cudaMemcpyPeerAsync hipMemcpyPeerAsync +#define cudaMemcpy2DAsync hipMemcpy2DAsync #define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice #define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost #define cudaMemcpyHostToDevice hipMemcpyHostToDevice @@ -163,7 +164,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) { const int8x4_t vb = reinterpret_cast(b); #if __has_builtin(__builtin_elementwise_sub_sat) const int8x4_t c = __builtin_elementwise_sub_sat(va, vb); - return reinterpret_cast(c); + return reinterpret_cast(c); #else int8x4_t c; int16_t tmp; @@ -174,7 +175,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) { if(tmp < std::numeric_limits::min()) tmp = std::numeric_limits::min(); c[i] = tmp; } - return reinterpret_cast(c); + return reinterpret_cast(c); #endif // __has_builtin(__builtin_elementwise_sub_sat) } @@ -212,6 +213,28 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); +[[noreturn]] +static void ggml_cuda_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) { + int id = -1; // in case cudaGetDevice fails + cudaGetDevice(&id); + + fprintf(stderr, "CUDA error: %s\n", msg); + fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line); + fprintf(stderr, " %s\n", stmt); + // abort with GGML_ASSERT to get a stack trace + GGML_ASSERT(!"CUDA error"); +} + +#define CUDA_CHECK_GEN(err, success, error_fn) \ + do { \ + auto err_ = (err); \ + if (err_ != (success)) { \ + ggml_cuda_error(#err, __func__, __FILE__, __LINE__, error_fn(err_)); \ + } \ + } while (0) + +#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString) + #if CUDART_VERSION >= 12000 static const char * cublas_get_error_str(const cublasStatus_t err) { return cublasGetStatusString(err); @@ -233,15 +256,7 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); } #endif // CUDART_VERSION >= 12000 -[[noreturn]] -static void ggml_cuda_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) { - fprintf(stderr, "CUDA error: %s: %s\n", stmt, msg); - fprintf(stderr, " in function %s at %s:%d\n", func, file, line); - GGML_ASSERT(!"CUDA error"); -} - -#define CUDA_CHECK(err) do { auto err_ = (err); if (err_ != cudaSuccess) ggml_cuda_error(#err, __func__, __FILE__, __LINE__, cudaGetErrorString(err_)); } while (0) -#define CUBLAS_CHECK(err) do { auto err_ = (err); if (err_ != CUBLAS_STATUS_SUCCESS) ggml_cuda_error(#err, __func__, __FILE__, __LINE__, cublas_get_error_str(err_)); } while (0) +#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str) #if !defined(GGML_USE_HIPBLAS) static const char * cu_get_error_str(CUresult err) { @@ -249,7 +264,7 @@ static const char * cu_get_error_str(CUresult err) { cuGetErrorString(err, &err_str); return err_str; } -#define CU_CHECK(err) do { auto err_ = (err); if (err_ != CUDA_SUCCESS) ggml_cuda_error(#err, __func__, __FILE__, __LINE__, cu_get_error_str(err_)); } while (0) +#define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str) #endif #if CUDART_VERSION >= 11100 @@ -306,10 +321,10 @@ typedef void (*ggml_cuda_func_t)(const ggml_tensor * src0, const ggml_tensor * s typedef void (*ggml_cuda_op_mul_mat_t)( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, const cudaStream_t & stream); + const int64_t src1_padded_row_size, cudaStream_t stream); typedef void (*ggml_cuda_op_flatten_t)( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream); + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream); // QK = number of values after dequantization // QR = QK / number of values before dequantization @@ -515,15 +530,15 @@ struct ggml_tensor_extra_gpu { // this is faster on Windows // probably because the Windows CUDA libraries forget to make this check before invoking the drivers -inline cudaError_t ggml_cuda_set_device(const int device) { +static void ggml_cuda_set_device(const int device) { int current_device; CUDA_CHECK(cudaGetDevice(¤t_device)); if (device == current_device) { - return cudaSuccess; + return; } - return cudaSetDevice(device); + CUDA_CHECK(cudaSetDevice(device)); } static int g_device_count = -1; @@ -538,7 +553,6 @@ struct cuda_device_capabilities { static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, false, 0} }; - static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default static size_t g_scratch_offset = 0; @@ -580,6 +594,7 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { static __device__ __forceinline__ float op_repeat(const float a, const float b) { return b; + GGML_UNUSED(a); } static __device__ __forceinline__ float op_add(const float a, const float b) { @@ -701,7 +716,7 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } -static __global__ void gelu_quick_f32(const float *x, float *dst, int k) { +static __global__ void gelu_quick_f32(const float * x, float * dst, int k) { const float GELU_QUICK_COEF = -1.702f; const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { @@ -710,7 +725,7 @@ static __global__ void gelu_quick_f32(const float *x, float *dst, int k) { dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i]))); } -static __global__ void tanh_f32(const float *x, float *dst, int k) { +static __global__ void tanh_f32(const float * x, float * dst, int k) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { return; @@ -727,7 +742,7 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) { dst[i] = fmaxf(x[i], 0); } -static __global__ void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope) { +static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { return; @@ -780,7 +795,7 @@ static __global__ void norm_f32(const float * x, float * dst, const int ncols, c } } -static __global__ void concat_f32(const float *x,const float *y, float *dst, const int ne0, const int ne02) { +static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; @@ -805,7 +820,7 @@ static __global__ void concat_f32(const float *x,const float *y, float *dst, c } } -static __global__ void upscale_f32(const float *x, float *dst, const int ne00, const int nb02, const int scale_factor) { +static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) { int ne0 = ne00 * scale_factor; int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { @@ -825,7 +840,7 @@ static __global__ void upscale_f32(const float *x, float *dst, const int ne00, dst[offset_dst] = x[offset_src]; } -static __global__ void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02) { +static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) { int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; @@ -4727,7 +4742,6 @@ static __global__ void mul_mat_p021_f16_f32( const int row_y = col_x; - // y is not transposed but permuted const int iy = channel*nrows_y + row_y; @@ -5402,7 +5416,7 @@ struct bin_bcast_cuda { cne[3] = 1; }; - auto collapse_nb = [](size_t cnb[], int64_t cne[]) { + auto collapse_nb = [](size_t cnb[], const int64_t cne[]) { cnb[1] *= cne[1]; cnb[2] *= cne[2]; cnb[3] *= cne[3]; @@ -6566,18 +6580,16 @@ struct scoped_spin_lock { static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT; // #define DEBUG_CUDA_MALLOC -struct cuda_buffer { +struct ggml_cuda_buffer { void * ptr = nullptr; size_t size = 0; }; -static cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS]; +static ggml_cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS]; static size_t g_cuda_pool_size[GGML_CUDA_MAX_DEVICES] = {0}; -static void * ggml_cuda_pool_malloc_leg(size_t size, size_t * actual_size) { +static void * ggml_cuda_pool_malloc_leg(int device, size_t size, size_t * actual_size) { scoped_spin_lock lock(g_cuda_pool_lock); - int id; - CUDA_CHECK(cudaGetDevice(&id)); #ifdef DEBUG_CUDA_MALLOC int nnz = 0; size_t max_size = 0; @@ -6585,7 +6597,7 @@ static void * ggml_cuda_pool_malloc_leg(size_t size, size_t * actual_size) { size_t best_diff = 1ull << 36; int ibest = -1; for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) { - cuda_buffer& b = g_cuda_buffer_pool[id][i]; + ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i]; if (b.ptr != nullptr) { #ifdef DEBUG_CUDA_MALLOC ++nnz; @@ -6608,7 +6620,7 @@ static void * ggml_cuda_pool_malloc_leg(size_t size, size_t * actual_size) { } } if (ibest >= 0) { - cuda_buffer& b = g_cuda_buffer_pool[id][ibest]; + ggml_cuda_buffer& b = g_cuda_buffer_pool[device][ibest]; void * ptr = b.ptr; *actual_size = b.size; b.ptr = nullptr; @@ -6618,9 +6630,10 @@ static void * ggml_cuda_pool_malloc_leg(size_t size, size_t * actual_size) { void * ptr; size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); + ggml_cuda_set_device(device); CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); *actual_size = look_ahead_size; - g_cuda_pool_size[id] += look_ahead_size; + g_cuda_pool_size[device] += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); @@ -6628,13 +6641,11 @@ static void * ggml_cuda_pool_malloc_leg(size_t size, size_t * actual_size) { return ptr; } -static void ggml_cuda_pool_free_leg(void * ptr, size_t size) { +static void ggml_cuda_pool_free_leg(int device, void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); - int id; - CUDA_CHECK(cudaGetDevice(&id)); for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) { - cuda_buffer& b = g_cuda_buffer_pool[id][i]; + ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i]; if (b.ptr == nullptr) { b.ptr = ptr; b.size = size; @@ -6642,73 +6653,73 @@ static void ggml_cuda_pool_free_leg(void * ptr, size_t size) { } } fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); + ggml_cuda_set_device(device); CUDA_CHECK(cudaFree(ptr)); - g_cuda_pool_size[id] -= size; + g_cuda_pool_size[device] -= size; } #if !defined(GGML_USE_HIPBLAS) // pool with virtual memory -static std::vector g_cuda_pool_handles[GGML_CUDA_MAX_DEVICES]; static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0}; static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0}; static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 36; // 64 GB -static void * ggml_cuda_pool_malloc_vmm(size_t size, size_t * actual_size) { +static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual_size) { scoped_spin_lock lock(g_cuda_pool_lock); - int id; - CUDA_CHECK(cudaGetDevice(&id)); // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types const size_t alignment = 128; size = alignment * ((size + alignment - 1) / alignment); - size_t avail = g_cuda_pool_size[id] - g_cuda_pool_used[id]; + size_t avail = g_cuda_pool_size[device] - g_cuda_pool_used[device]; if (size > avail) { // round up to the next multiple of the granularity size_t reserve_size = size - avail; - const size_t granularity = g_device_caps[id].vmm_granularity; + const size_t granularity = g_device_caps[device].vmm_granularity; reserve_size = granularity * ((reserve_size + granularity - 1) / granularity); - GGML_ASSERT(g_cuda_pool_size[id] + reserve_size <= CUDA_POOL_VMM_MAX_SIZE); + GGML_ASSERT(g_cuda_pool_size[device] + reserve_size <= CUDA_POOL_VMM_MAX_SIZE); // allocate more physical memory CUmemAllocationProp prop = {}; prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = id; + prop.location.id = device; CUmemGenericAllocationHandle handle; CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); // reserve virtual address space (if not already reserved) - if (g_cuda_pool_addr[id] == 0) { - CU_CHECK(cuMemAddressReserve(&g_cuda_pool_addr[id], CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)); + if (g_cuda_pool_addr[device] == 0) { + CU_CHECK(cuMemAddressReserve(&g_cuda_pool_addr[device], CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)); } // map at the end of the pool - CU_CHECK(cuMemMap(g_cuda_pool_addr[id] + g_cuda_pool_size[id], reserve_size, 0, handle, 0)); + CU_CHECK(cuMemMap(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, 0, handle, 0)); + + // the memory allocation handle is no longer needed after mapping + CU_CHECK(cuMemRelease(handle)); // set access CUmemAccessDesc access = {}; access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = id; + access.location.id = device; access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; - CU_CHECK(cuMemSetAccess(g_cuda_pool_addr[id] + g_cuda_pool_size[id], reserve_size, &access, 1)); + CU_CHECK(cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1)); // add to the pool - g_cuda_pool_handles[id].push_back(handle); - g_cuda_pool_size[id] += reserve_size; + g_cuda_pool_size[device] += reserve_size; //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n", // id, (unsigned long long) (g_cuda_pool_size[id]/1024/1024), // (unsigned long long) (reserve_size/1024/1024)); } - GGML_ASSERT(g_cuda_pool_addr[id] != 0); + GGML_ASSERT(g_cuda_pool_addr[device] != 0); - void * ptr = (void *) (g_cuda_pool_addr[id] + g_cuda_pool_used[id]); + void * ptr = (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device]); *actual_size = size; - g_cuda_pool_used[id] += size; + g_cuda_pool_used[device] += size; #ifdef DEBUG_CUDA_MALLOC printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr); @@ -6717,38 +6728,32 @@ static void * ggml_cuda_pool_malloc_vmm(size_t size, size_t * actual_size) { return ptr; } -static void ggml_cuda_pool_free_vmm(void * ptr, size_t size) { +static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); - int id; - CUDA_CHECK(cudaGetDevice(&id)); #ifdef DEBUG_CUDA_MALLOC printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); #endif - g_cuda_pool_used[id] -= size; + g_cuda_pool_used[device] -= size; // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (g_cuda_pool_addr[id] + g_cuda_pool_used[id])); + GGML_ASSERT(ptr == (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device])); } -static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); - if (g_device_caps[id].vmm) { - return ggml_cuda_pool_malloc_vmm(size, actual_size); +static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_size) { + if (g_device_caps[device].vmm) { + return ggml_cuda_pool_malloc_vmm(device, size, actual_size); } else { - return ggml_cuda_pool_malloc_leg(size, actual_size); + return ggml_cuda_pool_malloc_leg(device, size, actual_size); } } -static void ggml_cuda_pool_free(void * ptr, size_t size) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); - if (g_device_caps[id].vmm) { - ggml_cuda_pool_free_vmm(ptr, size); +static void ggml_cuda_pool_free(int device, void * ptr, size_t size) { + if (g_device_caps[device].vmm) { + ggml_cuda_pool_free_vmm(device, ptr, size); } else { - ggml_cuda_pool_free_leg(ptr, size); + ggml_cuda_pool_free_leg(device, ptr, size); } } #else @@ -6758,13 +6763,15 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { template struct cuda_pool_alloc { + int device = -1; T * ptr = nullptr; size_t actual_size = 0; // size is in number of elements T * alloc(size_t size) { GGML_ASSERT(ptr == nullptr); - ptr = (T *) ggml_cuda_pool_malloc(size * sizeof(T), &this->actual_size); + CUDA_CHECK(cudaGetDevice(&device)); + ptr = (T *) ggml_cuda_pool_malloc(device, size * sizeof(T), &this->actual_size); return ptr; } @@ -6774,7 +6781,7 @@ struct cuda_pool_alloc { ~cuda_pool_alloc() { if (ptr != nullptr) { - ggml_cuda_pool_free(ptr, actual_size); + ggml_cuda_pool_free(device, ptr, actual_size); } } @@ -6839,7 +6846,7 @@ void ggml_init_cublas() { alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; alloc_prop.location.id = id; - CU_CHECK(cuMemGetAllocationGranularity(&g_device_caps[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM)); + CU_CHECK(cuMemGetAllocationGranularity(&g_device_caps[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); } #endif // !defined(GGML_USE_HIPBLAS) g_device_caps[id].vmm = !!device_vmm; @@ -6861,7 +6868,7 @@ void ggml_init_cublas() { } for (int id = 0; id < g_device_count; ++id) { - CUDA_CHECK(ggml_cuda_set_device(id)); + ggml_cuda_set_device(id); // create cuda streams for (int is = 0; is < MAX_STREAMS; ++is) { @@ -6976,7 +6983,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d( static void ggml_cuda_op_get_rows( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & stream) { + const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t stream) { GGML_ASSERT(src1->type == GGML_TYPE_I32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -7018,9 +7025,9 @@ static void ggml_cuda_op_get_rows( } template -inline void ggml_cuda_op_bin_bcast( +static void ggml_cuda_op_bin_bcast( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -7039,7 +7046,7 @@ inline void ggml_cuda_op_bin_bcast( static void ggml_cuda_op_repeat( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & main_stream) { + const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t main_stream) { ggml_cuda_op_bin_bcast>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream); @@ -7047,16 +7054,16 @@ static void ggml_cuda_op_repeat( (void) src1_d; } -inline void ggml_cuda_op_add( +static void ggml_cuda_op_add( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { ggml_cuda_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -inline void ggml_cuda_op_acc( +static void ggml_cuda_op_acc( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -7073,23 +7080,23 @@ inline void ggml_cuda_op_acc( (void) dst; } -inline void ggml_cuda_op_mul( +static void ggml_cuda_op_mul( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { ggml_cuda_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -inline void ggml_cuda_op_div( +static void ggml_cuda_op_div( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { ggml_cuda_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -inline void ggml_cuda_op_gelu( +static void ggml_cuda_op_gelu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7101,9 +7108,9 @@ inline void ggml_cuda_op_gelu( (void) src1_dd; } -inline void ggml_cuda_op_silu( +static void ggml_cuda_op_silu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7115,9 +7122,9 @@ inline void ggml_cuda_op_silu( (void) src1_dd; } -inline void ggml_cuda_op_gelu_quick( +static void ggml_cuda_op_gelu_quick( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7129,9 +7136,9 @@ inline void ggml_cuda_op_gelu_quick( (void) src1_dd; } -inline void ggml_cuda_op_tanh( +static void ggml_cuda_op_tanh( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7143,9 +7150,9 @@ inline void ggml_cuda_op_tanh( (void) src1_dd; } -inline void ggml_cuda_op_relu( +static void ggml_cuda_op_relu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7157,9 +7164,9 @@ inline void ggml_cuda_op_relu( (void) src1_dd; } -inline void ggml_cuda_op_leaky_relu( +static void ggml_cuda_op_leaky_relu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7174,9 +7181,9 @@ inline void ggml_cuda_op_leaky_relu( (void) src1_dd; } -inline void ggml_cuda_op_sqr( +static void ggml_cuda_op_sqr( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7188,9 +7195,9 @@ inline void ggml_cuda_op_sqr( (void) src1_dd; } -inline void ggml_cuda_op_norm( +static void ggml_cuda_op_norm( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7208,10 +7215,9 @@ inline void ggml_cuda_op_norm( (void) src1_dd; } - -inline void ggml_cuda_op_group_norm( +static void ggml_cuda_op_group_norm( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7225,9 +7231,9 @@ inline void ggml_cuda_op_group_norm( (void) src1_dd; } -inline void ggml_cuda_op_concat( +static void ggml_cuda_op_concat( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -7241,9 +7247,9 @@ inline void ggml_cuda_op_concat( (void) dst; } -inline void ggml_cuda_op_upscale( +static void ggml_cuda_op_upscale( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -7258,9 +7264,9 @@ inline void ggml_cuda_op_upscale( (void) src1_dd; } -inline void ggml_cuda_op_pad( +static void ggml_cuda_op_pad( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -7275,9 +7281,9 @@ inline void ggml_cuda_op_pad( (void) src1_dd; } -inline void ggml_cuda_op_rms_norm( +static void ggml_cuda_op_rms_norm( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7295,10 +7301,10 @@ inline void ggml_cuda_op_rms_norm( (void) src1_dd; } -inline void ggml_cuda_op_mul_mat_q( +static void ggml_cuda_op_mul_mat_q( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, const cudaStream_t & stream) { + const int64_t src1_padded_row_size, cudaStream_t stream) { const int64_t ne00 = src0->ne[0]; @@ -7360,7 +7366,7 @@ inline void ggml_cuda_op_mul_mat_q( static int64_t get_row_rounding(ggml_type type) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { if (min_compute_capability > g_device_caps[id].cc) { min_compute_capability = g_device_caps[id].cc; @@ -7418,10 +7424,10 @@ static int64_t get_row_rounding(ggml_type type) { #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) } -inline void ggml_cuda_op_mul_mat_vec_q( +static void ggml_cuda_op_mul_mat_vec_q( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, const cudaStream_t & stream) { + const int64_t src1_padded_row_size, cudaStream_t stream) { GGML_ASSERT(ggml_nrows(src1) == 1); @@ -7471,10 +7477,10 @@ inline void ggml_cuda_op_mul_mat_vec_q( (void) src1_padded_row_size; } -inline void ggml_cuda_op_dequantize_mul_mat_vec( +static void ggml_cuda_op_dequantize_mul_mat_vec( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, const cudaStream_t & stream) { + const int64_t src1_padded_row_size, cudaStream_t stream) { const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; @@ -7545,10 +7551,10 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( (void) src1_padded_row_size; } -inline void ggml_cuda_op_mul_mat_cublas( +static void ggml_cuda_op_mul_mat_cublas( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, const cudaStream_t & stream) { + const int64_t src1_padded_row_size, cudaStream_t stream) { GGML_ASSERT(src0_dd_i != nullptr); GGML_ASSERT(src1_ddf_i != nullptr); @@ -7637,9 +7643,9 @@ inline void ggml_cuda_op_mul_mat_cublas( (void) src1_padded_row_size; } -inline void ggml_cuda_op_rope( +static void ggml_cuda_op_rope( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); @@ -7717,9 +7723,9 @@ inline void ggml_cuda_op_rope( (void) src1_dd; } -inline void ggml_cuda_op_alibi( +static void ggml_cuda_op_alibi( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7748,9 +7754,9 @@ inline void ggml_cuda_op_alibi( (void) src1_dd; } -inline void ggml_cuda_op_im2col( +static void ggml_cuda_op_im2col( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -7783,10 +7789,9 @@ inline void ggml_cuda_op_im2col( (void) src0_dd; } - -inline void ggml_cuda_op_sum_rows( +static void ggml_cuda_op_sum_rows( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7801,9 +7806,9 @@ inline void ggml_cuda_op_sum_rows( (void) src1_dd; } -inline void ggml_cuda_op_argsort( +static void ggml_cuda_op_argsort( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_I32); @@ -7820,9 +7825,9 @@ inline void ggml_cuda_op_argsort( (void) src1_dd; } -inline void ggml_cuda_op_diag_mask_inf( +static void ggml_cuda_op_diag_mask_inf( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7840,9 +7845,9 @@ inline void ggml_cuda_op_diag_mask_inf( (void) src1_dd; } -inline void ggml_cuda_op_soft_max( +static void ggml_cuda_op_soft_max( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7861,9 +7866,9 @@ inline void ggml_cuda_op_soft_max( (void) dst; } -inline void ggml_cuda_op_scale( +static void ggml_cuda_op_scale( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7879,9 +7884,9 @@ inline void ggml_cuda_op_scale( (void) src1_dd; } -inline void ggml_cuda_op_clamp( +static void ggml_cuda_op_clamp( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -7974,12 +7979,12 @@ static void ggml_cuda_set_peer_access(const int n_tokens) { #ifdef NDEBUG for (int id = 0; id < g_device_count; ++id) { - CUDA_CHECK(ggml_cuda_set_device(id)); + ggml_cuda_set_device(id); CUDA_CHECK(cudaDeviceSynchronize()); } for (int id = 0; id < g_device_count; ++id) { - CUDA_CHECK(ggml_cuda_set_device(id)); + ggml_cuda_set_device(id); for (int id_other = 0; id_other < g_device_count; ++id_other) { if (id == id_other) { @@ -8013,7 +8018,6 @@ static void ggml_cuda_op_mul_mat( const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; - const int64_t nrows0 = ggml_nrows(src0); const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; @@ -8056,27 +8060,29 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); - // dd = data device - char * src0_dd[GGML_CUDA_MAX_DEVICES] = {nullptr}; - float * src1_ddf[GGML_CUDA_MAX_DEVICES] = {nullptr}; // float - char * src1_ddq[GGML_CUDA_MAX_DEVICES] = {nullptr}; // q8_1 - float * dst_dd[GGML_CUDA_MAX_DEVICES] = {nullptr}; + struct dev_data { + cuda_pool_alloc src0_dd_alloc; + cuda_pool_alloc src1_ddf_alloc; + cuda_pool_alloc src1_ddq_alloc; + cuda_pool_alloc dst_dd_alloc; - // as = actual size - size_t src0_as[GGML_CUDA_MAX_DEVICES] = {0}; - size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0}; - size_t src1_asq[GGML_CUDA_MAX_DEVICES] = {0}; - size_t dst_as[GGML_CUDA_MAX_DEVICES] = {0}; + char * src0_dd = nullptr; + float * src1_ddf = nullptr; // float + char * src1_ddq = nullptr; // q8_1 + float * dst_dd = nullptr; - int64_t row_low[GGML_CUDA_MAX_DEVICES]; - int64_t row_high[GGML_CUDA_MAX_DEVICES]; + int64_t row_low; + int64_t row_high; + }; + + dev_data dev[GGML_CUDA_MAX_DEVICES]; int used_devices = 0; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { // by default, use all rows - row_low[id] = 0; - row_high[id] = ne01; + dev[id].row_low = 0; + dev[id].row_high = ne01; // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes @@ -8084,23 +8090,23 @@ static void ggml_cuda_op_mul_mat( const int64_t rounding = get_row_rounding(src0->type); if (id != 0) { - row_low[id] = ne01*g_tensor_split[id]; - if (row_low[id] < ne01) { - row_low[id] -= row_low[id] % rounding; + dev[id].row_low = ne01*g_tensor_split[id]; + if (dev[id].row_low < ne01) { + dev[id].row_low -= dev[id].row_low % rounding; } } if (id != g_device_count - 1) { - row_high[id] = ne01*g_tensor_split[id + 1]; - if (row_high[id] < ne01) { - row_high[id] -= row_high[id] % rounding; + dev[id].row_high = ne01*g_tensor_split[id + 1]; + if (dev[id].row_high < ne01) { + dev[id].row_high -= dev[id].row_high % rounding; } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device) || row_low[id] == row_high[id]) { + for (int id = 0; id < g_device_count; ++id) { + if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) { continue; } @@ -8110,42 +8116,41 @@ static void ggml_cuda_op_mul_mat( const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device; ggml_cuda_set_device(id); - const cudaStream_t stream = g_cudaStreams[id][0]; + cudaStream_t stream = g_cudaStreams[id][0]; if (src0_on_device && src0_is_contiguous) { - src0_dd[id] = (char *) src0_extra->data_device[id]; + dev[id].src0_dd = (char *) src0_extra->data_device[id]; } else { - // const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ggml_nbytes(src0)); } if (src1_on_device && src1_is_contiguous) { - src1_ddf[id] = (float *) src1_extra->data_device[id]; + dev[id].src1_ddf = (float *) src1_extra->data_device[id]; } else { - src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ggml_nelements(src1)); } if (convert_src1_to_q8_1) { - src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); + quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); CUDA_CHECK(cudaGetLastError()); } } if (dst_on_device) { - dst_dd[id] = (float *) dst_extra->data_device[id]; + dev[id].dst_dd = (float *) dst_extra->data_device[id]; } else { - const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]); + const size_t size_dst_ddf = split ? (dev[id].row_high - dev[id].row_low)*ne1 : ggml_nelements(dst); + dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(size_dst_ddf); } } // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data if (split && used_devices > 1) { - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0])); } @@ -8154,17 +8159,17 @@ static void ggml_cuda_op_mul_mat( const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device) || row_low[id] == row_high[id]) { + for (int id = 0; id < g_device_count; ++id) { + if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) { continue; } const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device; const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device; - const int64_t row_diff = row_high[id] - row_low[id]; + const int64_t row_diff = dev[id].row_high - dev[id].row_low; ggml_cuda_set_device(id); - const cudaStream_t stream = g_cudaStreams[id][is]; + cudaStream_t stream = g_cudaStreams[id][is]; // wait for main GPU data if necessary if (split && (id != g_main_device || is != 0)) { @@ -8178,34 +8183,34 @@ static void ggml_cuda_op_mul_mat( const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = src1_ddq[id] + src1_ddq_i_offset; - float * dst_dd_i = dst_dd[id] + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); + char * src0_dd_i = dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; + float * src1_ddf_i = dev[id].src1_ddf + (i0*ne11 + src1_col_0) * ne10; + char * src1_ddq_i = dev[id].src1_ddq + src1_ddq_i_offset; + float * dst_dd_i = dev[id].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) { - dst_dd_i += row_low[id]; // offset is 0 if no tensor split + dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) { if (id != g_main_device) { if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = src1_ddq[g_main_device] + src1_ddq_i_offset; - CUDA_CHECK(cudaMemcpyAsync(src1_ddq_i, src1_ddq_i_source, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, - cudaMemcpyDeviceToDevice, stream)); + char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; + CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, g_main_device, + src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream)); } else { float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - CUDA_CHECK(cudaMemcpyAsync(src1_ddf_i, src1_ddf_i_source, src1_ncols*ne10*sizeof(float), - cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, g_main_device, + src1_ncols*ne10*sizeof(float), stream)); } } } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) { CUDA_CHECK(ggml_cuda_cpy_tensor_2d( - src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); + src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); } else { GGML_ASSERT(false); } @@ -8216,12 +8221,12 @@ static void ggml_cuda_op_mul_mat( } if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { - CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream)); + CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream)); } // do the computation op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream); + dev[id].row_low, dev[id].row_high, src1_ncols, src1_padded_col_size, stream); CUDA_CHECK(cudaGetLastError()); // copy dst to host or other device if necessary @@ -8245,9 +8250,25 @@ static void ggml_cuda_op_mul_mat( // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + row_low[id]; - CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), dst_dd_i, row_diff*sizeof(float), - row_diff*sizeof(float), src1_ncols, kind, stream)); + dhf_dst_i += src1_col_0*ne0 + dev[id].row_low; +#if !defined(GGML_USE_HIPBLAS) + if (kind == cudaMemcpyDeviceToDevice) { + // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices + cudaMemcpy3DPeerParms p = {}; + p.dstDevice = g_main_device; + p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols); + p.srcDevice = id; + p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); + p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); + CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream)); + } else +#endif + { + CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), + dst_dd_i, row_diff*sizeof(float), + row_diff*sizeof(float), src1_ncols, + kind, stream)); + } } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); @@ -8264,35 +8285,14 @@ static void ggml_cuda_op_mul_mat( } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device) || row_low[id] == row_high[id]) { - continue; - } - CUDA_CHECK(ggml_cuda_set_device(id)); - - // free buffers again when done - if (dst_as[id] > 0) { - ggml_cuda_pool_free(dst_dd[id], dst_as[id]); - } - if (src1_asq[id] > 0) { - ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]); - } - if (src1_asf[id] > 0) { - ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); - } - if (src0_as[id] > 0) { - ggml_cuda_pool_free(src0_dd[id], src0_as[id]); - } - } - // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); - for (int64_t id = 0; id < g_device_count; ++id) { - if (row_low[id] == row_high[id]) { + ggml_cuda_set_device(g_main_device); + for (int id = 0; id < g_device_count; ++id) { + if (dev[id].row_low == dev[id].row_high) { continue; } for (int64_t is = 0; is < is_max; ++is) { @@ -8302,7 +8302,7 @@ static void ggml_cuda_op_mul_mat( } if (dst->backend == GGML_BACKEND_CPU) { - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); CUDA_CHECK(cudaDeviceSynchronize()); } } @@ -8412,7 +8412,7 @@ static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tens const int64_t ne12 = src1->ne[2]; - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; @@ -8444,7 +8444,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor const int64_t ne12 = src1->ne[2]; - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; @@ -8515,7 +8515,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const const int64_t ne1 = ggml_nelements(src1); const int64_t ne = ggml_nelements(dst); - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream)); @@ -8656,7 +8656,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { min_compute_capability = g_device_caps[id].cc; } @@ -8799,7 +8799,7 @@ static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) { const int64_t ne1 = ggml_nelements(src1); const int64_t ne = ggml_nelements(dst); - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream)); @@ -8917,7 +8917,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s std::vector ids_host(ggml_nbytes(ids)); - const cudaStream_t stream = g_cudaStreams[g_main_device][0]; + cudaStream_t stream = g_cudaStreams[g_main_device][0]; if (ids->backend == GGML_BACKEND_GPU) { const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; @@ -9073,7 +9073,7 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg const int64_t nb11 = src1->nb[1]; const int64_t nb12 = src1->nb[2]; - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; @@ -9163,7 +9163,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; memset(extra, 0, sizeof(*extra)); - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { if (backend == GGML_BACKEND_GPU && id != g_main_device) { continue; } @@ -9234,15 +9234,14 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { + ggml_cuda_set_device(id); if (extra->data_device[id] != nullptr) { - CUDA_CHECK(ggml_cuda_set_device(id)); CUDA_CHECK(cudaFree(extra->data_device[id])); } for (int64_t is = 0; is < MAX_STREAMS; ++is) { if (extra->events[id][is] != nullptr) { - CUDA_CHECK(ggml_cuda_set_device(id)); CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); } } @@ -9296,7 +9295,7 @@ static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scra force_inplace; const size_t size = ggml_nbytes(tensor); - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) { ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; @@ -9373,7 +9372,7 @@ void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) { GGML_ASSERT(ggml_is_contiguous(tensor)); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + ggml_cuda_set_device(g_main_device); CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice)); } diff --git a/ggml.c b/ggml.c index d24560480..ed56e60a8 100644 --- a/ggml.c +++ b/ggml.c @@ -4041,7 +4041,6 @@ static struct ggml_tensor * ggml_group_norm_impl( result->op = GGML_OP_GROUP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; // TODO: maybe store epsilon here? return result; } @@ -5541,7 +5540,6 @@ static struct ggml_tensor * ggml_upscale_impl( result->op_params[0] = scale_factor; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5846,7 +5844,6 @@ struct ggml_tensor * ggml_get_rel_pos( result->op = GGML_OP_GET_REL_POS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } diff --git a/llama.cpp b/llama.cpp index 0b99f1e03..4aa59c4c0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9519,7 +9519,8 @@ struct llama_context * llama_new_context_with_model( ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc); #if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) if (model->n_gpu_layers > 0) { - ggml_cuda_set_scratch_size(alloc_size); + // the CPU buffer adds this padding in case the malloc buffer is not aligned, so we need to do the same for the GPU buffer, since we use the same offsets + ggml_cuda_set_scratch_size(alloc_size + 64); LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0); // calculate total VRAM usage From f56d6077d0c37e6606ac0a4fa3169de70593acfe Mon Sep 17 00:00:00 2001 From: wonjun Jang Date: Wed, 27 Dec 2023 17:37:25 +0900 Subject: [PATCH 257/664] Add byte token type when tokenizer.model is not exists (#4641) * Add byte token type to hf format * remove unused variable --- convert.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/convert.py b/convert.py index 7a3cd615e..1f0c4f2f4 100755 --- a/convert.py +++ b/convert.py @@ -357,6 +357,7 @@ class VocabLoader: for tok in self.tokenizer.all_special_tokens } self.special_ids: set[int] = set(self.tokenizer.all_special_ids) + self.reverse_vocab = {id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()} self.vocab_size_base: int = self.tokenizer.vocab_size self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_dict) self.fname_tokenizer: Path = fname_tokenizer @@ -370,15 +371,13 @@ class VocabLoader: self.spm = None def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.tokenizer - reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.get_vocab().items()} added_tokens_ids = set(self.added_tokens_dict.values()) for i in range(self.vocab_size_base): if i in added_tokens_ids: continue - text = reverse_vocab[i].encode("utf-8") + text = self.reverse_vocab[i].encode("utf-8") yield text, self.get_token_score(i), self.get_token_type(i) def get_token_type(self, token_id: int) -> gguf.TokenType: @@ -394,10 +393,13 @@ class VocabLoader: if self.spm.is_byte(token_id): toktype = gguf.TokenType.BYTE else: + token = self.reverse_vocab[token_id] if token_id == self.unk_token_id: toktype = gguf.TokenType.UNKNOWN - if token_id in self.special_ids: + elif token_id in self.special_ids: toktype = gguf.TokenType.CONTROL + elif len(token) == 6 and token.startswith("<0x") and token.endswith(">"): + toktype = gguf.TokenType.BYTE return toktype From 951010fa53a0ffe81b7d2e87c4349e0d3cb3d19d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 27 Dec 2023 11:02:13 +0200 Subject: [PATCH 258/664] ggml : fix dot product for ARM (#4630) ggml-ci --- ggml-quants.c | 363 +++----------------------------------------------- 1 file changed, 22 insertions(+), 341 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index a15a24048..05ef8f9b7 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -407,6 +407,18 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { #define ggml_vld1q_s8_x4 vld1q_s8_x4 #endif + +#if !defined(__ARM_FEATURE_DOTPROD) + +inline static int32x4_t vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { + const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b)); + const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b)); + + return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))); +} + +#endif + #endif #if defined(__ARM_NEON) || defined(__wasm_simd128__) @@ -2468,32 +2480,12 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); -#if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); -#else - const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l)); - const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l)); - const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h)); - const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h)); - - const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l)); - const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l)); - const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h)); - const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h)); - - const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); - const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); - const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); - const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); -#endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -2776,32 +2768,12 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); -#if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d); -#else - const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l)); - const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l)); - const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h)); - const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h)); - - const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l)); - const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l)); - const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h)); - const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h)); - - const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); - const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); - const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); - const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d); -#endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; @@ -2963,32 +2935,12 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); -#if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); -#else - const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l)); - const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l)); - const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h)); - const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h)); - - const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l)); - const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l)); - const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h)); - const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h)); - - const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); - const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); - const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); - const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); -#endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -3275,32 +3227,12 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); -#if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); -#else - const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l)); - const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l)); - const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h)); - const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h)); - - const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l)); - const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l)); - const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h)); - const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h)); - - const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); - const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); - const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); - const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d); -#endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; @@ -3550,7 +3482,6 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri const int8x16_t y1_0 = vld1q_s8(y1->qs); const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); -#if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); @@ -3558,26 +3489,6 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - -#else - const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0)); - const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0)); - const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1)); - const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1)); - - const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0)); - const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0)); - const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1)); - const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1)); - - const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1)); - const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3)); - const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1)); - const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3)); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); -#endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -3650,12 +3561,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); const uint8x16_t m4 = vdupq_n_u8(0xF); -#if defined(__ARM_FEATURE_DOTPROD) - const int32x4_t vzero = vdupq_n_s32(0); -#endif + + const int32x4_t vzero = vdupq_n_s32(0); ggml_int8x16x2_t q2bytes; uint8_t aux[16]; @@ -3663,7 +3572,6 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri float sum = 0; for (int i = 0; i < nb; ++i) { - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); @@ -3689,20 +3597,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri // We use this macro instead of a function call because for some reason // the code runs 2-3% slower, even if the function is declared inline -#if defined(__ARM_FEATURE_DOTPROD) #define MULTIPLY_ACCUM_WITH_SCALE(index)\ isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; -#else -#define MULTIPLY_ACCUM_WITH_SCALE(index)\ - {\ - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])),\ - vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0])));\ - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])),\ - vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1])));\ - isum += vaddvq_s16(p1) * aux[is+(index)] + vaddvq_s16(p2) * aux[is+1+(index)];\ - } -#endif #define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ @@ -3710,26 +3607,23 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ MULTIPLY_ACCUM_WITH_SCALE((index)); - for (int j = 0; j < QK_K/128; ++j) { - const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); + MULTIPLY_ACCUM_WITH_SCALE(0); SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2); - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4); - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6); is += 8; } - sum += d * isum; + sum += d * isum; } *s = sum; @@ -4043,11 +3937,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); -#if defined(__ARM_FEATURE_DOTPROD) - const int32x4_t vzero = vdupq_n_s32(0); -#endif + + const int32x4_t vzero = vdupq_n_s32(0); ggml_int8x16x4_t q2bytes; @@ -4081,28 +3973,12 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); -#if defined(__ARM_FEATURE_DOTPROD) isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; -#else - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - isum1 += vaddvq_s16(p1) * scales[0]; - isum2 += vaddvq_s16(p2) * scales[1]; - const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q2bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - const int16x8_t p4 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q2bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - isum1 += vaddvq_s16(p3) * scales[2]; - isum2 += vaddvq_s16(p4) * scales[3]; -#endif sum += d * (isum1 + isum2); - } *s = sum; @@ -4328,9 +4204,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri uint32_t utmp[4]; const uint8x16_t m3b = vdupq_n_u8(0x3); -#ifdef __ARM_FEATURE_DOTPROD const int32x4_t vzero = vdupq_n_s32(0); -#endif const uint8x16_t m0 = vdupq_n_u8(1); const uint8x16_t m1 = vshlq_n_u8(m0, 1); @@ -4382,22 +4256,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; -#else - int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes_1.val[0])), - vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes_1.val[0]))); - int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes_1.val[1])), - vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes_1.val[1]))); - int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes_1.val[2])), - vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes_1.val[2]))); - int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes_1.val[3])), - vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes_1.val[3]))); - isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1] + vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3]; -#endif + scale += 4; q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); @@ -4410,22 +4273,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; -#else - p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes_2.val[0])), - vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes_2.val[0]))); - p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes_2.val[1])), - vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes_2.val[1]))); - p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes_2.val[2])), - vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes_2.val[2]))); - p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes_2.val[3])), - vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes_2.val[3]))); - isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1] + vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3]; -#endif + scale += 4; if (j == 0) { @@ -4864,10 +4716,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - -#ifdef __ARM_FEATURE_DOTPROD - const int32x4_t vzero = vdupq_n_s32(0); -#endif + const int32x4_t vzero = vdupq_n_s32(0); const uint8x16_t m3b = vdupq_n_u8(0x3); const uint8x16_t mh = vdupq_n_u8(4); @@ -4908,22 +4757,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; -#else - const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - isum += vaddvq_s16(p0) * scales[0] + vaddvq_s16(p1) * scales[2] + vaddvq_s16(p2) * scales[1] + vaddvq_s16(p3) * scales[3]; -#endif sum += d * isum; @@ -5228,11 +5065,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri uint32_t utmp[4]; #ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); -#ifdef __ARM_FEATURE_DOTPROD const int32x4_t mzero = vdupq_n_s32(0); -#endif ggml_int8x16x2_t q4bytes; ggml_int8x16x2_t q8bytes; @@ -5269,10 +5103,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri int32_t sumi2 = 0; for (int j = 0; j < QK_K/64; ++j) { - const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; -#ifdef __ARM_FEATURE_DOTPROD q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); @@ -5287,26 +5119,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi2 += vaddvq_s32(p2) * scales[2*j+1]; -#else - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) * scales[2*j+0]; - - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - sumi2 += vaddvq_s16(vaddq_s16(p2, p3)) * scales[2*j+1]; - -#endif } sumf += d * (sumi1 + sumi2); @@ -5603,12 +5415,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); -#ifdef __ARM_FEATURE_DOTPROD const int32x4_t mzero = vdupq_n_s32(0); -#endif float sumf = 0; @@ -5636,7 +5445,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); -#ifdef __ARM_FEATURE_DOTPROD q8bytes = ggml_vld1q_s8_x4(q8); q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); @@ -5650,27 +5458,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; -#else - q8bytes = ggml_vld1q_s8_x4(q8); - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - int32_t sumi1 = vaddvq_s16(vaddq_s16(p0, p1)) * scales[0]; - - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[2]))); - const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[3]))); - int32_t sumi2 = vaddvq_s16(vaddq_s16(p2, p3)) * scales[1]; - -#endif sumf += d * (sumi1 + sumi2); - } *s = sumf - sum_mins; @@ -5875,15 +5663,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri uint32_t utmp[4]; - #ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); const uint8x16_t mone = vdupq_n_u8(1); const uint8x16_t mtwo = vdupq_n_u8(2); -#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t mzero = vdupq_n_s32(0); -#endif ggml_int8x16x4_t q5bytes; @@ -5938,28 +5722,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) - sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; -#else - - const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - sumi += vaddvq_s16(vaddq_s16(p0, p1)) * *scales++; - - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - sumi += vaddvq_s16(vaddq_s16(p2, p3)) * *scales++; -#endif } sumf += d * sumi - dmin * sumi_mins; - } *s = sumf; @@ -6311,12 +6078,9 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); const uint8x16_t mh = vdupq_n_u8(16); -#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t mzero = vdupq_n_s32(0); -#endif ggml_int8x16x4_t q5bytes; ggml_uint8x16x4_t q5h; @@ -6348,32 +6112,12 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) - int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); - -#else - - const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - int32_t sumi = sc[0] * vaddvq_s16(p0) + sc[1] * vaddvq_s16(p1); - - const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - sumi += sc[2] * vaddvq_s16(p2) + sc[3] * vaddvq_s16(p3); - - sumf += d*sumi; -#endif - } *s = sumf; @@ -6600,13 +6344,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - float sum = 0; const uint8x16_t m4b = vdupq_n_u8(0xF); -#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); -#endif //const int8x16_t m32s = vdupq_n_s8(32); const uint8x16_t mone = vdupq_n_u8(3); @@ -6658,31 +6399,13 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + scale += 4; -#else - - int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1]; - scale += 2; - - int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - isum += vaddvq_s16(p2) * scale[0] + vaddvq_s16(p3) * scale[1]; - scale += 2; -#endif - q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; shifted = vshrq_n_u8(qhbits.val[0], 4); @@ -6703,34 +6426,11 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); -#if defined(__ARM_FEATURE_DOTPROD) - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; scale += 4; - - //for (int l = 0; l < 4; ++l) { - // const int32x4_t p = vdotq_s32(vzero, q6bytes.val[l], q8bytes.val[l]); - // isum += vaddvq_s32(p) * *scale++; - //} -#else - p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1]; - scale += 2; - - p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - isum += vaddvq_s16(p2) * scale[0] + vaddvq_s16(p3) * scale[1]; - scale += 2; -#endif - } //sum += isum * d_all * y[i].d; sum += d_all * y[i].d * (isum - 32 * isum_mins); @@ -7076,14 +6776,11 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const int nb = n / QK_K; #ifdef __ARM_NEON - float sum = 0; const uint8x16_t m4b = vdupq_n_u8(0xF); const int8x16_t m32s = vdupq_n_s8(32); -#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); -#endif const uint8x16_t mone = vdupq_n_u8(3); @@ -7119,26 +6816,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); -#if defined(__ARM_FEATURE_DOTPROD) - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; -#else - - int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])), - vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0]))); - int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])), - vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1]))); - isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1]; - - int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])), - vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2]))); - int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])), - vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3]))); - isum += vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3]; -#endif sum += isum * d_all * y[i].d; From b47879b0dda43f2d26415e88b6840295817e552a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 27 Dec 2023 11:15:31 +0200 Subject: [PATCH 259/664] scripts : add sync-ggml-am.sh --- scripts/sync-ggml-am.sh | 131 ++++++++++++++++++++++++++++++++++++++++ scripts/sync-ggml.last | 1 + 2 files changed, 132 insertions(+) create mode 100755 scripts/sync-ggml-am.sh create mode 100644 scripts/sync-ggml.last diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh new file mode 100755 index 000000000..83abe3681 --- /dev/null +++ b/scripts/sync-ggml-am.sh @@ -0,0 +1,131 @@ +#!/bin/bash +# +# Synchronize ggml changes to llama.cpp +# +# Usage: +# +# $ cd /path/to/llama.cpp +# $ ./scripts/sync-ggml-am.sh +# + +set -e + +sd=$(dirname $0) +cd $sd/../ + +SRC_LLAMA=$(pwd) +SRC_GGML=$(cd ../ggml; pwd) + +if [ ! -d $SRC_GGML ]; then + echo "ggml not found at $SRC_GGML" + exit 1 +fi + +lc=$(cat $SRC_LLAMA/scripts/sync-ggml.last) +echo "Syncing ggml changes since commit $lc" + +cd $SRC_GGML + +git log --oneline $lc..HEAD + +git format-patch $lc --stdout -- \ + include/ggml/ggml*.h \ + src/ggml*.h \ + src/ggml*.c \ + src/ggml*.cpp \ + src/ggml*.m \ + src/ggml*.metal \ + src/ggml*.cu \ + tests/test-opt.cpp \ + tests/test-grad0.cpp \ + tests/test-quantize-fns.cpp \ + tests/test-quantize-perf.cpp \ + tests/test-backend-ops.cpp \ + > $SRC_LLAMA/ggml-src.patch + +# delete files if empty +if [ ! -s $SRC_LLAMA/ggml-src.patch ]; then + rm -v $SRC_LLAMA/ggml-src.patch +fi + +cd $SRC_LLAMA + +if [ -f $SRC_LLAMA/ggml-src.patch ]; then + # replace PR numbers + # + # Subject: some text (#1234) + # Subject: some text (ggml/1234) + cat ggml-src.patch | sed -e 's/^Subject: \(.*\) (#\([0-9]*\))/Subject: \1 (ggml\/\2)/' > ggml-src.patch.tmp + mv ggml-src.patch.tmp ggml-src.patch + + cat ggml-src.patch | sed -e 's/^\(.*\) (#\([0-9]*\))$/\1 (ggml\/\2)/' > ggml-src.patch.tmp + mv ggml-src.patch.tmp ggml-src.patch + + # replace filenames: + # + # src/ggml.c -> ggml.c + # src/ggml-alloc.c -> ggml-alloc.c + # src/ggml-backend-impl.h -> ggml-backend-impl.h + # src/ggml-backend.c -> ggml-backend.c + # src/ggml-cuda.cu -> ggml-cuda.cu + # src/ggml-cuda.h -> ggml-cuda.h + # src/ggml-impl.h -> ggml-impl.h + # src/ggml-metal.h -> ggml-metal.h + # src/ggml-metal.m -> ggml-metal.m + # src/ggml-metal.metal -> ggml-metal.metal + # src/ggml-mpi.h -> ggml-mpi.h + # src/ggml-mpi.c -> ggml-mpi.c + # src/ggml-opencl.cpp -> ggml-opencl.cpp + # src/ggml-opencl.h -> ggml-opencl.h + # src/ggml-quants.c -> ggml-quants.c + # src/ggml-quants.h -> ggml-quants.h + # include/ggml/ggml.h -> ggml.h + # include/ggml/ggml-alloc.h -> ggml-alloc.h + # include/ggml/ggml-backend.h -> ggml-backend.h + # + # tests/test-opt.cpp -> tests/test-opt.cpp + # tests/test-grad0.cpp -> tests/test-grad0.cpp + # tests/test-quantize-fns.cpp -> tests/test-quantize-fns.cpp + # tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp + # tests/test-backend-ops.cpp -> tests/test-backend-ops.cpp + + cat ggml-src.patch | sed \ + -e 's/src\/ggml\.c/ggml.c/g' \ + -e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \ + -e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \ + -e 's/src\/ggml-backend\.c/ggml-backend.c/g' \ + -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \ + -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \ + -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \ + -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \ + -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \ + -e 's/src\/ggml-metal\.metal/ggml-metal.metal/g' \ + -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \ + -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \ + -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \ + -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \ + -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \ + -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \ + -e 's/include\/ggml\/ggml\.h/ggml.h/g' \ + -e 's/include\/ggml\/ggml-alloc\.h/ggml-alloc.h/g' \ + -e 's/include\/ggml\/ggml-backend\.h/ggml-backend.h/g' \ + -e 's/tests\/test-opt\.cpp/tests\/test-opt.cpp/g' \ + -e 's/tests\/test-grad0\.cpp/tests\/test-grad0.cpp/g' \ + -e 's/tests\/test-quantize-fns\.cpp/tests\/test-quantize-fns.cpp/g' \ + -e 's/tests\/test-quantize-perf\.cpp/tests\/test-quantize-perf.cpp/g' \ + -e 's/tests\/test-backend-ops\.cpp/tests\/test-backend-ops.cpp/g' \ + > ggml-src.patch.tmp + mv ggml-src.patch.tmp ggml-src.patch + + git am ggml-src.patch + + rm -v $SRC_LLAMA/ggml-src.patch +fi + +# update last commit +cd $SRC_GGML +git log -1 --format=%H > $SRC_LLAMA/scripts/sync-ggml.last + +echo "Done" + +exit 0 diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last new file mode 100644 index 000000000..1ec144116 --- /dev/null +++ b/scripts/sync-ggml.last @@ -0,0 +1 @@ +76e7f47b69e8334384dc718480c496dafbd47999 From 879b690a9e1eb1ab0a29b58236fc76978fb4d902 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 27 Dec 2023 15:16:55 +0100 Subject: [PATCH 260/664] finetune : fix output formatting in print_params (#4653) This commit fixes the output formatting in the print_params function which currently looks like this: ```console print_params: n_vocab: 32000 print_params: n_ctx: 128 print_params: n_embd: 4096 print_params: n_ff: 11008 print_params: n_head: 32 print_params: n_head_kv: 32 print_params: n_layer: 32 print_params: norm_rms_eps : 0.000010 print_params: rope_freq_base : 10000.000000 print_params: rope_freq_scale : 1.000000 ``` With this comit the output will look like this: ```console print_params: n_vocab : 32000 print_params: n_ctx : 128 print_params: n_embd : 4096 print_params: n_ff : 11008 print_params: n_head : 32 print_params: n_head_kv : 32 print_params: n_layer : 32 print_params: norm_rms_eps : 0.000010 print_params: rope_freq_base : 10000.000000 print_params: rope_freq_scale : 1.000000 ``` Signed-off-by: Daniel Bevenius --- examples/finetune/finetune.cpp | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 7b1333a9d..e0520f64c 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -196,13 +196,13 @@ static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down"; static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up"; static void print_params(struct my_llama_hparams * params) { - printf("%s: n_vocab: %u\n", __func__, params->n_vocab); - printf("%s: n_ctx: %u\n", __func__, params->n_ctx); - printf("%s: n_embd: %u\n", __func__, params->n_embd); - printf("%s: n_ff: %u\n", __func__, params->n_ff); - printf("%s: n_head: %u\n", __func__, params->n_head); - printf("%s: n_head_kv: %u\n", __func__, params->n_head_kv); - printf("%s: n_layer: %u\n", __func__, params->n_layer); + printf("%s: n_vocab : %u\n", __func__, params->n_vocab); + printf("%s: n_ctx : %u\n", __func__, params->n_ctx); + printf("%s: n_embd : %u\n", __func__, params->n_embd); + printf("%s: n_ff : %u\n", __func__, params->n_ff); + printf("%s: n_head : %u\n", __func__, params->n_head); + printf("%s: n_head_kv : %u\n", __func__, params->n_head_kv); + printf("%s: n_layer : %u\n", __func__, params->n_layer); printf("%s: norm_rms_eps : %f\n", __func__, params->f_norm_rms_eps); printf("%s: rope_freq_base : %f\n", __func__, params->rope_freq_base); printf("%s: rope_freq_scale : %f\n", __func__, params->rope_freq_scale); From f6793491b5af6da75edad34d6f503ef86d31b09f Mon Sep 17 00:00:00 2001 From: "Nam D. Tran" <42194884+namtranase@users.noreply.github.com> Date: Wed, 27 Dec 2023 22:39:45 +0700 Subject: [PATCH 261/664] llama : add AWQ for llama, llama2, mpt, and mistral models (#4593) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci --------- Co-authored-by: Trần Đức Nam Co-authored-by: Le Hoang Anh Co-authored-by: Georgi Gerganov --- awq-py/README.md | 116 +++++++++++++++ awq-py/awq/apply_awq.py | 254 +++++++++++++++++++++++++++++++++ awq-py/requirements.txt | 2 + convert-hf-to-gguf.py | 27 +++- convert.py | 14 ++ gguf-py/gguf/constants.py | 3 + gguf-py/gguf/tensor_mapping.py | 5 + llama.cpp | 27 +++- 8 files changed, 443 insertions(+), 5 deletions(-) create mode 100644 awq-py/README.md create mode 100644 awq-py/awq/apply_awq.py create mode 100644 awq-py/requirements.txt diff --git a/awq-py/README.md b/awq-py/README.md new file mode 100644 index 000000000..59354f4e3 --- /dev/null +++ b/awq-py/README.md @@ -0,0 +1,116 @@ +# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp +[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)] + +**Supported models:** + +- [X] LLaMA +- [x] LLaMA 2 +- [X] MPT +- [X] Mistral AI v0.1 +- [ ] Bloom +- [ ] Mixtral MoE + +**TODO:** +- [x] Update version work with both MPT and MPT-AWQ model +- [ ] Add OPT model +- [ ] Add Bloom model +- [ ] Add Mixtral MoE +- [ ] Support w3, w2 + + +## Contents + +- [Install](##Install) +- [Convert](##Convert) +- [Quantize](##Quantize) +- [Test](##Test) +- [Benchmark](##Benchmark) +- [Results](##Results) + +## Install +Install requirements +```bash +pip install -r requirements.txt +``` +Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT +```bash +git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache +``` + +## Convert +Example for llama model +```bash +# For llama7b and llama2 models +python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf +# For mistral and mpt models +python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf +``` + +## Quantize +```bash +# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types. +./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0 +``` + +## Test +```bash +# For all models. +./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time" +``` + +## Benchmark +The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. +```bash +# For llama and llama2, and mistral models. +./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw +``` + +## Results +Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison +We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k + +### Llama 7B (Build with OpenBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|-----------:|--------------|-------:|-------:|-------:|-------:| +|Llama 7B | perplexity | 5.9066 | 6.1214 | 6.0643 | 6.5808 | +|Llama 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G | +|Llama 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-LLama 7B| perplexity | 5.9175 | 6.0252 | 5.9987 | 6.3692 | +|AWQ-LLama 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G | +|AWQ-LLama 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | + + +### Llama2 7B (Build with CuBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|------------:|--------------|-------:|-------:|-------:|-------:| +|Llama2 7B | perplexity | 5.8664 | 6.0260 | 6.0656 | 6.4496 | +|Llama2 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G | +|Llama2 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-LLama2 7B| perplexity | 5.8801 | 6.0054 | 5.9849 | 6.3650 | +|AWQ-LLama2 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G | +|AWQ-LLama2 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | + + +### Mistral 7B v0.1 (Build with CuBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|-------------:|--------------|-------:|-------:|-------:|-------:| +|Mistral 7B | perplexity | 5.6931 | 5.8202 | 5.8268 | 6.1645 | +|Mistral 7B | file size | 14.5G | 4.1G | 4.5G | 3.1G | +|Mistral 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-Mistral 7B| perplexity | 5.6934 | 5.8020 | 5.7691 | 6.0426 | +|AWQ-Mistral 7B| file size | 14.5G | 4.1G | 4.5G | 3.1G | +|AWQ-Mistral 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | + +### MPT 7B (Build with OpenBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|---------:|--------------|-------:|-------:|-------:|--------:| +|MPT 7B | perplexity | 8.4369 | 8.7956 | 8.6265 | 11.4913 | +|MPT 7B | file size | 13.7G | 3.9G | 4.3G | 2.8G | +|MPT 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-MPT 7B| perplexity | 8.4944 | 8.7053 | 8.6750 | 10.2873| +|AWQ-MPT 7B| file size | 13.7G | 3.9G | 4.3G | 2.8G | +|AWQ-MPT 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | diff --git a/awq-py/awq/apply_awq.py b/awq-py/awq/apply_awq.py new file mode 100644 index 000000000..11132c5d2 --- /dev/null +++ b/awq-py/awq/apply_awq.py @@ -0,0 +1,254 @@ +""" +Implements the AWQ for llama.cpp use cases. +Original paper: https://arxiv.org/abs/2306.00978 + +This code is based on versions of the AWQ implementation found in the following repositories: +* https://github.com/mit-han-lab/llm-awq +* https://github.com/casper-hansen/AutoAWQ +""" + +import os +import torch +import torch.nn as nn + +from transformers import AutoModelForCausalLM, AutoConfig +from transformers.models.bloom.modeling_bloom import BloomGelu +from transformers.models.llama.modeling_llama import LlamaRMSNorm +from transformers.activations import GELUActivation + + +class ScaledActivation(nn.Module): + """ + ScaledActivation module wraps an existing activation function and applies a + scale factor to its output. + + Args: + module (nn.Module): The activation function to be scaled. + scales (torch.Tensor): A tensor of size (num_features,) containing the initial + scale factors for each feature. + + Returns: + torch.Tensor: The scaled output of the activation function. + """ + + def __init__(self, module, scales): + super().__init__() + self.act = module + self.scales = nn.Parameter(scales.data) + + def forward(self, x): + return self.act(x) / self.scales.view(1, 1, -1).to(x.device) + + +def set_op_by_name(layer, name, new_module): + """ + Set the new module for given module's name. + + Args: + layer (nn.Module): The layer in which to replace the submodule. + name (str): The path to the submodule to be replaced, using dot notation + to access nested modules. + new_module (nn.Module): The new module to replace the existing one. + """ + levels = name.split(".") + if len(levels) > 1: + mod_ = layer + for l_idx in range(len(levels) - 1): + if levels[l_idx].isdigit(): + mod_ = mod_[int(levels[l_idx])] + else: + mod_ = getattr(mod_, levels[l_idx]) + setattr(mod_, levels[-1], new_module) + else: + setattr(layer, name, new_module) + + +def get_op_by_name(module, op_name): + """ + Retrieves a submodule within a given layer based on its name. + + Args: + module (nn.Module): The layer containing the submodule to find. + op_name (str): The name of the submodule. + + Returns: + nn.Module: The requested submodule found within the given layer. + + Raises: + ValueError: If the specified submodule cannot be found within the layer. + """ + for name, m in module.named_modules(): + if name == op_name: + return m + raise ValueError(f"Cannot find op {op_name} in module {module}") + + +@torch.no_grad() +def scale_ln_fcs(ln, fcs, scales): + """ + Scales the weights of a LayerNorm and a list of fully-connected layers proportionally. + + Args: + ln (nn.LayerNorm): The LayerNorm module to be scaled. + fcs (List[nn.Linear]): A list of fully-connected layers to be scaled. + scales (torch.Tensor): A 1D tensor of size (num_features,). + """ + + if not isinstance(fcs, list): + fcs = [fcs] + + scales = scales.to(ln.weight.device) + + ln.weight.div_(scales) + if hasattr(ln, "bias") and ln.bias is not None: + ln.bias.div_(scales) + + for fc in fcs: + fc.weight.mul_(scales.view(1, -1)) + + for p in ln.parameters(): + assert torch.isnan(p).sum() == 0 + for fc in fcs: + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + +@torch.no_grad() +def scale_fc_fc(fc1, fc2, scales): + """ + Scales the weights of two fully-connected layers in a specific pattern. + + Args: + fc1 (nn.Linear): The first fully-connected layer to be scaled. + fc2 (nn.Linear): The second fully-connected layer to be scaled. + scales (torch.Tensor): A 1D tensor of size (num_features,). + """ + assert isinstance(fc1, nn.Linear) + assert isinstance(fc2, nn.Linear) + + scales = scales.to(fc1.weight.device) + + fc1.weight[-scales.size(0):].div_(scales.view(-1, 1)) + if fc1.bias is not None: + fc1.bias.div_(scales.view(-1)) + + fc2.weight.mul_(scales.view(1, -1)) + + for p in fc1.parameters(): + assert torch.isnan(p).sum() == 0 + for p in fc2.parameters(): + assert torch.isnan(p).sum() == 0 + + +@torch.no_grad() +def scale_gelu_fc(gelu, fc, scales): + """ + Scales the weight of a GELU activation and a fully-connected layer proportionally. + + Args: + gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled. + fc (nn.Linear): The fully-connected layer to be scaled. + scales (torch.Tensor): A 1D tensor of size (num_features,). + + Raises: + TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`. + TypeError: If the `fc` module is not of type `nn.Linear`. + """ + assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation)) + assert isinstance(fc, nn.Linear) + + fc.weight.mul_(scales.view(1, -1).to(fc.weight.device)) + + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + +def apply_scale(module, scales_list, input_feat_dict=None): + """ + Applies different scaling strategies to layers based on their type and hierarchy within a given module. + + Args: + module (nn.Module): The module containing the layers to be scaled. + scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing: + * prev_op_name (str): The name of the preceding operation or module, + relative to which the layers to be scaled are located. + * layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation. + * scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature. + input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding + input features (optional). + """ + for prev_op_name, layer_names, scales in scales_list: + prev_op = get_op_by_name(module, prev_op_name) + layers = [get_op_by_name(module, name) for name in layer_names] + + prev_op.cuda() + for layer in layers: + layer.cuda() + scales.cuda() + + if isinstance(prev_op, nn.Linear): + assert len(layers) == 1 + scale_fc_fc(prev_op, layers[0], scales) + elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower(): + scale_ln_fcs(prev_op, layers, scales) + elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)): + new_module = ScaledActivation(prev_op, scales) + set_op_by_name(module, prev_op_name, new_module) + scale_gelu_fc(prev_op, layers[0], scales) + else: + raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!") + + # apply the scaling to input feat if given; prepare it for clipping + if input_feat_dict is not None: + for layer_name in layer_names: + inp = input_feat_dict[layer_name] + inp.div_(scales.view(1, -1).to(inp.device)) + + prev_op.cpu() + for layer in layers: + layer.cpu() + scales.cpu() + + +@torch.no_grad() +def apply_clip(module, clip_list): + """ + Applies element-wise clipping to the weight of a specific layer within a given module. + + Args: + module (nn.Module): The module containing the layer to be clipped. + clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing: + * name (str): The name of the layer to be clipped, relative to the root of the module. + * max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight. + """ + for name, max_val in clip_list: + layer = get_op_by_name(module, name) + layer.cuda() + max_val = max_val.to(layer.weight.device) + org_shape = layer.weight.shape + layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1) + layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val) + layer.weight.data = layer.weight.data.reshape(org_shape) + layer.cpu() + + +def add_scale_weights(model_path, scale_path, tmp_path): + """ + Adds pre-computed Activation Weight Quantization (AWQ) results to a model, + including scaling factors and clipping bounds. + + Args: + model_path (str): Path to the pre-trained model to be equipped with AWQ. + scale_path (str): Path to the AWQ scale factors (.pt file). + tmp_path (str): Path to the temporary directory where the equipped model will be saved. + """ + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + model = AutoModelForCausalLM.from_pretrained( + model_path, config=config, trust_remote_code=True + ) + model.eval() + awq_results = torch.load(str(scale_path), map_location="cpu") + apply_scale(model, awq_results["scale"]) + apply_clip(model, awq_results["clip"]) + model.save_pretrained(str(tmp_path)) + os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}") diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt new file mode 100644 index 000000000..5fe604329 --- /dev/null +++ b/awq-py/requirements.txt @@ -0,0 +1,2 @@ +torch>=2.0.0 +transformers>=4.32.0 diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 303d08170..7dbc28147 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -46,7 +46,7 @@ class Model: self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) self.model_arch = self._get_model_architecture() - self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess) + self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) def set_vocab(self): self._set_vocab_gpt2() @@ -59,7 +59,7 @@ class Model: from safetensors import safe_open ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) else: - ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) + ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", weights_only=True)) with ctx as model_part: for name in model_part.keys(): @@ -464,7 +464,11 @@ class MPTModel(Model): data = data_torch.squeeze().numpy() # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if "scales" in name: + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales")) + new_name = new_name.replace("scales", "act.scales") + else: + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() @@ -1095,6 +1099,9 @@ def parse_args() -> argparse.Namespace: "--vocab-only", action="store_true", help="extract only the vocab", ) + parser.add_argument( + "--awq-path", type=Path, default=None, + help="Path to scale awq cache file") parser.add_argument( "--outfile", type=Path, help="path to write to; default: based on input", @@ -1115,6 +1122,20 @@ def parse_args() -> argparse.Namespace: args = parse_args() dir_model = args.model + +if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + dir_model = tmp_model_path + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + if not dir_model.is_dir(): print(f'Error: {args.model} is not a directory', file=sys.stderr) sys.exit(1) diff --git a/convert.py b/convert.py index 1f0c4f2f4..c3f3fc0a1 100755 --- a/convert.py +++ b/convert.py @@ -1187,6 +1187,7 @@ def main(args_in: list[str] | None = None) -> None: # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") + parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") @@ -1200,6 +1201,19 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--padvocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") args = parser.parse_args(args_in) + if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + args.model = tmp_model_path + if args.dump_single: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 4cd87cdda..c9be21119 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -120,6 +120,7 @@ class MODEL_TENSOR(IntEnum): FFN_GATE = auto() FFN_DOWN = auto() FFN_UP = auto() + FFN_ACT = auto() FFN_GATE_EXP = auto() FFN_DOWN_EXP = auto() FFN_UP_EXP = auto() @@ -169,6 +170,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", + MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate.{xid}", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}", @@ -269,6 +271,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_ACT, ], MODEL_ARCH.GPTJ: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 446c6b688..0b8f70417 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -188,6 +188,11 @@ class TensorNameMap: "model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral ), + # AWQ-activation gate + MODEL_TENSOR.FFN_ACT: ( + "transformer.blocks.{bid}.ffn.act", # mpt + ), + # Feed-forward gate MODEL_TENSOR.FFN_GATE: ( "model.layers.{bid}.mlp.gate_proj", # llama-hf refact diff --git a/llama.cpp b/llama.cpp index 4aa59c4c0..bf1b01a90 100644 --- a/llama.cpp +++ b/llama.cpp @@ -354,6 +354,7 @@ enum llm_tensor { LLM_TENSOR_FFN_GATE, LLM_TENSOR_FFN_DOWN, LLM_TENSOR_FFN_UP, + LLM_TENSOR_FFN_ACT, LLM_TENSOR_FFN_DOWN_EXP, LLM_TENSOR_FFN_GATE_EXP, LLM_TENSOR_FFN_UP_EXP, @@ -473,6 +474,7 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" }, }, }, { @@ -1285,6 +1287,7 @@ struct llama_hparams { float f_clamp_kqv; float f_max_alibi_bias; + bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; if (this->n_vocab != other.n_vocab) return true; @@ -1388,6 +1391,7 @@ struct llama_layer { // ff bias struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_up_b; // b3 + struct ggml_tensor * ffn_act; }; struct llama_kv_cell { @@ -3471,7 +3475,6 @@ static bool llm_load_tensors( case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - // output { ggml_backend_type backend_norm; @@ -3509,6 +3512,9 @@ static bool llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + + // AWQ ScaleActivation layer + layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false); } } break; case LLM_ARCH_STABLELM: @@ -4039,6 +4045,7 @@ static struct ggml_tensor * llm_build_ffn( struct ggml_tensor * gate_b, struct ggml_tensor * down, struct ggml_tensor * down_b, + struct ggml_tensor * act_scales, llm_ffn_op_type type_op, llm_ffn_gate_type type_gate, const llm_build_cb & cb, @@ -4083,6 +4090,10 @@ static struct ggml_tensor * llm_build_ffn( { cur = ggml_gelu(ctx, cur); cb(cur, "ffn_gelu", il); + if (act_scales != NULL) { + cur = ggml_div(ctx, cur, act_scales); + cb(cur, "ffn_act", il); + } } break; case LLM_FFN_RELU: { @@ -4401,6 +4412,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } else { @@ -4580,6 +4592,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -4694,6 +4707,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, NULL, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -4798,6 +4812,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5002,6 +5017,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5088,6 +5104,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5183,6 +5200,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5268,11 +5286,11 @@ struct llm_build_context { NULL, LLM_NORM, cb, il); cb(cur, "ffn_norm", il); - cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, NULL, NULL, model.layers[il].ffn_down, NULL, + model.layers[il].ffn_act, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5381,6 +5399,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5493,6 +5512,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5600,6 +5620,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(ffn_output, "ffn_out", il); } @@ -5703,6 +5724,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5887,6 +5909,7 @@ static const std::unordered_map k_offload_map { "ffn_gate", OFFLOAD_FUNC }, { "ffn_gate_b", OFFLOAD_FUNC }, { "ffn_gate_par", OFFLOAD_FUNC }, + { "ffn_act", OFFLOAD_FUNC }, { "ffn_down", OFFLOAD_FUNC }, { "ffn_down_b", OFFLOAD_FUNC }, { "ffn_out", OFFLOAD_FUNC }, From ea5497df5d138c83b2b0ca70aefdc4b1175c1001 Mon Sep 17 00:00:00 2001 From: manikbhandari Date: Thu, 28 Dec 2023 09:03:57 -0500 Subject: [PATCH 262/664] gpt2 : Add gpt2 architecture integration (#4555) --- README.md | 1 + convert-hf-to-gguf.py | 66 +++++++++++ gguf-py/gguf/constants.py | 11 +- gguf-py/gguf/tensor_mapping.py | 10 +- llama.cpp | 206 +++++++++++++++++++++++++++++++-- models/ggml-vocab-gpt2.gguf | Bin 0 -> 1766799 bytes tests/CMakeLists.txt | 1 + 7 files changed, 281 insertions(+), 14 deletions(-) create mode 100644 models/ggml-vocab-gpt2.gguf diff --git a/README.md b/README.md index 3b202a336..48dcd6464 100644 --- a/README.md +++ b/README.md @@ -103,6 +103,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) +- [x] [GPT-2](https://huggingface.co/gpt2) **Multimodal models:** diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 7dbc28147..3557a825e 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -182,6 +182,8 @@ class Model: return QwenModel if model_architecture == "MixtralForCausalLM": return MixtralModel + if model_architecture == "GPT2LMHeadModel": + return GPT2Model if model_architecture == "PhiForCausalLM": return Phi2Model if model_architecture == "PlamoForCausalLM": @@ -225,6 +227,8 @@ class Model: return gguf.MODEL_ARCH.QWEN if arch == "MixtralForCausalLM": return gguf.MODEL_ARCH.LLAMA + if arch == "GPT2LMHeadModel": + return gguf.MODEL_ARCH.GPT2 if arch == "PhiForCausalLM": return gguf.MODEL_ARCH.PHI2 if arch == "PlamoForCausalLM": @@ -993,6 +997,68 @@ class QwenModel(Model): self.gguf_writer.add_tensor(new_name, data) +class GPT2Model(Model): + def set_gguf_parameters(self): + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_ctx"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")): + continue + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): + data_torch = data_torch.transpose(1, 0) + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + # note: GPT2 output is tied to (same as) wte in original model + if new_name == "token_embd.weight": + print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor("output.weight", data) + + class Phi2Model(Model): def set_gguf_parameters(self): block_count = self.hparams["n_layer"] diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c9be21119..ae62cc575 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -370,7 +370,16 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.GPT2: [ - # TODO + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.PHI2: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 0b8f70417..80c1d5449 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -17,6 +17,7 @@ class TensorNameMap: "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert "language_model.embedding.word_embeddings", # persimmon + "wte", # gpt2 "transformer.embd.wte", # phi2 ), @@ -34,6 +35,7 @@ class TensorNameMap: MODEL_TENSOR.POS_EMBD: ( "transformer.wpe", # gpt2 "embeddings.position_embeddings", # bert + "wpe", # gpt2 ), # Output @@ -53,7 +55,7 @@ class TensorNameMap: "norm", # llama-pth "embeddings.LayerNorm", # bert "transformer.norm_f", # mpt - "ln_f", # refact bloom qwen + "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon "lm_head.ln", # phi2 ), @@ -78,6 +80,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi + "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo ), @@ -95,6 +98,7 @@ class TensorNameMap: "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 ), @@ -137,6 +141,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo ), @@ -159,6 +164,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi + "h.{bid}.ln_2", # gpt2 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -179,6 +185,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc_in", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen + "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo ), @@ -218,6 +225,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo ), diff --git a/llama.cpp b/llama.cpp index bf1b01a90..68c7cced6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -423,6 +423,15 @@ static std::map> LLM_TENSOR_NAMES = LLM_ARCH_GPT2, { { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_POS_EMBD, "position_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, }, }, { @@ -1256,6 +1265,10 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_SMALL, + MODEL_MEDIUM, + MODEL_LARGE, + MODEL_XL, }; static const size_t kiB = 1024; @@ -2552,18 +2565,22 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { - case MODEL_1B: return "1B"; - case MODEL_3B: return "3B"; - case MODEL_7B: return "7B"; - case MODEL_8B: return "8B"; - case MODEL_13B: return "13B"; - case MODEL_15B: return "15B"; - case MODEL_30B: return "30B"; - case MODEL_34B: return "34B"; - case MODEL_40B: return "40B"; - case MODEL_65B: return "65B"; - case MODEL_70B: return "70B"; - default: return "?B"; + case MODEL_1B: return "1B"; + case MODEL_3B: return "3B"; + case MODEL_7B: return "7B"; + case MODEL_8B: return "8B"; + case MODEL_13B: return "13B"; + case MODEL_15B: return "15B"; + case MODEL_30B: return "30B"; + case MODEL_34B: return "34B"; + case MODEL_40B: return "40B"; + case MODEL_65B: return "65B"; + case MODEL_70B: return "70B"; + case MODEL_SMALL: return "0.1B"; + case MODEL_MEDIUM: return "0.4B"; + case MODEL_LARGE: return "0.8B"; + case MODEL_XL: return "1.5B"; + default: return "?B"; } } @@ -2782,6 +2799,17 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GPT2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 12: model.type = e_model::MODEL_SMALL; break; + case 24: model.type = e_model::MODEL_MEDIUM; break; + case 36: model.type = e_model::MODEL_LARGE; break; + case 48: model.type = e_model::MODEL_XL; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3710,6 +3738,60 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); } } break; + case LLM_ARCH_GPT2: + { + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + + // output + { + ggml_backend_type backend_norm; + ggml_backend_type backend_output; + + if (n_gpu_layers > int(n_layer)) { + backend_norm = llama_backend_offload; + backend_output = llama_backend_offload_split; + } else { + backend_norm = GGML_BACKEND_CPU; + backend_output = GGML_BACKEND_CPU; + } + + model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); + model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); + model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + } + + const uint32_t n_ff = hparams.n_ff; + + const int i_gpu_start = n_layer - n_gpu_layers; + + model.layers.resize(n_layer); + + for (uint32_t i = 0; i < n_layer; ++i) { + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + + layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + + layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -5754,6 +5836,102 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_gpt2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + struct ggml_tensor * cur; + struct ggml_tensor * pos; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); + cb(pos, "pos_embd", -1); + + inpL = ggml_add(ctx0, inpL, pos); + cb(inpL, "inpL", -1); + + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, model, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + // add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // FF + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + } + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; // @@ -6269,6 +6447,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_plamo(); } break; + case LLM_ARCH_GPT2: + { + result = llm.build_gpt2(); + } break; default: GGML_ASSERT(false); } diff --git a/models/ggml-vocab-gpt2.gguf b/models/ggml-vocab-gpt2.gguf new file mode 100644 index 0000000000000000000000000000000000000000..1fbc72c1e4d9e210e5c5689b31e1debfa33d4b6a GIT binary patch literal 1766799 zcmZs^`I97Ba^FYYwgw*99`uE-njL~9w2aEis*35X zjNClBx`rf70(4UMeM>2Tph$@#MM&;i)TMzEv>ZYeBw{QJ3 zISylfbTS(zZ+<)(HuH6SE57m1`TfZ-Ur*nye>|Jc&({}iI{fEf@JFVXC)3l@etzx` 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being set to zero, which caused the token candidates array to get shrunk down to one element thus preventing any sampling. Note this only applies to OpenAI API compatible HTTP server requests. The solution is to use the default values that OpenAI documents, as well as ensuring we use the llama.cpp defaults for the rest. I've tested this change still ensures deterministic output by default. If a "temperature" greater than 0 is explicitly passed, then output is unique each time. If "seed" is specified in addition to "temperature" then the output becomes deterministic once more. See mozilla-Ocho/llamafile#117 See mozilla-Ocho/llamafile@9e4bf29 --- examples/server/server.cpp | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 72dfe452c..c5035e202 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -441,7 +441,6 @@ struct llama_client_slot } images.clear(); - // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } bool has_budget(gpt_params &global_params) { @@ -921,6 +920,7 @@ struct llama_server_context llama_sampling_free(slot->ctx_sampling); } slot->ctx_sampling = llama_sampling_init(slot->sparams); + llama_set_rng_seed(ctx, slot->params.seed); slot->command = LOAD_PROMPT; all_slots_are_idle = false; @@ -1215,7 +1215,7 @@ struct llama_server_context {"n_ctx", slot.n_ctx}, {"model", params.model_alias}, {"seed", slot.params.seed}, - {"temp", slot.sparams.temp}, + {"temperature", slot.sparams.temp}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, @@ -2437,26 +2437,33 @@ json oaicompat_completion_params_parse( llama_params["__oaicompat"] = true; // Map OpenAI parameters to llama.cpp parameters + // + // For parameters that are defined by the OpenAI documentation (e.g. + // temperature), we explicitly specify OpenAI's intended default; we + // need to do that because sometimes OpenAI disagrees with llama.cpp + // + // https://platform.openai.com/docs/api-reference/chat/create + llama_sampling_params default_sparams; llama_params["model"] = json_value(body, "model", std::string("uknown")); llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); - llama_params["temperature"] = json_value(body, "temperature", 0.8); - llama_params["top_k"] = json_value(body, "top_k", 40); - llama_params["top_p"] = json_value(body, "top_p", 0.95); + llama_params["temperature"] = json_value(body, "temperature", 0.0); + llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); + llama_params["top_p"] = json_value(body, "top_p", 1.0); llama_params["n_predict"] = json_value(body, "max_tokens", -1); llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); - llama_params["seed"] = json_value(body, "seed", 0); + llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); llama_params["stream"] = json_value(body, "stream", false); - llama_params["mirostat"] = json_value(body, "mirostat", false); - llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0); - llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0); - llama_params["penalize_nl"] = json_value(body, "penalize_nl", false); - llama_params["typical_p"] = json_value(body, "typical_p", 0.0); + llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); + llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); + llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0); llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); - llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0); + llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); if (body.count("grammar") != 0) { llama_params["grammar"] = json_value(body, "grammar", json::object()); From ca38b8d334baa724bd6c9402470931d26427466f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 14:41:36 +0200 Subject: [PATCH 264/664] scripts : do not sync commits from this repo --- scripts/sync-ggml-am.sh | 44 +++++++++++++++++++++++++++-------------- 1 file changed, 29 insertions(+), 15 deletions(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 83abe3681..93aad88a7 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -26,22 +26,36 @@ echo "Syncing ggml changes since commit $lc" cd $SRC_GGML -git log --oneline $lc..HEAD +git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits -git format-patch $lc --stdout -- \ - include/ggml/ggml*.h \ - src/ggml*.h \ - src/ggml*.c \ - src/ggml*.cpp \ - src/ggml*.m \ - src/ggml*.metal \ - src/ggml*.cu \ - tests/test-opt.cpp \ - tests/test-grad0.cpp \ - tests/test-quantize-fns.cpp \ - tests/test-quantize-perf.cpp \ - tests/test-backend-ops.cpp \ - > $SRC_LLAMA/ggml-src.patch +if [ ! -s $SRC_LLAMA/ggml-commits ]; then + rm -v $SRC_LLAMA/ggml-commits + echo "No new commits" + exit 0 +fi + +if [ -f $SRC_LLAMA/ggml-src.patch ]; then + rm -v $SRC_LLAMA/ggml-src.patch +fi + +while read c; do + git format-patch -k $c~1..$c --stdout -- \ + include/ggml/ggml*.h \ + src/ggml*.h \ + src/ggml*.c \ + src/ggml*.cpp \ + src/ggml*.m \ + src/ggml*.metal \ + src/ggml*.cu \ + tests/test-opt.cpp \ + tests/test-grad0.cpp \ + tests/test-quantize-fns.cpp \ + tests/test-quantize-perf.cpp \ + tests/test-backend-ops.cpp \ + >> $SRC_LLAMA/ggml-src.patch +done < $SRC_LLAMA/ggml-commits + +rm -v $SRC_LLAMA/ggml-commits # delete files if empty if [ ! -s $SRC_LLAMA/ggml-src.patch ]; then From afc8c192919f04613a92d40391bff4c8cd99856b Mon Sep 17 00:00:00 2001 From: bssrdf Date: Fri, 29 Dec 2023 03:32:31 -0500 Subject: [PATCH 265/664] ggml : fix some mul mat cases + add tests for src1 F16 (ggml/669) * fixed mul-mat error for old GPUs * style fixes * add mul mat src1 f16 test cases, fix more cases ggml-ci --------- Co-authored-by: bssrdf Co-authored-by: slaren --- ggml-backend.c | 8 +++- ggml-cuda.cu | 89 +++++++++++++++++++------------------- ggml.c | 2 +- tests/test-backend-ops.cpp | 14 +++--- 4 files changed, 60 insertions(+), 53 deletions(-) diff --git a/ggml-backend.c b/ggml-backend.c index 526ce732b..2c3752067 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -614,10 +614,14 @@ static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c } static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - return true; + switch (op->op) { + case GGML_OP_MUL_MAT: + return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; + default: + return true; + } GGML_UNUSED(backend); - GGML_UNUSED(op); } static struct ggml_backend_i cpu_backend_i = { diff --git a/ggml-cuda.cu b/ggml-cuda.cu index abad9cc39..9a9effcf5 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7485,6 +7485,8 @@ static void ggml_cuda_op_dequantize_mul_mat_vec( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + GGML_ASSERT(src1->type == GGML_TYPE_F32); + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_CUDA_F16 cuda_pool_alloc src1_dfloat_a; @@ -7577,6 +7579,7 @@ static void ggml_cuda_op_mul_mat_cublas( const int compute_capability = g_device_caps[id].cc; if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { + //printf("this branch\n"); // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 cuda_pool_alloc src0_as_f16; if (src0->type != GGML_TYPE_F16) { @@ -7614,9 +7617,9 @@ static void ggml_cuda_op_mul_mat_cublas( const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); - } - else { + } else { cuda_pool_alloc src0_ddq_as_f32; + cuda_pool_alloc src1_ddq_as_f32; if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); @@ -7624,7 +7627,15 @@ static void ggml_cuda_op_mul_mat_cublas( src0_ddq_as_f32.alloc(row_diff*ne00); to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); } + if (src1->type != GGML_TYPE_F32) { + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src1->type); + GGML_ASSERT(to_fp32_cuda != nullptr); + src1_ddq_as_f32.alloc(src1_ncols*ne10); + to_fp32_cuda(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + } + const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); const float alpha = 1.0f; const float beta = 0.0f; @@ -7633,9 +7644,9 @@ static void ggml_cuda_op_mul_mat_cublas( CUBLAS_CHECK( cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, row_diff, src1_ncols, ne10, - &alpha, src0_ddf_i, ne00, - src1_ddf_i, ne10, - &beta, dst_dd_i, ldc)); + &alpha, src0_ddf_i, ne00, + src1_ddf1_i, ne10, + &beta, dst_dd_i, ldc)); } (void) dst; @@ -8035,6 +8046,7 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -8481,9 +8493,9 @@ static __global__ void k_compute_batched_ptrs( int64_t i03 = i13 / r3; int64_t i02 = i12 / r2; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -8492,28 +8504,10 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00); - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; + GGML_TENSOR_BINARY_OP_LOCALS - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02); - const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03); - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); - const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); - - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; @@ -8522,7 +8516,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; void * src0_ddq = src0_extra->data_device[g_main_device]; - half * src0_as_f16 = (half *) src0_ddq; + half * src0_f16 = (half *) src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; @@ -8531,11 +8525,15 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - - cuda_pool_alloc src1_as_f16(ne1); - to_fp16_cuda(src1_ddf, src1_as_f16.get(), ne1, main_stream); + cuda_pool_alloc src1_f16_alloc; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + } + half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get(); cuda_pool_alloc dst_f16; char * dst_t; @@ -8557,7 +8555,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const const void * beta = &beta_f16; if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne); + dst_t = (char *) dst_f16.alloc(ne_dst); nbd2 /= sizeof(float) / sizeof(half); nbd3 /= sizeof(float) / sizeof(half); @@ -8604,9 +8602,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA - (const char *) src1_as_f16.get(), CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB - beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC + alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA + (const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB + beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC ne12*ne13, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); @@ -8619,12 +8617,13 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( - src0_as_f16, src1_as_f16.get(), dst_t, + src0_f16, src1_f16, dst_t, ptrs_src.get(), ptrs_dst.get(), ne12, ne13, ne23, nb02, nb03, - nb12, nb13, + src1->type == GGML_TYPE_F16 ? nb12 : nb12/2, + src1->type == GGML_TYPE_F16 ? nb13 : nb13/2, nbd2, nbd3, r2, r3); CUDA_CHECK(cudaGetLastError()); @@ -8632,8 +8631,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, + (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, ne23, cu_compute_type, @@ -8643,7 +8642,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const if (dst->op_params[0] == GGML_PREC_DEFAULT) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16.get(), dst_ddf, ne, main_stream); + to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream); } } @@ -8682,13 +8681,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { - if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) { + if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->type == GGML_TYPE_F32) { #ifdef GGML_CUDA_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else diff --git a/ggml.c b/ggml.c index ed56e60a8..a9e1ea9b4 100644 --- a/ggml.c +++ b/ggml.c @@ -9687,7 +9687,7 @@ static void ggml_compute_forward_mul_mat( const size_t row_size = ggml_row_size(vec_dot_type, ne10); assert(params->wsize >= ne11*ne12*ne13*row_size); - assert(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f3df8a8c6..b115299c0 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -350,13 +350,18 @@ struct test_case { fflush(stdout); // check if backends support op + bool supported = true; for (ggml_backend_t backend : {backend1, backend2}) { if (!ggml_backend_supports_op(backend, out)) { - printf("not supported\n"); - ggml_free(ctx); - return true; + printf("not supported [%s] ", ggml_backend_name(backend)); + supported = false; } } + if (!supported) { + printf("\n"); + ggml_free(ctx); + return true; + } // post-graph sentinel add_sentinel(ctx); @@ -1505,8 +1510,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } for (ggml_type type_a : all_types) { - for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) { - // FIXME: CPU crashes on f16xf16 + for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1})); From 38b3de4658292582a8941a2be5c77b40ce6ac0f2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 14:56:41 +0200 Subject: [PATCH 266/664] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 1ec144116..6ff2d5233 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -76e7f47b69e8334384dc718480c496dafbd47999 +168c43edd1f85ebdecd4c79262cacb32b74eda68 From 441f51dca004debf8b275f1bdc08e0f1af7fd8f8 Mon Sep 17 00:00:00 2001 From: Tamotsu Takahashi Date: Fri, 29 Dec 2023 19:23:27 +0900 Subject: [PATCH 267/664] ci : build with CLBlast + ggml-opencl use GGML_API (whisper/1576) * Build with CLBlast * Declare GGML_API After rebasing, examples/talk-llama failed: "D:\a\whisper.cpp\whisper.cpp\build\ALL_BUILD.vcxproj" (build target) (1) -> "D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj" (default target) (14) -> (Link target) -> llama.obj : error LNK2019: unresolved external symbol ggml_cl_free_data referenced in function "public: __cdecl llama_model::~llama_model(void)" (??1llama_model@@QEAA@XZ) [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj] llama.obj : error LNK2019: unresolved external symbol ggml_cl_transform_tensor referenced in function "public: void __cdecl llama_model_loader::load_all_data(struct ggml_context *,void (__cdecl*)(float,void *),void *,struct llama_mlock *)" (?load_all_data@llama_model_loader@@QEAAXPEAUggml_context@@P6AXMPEAX@Z1PEAUllama_mlock@@@Z) [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj] D:\a\whisper.cpp\whisper.cpp\build\bin\Release\talk-llama.exe : fatal error LNK1120: 2 unresolved externals [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj] --- ggml-opencl.h | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/ggml-opencl.h b/ggml-opencl.h index a92b445c9..44d05bd64 100644 --- a/ggml-opencl.h +++ b/ggml-opencl.h @@ -6,19 +6,19 @@ extern "C" { #endif -void ggml_cl_init(void); +GGML_API void ggml_cl_init(void); -void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); +GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); -void * ggml_cl_host_malloc(size_t size); -void ggml_cl_host_free(void * ptr); +GGML_API void * ggml_cl_host_malloc(size_t size); +GGML_API void ggml_cl_host_free(void * ptr); -void ggml_cl_free_data(const struct ggml_tensor* tensor); +GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor); -void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); +GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); #ifdef __cplusplus } From c8255f8a6b2a3b3ebc6cb340cc2487f39fc95ffc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 15:12:35 +0200 Subject: [PATCH 268/664] scripts : print list of sync commits --- scripts/sync-ggml-am.sh | 1 + scripts/sync-ggml.last | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 93aad88a7..91478f177 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -26,6 +26,7 @@ echo "Syncing ggml changes since commit $lc" cd $SRC_GGML +git log --oneline $lc..HEAD git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits if [ ! -s $SRC_LLAMA/ggml-commits ]; then diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 6ff2d5233..5b6a440f7 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -168c43edd1f85ebdecd4c79262cacb32b74eda68 +df098ea908764cba4a4889a1cbe7b026b2d31a14 From afd997ab6011dfefe9e917425b04ef4d83614841 Mon Sep 17 00:00:00 2001 From: Peter Sugihara Date: Fri, 29 Dec 2023 05:58:56 -0800 Subject: [PATCH 269/664] llama.swiftui : fix infinite loop, ouput timings, buff UI (#4674) * fix infinite loop * slight UI simplification, clearer UX * clearer UI text, add timings to completion log --- .../llama.cpp.swift/LibLlama.swift | 2 ++ .../llama.swiftui/Models/LlamaState.swift | 27 ++++++++++---- .../llama.swiftui/UI/ContentView.swift | 35 +++---------------- .../llama.swiftui/UI/DownloadButton.swift | 2 +- 4 files changed, 29 insertions(+), 37 deletions(-) diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 464fb3277..66244382f 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -1,5 +1,7 @@ import Foundation +// To use this in your own project, add llama.cpp as a swift package dependency +// and uncomment this import line. // import llama enum LlamaError: Error { diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift index 3393eb242..17cb5b9dd 100644 --- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift +++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift @@ -4,6 +4,7 @@ import Foundation class LlamaState: ObservableObject { @Published var messageLog = "" @Published var cacheCleared = false + let NS_PER_S = 1_000_000_000.0 private var llamaContext: LlamaContext? private var defaultModelUrl: URL? { @@ -20,12 +21,12 @@ class LlamaState: ObservableObject { } func loadModel(modelUrl: URL?) throws { - messageLog += "Loading model...\n" if let modelUrl { + messageLog += "Loading model...\n" llamaContext = try LlamaContext.create_context(path: modelUrl.path()) messageLog += "Loaded model \(modelUrl.lastPathComponent)\n" } else { - messageLog += "Could not locate model\n" + messageLog += "Load a model from the list below\n" } } @@ -34,15 +35,29 @@ class LlamaState: ObservableObject { return } + let t_start = DispatchTime.now().uptimeNanoseconds await llamaContext.completion_init(text: text) + let t_heat_end = DispatchTime.now().uptimeNanoseconds + let t_heat = Double(t_heat_end - t_start) / NS_PER_S + messageLog += "\(text)" - while await llamaContext.n_cur <= llamaContext.n_len { + while await llamaContext.n_cur < llamaContext.n_len { let result = await llamaContext.completion_loop() messageLog += "\(result)" } + + let t_end = DispatchTime.now().uptimeNanoseconds + let t_generation = Double(t_end - t_heat_end) / NS_PER_S + let tokens_per_second = Double(await llamaContext.n_len) / t_generation + await llamaContext.clear() - messageLog += "\n\ndone\n" + messageLog += """ + \n + Done + Heat up took \(t_heat)s + Generated \(tokens_per_second) t/s\n + """ } func bench() async { @@ -56,10 +71,10 @@ class LlamaState: ObservableObject { messageLog += await llamaContext.model_info() + "\n" let t_start = DispatchTime.now().uptimeNanoseconds - await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up + let _ = await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up let t_end = DispatchTime.now().uptimeNanoseconds - let t_heat = Double(t_end - t_start) / 1_000_000_000.0 + let t_heat = Double(t_end - t_start) / NS_PER_S messageLog += "Heat up time: \(t_heat) seconds, please wait...\n" // if more than 5 seconds, then we're probably running on a slow device diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index c78f107b3..147e0c63b 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -42,46 +42,27 @@ struct ContentView: View { Button("Send") { sendText() } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) Button("Bench") { bench() } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) Button("Clear") { clear() } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) Button("Copy") { UIPasteboard.general.string = llamaState.messageLog } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) - } + }.buttonStyle(.bordered) - VStack { + VStack(alignment: .leading) { DownloadButton( llamaState: llamaState, modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)", modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true", filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf" ) - .font(.system(size: 12)) - .padding(.top, 4) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -89,7 +70,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true", filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf" ) - .font(.system(size: 12)) DownloadButton( llamaState: llamaState, @@ -97,8 +77,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", filename: "tinyllama-1.1b-f16.gguf" ) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -106,7 +84,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true", filename: "phi-2-q4_0.gguf" ) - .font(.system(size: 12)) DownloadButton( llamaState: llamaState, @@ -114,8 +91,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true", filename: "phi-2-q8_0.gguf" ) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -123,15 +98,15 @@ struct ContentView: View { modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true", filename: "mistral-7b-v0.1.Q4_0.gguf" ) - .font(.system(size: 12)) Button("Clear downloaded models") { ContentView.cleanupModelCaches() llamaState.cacheCleared = true } - .padding(8) - .font(.system(size: 12)) } + .padding(.top, 4) + .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) } .padding() } diff --git a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift index 4bd75cb69..c9f322ca1 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift @@ -93,7 +93,7 @@ struct DownloadButton: View { print("Error: \(err.localizedDescription)") } }) { - Text("\(modelName) (Downloaded)") + Text("Load \(modelName)") } } else { Text("Unknown status") From 82d6eab224862a7044069fb9211dc4b29124264b Mon Sep 17 00:00:00 2001 From: andrijdavid Date: Fri, 29 Dec 2023 15:18:20 +0100 Subject: [PATCH 270/664] main-cmake-pkg : fix build issue (#4665) * Fix main-cmake-pkg compilation * Use glob to load common files * cmake : fix trailing whitespace --------- Co-authored-by: Georgi Gerganov --- examples/main-cmake-pkg/CMakeLists.txt | 27 ++++++-------------------- 1 file changed, 6 insertions(+), 21 deletions(-) diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index cb00edbbb..deb77d588 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -7,28 +7,13 @@ find_package(Llama 0.0.1 REQUIRED) # Bake common functionality in with target. Because applications # using the relocatable Llama package should be outside of the # source tree, main-cmake-pkg pretends the dependencies are built-in. - set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common") -add_library(common OBJECT - ${_common_path}/common.h - ${_common_path}/common.cpp - ${_common_path}/console.h - ${_common_path}/console.cpp - ${_common_path}/grammar-parser.h - ${_common_path}/grammar-parser.cpp - ${_common_path}/sampling.h - ${_common_path}/sampling.cpp - ) - -# WARNING: because build-info.h is auto-generated, it will only -# be available after the user has built the llama.cpp sources. -# -configure_file(${_common_path}/../build-info.h - ${CMAKE_CURRENT_BINARY_DIR}/build-info.h - COPYONLY) - -target_include_directories(common PUBLIC ${LLAMA_INCLUDE_DIR} - ${CMAKE_CURRENT_BINARY_DIR}) +add_library(common OBJECT) +file(GLOB _common_files + "${_common_path}/*.h" + "${_common_path}/*.cpp" +) +target_sources(common PRIVATE ${_common_files}) # If the common project was part of "main-cmake-pkg" the transient # defines would automatically be attached. Because the common func- From b93edd22f55d3e5268263c3edcdae1818505c078 Mon Sep 17 00:00:00 2001 From: Karthik Sethuraman Date: Fri, 29 Dec 2023 06:22:10 -0800 Subject: [PATCH 271/664] server : allow to generate multimodal embeddings (#4681) --- examples/server/README.md | 4 +++- examples/server/server.cpp | 12 +++++++++++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index f1e586a1c..718a7e064 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -166,7 +166,7 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. *Result JSON:* @@ -224,6 +224,8 @@ node index.js `content`: Set the text to process. + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + - **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. *Options:* diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c5035e202..31b8cf33d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3077,7 +3077,17 @@ int main(int argc, char **argv) { prompt = ""; } - const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1); + + json image_data; + if (body.count("image_data") != 0) { + image_data = body["image_data"]; + } + else + { + image_data = ""; + } + + const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); task_result result = llama.next_result(task_id); return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); From 60f55e888c29cbd87c4238dd19e85d0eef87245d Mon Sep 17 00:00:00 2001 From: SakuraUmi Date: Fri, 29 Dec 2023 22:22:44 +0800 Subject: [PATCH 272/664] server : fix OpenAI server sampling w.r.t. penalty. (#4675) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 31b8cf33d..035eb24ac 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2461,7 +2461,7 @@ json oaicompat_completion_params_parse( llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); - llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0); + llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); From db49ff8ed7f0bb201176703441cc02911b08ef2a Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Fri, 29 Dec 2023 06:24:12 -0800 Subject: [PATCH 273/664] server : replace sleep with condition variables (#4673) The server currently schedules tasks using a sleep(5ms) busy loop. This adds unnecessary latency since most sleep implementations do a round up to the system scheduling quantum (usually 10ms). Other libc sleep impls spin for smaller time intervals which results in the server's busy loop consuming all available cpu. Having the explicit notify() / wait() code also helps aid in the readability of the server code. See mozilla-Ocho/llamafile@711344b --- examples/server/server.cpp | 41 ++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 15 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 035eb24ac..0aada8e28 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -25,6 +25,7 @@ #include #include #include +#include #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 @@ -541,7 +542,9 @@ struct llama_server_context std::vector queue_results; std::vector queue_multitasks; std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks + std::condition_variable condition_tasks; std::mutex mutex_results; + std::condition_variable condition_results; ~llama_server_context() { @@ -1169,7 +1172,7 @@ struct llama_server_context void send_error(task_server& task, std::string error) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = task.id; res.multitask_id = task.multitask_id; @@ -1177,6 +1180,7 @@ struct llama_server_context res.error = true; res.result_json = { { "content", error } }; queue_results.push_back(res); + condition_results.notify_all(); } void add_multi_task(int id, std::vector& sub_ids) @@ -1186,6 +1190,7 @@ struct llama_server_context multi.id = id; std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); queue_multitasks.push_back(multi); + condition_tasks.notify_one(); } void update_multi_task(int multitask_id, int subtask_id, task_result& result) @@ -1197,6 +1202,7 @@ struct llama_server_context { multitask.subtasks_remaining.erase(subtask_id); multitask.results.push_back(result); + condition_tasks.notify_one(); } } } @@ -1244,7 +1250,7 @@ struct llama_server_context void send_partial_response(llama_client_slot &slot, completion_token_output tkn) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1280,11 +1286,12 @@ struct llama_server_context } queue_results.push_back(res); + condition_results.notify_all(); } void send_final_response(llama_client_slot &slot) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1340,11 +1347,12 @@ struct llama_server_context } queue_results.push_back(res); + condition_results.notify_all(); } void send_embedding(llama_client_slot &slot) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1372,6 +1380,7 @@ struct llama_server_context }; } queue_results.push_back(res); + condition_results.notify_all(); } int request_completion(json data, bool infill, bool embedding, int multitask_id) @@ -1395,6 +1404,7 @@ struct llama_server_context // otherwise, it's a single-prompt task, we actually queue it queue_tasks.push_back(task); + condition_tasks.notify_one(); return task.id; } @@ -1402,13 +1412,10 @@ struct llama_server_context { while (true) { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - std::lock_guard lock(mutex_results); - - if (queue_results.empty()) - { - continue; - } + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + return !queue_results.empty(); + }); for (int i = 0; i < (int) queue_results.size(); i++) { @@ -1504,12 +1511,13 @@ struct llama_server_context void request_cancel(int task_id) { - std::lock_guard lock(mutex_tasks); + std::unique_lock lock(mutex_tasks); task_server task; task.id = id_gen++; task.type = CANCEL_TASK; task.target_id = task_id; queue_tasks.push_back(task); + condition_tasks.notify_one(); } int split_multiprompt_task(task_server& multiprompt_task) @@ -1535,7 +1543,7 @@ struct llama_server_context void process_tasks() { - std::lock_guard lock(mutex_tasks); + std::unique_lock lock(mutex_tasks); while (!queue_tasks.empty()) { task_server task = queue_tasks.front(); @@ -1607,6 +1615,7 @@ struct llama_server_context std::lock_guard lock(mutex_results); queue_results.push_back(aggregate_result); + condition_results.notify_all(); queue_iterator = queue_multitasks.erase(queue_iterator); } @@ -1637,8 +1646,10 @@ struct llama_server_context LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); kv_cache_clear(); } - // avoid 100% usage of cpu all time - std::this_thread::sleep_for(std::chrono::milliseconds(5)); + std::unique_lock lock(mutex_tasks); + condition_tasks.wait(lock, [&]{ + return !queue_tasks.empty(); + }); } for (llama_client_slot &slot : slots) From 4af4801566bc262a38fb77f51edf278ac323c2bd Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Fri, 29 Dec 2023 06:38:38 -0800 Subject: [PATCH 274/664] llava-cli : refactor to use sampling library (#4669) This change makes it possible to use flags like `--grammar` when using the `llava-cli` program. The rest is just code cleanup deleting a long standing TODO comment. This change also ensures that logging information is emitted to stderr which helps the `llava-cli` command be more friendly to shell scripts. See Mozilla-Ocho/llamafile@1cd334f --- examples/llava/llava-cli.cpp | 85 ++++++------------------------------ 1 file changed, 13 insertions(+), 72 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 31f8cd8e0..502b788b1 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -39,73 +39,11 @@ static bool eval_string(struct llama_context * ctx_llama, const char* str, int n return true; } -// TODO: use common/sampling.h -static llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { - auto & sparams = params.sparams; - - // out of user input, sample next token - const float temp = sparams.temp; - const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k; - const float top_p = sparams.top_p; - const float tfs_z = sparams.tfs_z; - const float typical_p = sparams.typical_p; - // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n; - // const float repeat_penalty = sparams.repeat_penalty; - // const float alpha_presence = sparams.presence_penalty; - // const float alpha_frequency = sparams.frequency_penalty; - const int mirostat = sparams.mirostat; - const float mirostat_tau = sparams.mirostat_tau; - const float mirostat_eta = sparams.mirostat_eta; - // const bool penalize_nl = sparams.penalize_nl; - - llama_token id = 0; - { - auto logits = llama_get_logits(ctx_llama); - auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama)); - - // Apply params.logit_bias map - for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx_llama, &candidates_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1); - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token(ctx_llama, &candidates_p); - } - } - } - - return id; -} - -static const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) { - int id = sample_id(ctx_llama, params); +static const char * sample(struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_llama, + int * n_past) { + const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL); + llama_sampling_accept(ctx_sampling, ctx_llama, id, true); static std::string ret; if (id == llama_token_eos(llama_get_model(ctx_llama))) { ret = ""; @@ -174,8 +112,8 @@ struct llava_context { }; static void show_additional_info(int /*argc*/, char ** argv) { - printf("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); + fprintf(stderr, "\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n"); } static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { @@ -185,7 +123,7 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para auto prompt = params->prompt; if (prompt_contains_image(prompt)) { if (!params->image.empty()) { - printf("using base64 encoded image instead of command line image path\n"); + fprintf(stderr, "using base64 encoded image instead of command line image path\n"); } embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); if (!embed) { @@ -217,16 +155,19 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // generate the response - printf("\n"); + fprintf(stderr, "\n"); + + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); for (int i = 0; i < max_tgt_len; i++) { - const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past); + const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); if (strcmp(tmp, "") == 0) break; printf("%s", tmp); fflush(stdout); } + llama_sampling_free(ctx_sampling); printf("\n"); } From 97bbca6e8522d18041fcde6c3d0907a52ce36446 Mon Sep 17 00:00:00 2001 From: Cuong Trinh Manh Date: Fri, 29 Dec 2023 21:39:15 +0700 Subject: [PATCH 275/664] cmake : fix ld warning duplicate libraries libllama.a (#4671) * fix "ld: warning: ignoring duplicate libraries: '../libllama.a'" * fix warning in example. --- common/CMakeLists.txt | 2 +- examples/llava/CMakeLists.txt | 2 +- examples/server/CMakeLists.txt | 2 +- tests/CMakeLists.txt | 4 ++-- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index b5d5453d2..f79acfef1 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -65,4 +65,4 @@ endif() target_include_directories(${TARGET} PUBLIC .) target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE llama build_info) +target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 8ea3e5c83..48dae1506 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -32,5 +32,5 @@ endif() set(TARGET llava-cli) add_executable(llava-cli llava-cli.cpp) install(TARGETS llava-cli RUNTIME) -target_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(llava PRIVATE cxx_std_11) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 859cd12c6..81709e448 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 9b5e69d13..7c932240d 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -2,7 +2,7 @@ function(llama_build_executable source) get_filename_component(TEST_TARGET ${source} NAME_WE) add_executable(${TEST_TARGET} ${source}) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE llama common) + target_link_libraries(${TEST_TARGET} PRIVATE common) endfunction() function(llama_test_executable name source) @@ -14,7 +14,7 @@ function(llama_build_and_test_executable source) get_filename_component(TEST_TARGET ${source} NAME_WE) add_executable(${TEST_TARGET} ${source}) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE llama common) + target_link_libraries(${TEST_TARGET} PRIVATE common) add_test(NAME ${TEST_TARGET} COMMAND $ ${ARGN}) endfunction() From 68eccbdc5b56f2a2450f9a8463f9934388cafabf Mon Sep 17 00:00:00 2001 From: Philip Taron Date: Fri, 29 Dec 2023 06:42:26 -0800 Subject: [PATCH 276/664] flake.nix : rewrite (#4605) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * flake.lock: update to hotfix CUDA::cuda_driver Required to support https://github.com/ggerganov/llama.cpp/pull/4606 * flake.nix: rewrite 1. Split into separate files per output. 2. Added overlays, so that this flake can be integrated into others. The names in the overlay are `llama-cpp`, `llama-cpp-opencl`, `llama-cpp-cuda`, and `llama-cpp-rocm` so that they fit into the broader set of Nix packages from [nixpkgs](https://github.com/nixos/nixpkgs). 3. Use [callPackage](https://summer.nixos.org/blog/callpackage-a-tool-for-the-lazy/) rather than `with pkgs;` so that there's dependency injection rather than dependency lookup. 4. Add a description and meta information for each package. The description includes a bit about what's trying to accelerate each one. 5. Use specific CUDA packages instead of cudatoolkit on the advice of SomeoneSerge. 6. Format with `serokell/nixfmt` for a consistent style. 7. Update `flake.lock` with the latest goods. * flake.nix: use finalPackage instead of passing it manually * nix: unclutter darwin support * nix: pass most darwin frameworks unconditionally ...for simplicity * *.nix: nixfmt nix shell github:piegamesde/nixfmt/rfc101-style --command \ nixfmt flake.nix .devops/nix/*.nix * flake.nix: add maintainers * nix: move meta down to follow Nixpkgs style more closely * nix: add missing meta attributes nix: clarify the interpretation of meta.maintainers nix: clarify the meaning of "broken" and "badPlatforms" nix: passthru: expose the use* flags for inspection E.g.: ``` ❯ nix eval .#cuda.useCuda true ``` * flake.nix: avoid re-evaluating nixpkgs too many times * flake.nix: use flake-parts * nix: migrate to pname+version * flake.nix: overlay: expose both the namespace and the default attribute * ci: add the (Nix) flakestry workflow * nix: cmakeFlags: explicit OFF bools * nix: cuda: reduce runtime closure * nix: fewer rebuilds * nix: respect config.cudaCapabilities * nix: add the impure driver's location to the DT_RUNPATHs * nix: clean sources more thoroughly ...this way outPaths change less frequently, and so there are fewer rebuilds * nix: explicit mpi support * nix: explicit jetson support * flake.nix: darwin: only expose the default --------- Co-authored-by: Someone Serge --- .devops/nix/apps.nix | 22 +++ .devops/nix/devshells.nix | 13 ++ .devops/nix/jetson-support.nix | 32 ++++ .devops/nix/nixpkgs-instances.nix | 35 ++++ .devops/nix/package.nix | 265 ++++++++++++++++++++++++++++ .devops/nix/scope.nix | 12 ++ .github/workflows/nix-flakestry.yml | 23 +++ flake.lock | 55 +++--- flake.nix | 226 ++++++++++-------------- 9 files changed, 524 insertions(+), 159 deletions(-) create mode 100644 .devops/nix/apps.nix create mode 100644 .devops/nix/devshells.nix create mode 100644 .devops/nix/jetson-support.nix create mode 100644 .devops/nix/nixpkgs-instances.nix create mode 100644 .devops/nix/package.nix create mode 100644 .devops/nix/scope.nix create mode 100644 .github/workflows/nix-flakestry.yml diff --git a/.devops/nix/apps.nix b/.devops/nix/apps.nix new file mode 100644 index 000000000..b8a12cc0a --- /dev/null +++ b/.devops/nix/apps.nix @@ -0,0 +1,22 @@ +{ + perSystem = + { config, lib, ... }: + { + apps = + let + inherit (config.packages) default; + binaries = [ + "llama" + "llama-embedding" + "llama-server" + "quantize" + "train-text-from-scratch" + ]; + mkApp = name: { + type = "app"; + program = "${default}/bin/${name}"; + }; + in + lib.genAttrs binaries mkApp; + }; +} diff --git a/.devops/nix/devshells.nix b/.devops/nix/devshells.nix new file mode 100644 index 000000000..1862f0f08 --- /dev/null +++ b/.devops/nix/devshells.nix @@ -0,0 +1,13 @@ +{ + perSystem = + { config, lib, ... }: + { + devShells = + lib.concatMapAttrs + (name: package: { + ${name} = package.passthru.shell; + ${name + "-extra"} = package.passthru.shell-extra; + }) + config.packages; + }; +} diff --git a/.devops/nix/jetson-support.nix b/.devops/nix/jetson-support.nix new file mode 100644 index 000000000..08426d2ab --- /dev/null +++ b/.devops/nix/jetson-support.nix @@ -0,0 +1,32 @@ +{ inputs, ... }: +{ + perSystem = + { + config, + system, + lib, + pkgsCuda, + ... + }: + lib.optionalAttrs (system == "aarch64-linux") { + packages = + let + caps.jetson-xavier = "7.2"; + caps.jetson-orin = "8.7"; + caps.jetson-nano = "5.3"; + + pkgsFor = + cap: + import inputs.nixpkgs { + inherit system; + config = { + cudaSupport = true; + cudaCapabilities = [ cap ]; + cudaEnableForwardCompat = false; + inherit (pkgsCuda.config) allowUnfreePredicate; + }; + }; + in + builtins.mapAttrs (name: cap: ((pkgsFor cap).callPackage ./scope.nix { }).llama-cpp) caps; + }; +} diff --git a/.devops/nix/nixpkgs-instances.nix b/.devops/nix/nixpkgs-instances.nix new file mode 100644 index 000000000..6e9872b28 --- /dev/null +++ b/.devops/nix/nixpkgs-instances.nix @@ -0,0 +1,35 @@ +{ inputs, ... }: +{ + # The _module.args definitions are passed on to modules as arguments. E.g. + # the module `{ pkgs ... }: { /* config */ }` implicitly uses + # `_module.args.pkgs` (defined in this case by flake-parts). + perSystem = + { system, ... }: + { + _module.args = { + pkgsCuda = import inputs.nixpkgs { + inherit system; + # Ensure dependencies use CUDA consistently (e.g. that openmpi, ucc, + # and ucx are built with CUDA support) + config.cudaSupport = true; + config.allowUnfreePredicate = + p: + builtins.all + ( + license: + license.free + || builtins.elem license.shortName [ + "CUDA EULA" + "cuDNN EULA" + ] + ) + (p.meta.licenses or [ p.meta.license ]); + }; + # Ensure dependencies use ROCm consistently + pkgsRocm = import inputs.nixpkgs { + inherit system; + config.rocmSupport = true; + }; + }; + }; +} diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix new file mode 100644 index 000000000..5f2a7c9f4 --- /dev/null +++ b/.devops/nix/package.nix @@ -0,0 +1,265 @@ +{ + lib, + config, + stdenv, + mkShell, + cmake, + ninja, + pkg-config, + git, + python3, + mpi, + openblas, # TODO: Use the generic `blas` so users could switch betwen alternative implementations + cudaPackages, + darwin, + rocmPackages, + clblast, + useBlas ? builtins.all (x: !x) [ + useCuda + useMetalKit + useOpenCL + useRocm + ], + useCuda ? config.cudaSupport, + useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, + useMpi ? false, # Increases the runtime closure size by ~700M + useOpenCL ? false, + useRocm ? config.rocmSupport, + llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake +}@inputs: + +let + inherit (lib) + cmakeBool + cmakeFeature + optionals + strings + versionOlder + ; + + # It's necessary to consistently use backendStdenv when building with CUDA support, + # otherwise we get libstdc++ errors downstream. + stdenv = throw "Use effectiveStdenv instead"; + effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv; + + suffices = + lib.optionals useBlas [ "BLAS" ] + ++ lib.optionals useCuda [ "CUDA" ] + ++ lib.optionals useMetalKit [ "MetalKit" ] + ++ lib.optionals useMpi [ "MPI" ] + ++ lib.optionals useOpenCL [ "OpenCL" ] + ++ lib.optionals useRocm [ "ROCm" ]; + + pnameSuffix = + strings.optionalString (suffices != [ ]) + "-${strings.concatMapStringsSep "-" strings.toLower suffices}"; + descriptionSuffix = + strings.optionalString (suffices != [ ]) + ", accelerated with ${strings.concatStringsSep ", " suffices}"; + + # TODO: package the Python in this repository in a Nix-like way. + # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo + # is PEP 517-compatible, and ensure the correct .dist-info is generated. + # https://peps.python.org/pep-0517/ + llama-python = python3.withPackages ( + ps: [ + ps.numpy + ps.sentencepiece + ] + ); + + # TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime + llama-python-extra = python3.withPackages ( + ps: [ + ps.numpy + ps.sentencepiece + ps.torchWithoutCuda + ps.transformers + ] + ); + + # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64 + # separately + darwinBuildInputs = + with darwin.apple_sdk.frameworks; + [ + Accelerate + CoreVideo + CoreGraphics + ] + ++ optionals useMetalKit [ MetalKit ]; + + cudaBuildInputs = with cudaPackages; [ + cuda_cccl.dev # + + # A temporary hack for reducing the closure size, remove once cudaPackages + # have stopped using lndir: https://github.com/NixOS/nixpkgs/issues/271792 + cuda_cudart.dev + cuda_cudart.lib + cuda_cudart.static + libcublas.dev + libcublas.lib + libcublas.static + ]; + + rocmBuildInputs = with rocmPackages; [ + clr + hipblas + rocblas + ]; +in + +effectiveStdenv.mkDerivation ( + finalAttrs: { + pname = "llama-cpp${pnameSuffix}"; + version = llamaVersion; + + src = lib.cleanSourceWith { + filter = + name: type: + !(builtins.any (_: _) [ + (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths + (name == "README.md") # Ignore *.md changes whe computing outPaths + (lib.hasPrefix "." name) # Skip hidden files and directories + ]); + src = lib.cleanSource ../../.; + }; + + postPatch = '' + substituteInPlace ./ggml-metal.m \ + --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" + + # TODO: Package up each Python script or service appropriately. + # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`, + # we could make those *.py into setuptools' entrypoints + substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python" + ''; + + nativeBuildInputs = + [ + cmake + ninja + pkg-config + git + ] + ++ optionals useCuda [ + cudaPackages.cuda_nvcc + + # TODO: Replace with autoAddDriverRunpath + # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged + cudaPackages.autoAddOpenGLRunpathHook + ]; + + buildInputs = + optionals effectiveStdenv.isDarwin darwinBuildInputs + ++ optionals useCuda cudaBuildInputs + ++ optionals useMpi [ mpi ] + ++ optionals useOpenCL [ clblast ] + ++ optionals useRocm rocmBuildInputs; + + cmakeFlags = + [ + (cmakeBool "LLAMA_NATIVE" true) + (cmakeBool "LLAMA_BUILD_SERVER" true) + (cmakeBool "BUILD_SHARED_LIBS" true) + (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) + (cmakeBool "LLAMA_BLAS" useBlas) + (cmakeBool "LLAMA_CLBLAST" useOpenCL) + (cmakeBool "LLAMA_CUBLAS" useCuda) + (cmakeBool "LLAMA_HIPBLAS" useRocm) + (cmakeBool "LLAMA_METAL" useMetalKit) + (cmakeBool "LLAMA_MPI" useMpi) + ] + ++ optionals useCuda [ + ( + with cudaPackages.flags; + cmakeFeature "CMAKE_CUDA_ARCHITECTURES" ( + builtins.concatStringsSep ";" (map dropDot cudaCapabilities) + ) + ) + ] + ++ optionals useRocm [ + (cmakeFeature "CMAKE_C_COMPILER" "hipcc") + (cmakeFeature "CMAKE_CXX_COMPILER" "hipcc") + + # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM + # in https://github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt + # and select the line that matches the current nixpkgs version of rocBLAS. + # Should likely use `rocmPackages.clr.gpuTargets`. + "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102" + ] + ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") ] + ++ optionals useBlas [ (lib.cmakeFeature "LLAMA_BLAS_VENDOR" "OpenBLAS") ]; + + # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, + # if they haven't been added yet. + postInstall = '' + mv $out/bin/main $out/bin/llama + mv $out/bin/server $out/bin/llama-server + mkdir -p $out/include + cp $src/llama.h $out/include/ + ''; + + # Define the shells here, but don't add in the inputsFrom to avoid recursion. + passthru = { + inherit + useBlas + useCuda + useMetalKit + useMpi + useOpenCL + useRocm + ; + + shell = mkShell { + name = "shell-${finalAttrs.finalPackage.name}"; + description = "contains numpy and sentencepiece"; + buildInputs = [ llama-python ]; + inputsFrom = [ finalAttrs.finalPackage ]; + }; + + shell-extra = mkShell { + name = "shell-extra-${finalAttrs.finalPackage.name}"; + description = "contains numpy, sentencepiece, torchWithoutCuda, and transformers"; + buildInputs = [ llama-python-extra ]; + inputsFrom = [ finalAttrs.finalPackage ]; + }; + }; + + meta = { + # Configurations we don't want even the CI to evaluate. Results in the + # "unsupported platform" messages. This is mostly a no-op, because + # cudaPackages would've refused to evaluate anyway. + badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + + # Configurations that are known to result in build failures. Can be + # overridden by importing Nixpkgs with `allowBroken = true`. + broken = (useMetalKit && !effectiveStdenv.isDarwin); + + description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; + homepage = "https://github.com/ggerganov/llama.cpp/"; + license = lib.licenses.mit; + + # Accommodates `nix run` and `lib.getExe` + mainProgram = "llama"; + + # These people might respond, on the best effort basis, if you ping them + # in case of Nix-specific regressions or for reviewing Nix-specific PRs. + # Consider adding yourself to this list if you want to ensure this flake + # stays maintained and you're willing to invest your time. Do not add + # other people without their consent. Consider removing people after + # they've been unreachable for long periods of time. + + # Note that lib.maintainers is defined in Nixpkgs, but you may just add + # an attrset following the same format as in + # https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix + maintainers = with lib.maintainers; [ + philiptaron + SomeoneSerge + ]; + + # Extend `badPlatforms` instead + platforms = lib.platforms.all; + }; + } +) diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix new file mode 100644 index 000000000..7932ac1e8 --- /dev/null +++ b/.devops/nix/scope.nix @@ -0,0 +1,12 @@ +{ + lib, + newScope, + llamaVersion ? "0.0.0", +}: + +lib.makeScope newScope ( + self: { + inherit llamaVersion; + llama-cpp = self.callPackage ./package.nix { }; + } +) diff --git a/.github/workflows/nix-flakestry.yml b/.github/workflows/nix-flakestry.yml new file mode 100644 index 000000000..3abfb3509 --- /dev/null +++ b/.github/workflows/nix-flakestry.yml @@ -0,0 +1,23 @@ +# Make the flake discoverable on https://flakestry.dev +name: "Publish a flake to flakestry" +on: + push: + tags: + - "v?[0-9]+.[0-9]+.[0-9]+" + - "v?[0-9]+.[0-9]+" + workflow_dispatch: + inputs: + tag: + description: "The existing tag to publish" + type: "string" + required: true +jobs: + publish-flake: + runs-on: ubuntu-latest + permissions: + id-token: "write" + contents: "read" + steps: + - uses: flakestry/flakestry-publish@main + with: + version: "${{ inputs.tag || github.ref_name }}" diff --git a/flake.lock b/flake.lock index 0455f6561..3fcd1f45d 100644 --- a/flake.lock +++ b/flake.lock @@ -1,30 +1,30 @@ { "nodes": { - "flake-utils": { + "flake-parts": { "inputs": { - "systems": "systems" + "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1694529238, - "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "ff7b65b44d01cf9ba6a71320833626af21126384", + "lastModified": 1701473968, + "narHash": "sha256-YcVE5emp1qQ8ieHUnxt1wCZCC3ZfAS+SRRWZ2TMda7E=", + "owner": "hercules-ci", + "repo": "flake-parts", + "rev": "34fed993f1674c8d06d58b37ce1e0fe5eebcb9f5", "type": "github" }, "original": { - "owner": "numtide", - "repo": "flake-utils", + "owner": "hercules-ci", + "repo": "flake-parts", "type": "github" } }, "nixpkgs": { "locked": { - "lastModified": 1698318101, - "narHash": "sha256-gUihHt3yPD7bVqg+k/UVHgngyaJ3DMEBchbymBMvK1E=", + "lastModified": 1703559957, + "narHash": "sha256-x9PUuMEPGUOMB51zNxrDr2QoHbYWlCS2xhFedm9MC5Q=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "63678e9f3d3afecfeafa0acead6239cdb447574c", + "rev": "75dd68c36f458c6593c5bbb48abfd3e59bfed380", "type": "github" }, "original": { @@ -34,26 +34,29 @@ "type": "github" } }, - "root": { - "inputs": { - "flake-utils": "flake-utils", - "nixpkgs": "nixpkgs" - } - }, - "systems": { + "nixpkgs-lib": { "locked": { - "lastModified": 1681028828, - "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", - "owner": "nix-systems", - "repo": "default", - "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", + "dir": "lib", + "lastModified": 1701253981, + "narHash": "sha256-ztaDIyZ7HrTAfEEUt9AtTDNoCYxUdSd6NrRHaYOIxtk=", + "owner": "NixOS", + "repo": "nixpkgs", + "rev": "e92039b55bcd58469325ded85d4f58dd5a4eaf58", "type": "github" }, "original": { - "owner": "nix-systems", - "repo": "default", + "dir": "lib", + "owner": "NixOS", + "ref": "nixos-unstable", + "repo": "nixpkgs", "type": "github" } + }, + "root": { + "inputs": { + "flake-parts": "flake-parts", + "nixpkgs": "nixpkgs" + } } }, "root": "root", diff --git a/flake.nix b/flake.nix index 4cf28d5c1..2209070aa 100644 --- a/flake.nix +++ b/flake.nix @@ -1,139 +1,99 @@ { + description = "Port of Facebook's LLaMA model in C/C++"; + inputs = { nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable"; - flake-utils.url = "github:numtide/flake-utils"; + flake-parts.url = "github:hercules-ci/flake-parts"; }; - outputs = { self, nixpkgs, flake-utils }: - flake-utils.lib.eachDefaultSystem (system: - let - name = "llama.cpp"; - src = ./.; - meta.mainProgram = "llama"; - inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin; - buildInputs = with pkgs; [ openmpi ]; - osSpecific = with pkgs; buildInputs ++ ( - if isAarch64 && isDarwin then - with pkgs.darwin.apple_sdk_11_0.frameworks; [ - Accelerate - MetalKit - ] - else if isAarch32 && isDarwin then - with pkgs.darwin.apple_sdk.frameworks; [ - Accelerate - CoreGraphics - CoreVideo - ] - else if isDarwin then - with pkgs.darwin.apple_sdk.frameworks; [ - Accelerate - CoreGraphics - CoreVideo - ] - else - with pkgs; [ openblas ] - ); - pkgs = import nixpkgs { inherit system; }; - nativeBuildInputs = with pkgs; [ cmake ninja pkg-config ]; - cudatoolkit_joined = with pkgs; symlinkJoin { - # HACK(Green-Sky): nix currently has issues with cmake findcudatoolkit - # see https://github.com/NixOS/nixpkgs/issues/224291 - # copied from jaxlib - name = "${cudaPackages.cudatoolkit.name}-merged"; - paths = [ - cudaPackages.cudatoolkit.lib - cudaPackages.cudatoolkit.out - ] ++ lib.optionals (lib.versionOlder cudaPackages.cudatoolkit.version "11") [ - # for some reason some of the required libs are in the targets/x86_64-linux - # directory; not sure why but this works around it - "${cudaPackages.cudatoolkit}/targets/${system}" - ]; - }; - llama-python = - pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]); - # TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime - llama-python-extra = - pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece torchWithoutCuda transformers ]); - postPatch = '' - substituteInPlace ./ggml-metal.m \ - --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" - substituteInPlace ./*.py --replace '/usr/bin/env python' '${llama-python}/bin/python' - ''; - postInstall = '' - mv $out/bin/main $out/bin/llama - mv $out/bin/server $out/bin/llama-server - mkdir -p $out/include - cp ${src}/llama.h $out/include/ - ''; - cmakeFlags = [ "-DLLAMA_NATIVE=OFF" "-DLLAMA_BUILD_SERVER=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ]; - in + + # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl: + # + # ```bash + # ❯ nix repl + # nix-repl> :lf github:ggerganov/llama.cpp + # Added 13 variables. + # nix-repl> outputs.apps.x86_64-linux.quantize + # { program = "/nix/store/00000000000000000000000000000000-llama.cpp/bin/quantize"; type = "app"; } + # ``` + outputs = + { self, flake-parts, ... }@inputs: + let + # We could include the git revisions in the package names but those would + # needlessly trigger rebuilds: + # llamaVersion = self.dirtyShortRev or self.shortRev; + + # Nix already uses cryptographic hashes for versioning, so we'll just fix + # the fake semver for now: + llamaVersion = "0.0.0"; + in + flake-parts.lib.mkFlake { inherit inputs; } + { - packages.default = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = osSpecific; - cmakeFlags = cmakeFlags - ++ (if isAarch64 && isDarwin then [ - "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1" - "-DLLAMA_METAL=ON" - ] else [ - "-DLLAMA_BLAS=ON" - "-DLLAMA_BLAS_VENDOR=OpenBLAS" - ]); - }; - packages.opencl = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs; buildInputs ++ [ clblast ]; - cmakeFlags = cmakeFlags ++ [ - "-DLLAMA_CLBLAST=ON" - ]; - }; - packages.cuda = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs; buildInputs ++ [ cudatoolkit_joined ]; - cmakeFlags = cmakeFlags ++ [ - "-DLLAMA_CUBLAS=ON" - ]; - }; - packages.rocm = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs.rocmPackages; buildInputs ++ [ clr hipblas rocblas ]; - cmakeFlags = cmakeFlags ++ [ - "-DLLAMA_HIPBLAS=1" - "-DCMAKE_C_COMPILER=hipcc" - "-DCMAKE_CXX_COMPILER=hipcc" - # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM - # in github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt - # and select the line that matches the current nixpkgs version of rocBLAS. - "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102" - ]; - }; - apps.llama-server = { - type = "app"; - program = "${self.packages.${system}.default}/bin/llama-server"; - }; - apps.llama-embedding = { - type = "app"; - program = "${self.packages.${system}.default}/bin/embedding"; - }; - apps.llama = { - type = "app"; - program = "${self.packages.${system}.default}/bin/llama"; - }; - apps.quantize = { - type = "app"; - program = "${self.packages.${system}.default}/bin/quantize"; - }; - apps.train-text-from-scratch = { - type = "app"; - program = "${self.packages.${system}.default}/bin/train-text-from-scratch"; - }; - apps.default = self.apps.${system}.llama; - devShells.default = pkgs.mkShell { - buildInputs = [ llama-python ]; - packages = nativeBuildInputs ++ osSpecific; - }; - devShells.extra = pkgs.mkShell { - buildInputs = [ llama-python-extra ]; - packages = nativeBuildInputs ++ osSpecific; - }; - }); + + imports = [ + .devops/nix/nixpkgs-instances.nix + .devops/nix/apps.nix + .devops/nix/devshells.nix + .devops/nix/jetson-support.nix + ]; + + # An overlay can be used to have a more granular control over llama-cpp's + # dependencies and configuration, than that offered by the `.override` + # mechanism. Cf. https://nixos.org/manual/nixpkgs/stable/#chap-overlays. + # + # E.g. in a flake: + # ``` + # { nixpkgs, llama-cpp, ... }: + # let pkgs = import nixpkgs { + # overlays = [ (llama-cpp.overlays.default) ]; + # system = "aarch64-linux"; + # config.allowUnfree = true; + # config.cudaSupport = true; + # config.cudaCapabilities = [ "7.2" ]; + # config.cudaEnableForwardCompat = false; + # }; in { + # packages.aarch64-linux.llamaJetsonXavier = pkgs.llamaPackages.llama-cpp; + # } + # ``` + # + # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format + flake.overlays.default = + (final: prev: { + llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + inherit (final.llamaPackages) llama-cpp; + }); + + systems = [ + "aarch64-darwin" + "aarch64-linux" + "x86_64-darwin" # x86_64-darwin isn't tested (and likely isn't relevant) + "x86_64-linux" + ]; + + perSystem = + { + config, + lib, + pkgs, + pkgsCuda, + pkgsRocm, + ... + }: + { + # We don't use the overlay here so as to avoid making too many instances of nixpkgs, + # cf. https://zimbatm.com/notes/1000-instances-of-nixpkgs + packages = + { + default = (pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + } + // lib.optionalAttrs pkgs.stdenv.isLinux { + opencl = config.packages.default.override { useOpenCL = true; }; + cuda = (pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + rocm = (pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + + mpi-cpu = config.packages.default.override { useMpi = true; }; + mpi-cuda = config.packages.default.override { useMpi = true; }; + }; + }; + }; } From 04ac0607e913ab91234dfb240e12a76509e30982 Mon Sep 17 00:00:00 2001 From: crasm Date: Fri, 29 Dec 2023 09:50:29 -0500 Subject: [PATCH 277/664] python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel --- .devops/full-cuda.Dockerfile | 3 +- .devops/full-rocm.Dockerfile | 3 +- .devops/full.Dockerfile | 3 +- .devops/main-rocm.Dockerfile | 3 +- .../workflows/python-check-requirements.yml | 29 +++ convert-hf-to-gguf.py | 95 +++++----- convert-lora-to-ggml.py | 147 +++++++-------- convert-persimmon-to-gguf.py | 1 + requirements-hf-to-gguf.txt | 3 - requirements.txt | 17 +- .../requirements-convert-hf-to-gguf.txt | 2 + ...equirements-convert-llama-ggml-to-gguf.txt | 1 + .../requirements-convert-lora-to-ggml.txt | 2 + ...requirements-convert-persimmon-to-gguf.txt | 2 + requirements/requirements-convert.txt | 5 + scripts/check-requirements.sh | 174 ++++++++++++++++++ 16 files changed, 360 insertions(+), 130 deletions(-) create mode 100644 .github/workflows/python-check-requirements.yml mode change 100644 => 100755 convert-persimmon-to-gguf.py delete mode 100644 requirements-hf-to-gguf.txt create mode 100644 requirements/requirements-convert-hf-to-gguf.txt create mode 100644 requirements/requirements-convert-llama-ggml-to-gguf.txt create mode 100644 requirements/requirements-convert-lora-to-ggml.txt create mode 100644 requirements/requirements-convert-persimmon-to-gguf.txt create mode 100644 requirements/requirements-convert.txt create mode 100755 scripts/check-requirements.sh diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 360602d65..77a9ddc14 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -14,7 +14,8 @@ ARG CUDA_DOCKER_ARCH=all RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip git -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile index 6c521e9b4..8b9633dc4 100644 --- a/.devops/full-rocm.Dockerfile +++ b/.devops/full-rocm.Dockerfile @@ -23,7 +23,8 @@ ARG ROCM_DOCKER_ARCH=\ gfx1101 \ gfx1102 -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 687628b35..cef1297d3 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -5,7 +5,8 @@ FROM ubuntu:$UBUNTU_VERSION as build RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip git -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile index 789deff6d..0a706dc73 100644 --- a/.devops/main-rocm.Dockerfile +++ b/.devops/main-rocm.Dockerfile @@ -23,7 +23,8 @@ ARG ROCM_DOCKER_ARCH=\ gfx1101 \ gfx1102 -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml new file mode 100644 index 000000000..92e1108b3 --- /dev/null +++ b/.github/workflows/python-check-requirements.yml @@ -0,0 +1,29 @@ +name: Python check requirements.txt + +on: + push: + paths: + - 'scripts/check-requirements.sh' + - 'convert*.py' + - 'requirements.txt' + - 'requirements/*.txt' + pull_request: + paths: + - 'scripts/check-requirements.sh' + - 'convert*.py' + - 'requirements.txt' + - 'requirements/*.txt' + +jobs: + python-check-requirements: + runs-on: ubuntu-latest + name: check-requirements + steps: + - name: Check out source repository + uses: actions/checkout@v3 + - name: Set up Python environment + uses: actions/setup-python@v4 + with: + python-version: "3.11" + - name: Run check-requirements.sh script + run: bash scripts/check-requirements.sh nocleanup diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 3557a825e..51724c0df 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -242,7 +242,7 @@ class Model: tokens: list[bytearray] = [] toktypes: list[int] = [] - from transformers import AutoTokenizer # type: ignore[attr-defined] + from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model) vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size @@ -856,7 +856,7 @@ class StableLMModel(Model): hparams = self.hparams block_count = hparams["num_hidden_layers"] - self.gguf_writer.add_name(dir_model.name) + self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) @@ -902,7 +902,7 @@ class QwenModel(Model): tokens: list[bytearray] = [] toktypes: list[int] = [] - from transformers import AutoTokenizer # type: ignore[attr-defined] + from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) vocab_size = hparams["vocab_size"] assert max(tokenizer.get_vocab().values()) < vocab_size @@ -1185,57 +1185,62 @@ def parse_args() -> argparse.Namespace: return parser.parse_args() -args = parse_args() +def main() -> None: + args = parse_args() -dir_model = args.model + dir_model = args.model -if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights - tmp_model_path = args.model / "weighted_model" - dir_model = tmp_model_path - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") + if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + dir_model = tmp_model_path + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + + if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file=sys.stderr) + sys.exit(1) + + ftype_map = { + "f32": gguf.GGMLQuantizationType.F32, + "f16": gguf.GGMLQuantizationType.F16, + } + + if args.outfile is not None: + fname_out = args.outfile else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file=sys.stderr) - sys.exit(1) + print(f"Loading model: {dir_model.name}") -ftype_map = { - "f32": gguf.GGMLQuantizationType.F32, - "f16": gguf.GGMLQuantizationType.F16, -} + hparams = Model.load_hparams(dir_model) -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + with torch.inference_mode(): + model_class = Model.from_model_architecture(hparams["architectures"][0]) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) -print(f"Loading model: {dir_model.name}") + print("Set model parameters") + model_instance.set_gguf_parameters() -hparams = Model.load_hparams(dir_model) + print("Set model tokenizer") + model_instance.set_vocab() -with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + if args.vocab_only: + print(f"Exporting model vocab to '{fname_out}'") + model_instance.write_vocab() + else: + print(f"Exporting model to '{fname_out}'") + model_instance.write() - print("Set model parameters") - model_instance.set_gguf_parameters() + print(f"Model successfully exported to '{fname_out}'") - print("Set model tokenizer") - model_instance.set_vocab() - if args.vocab_only: - print(f"Exporting model vocab to '{fname_out}'") - model_instance.write_vocab() - else: - print(f"Exporting model to '{fname_out}'") - model_instance.write() - - print(f"Model successfully exported to '{fname_out}'") +if __name__ == '__main__': + main() diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 53bb8a3d9..35ce152f4 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -47,95 +47,96 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty fout.seek((fout.tell() + 31) & -32) -if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} [arch]") - print( - "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" - ) - print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) +if __name__ == '__main__': + if len(sys.argv) < 2: + print(f"Usage: python {sys.argv[0]} [arch]") + print( + "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" + ) + print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") + sys.exit(1) -input_json = os.path.join(sys.argv[1], "adapter_config.json") -input_model = os.path.join(sys.argv[1], "adapter_model.bin") -output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") + input_json = os.path.join(sys.argv[1], "adapter_config.json") + input_model = os.path.join(sys.argv[1], "adapter_model.bin") + output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") -model = torch.load(input_model, map_location="cpu") -arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" + model = torch.load(input_model, map_location="cpu") + arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" -if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - print(f"Error: unsupported architecture {arch_name}") - sys.exit(1) + if arch_name not in gguf.MODEL_ARCH_NAMES.values(): + print(f"Error: unsupported architecture {arch_name}") + sys.exit(1) -arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] -name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone + arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] + name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone -with open(input_json, "r") as f: - params = json.load(f) + with open(input_json, "r") as f: + params = json.load(f) -if params["peft_type"] != "LORA": - print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) + if params["peft_type"] != "LORA": + print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") + sys.exit(1) -if params["fan_in_fan_out"] is True: - print("Error: param fan_in_fan_out is not supported") - sys.exit(1) + if params["fan_in_fan_out"] is True: + print("Error: param fan_in_fan_out is not supported") + sys.exit(1) -if params["bias"] is not None and params["bias"] != "none": - print("Error: param bias is not supported") - sys.exit(1) + if params["bias"] is not None and params["bias"] != "none": + print("Error: param bias is not supported") + sys.exit(1) -# TODO: these seem to be layers that have been trained but without lora. -# doesn't seem widely used but eventually should be supported -if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - print("Error: param modules_to_save is not supported") - sys.exit(1) + # TODO: these seem to be layers that have been trained but without lora. + # doesn't seem widely used but eventually should be supported + if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: + print("Error: param modules_to_save is not supported") + sys.exit(1) -with open(output_path, "wb") as fout: - fout.truncate() + with open(output_path, "wb") as fout: + fout.truncate() - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: + write_file_header(fout, params) + for k, v in model.items(): + orig_k = k + if k.endswith(".default.weight"): + k = k.replace(".default.weight", ".weight") + if k in ["llama_proj.weight", "llama_proj.bias"]: + continue + if k.endswith("lora_A.weight"): + if v.dtype != torch.float16 and v.dtype != torch.float32: + v = v.float() + v = v.T + else: v = v.float() - v = v.T - else: - v = v.float() - t = v.detach().numpy() + t = v.detach().numpy() - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] + prefix = "base_model.model." + if k.startswith(prefix): + k = k[len(prefix) :] - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - print(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) + lora_suffixes = (".lora_A.weight", ".lora_B.weight") + if k.endswith(lora_suffixes): + suffix = k[-len(lora_suffixes[0]):] + k = k[: -len(lora_suffixes[0])] + else: + print(f"Error: unrecognized tensor name {orig_k}") + sys.exit(1) - tname = name_map.get_name(k) - if tname is None: - print(f"Error: could not map tensor name {orig_k}") - print(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) + tname = name_map.get_name(k) + if tname is None: + print(f"Error: could not map tensor name {orig_k}") + print(" Note: the arch parameter must be specified if the model is not llama") + sys.exit(1) - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False + if suffix == ".lora_A.weight": + tname += ".weight.loraA" + elif suffix == ".lora_B.weight": + tname += ".weight.loraB" + else: + assert False - print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) + print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") + write_tensor_header(fout, tname, t.shape, t.dtype) + t.tofile(fout) -print(f"Converted {input_json} and {input_model} to {output_path}") + print(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py old mode 100644 new mode 100755 index 206b7d5ff..1ba5864dc --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 import torch import os from pprint import pprint diff --git a/requirements-hf-to-gguf.txt b/requirements-hf-to-gguf.txt deleted file mode 100644 index f4600539e..000000000 --- a/requirements-hf-to-gguf.txt +++ /dev/null @@ -1,3 +0,0 @@ --r requirements.txt -torch==2.1.1 -transformers==4.35.2 diff --git a/requirements.txt b/requirements.txt index 1a1162566..d36f74520 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,12 @@ -numpy==1.24.4 -sentencepiece==0.1.98 -transformers>=4.34.0 -gguf>=0.1.0 -protobuf>=4.21.0 +# These requirements include all dependencies for all top-level python scripts +# for llama.cpp. Avoid adding packages here directly. +# +# Package versions must stay compatible across all top-level python scripts. +# + +-r ./requirements/requirements-convert.txt + +-r ./requirements/requirements-convert-hf-to-gguf.txt +-r ./requirements/requirements-convert-llama-ggml-to-gguf.txt +-r ./requirements/requirements-convert-lora-to-ggml.txt +-r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt new file mode 100644 index 000000000..6ac402610 --- /dev/null +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt new file mode 100644 index 000000000..a0f37cd1c --- /dev/null +++ b/requirements/requirements-convert-llama-ggml-to-gguf.txt @@ -0,0 +1 @@ +-r ./requirements-convert.txt diff --git a/requirements/requirements-convert-lora-to-ggml.txt b/requirements/requirements-convert-lora-to-ggml.txt new file mode 100644 index 000000000..6ac402610 --- /dev/null +++ b/requirements/requirements-convert-lora-to-ggml.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert-persimmon-to-gguf.txt b/requirements/requirements-convert-persimmon-to-gguf.txt new file mode 100644 index 000000000..6ac402610 --- /dev/null +++ b/requirements/requirements-convert-persimmon-to-gguf.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert.txt new file mode 100644 index 000000000..a3d6ecec0 --- /dev/null +++ b/requirements/requirements-convert.txt @@ -0,0 +1,5 @@ +numpy~=1.24.4 +sentencepiece~=0.1.98 +transformers>=4.35.2,<5.0.0 +gguf>=0.1.0 +protobuf>=4.21.0,<5.0.0 diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh new file mode 100755 index 000000000..af7bab753 --- /dev/null +++ b/scripts/check-requirements.sh @@ -0,0 +1,174 @@ +#!/bin/bash +set -euo pipefail + +# +# check-requirements.sh checks all requirements files for each top-level +# convert*.py script. +# +# WARNING: This is quite IO intensive, because a fresh venv is set up for every +# python script. As of 2023-12-22, this writes ~2.7GB of data. An adequately +# sized tmpfs /tmp or ramdisk is recommended if running this frequently. +# +# usage: check-requirements.sh [] +# check-requirements.sh nocleanup [] +# +# where: +# - is a directory that can be used as the base for +# setting up the venvs. Defaults to `/tmp`. +# - 'nocleanup' as the first argument will disable automatic cleanup +# of the files created by this script. +# +# requires: +# - bash >= 3.2.57 +# - shellcheck +# +# For each script, it creates a fresh venv, `pip install`s the requirements, and +# finally imports the python script to check for `ImportError`. +# + +log() { + local level=$1 msg=$2 + printf >&2 '%s: %s\n' "$level" "$msg" +} + +debug() { + log DEBUG "$@" +} + +info() { + log INFO "$@" +} + +fatal() { + log FATAL "$@" + exit 1 +} + +cleanup() { + if [[ -n ${workdir+x} && -d $workdir && -w $workdir ]]; then + info "Removing $workdir" + local count=0 + rm -rfv -- "$workdir" | while read -r; do + if (( count++ > 750 )); then + printf . + count=0 + fi + done + printf '\n' + info "Removed $workdir" + fi +} + +do_cleanup=1 +if [[ ${1-} == nocleanup ]]; then + do_cleanup=0; shift +fi + +if (( do_cleanup )); then + trap exit INT TERM + trap cleanup EXIT +fi + +this=$(realpath -- "$0"); readonly this +cd "$(dirname "$this")/.." # PWD should stay in llama.cpp project directory + +shellcheck "$this" + +readonly reqs_dir=requirements + +if [[ ${1+x} ]]; then + tmp_dir=$(realpath -- "$1") + if [[ ! ( -d $tmp_dir && -w $tmp_dir ) ]]; then + fatal "$tmp_dir is not a writable directory" + fi +else + tmp_dir=/tmp +fi + +workdir=$(mktemp -d "$tmp_dir/check-requirements.XXXX"); readonly workdir +info "Working directory: $workdir" + +check_requirements() { + local reqs=$1 + + info "$reqs: beginning check" + pip --disable-pip-version-check install -qr "$reqs" + info "$reqs: OK" +} + +check_convert_script() { + local py=$1 # e.g. ./convert-hf-to-gguf.py + local pyname=${py##*/} # e.g. convert-hf-to-gguf.py + pyname=${pyname%.py} # e.g. convert-hf-to-gguf + + info "$py: beginning check" + + local reqs="$reqs_dir/requirements-$pyname.txt" + if [[ ! -r $reqs ]]; then + fatal "$py missing requirements. Expected: $reqs" + fi + + local venv="$workdir/$pyname-venv" + python3 -m venv "$venv" + + ( + # shellcheck source=/dev/null + source "$venv/bin/activate" + + check_requirements "$reqs" + + python - "$py" "$pyname" <<'EOF' +import sys +from importlib.machinery import SourceFileLoader +py, pyname = sys.argv[1:] +SourceFileLoader(pyname, py).load_module() +EOF + ) + + if (( do_cleanup )); then + rm -rf -- "$venv" + fi + + info "$py: imports OK" +} + +readonly ignore_eq_eq='check_requirements: ignore "=="' + +for req in "$reqs_dir"/*; do + # Check that all sub-requirements are added to top-level requirements.txt + if ! grep -qF "$req" requirements.txt; then + fatal "$req needs to be added to requirements.txt" + fi + + # Make sure exact release versions aren't being pinned in the requirements + # Filters out the ignore string + if grep -vF "$ignore_eq_eq" "$req" | grep -q '=='; then + tab=$'\t' + cat >&2 < Date: Sat, 30 Dec 2023 00:31:19 +0800 Subject: [PATCH 278/664] cuda: fix vmm oom issue on NVIDIA AGX Orin (#4687) Signed-off-by: hydai --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9a9effcf5..09585b07d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6662,7 +6662,7 @@ static void ggml_cuda_pool_free_leg(int device, void * ptr, size_t size) { // pool with virtual memory static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0}; static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0}; -static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 36; // 64 GB +static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual_size) { scoped_spin_lock lock(g_cuda_pool_lock); From ce18d727a47f2473ca863a6f78bf3ad480008f72 Mon Sep 17 00:00:00 2001 From: Steward Garcia <57494570+FSSRepo@users.noreply.github.com> Date: Fri, 29 Dec 2023 11:52:15 -0500 Subject: [PATCH 279/664] clip : enable gpu backend (#4205) * clip: enable CUDA backend * add missing kernels * add enough padding for alignment * remove ggml_repeat of clip.cpp * add metal backend * llava : fixes - avoid ggml_repeat - use GGML_USE_ instead of CLIP_USE_ macros - remove unused vars --------- Co-authored-by: Georgi Gerganov --- examples/llava/CMakeLists.txt | 3 +- examples/llava/clip.cpp | 231 +++++++++++++++++++--------------- 2 files changed, 131 insertions(+), 103 deletions(-) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 48dae1506..2985caff8 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -24,7 +24,8 @@ endif() if (NOT MSVC) target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h - endif() +endif() + if(TARGET BUILD_INFO) add_dependencies(llava BUILD_INFO) endif() diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f06ec400d..f9326a5cc 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -16,12 +16,19 @@ #include "clip.h" #include "ggml.h" #include "ggml-alloc.h" +#include "ggml-backend.h" + +#ifdef GGML_USE_CUBLAS +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" -#define CLIP_DEBUG - static std::string format(const char * fmt, ...) { va_list ap; va_list ap2; @@ -196,20 +203,6 @@ struct clip_vision_model { struct ggml_tensor * mm_2_b; }; -// Replacement for std::vector that doesn't require zero-initialization. -struct clip_buffer { - uint8_t * data = NULL; - size_t size = 0; - - void resize(size_t size) { - delete[] data; - data = new uint8_t[size]; - this->size = size; - } - - ~clip_buffer() { delete[] data; } -}; - struct clip_ctx { bool has_text_encoder = false; bool has_vision_encoder = false; @@ -223,9 +216,10 @@ struct clip_ctx { struct gguf_context * ctx_gguf; // memory buffers to evaluate the model - clip_buffer buf_compute; - clip_buffer buf_alloc; - ggml_allocr * alloc = NULL; + ggml_backend_buffer_t params_buffer = NULL; + ggml_backend_buffer_t compute_buffer = NULL; + ggml_backend_t backend = NULL; + ggml_allocr * compute_alloc = NULL; }; static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) { @@ -252,25 +246,20 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima if(ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); } - - const auto & buf_compute = ctx->buf_compute; - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ false, + /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, }; - params.no_alloc = true; - struct ggml_context * ctx0 = ggml_init(params); struct ggml_cgraph * gf = ggml_new_graph(ctx0); struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size); - ggml_allocr_alloc(ctx->alloc, inp_raw); + ggml_allocr_alloc(ctx->compute_alloc, inp_raw); - if (!ggml_allocr_is_measure(ctx->alloc)) { - float * data = (float *)ggml_get_data(inp_raw); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + float * data = (float *)malloc(ggml_nbytes(inp_raw)); for (size_t i = 0; i < imgs->size; i++) { const int nx = imgs->data[i].nx; @@ -289,6 +278,8 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima } } } + ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); + free(data); } struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1); @@ -298,36 +289,39 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima // concat class_embeddings and patch_embeddings struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_allocr_alloc(ctx->alloc, embeddings); - if (!ggml_allocr_is_measure(ctx->alloc)) { - ggml_set_zero(embeddings); + ggml_allocr_alloc(ctx->compute_alloc, embeddings); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); } - struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size); - ggml_allocr_alloc(ctx->alloc, temp); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - embeddings = ggml_acc(ctx0, embeddings, ggml_repeat(ctx0, model.class_embedding, temp), embeddings->nb[1], - embeddings->nb[2], embeddings->nb[3], 0); - embeddings = - ggml_acc(ctx0, embeddings, inp, embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); - ggml_allocr_alloc(ctx->alloc, positions); - if (!ggml_allocr_is_measure(ctx->alloc)) { + ggml_allocr_alloc(ctx->compute_alloc, positions); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + int* positions_data = (int*)malloc(ggml_nbytes(positions)); for (int i = 0; i < num_positions; i++) { - ggml_set_i32_1d(positions, i, i); + positions_data[i] = i; } + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); } embeddings = - ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, model.position_embeddings, positions), embeddings)); + ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); // pre-layernorm { embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.pre_ln_w, embeddings), embeddings), - ggml_repeat(ctx0, model.pre_ln_b, embeddings)); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b); } // loop over layers @@ -340,15 +334,15 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima { cur = ggml_norm(ctx0, cur, eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_w, cur), cur), - ggml_repeat(ctx0, model.layers[il].ln_1_b, cur)); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), + model.layers[il].ln_1_b); } // self-attention { struct ggml_tensor * Q = - ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur)); + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); @@ -356,14 +350,14 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); struct ggml_tensor * K = - ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, cur), ggml_mul_mat(ctx0, model.layers[il].k_w, cur)); + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); struct ggml_tensor * V = - ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, cur), ggml_mul_mat(ctx0, model.layers[il].v_w, cur)); + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b); V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); @@ -379,7 +373,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima } // attention output - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].o_b, cur), ggml_mul_mat(ctx0, model.layers[il].o_w, cur)); + cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b); // re-add the layer input, e.g., residual cur = ggml_add(ctx0, cur, embeddings); @@ -390,12 +384,11 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima { cur = ggml_norm(ctx0, cur, eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_w, cur), cur), - ggml_repeat(ctx0, model.layers[il].ln_2_b, cur)); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); } cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_i_b, cur), cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); if (ctx->use_gelu) { cur = ggml_gelu_inplace(ctx0, cur); @@ -404,7 +397,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima } cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_o_b, cur), cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b); // residual 2 cur = ggml_add(ctx0, embeddings, cur); @@ -417,23 +410,26 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); - ggml_allocr_alloc(ctx->alloc, patches); - if (!ggml_allocr_is_measure(ctx->alloc)) { - for (int i = 0; i < num_patches; ++i) { - ggml_set_i32_1d(patches, i, i+1); + ggml_allocr_alloc(ctx->compute_alloc, patches); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + int* patches_data = (int*)malloc(ggml_nbytes(patches)); + for (int i = 0; i < num_positions; i++) { + patches_data[i] = i + 1; } + ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); + free(patches_data); } embeddings = ggml_get_rows(ctx0, embeddings, patches); // mm projection 0 embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_0_b, embeddings), embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); embeddings = ggml_gelu(ctx0, embeddings); embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_2_b, embeddings), embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); } // build the graph @@ -446,7 +442,6 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima // read and create ggml_context containing the tensors and their data struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { - struct ggml_context * meta = NULL; struct gguf_init_params params = { @@ -479,7 +474,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("%s: ftype: %s\n", __func__, ftype_str.c_str()); printf("\n"); } - + const int n_tensors = gguf_get_n_tensors(ctx); // kv if (verbosity >= 3) { const int n_kv = gguf_get_n_kv(ctx); @@ -493,27 +488,38 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } // data - size_t ctx_size = 0; + size_t buffer_size = 0; { - const int n_tensors = gguf_get_n_tensors(ctx); - for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - struct ggml_tensor * cur = ggml_get_tensor(meta, name); - ctx_size += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE; size_t tensor_size = ggml_nbytes(cur); - size_t padded_size = ggml_nbytes_pad(cur); - ctx_size += padded_size; + buffer_size += tensor_size; if (verbosity >= 3) { - printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i, - ggml_n_dims(cur), cur->name, tensor_size, padded_size, offset); + printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i, + ggml_n_dims(cur), cur->name, tensor_size, offset); } } } + buffer_size += n_tensors * 128 /* CLIP PADDING */; + clip_ctx * new_clip = new clip_ctx; +#ifdef GGML_USE_CUBLAS + new_clip->backend = ggml_backend_cuda_init(0); + printf("%s: CLIP using CUDA backend\n", __func__); +#endif + +#ifdef GGML_USE_METAL + new_clip->backend = ggml_backend_metal_init(); + printf("%s: CLIP using Metal backend\n", __func__); +#endif + + if (!new_clip->backend) { + new_clip->backend = ggml_backend_cpu_init(); + printf("%s: CLIP using CPU backend\n", __func__); + } // model size and capabilities { @@ -539,17 +545,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - printf("%s: model size: %.2f MB\n", __func__, (ctx_size / 1024.0 / 1024.0)); + printf("%s: model size: %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0); printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } } + printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors); + // load tensors { + std::vector read_buf; struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, + /*.mem_size =*/ (n_tensors + 1) * ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ false, + /*.no_alloc =*/ true, }; new_clip->ctx = ggml_init(params); @@ -566,13 +575,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return nullptr; } - const int n_tensors = gguf_get_n_tensors(ctx); + // add tensors to context for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * t = ggml_get_tensor(meta, name); struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t); ggml_set_name(cur, name); + } + // alloc memory and offload data + new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size); + ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer); + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name(ctx, i); + struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx, name); + ggml_allocr_alloc(alloc, cur); const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); if (!fin) { @@ -580,10 +597,22 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { clip_free(new_clip); return nullptr; } - - fin.read(reinterpret_cast(cur->data), ggml_nbytes(t)); + int num_bytes = ggml_nbytes(cur); + if (ggml_backend_is_cpu(new_clip->backend) +#ifdef GGML_USE_METAL + || ggml_backend_is_metal(new_clip->backend) +#endif + ) { + // for the CPU and Metal backend, we can read directly into the tensor + fin.read(reinterpret_cast(cur->data), num_bytes); + } else { + // read into a temporary buffer first, then copy to device memory + read_buf.resize(num_bytes); + fin.read(reinterpret_cast(read_buf.data()), num_bytes); + ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes); + } } - + ggml_allocr_free(alloc); fin.close(); } @@ -657,18 +686,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // measure mem requirement and allocate { - static const size_t tensor_alignment = 32; - new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead()); - new_clip->alloc = ggml_allocr_new_measure(tensor_alignment); + new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend); clip_image_f32_batch batch; batch.size = 1; ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch); - size_t alloc_size = ggml_allocr_alloc_graph(new_clip->alloc, gf) + tensor_alignment; - ggml_allocr_free(new_clip->alloc); - new_clip->buf_alloc.resize(alloc_size); - new_clip->alloc = ggml_allocr_new(new_clip->buf_alloc.data, new_clip->buf_alloc.size, tensor_alignment); + size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf); + ggml_allocr_free(new_clip->compute_alloc); + new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size); + new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer); - printf("%s: total allocated memory: %.2f MB\n", __func__, (new_clip->buf_compute.size + alloc_size)/1024.0/1024.0); + printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); } return new_clip; @@ -852,29 +879,29 @@ bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const cl } // reset alloc buffer to clean the memory from previous invocations - ggml_allocr_reset(ctx->alloc); + ggml_allocr_reset(ctx->compute_alloc); // build the inference graph ggml_cgraph * gf = clip_image_build_graph(ctx, imgs); - ggml_allocr_alloc_graph(ctx->alloc, gf); + ggml_allocr_alloc_graph(ctx->compute_alloc, gf); - struct ggml_cplan plan = ggml_graph_plan(gf, n_threads); - if (plan.work_size > 0) { - plan.work_data = (uint8_t *)malloc(plan.work_size); + if (ggml_backend_is_cpu(ctx->backend)) { + ggml_backend_cpu_set_n_threads(ctx->backend, n_threads); } - ggml_graph_compute(gf, &plan); +#ifdef GGML_USE_METAL + if (ggml_backend_is_metal(ctx->backend)) { + ggml_backend_metal_set_n_cb(ctx->backend, n_threads); + } +#endif + + ggml_backend_graph_compute(ctx->backend, gf); // the last node is the embedding tensor -struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1]; // copy the embeddings to the location passed by the user - memcpy(vec, ggml_get_data_f32(embeddings), ggml_nbytes(embeddings)); - - if (plan.work_size > 0) { - free(plan.work_data); - } - + ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); return true; } @@ -1045,8 +1072,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i gguf_free(ctx_out); { - printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); - printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); + printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); + printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); int64_t sum_all = 0; for (size_t i = 0; i < hist_all.size(); ++i) { From 0235b9b571f3cc7d2b8836409a5404b41ce1379c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 18:53:34 +0200 Subject: [PATCH 280/664] clip : use ggml_backend_buffer_is_host (#4205) --- examples/llava/clip.cpp | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f9326a5cc..6a731eeec 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -598,11 +598,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return nullptr; } int num_bytes = ggml_nbytes(cur); - if (ggml_backend_is_cpu(new_clip->backend) -#ifdef GGML_USE_METAL - || ggml_backend_is_metal(new_clip->backend) -#endif - ) { + if (ggml_backend_buffer_is_host(new_clip->params_buffer)) { // for the CPU and Metal backend, we can read directly into the tensor fin.read(reinterpret_cast(cur->data), num_bytes); } else { From a20f3c7465d6d1b33767757c2760643b799a81bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 29 Dec 2023 23:12:53 +0100 Subject: [PATCH 281/664] CUDA: fix tensor core logic for Pascal and HIP (#4682) --- ggml-cuda.cu | 72 ++++++++++++++++++++++++++++------------------------ 1 file changed, 39 insertions(+), 33 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 09585b07d..71a64ca09 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -123,24 +123,6 @@ #define GGML_CUDA_MAX_NODES 8192 -// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication -// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant -// for large computational tasks. the drawback is that this requires some extra amount of VRAM: -// - 7B quantum model: +100-200 MB -// - 13B quantum model: +200-400 MB -// -//#define GGML_CUDA_FORCE_MMQ - -// TODO: improve this to be correct for more hardware -// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores -// probably other such cases, and not sure what happens on AMD hardware -#if !defined(GGML_CUDA_FORCE_MMQ) -#define CUDA_USE_TENSOR_CORES -#endif - -// max batch size to use MMQ kernels when tensor cores are available -#define MMQ_MAX_BATCH_SIZE 32 - #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -207,6 +189,23 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { } #endif // defined(GGML_USE_HIPBLAS) +// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication +// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant +// for large computational tasks. the drawback is that this requires some extra amount of VRAM: +// - 7B quantum model: +100-200 MB +// - 13B quantum model: +200-400 MB +// +//#define GGML_CUDA_FORCE_MMQ + +// TODO: improve this to be correct for more hardware +// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores +#if !defined(GGML_CUDA_FORCE_MMQ) && (!defined(GGML_USE_HIPBLAS) || defined(RDNA3)) +#define CUDA_USE_TENSOR_CORES +#endif + +// max batch size to use MMQ kernels when tensor cores are available +#define MMQ_MAX_BATCH_SIZE 32 + #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif @@ -8661,11 +8660,26 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } } -#ifdef CUDA_USE_TENSOR_CORES - const bool use_tensor_cores = true; +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + const bool fp16_performance_good = true; + +#ifdef RDNA3 + const bool use_mul_mat_q = false; #else - const bool use_tensor_cores = false; -#endif + const bool use_mul_mat_q = true; +#endif // RDNA3 + +#else + + const bool fp16_performance_good = min_compute_capability >= CC_VOLTA; + bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); +#ifdef CUDA_USE_TENSOR_CORES + // when tensor cores are available, use them for large batch size + // ref: https://github.com/ggerganov/llama.cpp/pull/3776 + use_mul_mat_q = use_mul_mat_q && !(fp16_performance_good && src1->ne[1] > MMQ_MAX_BATCH_SIZE); +#endif // CUDA_USE_TENSOR_CORES + +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) // debug helpers //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); @@ -8675,13 +8689,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { // KQ single-batch ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { @@ -8701,14 +8715,6 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); - - // when tensor cores are available, use them for large batch size - // ref: https://github.com/ggerganov/llama.cpp/pull/3776 - if (use_tensor_cores && min_compute_capability >= CC_VOLTA && src1->ne[1] > MMQ_MAX_BATCH_SIZE) { - use_mul_mat_q = false; - } - if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); } else { From 24a447e20af425fa44cf10feaa632b6bb596c80f Mon Sep 17 00:00:00 2001 From: automaticcat Date: Sat, 30 Dec 2023 15:07:48 +0700 Subject: [PATCH 282/664] ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- README.md | 30 ++++++++++++++++++++++-------- common/common.cpp | 1 + ggml.c | 8 ++++++++ ggml.h | 1 + llama.cpp | 1 + 5 files changed, 33 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 48dcd6464..ca6d14e17 100644 --- a/README.md +++ b/README.md @@ -385,16 +385,30 @@ Building the program with BLAS support may lead to some performance improvements Check [BLIS.md](docs/BLIS.md) for more information. -- #### Intel MKL +- #### Intel oneMKL + - Using manual oneAPI installation: + By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: + ```bash + mkdir build + cd build + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-runtime docker image, only required for manual installation + cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON + cmake --build . --config Release + ``` - By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. You may also specify it by: + - Using oneAPI docker image: + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-runtime](https://hub.docker.com/r/intel/oneapi-runtime) - ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx - cmake --build . --config Release - ``` + ```bash + mkdir build + cd build + cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON + cmake --build . --config Release + ``` + + Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. + + Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. - #### cuBLAS diff --git a/common/common.cpp b/common/common.cpp index b3425ab09..eacaee18e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1394,6 +1394,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); + fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); diff --git a/ggml.c b/ggml.c index a9e1ea9b4..bcec200f6 100644 --- a/ggml.c +++ b/ggml.c @@ -19638,6 +19638,14 @@ int ggml_cpu_has_avx(void) { #endif } +int ggml_cpu_has_avx_vnni(void) { +#if defined(__AVXVNNI__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_avx2(void) { #if defined(__AVX2__) return 1; diff --git a/ggml.h b/ggml.h index 67d6bc4f1..64f4e45e8 100644 --- a/ggml.h +++ b/ggml.h @@ -2198,6 +2198,7 @@ extern "C" { // GGML_API int ggml_cpu_has_avx (void); + GGML_API int ggml_cpu_has_avx_vnni (void); GGML_API int ggml_cpu_has_avx2 (void); GGML_API int ggml_cpu_has_avx512 (void); GGML_API int ggml_cpu_has_avx512_vbmi(void); diff --git a/llama.cpp b/llama.cpp index 68c7cced6..a833d4c15 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10780,6 +10780,7 @@ const char * llama_print_system_info(void) { s = ""; s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | "; + s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | "; s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | "; s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; From 39d8bc71edcb8b6f99d46fa4216af7a15232e218 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 30 Dec 2023 13:52:01 +0100 Subject: [PATCH 283/664] CUDA: fixed tensor cores not being used on RDNA3 (#4697) --- ggml-cuda.cu | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 71a64ca09..8c2712308 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -119,10 +119,29 @@ #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 #define CC_OFFSET_AMD 1000000 +#define CC_RDNA1 (CC_OFFSET_AMD + 1010) #define CC_RDNA2 (CC_OFFSET_AMD + 1030) +#define CC_RDNA3 (CC_OFFSET_AMD + 1100) #define GGML_CUDA_MAX_NODES 8192 +// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication +// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant +// for large computational tasks. the drawback is that this requires some extra amount of VRAM: +// - 7B quantum model: +100-200 MB +// - 13B quantum model: +200-400 MB +// +//#define GGML_CUDA_FORCE_MMQ + +// TODO: improve this to be correct for more hardware +// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores +#if !defined(GGML_CUDA_FORCE_MMQ) +#define CUDA_USE_TENSOR_CORES +#endif + +// max batch size to use MMQ kernels when tensor cores are available +#define MMQ_MAX_BATCH_SIZE 32 + #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -189,23 +208,6 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { } #endif // defined(GGML_USE_HIPBLAS) -// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication -// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant -// for large computational tasks. the drawback is that this requires some extra amount of VRAM: -// - 7B quantum model: +100-200 MB -// - 13B quantum model: +200-400 MB -// -//#define GGML_CUDA_FORCE_MMQ - -// TODO: improve this to be correct for more hardware -// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores -#if !defined(GGML_CUDA_FORCE_MMQ) && (!defined(GGML_USE_HIPBLAS) || defined(RDNA3)) -#define CUDA_USE_TENSOR_CORES -#endif - -// max batch size to use MMQ kernels when tensor cores are available -#define MMQ_MAX_BATCH_SIZE 32 - #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif @@ -8661,13 +8663,12 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - const bool fp16_performance_good = true; -#ifdef RDNA3 - const bool use_mul_mat_q = false; -#else - const bool use_mul_mat_q = true; -#endif // RDNA3 + const bool fp16_performance_good = min_compute_capability >= CC_RDNA1; + bool use_mul_mat_q = ggml_is_quantized(src0->type); +#ifdef CUDA_USE_TENSOR_CORES + use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3; +#endif // CUDA_USE_TENSOR_CORES #else From 9fbda719de18a9400a064c28759c39d55d687d3e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 30 Dec 2023 23:24:42 +0200 Subject: [PATCH 284/664] clip : refactor + bug fixes (#4696) * clip : refactor + bug fixes ggml-ci * server : add log message --- examples/llava/clip.cpp | 241 +++++++++++++++++++++---------------- examples/llava/clip.h | 48 +++----- examples/llava/llava.cpp | 4 +- examples/server/server.cpp | 38 +++--- 4 files changed, 169 insertions(+), 162 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 6a731eeec..cfb79e789 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -146,6 +146,27 @@ static std::string get_ftype(int ftype) { } } +// +// image data +// + +// RGB uint8 image +struct clip_image_u8 { + int nx; + int ny; + + std::vector buf; +}; + +// RGB float32 image (NHWC) +// Memory layout: RGBRGBRGB... +struct clip_image_f32 { + int nx; + int ny; + + std::vector buf; +}; + // // clip layers // @@ -204,16 +225,21 @@ struct clip_vision_model { }; struct clip_ctx { - bool has_text_encoder = false; - bool has_vision_encoder = false; + bool has_text_encoder = false; + bool has_vision_encoder = false; bool has_llava_projector = false; + struct clip_vision_model vision_model; + float image_mean[3]; float image_std[3]; bool use_gelu = false; int32_t ftype = 1; - struct ggml_context * ctx; + struct gguf_context * ctx_gguf; + struct ggml_context * ctx_data; + + std::vector buf_compute_meta; // memory buffers to evaluate the model ggml_backend_buffer_t params_buffer = NULL; @@ -222,7 +248,7 @@ struct clip_ctx { ggml_allocr * compute_alloc = NULL; }; -static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) { +static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return nullptr; @@ -243,13 +269,14 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima //const int projection_dim = hparams.projection_dim; const float eps = hparams.eps; int batch_size = imgs->size; - if(ctx->has_llava_projector) { + if (ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); } + struct ggml_init_params params = { - /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, + /*.mem_size =*/ ctx->buf_compute_meta.size(), + /*.mem_buffer =*/ ctx->buf_compute_meta.data(), + /*.no_alloc =*/ true, }; struct ggml_context * ctx0 = ggml_init(params); @@ -272,7 +299,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima for (int k = 0; k < 3; k++) { for (int y = 0; y < ny; y++) { for (int x = 0; x < nx; x++) { - data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].data[3 * (y * nx + x) + k]; + data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k]; } } } @@ -413,7 +440,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima ggml_allocr_alloc(ctx->compute_alloc, patches); if (!ggml_allocr_is_measure(ctx->compute_alloc)) { int* patches_data = (int*)malloc(ggml_nbytes(patches)); - for (int i = 0; i < num_positions; i++) { + for (int i = 0; i < num_patches; i++) { patches_data[i] = i + 1; } ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); @@ -561,8 +588,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { /*.no_alloc =*/ true, }; - new_clip->ctx = ggml_init(params); - if (!new_clip->ctx) { + new_clip->ctx_data = ggml_init(params); + if (!new_clip->ctx_data) { fprintf(stderr, "%s: ggml_init() failed\n", __func__); clip_free(new_clip); return nullptr; @@ -579,7 +606,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * t = ggml_get_tensor(meta, name); - struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t); + struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t); ggml_set_name(cur, name); } @@ -588,7 +615,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); - struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx, name); + struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name); ggml_allocr_alloc(alloc, cur); const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); @@ -617,20 +644,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // load vision model auto & vision_model = new_clip->vision_model; auto & hparams = vision_model.hparams; - hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision")); - hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision")); + hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision")); + hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision")); hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision")); - hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision")); - hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE); - hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE); + hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision")); + hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE); + hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE); hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision")); - hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision")); + hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision")); int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); - int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); + int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); for (int i = 0; i < 3; ++i) { new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean)); - new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); + new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); } if (verbosity >= 2) { @@ -644,35 +671,35 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("v_n_layer %d\n", hparams.n_layer); } - vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD); - vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "bias")); - vision_model.mm_0_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "weight")); - vision_model.mm_0_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "bias")); - vision_model.mm_2_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "weight")); - vision_model.mm_2_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "bias")); + vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); + vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); + vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); + vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); vision_model.layers.resize(hparams.n_layer); for (int il = 0; il < hparams.n_layer; ++il) { auto & layer = vision_model.layers[il]; - layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "weight")); - layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "weight")); - layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "weight")); - layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "weight")); - layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "weight")); - layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "weight")); - layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "weight")); - layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "weight")); - layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "bias")); - layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "bias")); - layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "bias")); - layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "bias")); - layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "bias")); - layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "bias")); - layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "bias")); - layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "bias")); + layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight")); + layer.q_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "weight")); + layer.v_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "weight")); + layer.o_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight")); + layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "weight")); + layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "weight")); + layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "weight")); + layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "weight")); + layer.k_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "bias")); + layer.q_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "bias")); + layer.v_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "bias")); + layer.o_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias")); + layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "bias")); + layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "bias")); + layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "bias")); + layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "bias")); } } @@ -680,8 +707,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->ctx_gguf = ctx; -// measure mem requirement and allocate + // measure mem requirement and allocate { + new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead()); new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend); clip_image_f32_batch batch; batch.size = 1; @@ -697,26 +725,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return new_clip; } -clip_image_u8 * make_clip_image_u8() { - auto img = new clip_image_u8(); - return img; +struct clip_image_u8 * clip_image_u8_init() { + return new clip_image_u8(); } -clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); } -void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; } -void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; } +struct clip_image_f32 * clip_image_f32_init() { + return new clip_image_f32(); +} + +void clip_image_u8_free (struct clip_image_u8 * img) { delete img; } +void clip_image_f32_free(struct clip_image_f32 * img) { delete img; } static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) { img->nx = nx; img->ny = ny; - img->size = nx * ny * 3; - img->data = new uint8_t[img->size](); - memcpy(img->data, data, img->size); + img->buf.resize(3 * nx * ny); + memcpy(img->buf.data(), data, img->buf.size()); } bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { int nx, ny, nc; - auto data = stbi_load(fname, &nx, &ny, &nc, 3); + auto * data = stbi_load(fname, &nx, &ny, &nc, 3); if (!data) { fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); return false; @@ -728,7 +757,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) { int nx, ny, nc; - auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); + auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); if (!data) { fprintf(stderr, "%s: failed to decode image bytes\n", __func__); return false; @@ -740,7 +769,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length // normalize: x = (x - mean) / std // TODO: implement bicubic interpolation instead of linear. -bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) { +bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; @@ -749,18 +778,17 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 - clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily + clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily if (pad2square && img->nx != img->ny) { int longer_side = std::max(img->nx, img->ny); temp->nx = longer_side; temp->ny = longer_side; - temp->size = 3 * longer_side * longer_side; - temp->data = new uint8_t[temp->size](); - uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA + temp->buf.resize(3 * longer_side * longer_side); + const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA // fill with background color - for (size_t i = 0; i < temp->size; i++) { - temp->data[i] = bc[i % 3]; + for (size_t i = 0; i < temp->buf.size(); i++) { + temp->buf[i] = bc[i % 3]; } // copy from the input image @@ -768,17 +796,16 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip for (int x = 0; x < img->nx; x++) { const int i = 3 * (y * img->nx + x); const int j = 3 * (y * temp->nx + x); - temp->data[j] = img->data[i]; - temp->data[j+1] = img->data[i+1]; - temp->data[j+2] = img->data[i+2]; + temp->buf[j] = img->buf[i]; + temp->buf[j+1] = img->buf[i+1]; + temp->buf[j+2] = img->buf[i+2]; } } } else { - temp->nx = img->nx; - temp->ny = img->ny; - temp->size = img->size; - temp->data = new uint8_t[temp->size](); - memcpy(&temp->data[0], &img->data[0], temp->size); // copy + temp->nx = img->nx; + temp->ny = img->ny; + temp->buf.resize(img->buf.size()); + memcpy(temp->buf.data(), img->buf.data(), temp->buf.size()); } const int nx = temp->nx; @@ -789,8 +816,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip res->nx = nx2; res->ny = ny2; - res->size = 3 * nx2 * ny2; - res->data = new float[res->size](); + res->buf.resize(3 * nx2 * ny2); const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size; @@ -821,10 +847,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip const int j10 = 3 * (y1 * nx + x0) + c; const int j11 = 3 * (y1 * nx + x1) + c; - const float v00 = temp->data[j00]; - const float v01 = temp->data[j01]; - const float v10 = temp->data[j10]; - const float v11 = temp->data[j11]; + const float v00 = temp->buf[j00]; + const float v01 = temp->buf[j01]; + const float v10 = temp->buf[j10]; + const float v11 = temp->buf[j11]; const float v0 = v00 * (1.0f - dx) + v01 * dx; const float v1 = v10 * (1.0f - dx) + v11 * dx; @@ -835,7 +861,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip const int i = 3 * (y * nx3 + x) + c; - res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c]; + res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c]; } } } @@ -845,12 +871,13 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip } void clip_free(clip_ctx * ctx) { - ggml_free(ctx->ctx); + ggml_free(ctx->ctx_data); gguf_free(ctx->ctx_gguf); + delete ctx; } -bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { +bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; @@ -862,8 +889,7 @@ bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 return clip_image_batch_encode(ctx, n_threads, &imgs, vec); } -bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { - +bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; @@ -906,31 +932,32 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i ggml_type type = GGML_TYPE_Q4_1; switch (itype) { - case 2: - type = GGML_TYPE_Q4_0; - break; - case 3: - type = GGML_TYPE_Q4_1; - break; - case 6: - type = GGML_TYPE_Q5_0; - break; - case 7: - type = GGML_TYPE_Q5_1; - break; - case 8: - type = GGML_TYPE_Q8_0; - break; - default: - fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); - return false; + case 2: + type = GGML_TYPE_Q4_0; + break; + case 3: + type = GGML_TYPE_Q4_1; + break; + case 6: + type = GGML_TYPE_Q5_0; + break; + case 7: + type = GGML_TYPE_Q5_1; + break; + case 8: + type = GGML_TYPE_Q8_0; + break; + default: + fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); + return false; }; - auto ctx_clip = clip_model_load(fname_inp, 2); - const auto & ctx_src = ctx_clip->ctx_gguf; - const auto & ctx_data = ctx_clip->ctx; + auto * ctx_clip = clip_model_load(fname_inp, 2); - auto ctx_out = gguf_init_empty(); + const auto & ctx_src = ctx_clip->ctx_gguf; + const auto & ctx_data = ctx_clip->ctx_data; + + auto * ctx_out = gguf_init_empty(); gguf_set_kv(ctx_out, ctx_src); gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); gguf_set_val_u32(ctx_out, "general.file_type", itype); diff --git a/examples/llava/clip.h b/examples/llava/clip.h index f11df85de..458a256a1 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -35,31 +35,14 @@ struct clip_vision_hparams { float eps; }; -/** load mmproj model */ -CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity); -/** free mmproj model */ +CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity); + CLIP_API void clip_free(struct clip_ctx * ctx); -size_t clip_embd_nbytes(const struct clip_ctx * ctx); -int clip_n_patches(const struct clip_ctx * ctx); -int clip_n_mmproj_embd(const struct clip_ctx * ctx); +CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); -// RGB uint8 image -struct clip_image_u8 { - int nx; - int ny; - uint8_t * data = NULL; - size_t size; -}; - -// RGB float32 image (NHWC) -// Memory layout: RGBRGBRGB... -struct clip_image_f32 { - int nx; - int ny; - float * data = NULL; - size_t size; -}; +CLIP_API int clip_n_patches (const struct clip_ctx * ctx); +CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); struct clip_image_u8_batch { struct clip_image_u8 * data; @@ -71,21 +54,22 @@ struct clip_image_f32_batch { size_t size; }; -struct clip_image_u8 * make_clip_image_u8(); -struct clip_image_f32 * make_clip_image_f32(); -CLIP_API void clip_image_u8_free(clip_image_u8 * img); -CLIP_API void clip_image_f32_free(clip_image_f32 * img); +CLIP_API struct clip_image_u8 * clip_image_u8_init (); +CLIP_API struct clip_image_f32 * clip_image_f32_init(); + +CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); +CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); + CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); + /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); -bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square); -bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec); +CLIP_API bool clip_image_preprocess (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square); +CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec); +CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec); -bool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs, - float * vec); - -bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype); +CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype); #ifdef __cplusplus } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 0cae8c4b1..d42e7582e 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -10,7 +10,7 @@ #include "base64.hpp" static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { - clip_image_f32 * img_res = make_clip_image_f32(); + clip_image_f32 * img_res = clip_image_f32_init(); if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) { fprintf(stderr, "%s: unable to preprocess image\n", __func__); clip_image_f32_free(img_res); @@ -86,7 +86,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ } LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { - clip_image_u8 * img = make_clip_image_u8(); + clip_image_u8 * img = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { clip_image_u8_free(img); fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0aada8e28..52d9b9768 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -82,7 +82,7 @@ static inline bool is_base64(uint8_t c) return (isalnum(c) || (c == '+') || (c == '/')); } -static std::vector base64_decode(std::string const &encoded_string) +static std::vector base64_decode(const std::string & encoded_string) { int i = 0; int j = 0; @@ -209,10 +209,10 @@ struct slot_image int32_t id; bool request_encode_image = false; - float* image_embedding = nullptr; + float * image_embedding = nullptr; int32_t image_tokens = 0; - clip_image_u8 img_data; + clip_image_u8 * img_data; std::string prefix_prompt; // before of this image }; @@ -434,10 +434,12 @@ struct llama_client_slot generated_token_probs.clear(); - for (slot_image &img : images) + for (slot_image & img : images) { free(img.image_embedding); - delete[] img.img_data.data; + if (img.img_data) { + clip_image_u8_free(img.img_data); + } img.prefix_prompt = ""; } @@ -851,24 +853,17 @@ struct llama_server_context { for (const auto &img : *images_data) { - std::string data_b64 = img["data"].get(); + const std::vector image_buffer = base64_decode(img["data"].get()); + slot_image img_sl; img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); - int width, height, channels; - std::vector image_buffer = base64_decode(data_b64); - data_b64.clear(); - auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); - if (!data) { + img_sl.img_data = clip_image_u8_init(); + if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data)) + { LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id); return false; } - LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height); - img_sl.img_data.nx = width; - img_sl.img_data.ny = height; - img_sl.img_data.size = width * height * 3; - img_sl.img_data.data = new uint8_t[width * height * 3](); - memcpy(img_sl.img_data.data, data, width * height * 3); - stbi_image_free(data); + LOG_TEE("slot %i - loaded image\n", slot->id); img_sl.request_encode_image = true; slot->images.push_back(img_sl); } @@ -1143,8 +1138,8 @@ struct llama_server_context { continue; } - clip_image_f32 img_res; - if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) + clip_image_f32 * img_res = clip_image_f32_init(); + if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true)) { LOG_TEE("Error processing the given image"); clip_free(clp_ctx); @@ -1159,11 +1154,12 @@ struct llama_server_context return false; } LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id); - if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) + if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding)) { LOG_TEE("Unable to encode image\n"); return false; } + clip_image_f32_free(img_res); img.request_encode_image = false; } From e39106c0554cbd0e9310e08fb3b2a577ea4b6273 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 31 Dec 2023 11:43:31 +0200 Subject: [PATCH 285/664] ggml : add ggml_vdotq_s32 alias (#4715) ggml-ci --- ggml-quants.c | 118 ++++++++++++++++++++++++++------------------------ 1 file changed, 61 insertions(+), 57 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 05ef8f9b7..55a9496d1 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -410,13 +410,17 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { #if !defined(__ARM_FEATURE_DOTPROD) -inline static int32x4_t vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { +inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b)); const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b)); return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))); } +#else + +#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c) + #endif #endif @@ -2481,8 +2485,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); // dot product into int32x4_t - const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); - const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); @@ -2769,8 +2773,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); // dot product into int32x4_t - const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); - const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d); @@ -2936,11 +2940,11 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -3228,11 +3232,11 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; @@ -3483,12 +3487,12 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), - vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), + ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), - vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), + ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -3598,8 +3602,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri // We use this macro instead of a function call because for some reason // the code runs 2-3% slower, even if the function is declared inline #define MULTIPLY_ACCUM_WITH_SCALE(index)\ - isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ - isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; #define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ @@ -3973,10 +3977,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); - isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; - isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; - isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; - isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; + isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; + isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; + isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; + isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; sum += d * (isum1 + isum2); } @@ -4256,10 +4260,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; scale += 4; @@ -4273,10 +4277,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; scale += 4; @@ -4757,10 +4761,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; sum += d * isum; @@ -5109,14 +5113,14 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi1 += vaddvq_s32(p1) * scales[2*j+0]; q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi2 += vaddvq_s32(p2) * scales[2*j+1]; } @@ -5449,13 +5453,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); const int32_t sumi1 = vaddvq_s32(p1) * scales[0]; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; sumf += d * (sumi1 + sumi2); @@ -5722,8 +5726,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); - sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; - sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; } sumf += d * sumi - dmin * sumi_mins; @@ -6112,10 +6116,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); - int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); - int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); - int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); - int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); + int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); + int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); + int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); + int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); } @@ -6399,10 +6403,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; scale += 4; @@ -6426,10 +6430,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; scale += 4; } //sum += isum * d_all * y[i].d; @@ -6816,10 +6820,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; sum += isum * d_all * y[i].d; From 1e3900ebacb3a0b385271389686403c97ad76d88 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Fri, 29 Dec 2023 16:15:37 +0000 Subject: [PATCH 286/664] flake.nix: expose full scope in legacyPackages --- .devops/nix/jetson-support.nix | 19 +++++++++++++------ flake.nix | 20 +++++++++++++++++--- 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/.devops/nix/jetson-support.nix b/.devops/nix/jetson-support.nix index 08426d2ab..78e2e40e0 100644 --- a/.devops/nix/jetson-support.nix +++ b/.devops/nix/jetson-support.nix @@ -8,12 +8,13 @@ pkgsCuda, ... }: - lib.optionalAttrs (system == "aarch64-linux") { - packages = + { + legacyPackages = let - caps.jetson-xavier = "7.2"; - caps.jetson-orin = "8.7"; - caps.jetson-nano = "5.3"; + caps.llamaPackagesXavier = "7.2"; + caps.llamaPackagesOrin = "8.7"; + caps.llamaPackagesTX2 = "6.2"; + caps.llamaPackagesNano = "5.3"; pkgsFor = cap: @@ -27,6 +28,12 @@ }; }; in - builtins.mapAttrs (name: cap: ((pkgsFor cap).callPackage ./scope.nix { }).llama-cpp) caps; + builtins.mapAttrs (name: cap: (pkgsFor cap).callPackage ./scope.nix { }) caps; + + packages = lib.optionalAttrs (system == "aarch64-linux") { + jetson-xavier = config.legacyPackages.llamaPackagesXavier.llama-cpp; + jetson-orin = config.legacyPackages.llamaPackagesOrin.llama-cpp; + jetson-nano = config.legacyPackages.llamaPackagesNano.llama-cpp; + }; }; } diff --git a/flake.nix b/flake.nix index 2209070aa..6785b52f4 100644 --- a/flake.nix +++ b/flake.nix @@ -80,16 +80,30 @@ ... }: { + # Unlike `.#packages`, legacyPackages may contain values of + # arbitrary types (including nested attrsets) and may even throw + # exceptions. This attribute isn't recursed into by `nix flake + # show` either. + # + # You can add arbitrary scripts to `.devops/nix/scope.nix` and + # access them as `nix build .#llamaPackages.${scriptName}` using + # the same path you would with an overlay. + legacyPackages = { + llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + }; + # We don't use the overlay here so as to avoid making too many instances of nixpkgs, # cf. https://zimbatm.com/notes/1000-instances-of-nixpkgs packages = { - default = (pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + default = config.legacyPackages.llamaPackages.llama-cpp; } // lib.optionalAttrs pkgs.stdenv.isLinux { opencl = config.packages.default.override { useOpenCL = true; }; - cuda = (pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; - rocm = (pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp; + rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; From a5c088d8c698299b973d2709153e5d95295606d9 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Tue, 26 Dec 2023 23:34:40 +0000 Subject: [PATCH 287/664] flake.nix: rocm not yet supported on aarch64, so hide the output --- flake.nix | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/flake.nix b/flake.nix index 6785b52f4..920a79906 100644 --- a/flake.nix +++ b/flake.nix @@ -74,6 +74,7 @@ { config, lib, + system, pkgs, pkgsCuda, pkgsRocm, @@ -103,10 +104,12 @@ // lib.optionalAttrs pkgs.stdenv.isLinux { opencl = config.packages.default.override { useOpenCL = true; }; cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp; - rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; + } + // lib.optionalAttrs (system == "x86_64-linux") { + rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; }; }; }; From 356ea17e0f92bfbbf28a4f69261bed48eff68d9c Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Fri, 29 Dec 2023 16:21:50 +0000 Subject: [PATCH 288/664] flake.nix: expose checks --- flake.nix | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/flake.nix b/flake.nix index 920a79906..8d0f095d7 100644 --- a/flake.nix +++ b/flake.nix @@ -111,6 +111,11 @@ // lib.optionalAttrs (system == "x86_64-linux") { rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; }; + + # Packages exposed in `.#checks` will be built by the CI and by + # `nix flake check`. Currently we expose all packages, but we could + # make more granular choices + checks = config.packages; }; }; } From 7adedecbe39bd552bc14142f496246d55a43ac4e Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Tue, 26 Dec 2023 19:17:26 +0000 Subject: [PATCH 289/664] workflows: nix-ci: init; build flake outputs --- .github/workflows/build.yml | 1 - .github/workflows/nix-ci.yml | 44 ++++++++++++++++++++++++++++++++++++ 2 files changed, 44 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/nix-ci.yml diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index a5090e398..0a28a1111 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -515,7 +515,6 @@ jobs: - name: Build Xcode project run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build - # freeBSD-latest: # runs-on: macos-12 # steps: diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml new file mode 100644 index 000000000..f82b2cb3d --- /dev/null +++ b/.github/workflows/nix-ci.yml @@ -0,0 +1,44 @@ +name: Nix CI + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + +jobs: + nix-build: + if: ${{ vars.CACHIX_NAME != '' }} + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, macos-latest ] + runs-on: ${{ matrix.os }} + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: Set-up cachix to push the results to + uses: cachix/cachix-action@v13 + with: + authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' + name: ${{ vars.CACHIX_NAME }} + - name: Build + run: > + nix run github:Mic92/nix-fast-build + -- --skip-cached --no-nom + --flake + ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" From 1e9ae54cf24d27afe3900d1250634a2a33423db1 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 17:19:11 +0000 Subject: [PATCH 290/664] workflows: nix-ci: add a job for eval --- .github/workflows/nix-ci.yml | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index f82b2cb3d..845b93bfb 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -11,6 +11,33 @@ on: paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] jobs: + nix-eval: + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, macos-latest ] + runs-on: ${{ matrix.os }} + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: List all flake outputs + run: nix flake show --all-systems + - name: Show all output paths + run: > + nix run github:nix-community/nix-eval-jobs + -- --gc-roots-dir gcroot + --flake + ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)" nix-build: if: ${{ vars.CACHIX_NAME != '' }} strategy: From c5239944bab0ff71915df8f2dc7e42fc2c138ff6 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 16:38:36 +0000 Subject: [PATCH 291/664] workflows: weekly `nix flake update` --- .github/workflows/nix-flake-update.yml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 .github/workflows/nix-flake-update.yml diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml new file mode 100644 index 000000000..fa9360841 --- /dev/null +++ b/.github/workflows/nix-flake-update.yml @@ -0,0 +1,22 @@ +name: update-flake-lock +on: + workflow_dispatch: + schedule: + - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00 + +jobs: + lockfile: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@main + - name: Update flake.lock + uses: DeterminateSystems/update-flake-lock@main + with: + pr-title: "nix: update flake.lock" + pr-labels: | + nix + pr-reviewers: philiptaron,SomeoneSerge + token: ${{ secrets.GITHUB_TOKEN }} From 06f2a5d1909a1385b1a16dab4ade68377e121bdd Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 17:36:08 +0000 Subject: [PATCH 292/664] workflows: nix-flakestry: drop tag filters ...and add a job for flakehub.com --- .github/workflows/nix-flakestry.yml | 23 ---------------- .github/workflows/nix-publish-flake.yml | 36 +++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 23 deletions(-) delete mode 100644 .github/workflows/nix-flakestry.yml create mode 100644 .github/workflows/nix-publish-flake.yml diff --git a/.github/workflows/nix-flakestry.yml b/.github/workflows/nix-flakestry.yml deleted file mode 100644 index 3abfb3509..000000000 --- a/.github/workflows/nix-flakestry.yml +++ /dev/null @@ -1,23 +0,0 @@ -# Make the flake discoverable on https://flakestry.dev -name: "Publish a flake to flakestry" -on: - push: - tags: - - "v?[0-9]+.[0-9]+.[0-9]+" - - "v?[0-9]+.[0-9]+" - workflow_dispatch: - inputs: - tag: - description: "The existing tag to publish" - type: "string" - required: true -jobs: - publish-flake: - runs-on: ubuntu-latest - permissions: - id-token: "write" - contents: "read" - steps: - - uses: flakestry/flakestry-publish@main - with: - version: "${{ inputs.tag || github.ref_name }}" diff --git a/.github/workflows/nix-publish-flake.yml b/.github/workflows/nix-publish-flake.yml new file mode 100644 index 000000000..2c3c1ebda --- /dev/null +++ b/.github/workflows/nix-publish-flake.yml @@ -0,0 +1,36 @@ +# Make the flake discoverable on https://flakestry.dev and https://flakehub.com/flakes +name: "Publish a flake to flakestry & flakehub" +on: + push: + tags: + - "*" + workflow_dispatch: + inputs: + tag: + description: "The existing tag to publish" + type: "string" + required: true +jobs: + flakestry-publish: + runs-on: ubuntu-latest + permissions: + id-token: "write" + contents: "read" + steps: + - uses: flakestry/flakestry-publish@main + with: + version: "${{ inputs.tag || github.ref_name }}" + flakehub-publish: + runs-on: "ubuntu-latest" + permissions: + id-token: "write" + contents: "read" + steps: + - uses: "actions/checkout@v4" + with: + ref: "${{ (inputs.tag != null) && format('refs/tags/{0}', inputs.tag) || '' }}" + - uses: "DeterminateSystems/nix-installer-action@main" + - uses: "DeterminateSystems/flakehub-push@main" + with: + visibility: "public" + tag: "${{ inputs.tag }}" From d8361747317c5cb2e00e7fb3b59ff4dce5a176a5 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 18:01:07 +0000 Subject: [PATCH 293/664] workflows: nix-ci: add a qemu job for jetsons --- .github/workflows/nix-ci.yml | 41 ++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index 845b93bfb..a38c6ead4 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -69,3 +69,44 @@ jobs: -- --skip-cached --no-nom --flake ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" + nix-build-aarch64: + if: ${{ vars.CACHIX_NAME != '' }} + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install QEMU + # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 + run: | + sudo apt-get install -y qemu-user-static qemu-system-aarch64 + sudo usermod -a -G kvm $USER + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-platforms = aarch64-linux + extra-system-features = nixos-test kvm + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: Set-up cachix to push the results to + uses: cachix/cachix-action@v13 + with: + authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' + name: ${{ vars.CACHIX_NAME }} + - name: Show all output paths + run: > + nix run github:nix-community/nix-eval-jobs + -- --gc-roots-dir gcroot + --flake + ".#packages.aarch64-linux" + - name: Build + run: > + nix run github:Mic92/nix-fast-build + -- --skip-cached --no-nom + --systems aarch64-linux + --flake + ".#checks.aarch64-linux" From 198ed7ebfc89b8f2b35a8b1655d57bfb57530c1a Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 18:25:25 +0000 Subject: [PATCH 294/664] flake.nix: suggest the binary caches --- flake.nix | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/flake.nix b/flake.nix index 8d0f095d7..488ed6c59 100644 --- a/flake.nix +++ b/flake.nix @@ -6,6 +6,29 @@ flake-parts.url = "github:hercules-ci/flake-parts"; }; + # Optional binary cache + nixConfig = { + extra-substituters = [ + # Populated by the CI in ggerganov/llama.cpp + "https://llama-cpp.cachix.org" + + # A development cache for nixpkgs imported with `config.cudaSupport = true`. + # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci. + # This lets one skip building e.g. the CUDA-enabled openmpi. + # TODO: Replace once nix-community obtains an official one. + "https://cuda-maintainers.cachix.org" + ]; + + # Verify these are the same keys as published on + # - https://app.cachix.org/cache/llama-cpp + # - https://app.cachix.org/cache/cuda-maintainers + extra-trusted-public-keys = [ + "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc=" + "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=" + ]; + }; + + # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl: # # ```bash From edd1ab7bc34c10a780ee7f9a4499f7689cdad36d Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sun, 31 Dec 2023 17:42:22 +0000 Subject: [PATCH 295/664] flake.lock: update to a commit recently cached by nixpkgs-cuda-ci --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 3fcd1f45d..15a0a1a8e 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1703559957, - "narHash": "sha256-x9PUuMEPGUOMB51zNxrDr2QoHbYWlCS2xhFedm9MC5Q=", + "lastModified": 1703637592, + "narHash": "sha256-8MXjxU0RfFfzl57Zy3OfXCITS0qWDNLzlBAdwxGZwfY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "75dd68c36f458c6593c5bbb48abfd3e59bfed380", + "rev": "cfc3698c31b1fb9cdcf10f36c9643460264d0ca8", "type": "github" }, "original": { From 58ba655af054715c0516ee270ad028ad9e74f357 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 2 Jan 2024 10:57:44 +0200 Subject: [PATCH 296/664] metal : enable shader debugging (cmake option) (#4705) * ggml : disable fast-math for Metal (cmake build only) ggml-ci * metal : fix Metal API debug warnings * cmake : add -fno-inline for Metal build (#4545) * metal : fix API debug warnings * metal : fix compile warnings * metal : use uint64_t for strides * cmake : rename option to LLAMA_METAL_SHADER_DEBUG * metal : fix mat-vec Q8_0 kernel for BS > 1 * metal : normalize mat-vec kernel signatures * cmake : respect LLAMA_QKK_64 option * metal : fix mat-vec Q4_K kernel for QK_K == 64 ggml-ci --- CMakeLists.txt | 34 ++- ci/run.sh | 14 +- ggml-metal.m | 28 ++- ggml-metal.metal | 475 +++++++++++++++++++++---------------- tests/test-backend-ops.cpp | 8 +- 5 files changed, 329 insertions(+), 230 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 545aab267..57ae4c2df 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -95,6 +95,7 @@ option(LLAMA_HIP_UMA "llama: use HIP unified memory arch option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) +option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) @@ -154,9 +155,9 @@ if (APPLE AND LLAMA_ACCELERATE) endif() if (LLAMA_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + find_library(FOUNDATION_LIBRARY Foundation REQUIRED) + find_library(METAL_FRAMEWORK Metal REQUIRED) + find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) message(STATUS "Metal framework found") set(GGML_HEADERS_METAL ggml-metal.h) @@ -173,6 +174,33 @@ if (LLAMA_METAL) # copy ggml-metal.metal to bin directory configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) + if (LLAMA_METAL_SHADER_DEBUG) + # custom command to do the following: + # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air + # xcrun -sdk macosx metallib ggml-metal.air -o ggml.metallib + # + # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works + # disabling fast math is needed in order to pass tests/test-backend-ops + # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 + set(XC_FLAGS -fno-fast-math -fno-inline -g) + if (LLAMA_QKK_64) + set(XC_FLAGS ${XC_FLAGS} -DQK_K=64) + endif() + + add_custom_command( + OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib + COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air + COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib + DEPENDS ggml-metal.metal + COMMENT "Compiling Metal kernels" + ) + + add_custom_target( + ggml-metal ALL + DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib + ) + endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${FOUNDATION_LIBRARY} ${METAL_FRAMEWORK} diff --git a/ci/run.sh b/ci/run.sh index 2e3343831..47a254f4c 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -30,6 +30,12 @@ sd=`dirname $0` cd $sd/../ SRC=`pwd` +CMAKE_EXTRA="" + +if [ ! -z ${GG_BUILD_METAL} ]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON" +fi + ## helpers # download a file if it does not exist or if it is outdated @@ -81,8 +87,8 @@ function gg_run_ctest_debug { set -e - (time cmake -DCMAKE_BUILD_TYPE=Debug .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log @@ -109,8 +115,8 @@ function gg_run_ctest_release { set -e - (time cmake -DCMAKE_BUILD_TYPE=Release .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log if [ -z ${GG_BUILD_LOW_PERF} ]; then (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log diff --git a/ggml-metal.m b/ggml-metal.m index 51a72ae33..cd9d00456 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -257,13 +257,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; #endif NSError * error = nil; - NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"]; + NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"]; if (libPath != nil) { + // pre-compiled library found NSURL * libURL = [NSURL fileURLWithPath:libPath]; GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]); ctx->library = [ctx->device newLibraryWithURL:libURL error:&error]; } else { - GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); + GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__); NSString * sourcePath; NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; @@ -291,6 +292,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { options = [MTLCompileOptions new]; options.preprocessorMacros = @{ @"QK_K" : @(64) }; #endif + // try to disable fast-math + // NOTE: this seems to have no effect whatsoever + // instead, in order to disable fast-math, we have to build ggml.metallib from the command line + // using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air + // and go through the "pre-compiled library found" path above + //[options setFastMathEnabled:false]; + ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; } @@ -1230,7 +1238,7 @@ void ggml_metal_graph_compute( // not sure how to avoid this // TODO: make a simpler cpy_bytes kernel - const int nth = MIN(1024, ne00); + const int nth = MIN((int) ctx->pipeline_cpy_f32_f32.maxTotalThreadsPerThreadgroup, ne00); [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1285,7 +1293,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - const int nth = MIN(1024, ne0); + const int nth = MIN((int) ctx->pipeline_add.maxTotalThreadsPerThreadgroup, ne00); [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1785,8 +1793,9 @@ void ggml_metal_graph_compute( [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; [encoder setBytes:&idx length:sizeof(idx) atIndex:18]; // TODO: how to make this an array? read Metal docs - for (int j = 0; j < n_as; ++j) { - struct ggml_tensor * src_cur = dst->src[2 + j]; + for (int j = 0; j < 8; ++j) { + // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 + struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; size_t offs_src_cur = 0; id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); @@ -1909,8 +1918,9 @@ void ggml_metal_graph_compute( [encoder setBytes:&r3 length:sizeof(r3) atIndex:21]; [encoder setBytes:&idx length:sizeof(idx) atIndex:22]; // TODO: how to make this an array? read Metal docs - for (int j = 0; j < n_as; ++j) { - struct ggml_tensor * src_cur = dst->src[2 + j]; + for (int j = 0; j < 8; ++j) { + // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 + struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; size_t offs_src_cur = 0; id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); @@ -2229,7 +2239,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; - const int nth = MIN(1024, ne0); + const int nth = MIN((int) ctx->pipeline_upscale_f32.maxTotalThreadsPerThreadgroup, ne0); [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index d5b54e112..1d5b8f6f4 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -59,26 +59,26 @@ kernel void kernel_add( constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant int64_t & nb00, - constant int64_t & nb01, - constant int64_t & nb02, - constant int64_t & nb03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, - constant int64_t & nb13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, - constant int64_t & nb0, - constant int64_t & nb1, - constant int64_t & nb2, - constant int64_t & nb3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, constant int64_t & offs, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -109,26 +109,26 @@ kernel void kernel_mul( constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant int64_t & nb00, - constant int64_t & nb01, - constant int64_t & nb02, - constant int64_t & nb03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, - constant int64_t & nb13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, - constant int64_t & nb0, - constant int64_t & nb1, - constant int64_t & nb2, - constant int64_t & nb3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { @@ -158,26 +158,26 @@ kernel void kernel_div( constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant int64_t & nb00, - constant int64_t & nb01, - constant int64_t & nb02, - constant int64_t & nb03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, - constant int64_t & nb13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, - constant int64_t & nb0, - constant int64_t & nb1, - constant int64_t & nb2, - constant int64_t & nb3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { @@ -205,7 +205,7 @@ kernel void kernel_add_row( device const float4 * src0, device const float4 * src1, device float4 * dst, - constant int64_t & nb [[buffer(28)]], + constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] + src1[tpig % nb]; } @@ -214,7 +214,7 @@ kernel void kernel_mul_row( device const float4 * src0, device const float4 * src1, device float4 * dst, - constant int64_t & nb [[buffer(28)]], + constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] * src1[tpig % nb]; } @@ -223,7 +223,7 @@ kernel void kernel_div_row( device const float4 * src0, device const float4 * src1, device float4 * dst, - constant int64_t & nb [[buffer(28)]], + constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] / src1[tpig % nb]; } @@ -307,26 +307,26 @@ kernel void kernel_sum_rows( constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant int64_t & nb00, - constant int64_t & nb01, - constant int64_t & nb02, - constant int64_t & nb03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, - constant int64_t & nb13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, - constant int64_t & nb0, - constant int64_t & nb1, - constant int64_t & nb2, - constant int64_t & nb3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, uint3 tpig[[thread_position_in_grid]]) { int64_t i3 = tpig.z; int64_t i2 = tpig.y; @@ -920,14 +920,21 @@ kernel void kernel_mul_mv_q4_0_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -939,14 +946,21 @@ kernel void kernel_mul_mv_q4_1_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -958,14 +972,21 @@ kernel void kernel_mul_mv_q5_0_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -977,14 +998,21 @@ kernel void kernel_mul_mv_q5_1_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -1071,12 +1099,19 @@ kernel void kernel_mul_mv_q8_0_f32( constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, constant int64_t & ne10, + constant int64_t & ne11, constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -1182,8 +1217,8 @@ kernel void kernel_mul_mv_f32_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]]) { kernel_mul_mv_f32_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg); @@ -1209,8 +1244,8 @@ kernel void kernel_mul_mv_f16_f16( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]]) { @@ -1346,8 +1381,8 @@ kernel void kernel_mul_mv_f16_f32_1row( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]]) { kernel_mul_mv_f16_f32_1row_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg); @@ -1452,8 +1487,8 @@ kernel void kernel_mul_mv_f16_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]]) { kernel_mul_mv_f16_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg); @@ -1478,8 +1513,8 @@ kernel void kernel_mul_mv_f16_f32_l4( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]]) { @@ -1543,7 +1578,8 @@ kernel void kernel_alibi_f32( const int64_t i3 = n / (ne2*ne1*ne0); const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); + //const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); + const int64_t k = i3*ne3 + i2; float m_k; @@ -2410,22 +2446,6 @@ typedef struct { } block_q6_K; // 210 bytes / block -static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) { - uchar4 r; - if (j < 4) { - r[0] = q[j+0] & 63; - r[2] = q[j+1] & 63; - r[1] = q[j+4] & 63; - r[3] = q[j+5] & 63; - } else { - r[0] = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); - r[2] = (q[j+5] & 0xF) | ((q[j-3] >> 6) << 4); - r[1] = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); - r[3] = (q[j+5] >> 4) | ((q[j+1] >> 6) << 4); - } - return r; -} - //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -2584,14 +2604,21 @@ kernel void kernel_mul_mv_q2_K_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -2841,14 +2868,21 @@ kernel void kernel_mul_mv_q3_K_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -2984,8 +3018,8 @@ void kernel_mul_mv_q4_K_f32_impl( constant uint & r2, constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { const int ix = tiisg/4; // 0...7 const int it = tiisg%4; // 0...3 @@ -2994,7 +3028,7 @@ void kernel_mul_mv_q4_K_f32_impl( const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int first_row = r0 * N_DST; const int ib_row = first_row * nb; const uint i12 = im%ne12; @@ -3060,7 +3094,7 @@ void kernel_mul_mv_q4_K_f32_impl( for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0+ im*ne0*ne1 + first_row + row] = all_sum; + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; } } } @@ -3072,14 +3106,21 @@ kernel void kernel_mul_mv_q4_K_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -3271,14 +3312,21 @@ kernel void kernel_mul_mv_q5_K_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -3398,14 +3446,21 @@ kernel void kernel_mul_mv_q6_K_f32( device const float * src1, device float * dst, constant int64_t & ne00, - constant int64_t & ne01[[buffer(4)]], - constant int64_t & ne02[[buffer(5)]], - constant int64_t & ne10[[buffer(9)]], - constant int64_t & ne12[[buffer(11)]], - constant int64_t & ne0 [[buffer(15)]], - constant int64_t & ne1 [[buffer(16)]], - constant uint & r2 [[buffer(17)]], - constant uint & r3 [[buffer(18)]], + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -3523,7 +3578,7 @@ void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg device const int8_t * qs = ((device const int8_t *)xb->qs); const half d = xb->d; - for (int i=0;i<16;i++) { + for (int i = 0; i < 16; i++) { reg[i/4][i%4] = (qs[i + 16*il] * d); } } @@ -3792,12 +3847,12 @@ void kernel_mul_mm_impl(device const uchar * src0, device float * dst, constant int64_t & ne00, constant int64_t & ne02, - constant int64_t & nb01, - constant int64_t & nb02, + constant uint64_t & nb01, + constant uint64_t & nb02, constant int64_t & ne12, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -3924,12 +3979,12 @@ kernel void kernel_mul_mm(device const uchar * src0, device float * dst, constant int64_t & ne00, constant int64_t & ne02, - constant int64_t & nb01, - constant int64_t & nb02, + constant uint64_t & nb01, + constant uint64_t & nb02, constant int64_t & ne12, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -3965,19 +4020,19 @@ kernel void kernel_mul_mm_id( device const uchar * ids, device const uchar * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne02, - constant int64_t & nb01, - constant int64_t & nb02, + constant uint64_t & nb01, + constant uint64_t & nb02, constant int64_t & ne12, constant int64_t & ne13, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4070,12 +4125,12 @@ typedef void (mat_mm_t)( device float * dst, constant int64_t & ne00, constant int64_t & ne02, - constant int64_t & nb01, - constant int64_t & nb02, + constant uint64_t & nb01, + constant uint64_t & nb02, constant int64_t & ne12, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4104,19 +4159,19 @@ typedef void (mat_mm_id_t)( device const uchar * ids, device const uchar * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne02, - constant int64_t & nb01, - constant int64_t & nb02, + constant uint64_t & nb01, + constant uint64_t & nb02, constant int64_t & ne12, constant int64_t & ne13, - constant int64_t & nb10, - constant int64_t & nb11, - constant int64_t & nb12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4153,7 +4208,7 @@ kernel void kernel_mul_mv_id_f32_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4169,7 +4224,7 @@ kernel void kernel_mul_mv_id_f32_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4222,7 +4277,7 @@ kernel void kernel_mul_mv_id_f16_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4238,7 +4293,7 @@ kernel void kernel_mul_mv_id_f16_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4291,7 +4346,7 @@ kernel void kernel_mul_mv_id_q8_0_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4307,7 +4362,7 @@ kernel void kernel_mul_mv_id_q8_0_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4354,7 +4409,7 @@ kernel void kernel_mul_mv_id_q4_0_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4370,7 +4425,7 @@ kernel void kernel_mul_mv_id_q4_0_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4417,7 +4472,7 @@ kernel void kernel_mul_mv_id_q4_1_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4433,7 +4488,7 @@ kernel void kernel_mul_mv_id_q4_1_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4480,7 +4535,7 @@ kernel void kernel_mul_mv_id_q5_0_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4496,7 +4551,7 @@ kernel void kernel_mul_mv_id_q5_0_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4543,7 +4598,7 @@ kernel void kernel_mul_mv_id_q5_1_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4559,7 +4614,7 @@ kernel void kernel_mul_mv_id_q5_1_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4606,7 +4661,7 @@ kernel void kernel_mul_mv_id_q2_K_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4622,7 +4677,7 @@ kernel void kernel_mul_mv_id_q2_K_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4669,7 +4724,7 @@ kernel void kernel_mul_mv_id_q3_K_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4685,7 +4740,7 @@ kernel void kernel_mul_mv_id_q3_K_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4732,7 +4787,7 @@ kernel void kernel_mul_mv_id_q4_K_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4748,7 +4803,7 @@ kernel void kernel_mul_mv_id_q4_K_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4795,7 +4850,7 @@ kernel void kernel_mul_mv_id_q5_K_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4811,7 +4866,7 @@ kernel void kernel_mul_mv_id_q5_K_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, @@ -4858,7 +4913,7 @@ kernel void kernel_mul_mv_id_q6_K_f32( device const char * ids, device const char * src1, device uchar * dst, - constant int64_t & nbi1, + constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -4874,7 +4929,7 @@ kernel void kernel_mul_mv_id_q6_K_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - constant int64_t & nb1, + constant uint64_t & nb1, constant uint & r2, constant uint & r3, constant int & idx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b115299c0..eff063b2d 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -15,19 +15,18 @@ #include #include - static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { size_t size = ggml_nelements(tensor); std::vector data(size); #if 0 - std::default_random_engine generator(rd()); + static std::default_random_engine generator(1234); std::uniform_real_distribution distribution(min, max); for (size_t i = 0; i < size; i++) { data[i] = distribution(generator); } -#endif +#else auto init_thread = [&](size_t start, size_t end) { std::random_device rd; std::default_random_engine generator(rd()); @@ -49,6 +48,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m for (auto & t : threads) { t.join(); } +#endif if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) { ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float)); @@ -437,7 +437,7 @@ struct test_case { double err = nmse(f1.data(), f2.data(), f1.size()); if (err > ud->max_err) { printf("[%s] NMSE = %f ", ggml_op_desc(t1), err); - //for (int i = 0; i < f1.size(); i++) { + //for (int i = 0; i < (int) f1.size(); i++) { // printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]); //} //printf("\n"); From 775ac8712a7b42cfead2585f42cec0dfd56644ab Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 2 Jan 2024 10:16:55 +0100 Subject: [PATCH 297/664] finetune: fix typo in README.md (#4733) Signed-off-by: Daniel Bevenius --- examples/finetune/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/finetune/README.md b/examples/finetune/README.md index a2a2c1281..a884706c5 100644 --- a/examples/finetune/README.md +++ b/examples/finetune/README.md @@ -61,7 +61,7 @@ For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' L --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin ``` -The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values. +The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values. Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime. If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`. From 26f3071d714f0b27ad7f021a46a66a1085480258 Mon Sep 17 00:00:00 2001 From: "Nam D. Tran" <42194884+namtranase@users.noreply.github.com> Date: Tue, 2 Jan 2024 16:23:38 +0700 Subject: [PATCH 298/664] py : re-enable mmap in convert hf (#4732) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci * fix: update torch version --------- Co-authored-by: Trần Đức Nam Co-authored-by: Le Hoang Anh Co-authored-by: Georgi Gerganov --- awq-py/requirements.txt | 2 +- convert-hf-to-gguf.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt index 5fe604329..991896116 100644 --- a/awq-py/requirements.txt +++ b/awq-py/requirements.txt @@ -1,2 +1,2 @@ -torch>=2.0.0 +torch>=2.1.1 transformers>=4.32.0 diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 51724c0df..203eaf64b 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -59,7 +59,7 @@ class Model: from safetensors import safe_open ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) else: - ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", weights_only=True)) + ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) with ctx as model_part: for name in model_part.keys(): From 5d7002d4372ebf107cfaf46fcd90df27b204f330 Mon Sep 17 00:00:00 2001 From: minarchist Date: Tue, 2 Jan 2024 04:38:15 -0600 Subject: [PATCH 299/664] server : add --override-kv parameter (#4710) * Changes to server to allow metadata override * documentation * flake.nix: expose full scope in legacyPackages * flake.nix: rocm not yet supported on aarch64, so hide the output * flake.nix: expose checks * workflows: nix-ci: init; build flake outputs * workflows: nix-ci: add a job for eval * workflows: weekly `nix flake update` * workflows: nix-flakestry: drop tag filters ...and add a job for flakehub.com * workflows: nix-ci: add a qemu job for jetsons * flake.nix: suggest the binary caches * flake.lock: update to a commit recently cached by nixpkgs-cuda-ci --------- Co-authored-by: John Co-authored-by: Someone Serge --- examples/server/server.cpp | 51 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 52d9b9768..b77d3f079 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2016,6 +2016,10 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-disable disables logging to a file.\n"); printf("\n"); + printf(" --override-kv KEY=TYPE:VALUE\n"); + printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); + printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf("\n"); } static void server_params_parse(int argc, char **argv, server_params &sparams, @@ -2379,6 +2383,49 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); } + else if (arg == "--override-kv") + { + if (++i >= argc) { + invalid_param = true; + break; + } + char * sep = strchr(argv[i], '='); + if (sep == nullptr || sep - argv[i] >= 128) { + fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); + invalid_param = true; + break; + } + struct llama_model_kv_override kvo; + std::strncpy(kvo.key, argv[i], sep - argv[i]); + kvo.key[sep - argv[i]] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_INT; + kvo.int_value = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_FLOAT; + kvo.float_value = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.bool_value = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.bool_value = false; + } else { + fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); + invalid_param = true; + break; + } + } else { + fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); + invalid_param = true; + break; + } + params.kv_overrides.push_back(kvo); + } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); @@ -2386,6 +2433,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, exit(1); } } + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.back().key[0] = 0; + } if (invalid_param) { From 32866c5edde402f42ff4233bb89dcfcede34fd22 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 2 Jan 2024 13:28:15 +0200 Subject: [PATCH 300/664] editorconfig : fix whitespace and indentation #4710 --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b77d3f079..e45ea809a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2383,8 +2383,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); } - else if (arg == "--override-kv") - { + else if (arg == "--override-kv") + { if (++i >= argc) { invalid_param = true; break; From 83e633c27efdf0eb0ba54249e784b0ea760b1007 Mon Sep 17 00:00:00 2001 From: postmasters Date: Tue, 2 Jan 2024 03:51:28 -0800 Subject: [PATCH 301/664] llama : differentiate the KV dims in the attention (#4657) * Add n_key_dim and n_value_dim Some models use values that are not derived from `n_embd`. Also remove `n_embd_head` and `n_embd_gqa` because it is not clear which "head" is referred to (key or value). Fix issue #4648. * Fix `llm_build_kqv` to use `n_value_gqa` * Rebase * Rename variables * Fix llm_build_kqv to be more generic wrt n_embd_head_k * Update default values for n_embd_head_k and n_embd_head_v Co-authored-by: Georgi Gerganov * Fix llm_load_tensors: the asserts were not backcompat --------- Co-authored-by: Georgi Gerganov --- gguf-py/gguf/constants.py | 2 + gguf-py/gguf/gguf_writer.py | 6 + llama.cpp | 271 +++++++++++++++++++++++++----------- 3 files changed, 201 insertions(+), 78 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index ae62cc575..f0a1c51f8 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -46,6 +46,8 @@ class Keys: HEAD_COUNT_KV = "{arch}.attention.head_count_kv" MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" CLAMP_KQV = "{arch}.attention.clamp_kqv" + KEY_LENGTH = "{arch}.attention.key_length" + VALUE_LENGTH = "{arch}.attention.value_length" LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 73e021607..d93aaa877 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -333,6 +333,12 @@ class GGUFWriter: def add_head_count_kv(self, count: int) -> None: self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + def add_key_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length) + + def add_value_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length) + def add_max_alibi_bias(self, bias: float) -> None: self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) diff --git a/llama.cpp b/llama.cpp index a833d4c15..704464039 100644 --- a/llama.cpp +++ b/llama.cpp @@ -245,6 +245,8 @@ enum llm_kv { LLM_KV_ATTENTION_HEAD_COUNT_KV, LLM_KV_ATTENTION_MAX_ALIBI_BIAS, LLM_KV_ATTENTION_CLAMP_KQV, + LLM_KV_ATTENTION_KEY_LENGTH, + LLM_KV_ATTENTION_VALUE_LENGTH, LLM_KV_ATTENTION_LAYERNORM_EPS, LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, @@ -297,6 +299,8 @@ static std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, { LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" }, { LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" }, + { LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" }, + { LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" }, { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, @@ -1284,6 +1288,8 @@ struct llama_hparams { uint32_t n_head_kv; uint32_t n_layer; uint32_t n_rot; + uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads + uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head uint32_t n_ff; uint32_t n_expert = 0; uint32_t n_expert_used = 0; @@ -1310,6 +1316,8 @@ struct llama_hparams { if (this->n_head_kv != other.n_head_kv) return true; if (this->n_layer != other.n_layer) return true; if (this->n_rot != other.n_rot) return true; + if (this->n_embd_head_k != other.n_embd_head_k) return true; + if (this->n_embd_head_v != other.n_embd_head_v) return true; if (this->n_ff != other.n_ff) return true; if (this->n_expert != other.n_expert) return true; if (this->n_expert_used != other.n_expert_used) return true; @@ -1331,12 +1339,12 @@ struct llama_hparams { return n_head/n_head_kv; } - uint32_t n_embd_head() const { - return n_embd/n_head; + uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads + return n_embd_head_k * n_head_kv; } - uint32_t n_embd_gqa() const { - return n_embd/n_gqa(); + uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads + return n_embd_head_v * n_head_kv; } }; @@ -1645,8 +1653,9 @@ static bool llama_kv_cache_init( uint32_t n_ctx, int n_gpu_layers, bool offload) { - const uint32_t n_embd = hparams.n_embd_gqa(); - const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_layer = hparams.n_layer; cache.has_shift = false; @@ -1677,8 +1686,8 @@ static bool llama_kv_cache_init( const int i_gpu_start = (int) n_layer - n_gpu_layers; for (int i = 0; i < (int) n_layer; i++) { - ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd*n_ctx); - ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd*n_ctx); + ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx); + ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx); ggml_format_name(k, "cache_k_l%d", i); ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); @@ -2672,6 +2681,12 @@ static void llm_load_hparams( // gpt-j n_rot = rotary_dim } + hparams.n_embd_head_k = hparams.n_embd / hparams.n_head; + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); + + hparams.n_embd_head_v = hparams.n_embd / hparams.n_head; + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); + // arch-specific KVs switch (model.arch) { case LLM_ARCH_LLAMA: @@ -3082,8 +3097,12 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head); LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv); LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer); - LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim + LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); + LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k); + LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v); LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa()); + LLAMA_LOG_INFO("%s: n_embd_k_gqa = %u\n", __func__, hparams.n_embd_k_gqa()); + LLAMA_LOG_INFO("%s: n_embd_v_gqa = %u\n", __func__, hparams.n_embd_v_gqa()); LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps); LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps); LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv); @@ -3173,10 +3192,11 @@ static bool llm_load_tensors( // create tensors for the weights { - const int64_t n_embd = hparams.n_embd; - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - const int64_t n_layer = hparams.n_layer; - const int64_t n_vocab = hparams.n_vocab; + const int64_t n_embd = hparams.n_embd; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const int64_t n_layer = hparams.n_layer; + const int64_t n_vocab = hparams.n_vocab; const auto tn = LLM_TN(model.arch); switch (model.arch) { @@ -3202,7 +3222,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3270,7 +3293,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3318,7 +3344,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3368,7 +3397,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3420,7 +3452,11 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); + const int i_gpu_start = n_layer - n_gpu_layers; model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { @@ -3469,7 +3505,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3520,7 +3559,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3567,7 +3609,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3665,7 +3710,10 @@ static bool llm_load_tensors( model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3714,7 +3762,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -3761,7 +3812,10 @@ static bool llm_load_tensors( model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); } - const uint32_t n_ff = hparams.n_ff; + const uint32_t n_ff = hparams.n_ff; + const int64_t n_embd_gqa = n_embd_v_gqa; + GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); const int i_gpu_start = n_layer - n_gpu_layers; @@ -4000,8 +4054,8 @@ static struct ggml_tensor * llm_build_inp_embd( return inpL; } -// Persimmon: n_rot = n_embd_head/2 -// Other: n_rot = n_embd_head +// Persimmon: n_rot = n_embd_head_k/2 +// Other: n_rot = n_embd_head_k static void llm_build_k_shift( struct ggml_context * ctx, const llama_hparams & hparams, @@ -4014,17 +4068,17 @@ static void llm_build_k_shift( float freq_base, float freq_scale, const llm_build_cb & cb) { - const int64_t n_layer = hparams.n_layer; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_gqa = hparams.n_embd_gqa(); - const int64_t n_embd_head = hparams.n_embd_head(); - const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; - const float ext_factor = cparams.yarn_ext_factor; - const float attn_factor = cparams.yarn_attn_factor; - const float beta_fast = cparams.yarn_beta_fast; - const float beta_slow = cparams.yarn_beta_slow; + const int64_t n_layer = hparams.n_layer; + const int64_t n_head_kv = hparams.n_head_kv; + const int64_t n_embd_head_k = hparams.n_embd_head_k; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; + const float ext_factor = cparams.yarn_ext_factor; + const float attn_factor = cparams.yarn_attn_factor; + const float beta_fast = cparams.yarn_beta_fast; + const float beta_slow = cparams.yarn_beta_slow; - GGML_ASSERT(n_embd_head % n_rot == 0); + GGML_ASSERT(n_embd_head_k % n_rot == 0); struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx); cb(K_shift, "K_shift", -1); @@ -4042,9 +4096,9 @@ static void llm_build_k_shift( // we rotate only the first n_rot dimensions ggml_rope_custom_inplace(ctx, ggml_view_3d(ctx, kv.k_l[il], - n_embd_head, n_head_kv, n_ctx, - ggml_row_size(kv.k_l[il]->type, n_embd_head), - ggml_row_size(kv.k_l[il]->type, n_embd_gqa), + n_embd_head_k, n_head_kv, n_ctx, + ggml_row_size(kv.k_l[il]->type, n_embd_head_k), + ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa), 0), K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); @@ -4065,18 +4119,19 @@ static void llm_build_kv_store( int32_t kv_head, const llm_build_cb & cb, int64_t il) { - const int64_t n_embd_gqa = hparams.n_embd_gqa(); + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); // compute the transposed [n_tokens, n_embd] V matrix - struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens)); + struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens)); //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed cb(v_cur_t, "v_cur_t", il); - struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_gqa, - (ggml_row_size(kv.k_l[il]->type, n_embd_gqa))*kv_head); + struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa, + (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head); cb(k_cache_view, "k_cache_view", il); - struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_gqa, + struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa, ( n_ctx)*ggml_element_size(kv.v_l[il]), (kv_head)*ggml_element_size(kv.v_l[il])); cb(v_cache_view, "v_cache_view", il); @@ -4226,20 +4281,20 @@ static struct ggml_tensor * llm_build_kqv( float kq_scale, const llm_build_cb & cb, int il) { - const int64_t n_embd = hparams.n_embd; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head = hparams.n_embd_head(); - const int64_t n_embd_gqa = hparams.n_embd_gqa(); + const int64_t n_head = hparams.n_head; + const int64_t n_head_kv = hparams.n_head_kv; + const int64_t n_embd_head_k = hparams.n_embd_head_k; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_head_v = hparams.n_embd_head_v; struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); cb(q, "q", il); struct ggml_tensor * k = ggml_view_3d(ctx, kv.k_l[il], - n_embd_head, n_kv, n_head_kv, - ggml_row_size(kv.k_l[il]->type, n_embd_gqa), - ggml_row_size(kv.k_l[il]->type, n_embd_head), + n_embd_head_k, n_kv, n_head_kv, + ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa), + ggml_row_size(kv.k_l[il]->type, n_embd_head_k), 0); cb(k, "k", il); @@ -4278,9 +4333,9 @@ static struct ggml_tensor * llm_build_kqv( // split cached v into n_head heads struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], - n_kv, n_embd_head, n_head_kv, + n_kv, n_embd_head_v, n_head_kv, ggml_element_size(kv.v_l[il])*n_ctx, - ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head, + ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v, 0); cb(v, "v", il); @@ -4290,7 +4345,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); cb(kqv_merged, "kqv_merged", il); - struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); + struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens); cb(cur, "kqv_merged_cont", il); cur = ggml_mul_mat(ctx, wo, cur); @@ -4317,8 +4372,10 @@ struct llm_build_context { const int64_t n_ctx; // user-specified context size (can be different from n_ctx_train) const int64_t n_head; const int64_t n_head_kv; - const int64_t n_embd_head; - const int64_t n_embd_gqa; + const int64_t n_embd_head_k; + const int64_t n_embd_k_gqa; + const int64_t n_embd_head_v; + const int64_t n_embd_v_gqa; const int64_t n_expert; const int64_t n_expert_used; @@ -4360,8 +4417,10 @@ struct llm_build_context { n_ctx (cparams.n_ctx), n_head (hparams.n_head), n_head_kv (hparams.n_head_kv), - n_embd_head (hparams.n_embd_head()), - n_embd_gqa (hparams.n_embd_gqa()), + n_embd_head_k (hparams.n_embd_head_k), + n_embd_k_gqa (hparams.n_embd_k_gqa()), + n_embd_head_v (hparams.n_embd_head_v), + n_embd_v_gqa (hparams.n_embd_v_gqa()), n_expert (hparams.n_expert), n_expert_used (hparams.n_expert_used), freq_base (cparams.rope_freq_base), @@ -4404,6 +4463,8 @@ struct llm_build_context { struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; @@ -4588,6 +4649,9 @@ struct llm_build_context { struct ggml_cgraph * build_baichuan() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4705,6 +4769,11 @@ struct llm_build_context { struct ggml_cgraph * build_falcon() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4824,6 +4893,11 @@ struct llm_build_context { struct ggml_cgraph * build_starcoder() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * pos; struct ggml_tensor * inpL; @@ -4920,7 +4994,12 @@ struct llm_build_context { struct ggml_cgraph * build_persimmon() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - const int64_t n_rot = n_embd_head / 2; + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + + const int64_t n_rot = n_embd_head_k / 2; struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5129,6 +5208,11 @@ struct llm_build_context { struct ggml_cgraph * build_refact() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5217,6 +5301,11 @@ struct llm_build_context { struct ggml_cgraph * build_bloom() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5308,6 +5397,11 @@ struct llm_build_context { struct ggml_cgraph * build_mpt() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5403,6 +5497,9 @@ struct llm_build_context { struct ggml_cgraph * build_stablelm() { struct ggml_cgraph * gf = ggml_new_graph(ctx0); + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5513,6 +5610,9 @@ struct llm_build_context { struct ggml_cgraph * build_qwen() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5624,6 +5724,11 @@ struct llm_build_context { struct ggml_cgraph * build_phi2() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * attn_norm_output; struct ggml_tensor * ffn_output; @@ -5736,6 +5841,9 @@ struct llm_build_context { struct ggml_cgraph * build_plamo() { struct ggml_cgraph * gf = ggml_new_graph(ctx0); + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5840,6 +5948,11 @@ struct llm_build_context { struct ggml_cgraph * build_gpt2() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_gqa == n_embd); + struct ggml_tensor * cur; struct ggml_tensor * pos; struct ggml_tensor * inpL; @@ -9627,8 +9740,8 @@ struct llama_context * llama_new_context_with_model( const ggml_type type_k = params.type_k; const ggml_type type_v = params.type_v; - GGML_ASSERT(hparams.n_embd_head() % ggml_blck_size(type_k) == 0); - GGML_ASSERT(hparams.n_embd_head() % ggml_blck_size(type_v) == 0); + GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); + GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); // reserve memory for context buffers if (!hparams.vocab_only) { @@ -10172,9 +10285,10 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto & hparams = ctx->model.hparams; const auto & cparams = ctx->cparams; - const auto n_layer = hparams.n_layer; - const auto n_embd = hparams.n_embd_gqa(); - const auto n_ctx = cparams.n_ctx; + const auto n_layer = hparams.n_layer; + const auto n_embd_k_gqa = hparams.n_embd_k_gqa(); + const auto n_embd_v_gqa = hparams.n_embd_v_gqa(); + const auto n_ctx = cparams.n_ctx; const size_t kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf); const uint32_t kv_head = kv_self.head; @@ -10196,15 +10310,15 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat std::vector vout2d(n_layer); for (int il = 0; il < (int) n_layer; ++il) { - kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head); - vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd); + kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head); + vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa); ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], - n_embd, kv_head, - elt_size*n_embd, 0); + n_embd_k_gqa, kv_head, + elt_size*n_embd_k_gqa, 0); ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il], - kv_head, n_embd, + kv_head, n_embd_v_gqa, elt_size*n_ctx, 0); ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il])); @@ -10311,9 +10425,10 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { const auto & hparams = ctx->model.hparams; const auto & cparams = ctx->cparams; - const int n_layer = hparams.n_layer; - const int n_embd = hparams.n_embd_gqa(); - const int n_ctx = cparams.n_ctx; + const int n_layer = hparams.n_layer; + const int n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int n_embd_v_gqa = hparams.n_embd_v_gqa(); + const int n_ctx = cparams.n_ctx; size_t kv_buf_size; uint32_t kv_head; @@ -10337,15 +10452,15 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { std::vector vin2d(n_layer); for (int il = 0; il < n_layer; ++il) { - kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head); - vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd); + kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head); + vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa); ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], - n_embd, kv_head, - elt_size*n_embd, 0); + n_embd_k_gqa, kv_head, + elt_size*n_embd_k_gqa, 0); ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il], - kv_head, n_embd, + kv_head, n_embd_v_gqa, elt_size*n_ctx, 0); ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d)); From 0040d42eeb237197054cc7790df5776eacfa608e Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Tue, 2 Jan 2024 06:15:16 -0800 Subject: [PATCH 302/664] llama : replace all API facing `int`'s with `int32_t` (#4577) * replaced all API facing `int`'s with `int32_t` * formatting and missed `int` in `llama_token_to_piece` --- llama.cpp | 50 +++++++++++++++++++++---------------------- llama.h | 63 +++++++++++++++++++++++++++---------------------------- 2 files changed, 56 insertions(+), 57 deletions(-) diff --git a/llama.cpp b/llama.cpp index 704464039..2e34cb395 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8030,7 +8030,7 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c } } -void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) { +void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) { const int64_t t_start_sample_us = ggml_time_us(); k = std::max(k, (int) min_keep); @@ -8390,7 +8390,7 @@ void llama_sample_classifier_free_guidance( } } -llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) { +llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) { GGML_ASSERT(ctx); auto N = float(llama_n_vocab(llama_get_model(ctx))); @@ -9598,7 +9598,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { return result; } -int llama_max_devices(void) { +int32_t llama_max_devices(void) { return LLAMA_MAX_DEVICES; } @@ -9909,15 +9909,15 @@ enum llama_vocab_type llama_vocab_type(const struct llama_model * model) { return model->vocab.type; } -int llama_n_vocab(const struct llama_model * model) { +int32_t llama_n_vocab(const struct llama_model * model) { return model->vocab.id_to_token.size(); } -int llama_n_ctx_train(const struct llama_model * model) { +int32_t llama_n_ctx_train(const struct llama_model * model) { return model->hparams.n_ctx_train; } -int llama_n_embd(const struct llama_model * model) { +int32_t llama_n_embd(const struct llama_model * model) { return model->hparams.n_embd; } @@ -9925,7 +9925,7 @@ float llama_rope_freq_scale_train(const struct llama_model * model) { return model->hparams.rope_freq_scale_train; } -int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) { +int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) { const auto & it = model->gguf_kv.find(key); if (it == model->gguf_kv.end()) { if (buf_size > 0) { @@ -9936,11 +9936,11 @@ int llama_model_meta_val_str(const struct llama_model * model, const char * key, return snprintf(buf, buf_size, "%s", it->second.c_str()); } -int llama_model_meta_count(const struct llama_model * model) { +int32_t llama_model_meta_count(const struct llama_model * model) { return (int)model->gguf_kv.size(); } -int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) { +int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) { if (i < 0 || i >= (int)model->gguf_kv.size()) { if (buf_size > 0) { buf[0] = '\0'; @@ -9952,7 +9952,7 @@ int llama_model_meta_key_by_index(const struct llama_model * model, int i, char return snprintf(buf, buf_size, "%s", it->first.c_str()); } -int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) { +int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) { if (i < 0 || i >= (int)model->gguf_kv.size()) { if (buf_size > 0) { buf[0] = '\0'; @@ -9964,7 +9964,7 @@ int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, c return snprintf(buf, buf_size, "%s", it->second.c_str()); } -int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { +int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { return snprintf(buf, buf_size, "%s %s %s", llama_model_arch_name(model->arch).c_str(), llama_model_type_name(model->type), @@ -9991,7 +9991,7 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch return ggml_get_tensor(model->ctx, name); } -int llama_model_quantize( +uint32_t llama_model_quantize( const char * fname_inp, const char * fname_out, const llama_model_quantize_params * params) { @@ -10004,7 +10004,7 @@ int llama_model_quantize( } } -int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) { +int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) { try { return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads); } catch (const std::exception & err) { @@ -10013,7 +10013,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor } } -int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int n_threads) { +int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) { try { return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads); } catch (const std::exception & err) { @@ -10111,7 +10111,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k } } -int llama_get_kv_cache_token_count(const struct llama_context * ctx) { +int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) { int result = 0; for (uint32_t i = 0; i < ctx->kv_self.size; i++) { @@ -10121,7 +10121,7 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) { return result; } -int llama_get_kv_cache_used_cells(const struct llama_context * ctx) { +int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) { return ctx->kv_self.used; } @@ -10603,7 +10603,7 @@ int llama_eval( struct llama_context * ctx, llama_token * tokens, int32_t n_tokens, - int n_past) { + int32_t n_past) { llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1); const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0)); @@ -10618,7 +10618,7 @@ int llama_eval_embd( struct llama_context * ctx, float * embd, int32_t n_tokens, - int n_past) { + int32_t n_past) { llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1); llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, }; @@ -10689,7 +10689,7 @@ void llama_batch_free(struct llama_batch batch) { if (batch.logits) free(batch.logits); } -int llama_decode( +int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch) { const int ret = llama_decode_internal(*ctx, batch); @@ -10737,11 +10737,11 @@ llama_token llama_token_nl(const struct llama_model * model) { return model->vocab.linefeed_id; } -int llama_add_bos_token(const struct llama_model * model) { +int32_t llama_add_bos_token(const struct llama_model * model) { return model->vocab.special_add_bos; } -int llama_add_eos_token(const struct llama_model * model) { +int32_t llama_add_eos_token(const struct llama_model * model) { return model->vocab.special_add_eos; } @@ -10761,12 +10761,12 @@ llama_token llama_token_eot(const struct llama_model * model) { return model->vocab.special_eot_id; } -int llama_tokenize( +int32_t llama_tokenize( const struct llama_model * model, const char * text, - int text_len, + int32_t text_len, llama_token * tokens, - int n_max_tokens, + int32_t n_max_tokens, bool add_bos, bool special) { auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special); @@ -10794,7 +10794,7 @@ static std::string llama_decode_text(const std::string & text) { } // does not write null-terminator to buf -int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) { +int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) { if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { case LLAMA_VOCAB_TYPE_SPM: { diff --git a/llama.h b/llama.h index af76bae2d..461d4604a 100644 --- a/llama.h +++ b/llama.h @@ -226,7 +226,7 @@ extern "C" { // model quantization parameters typedef struct llama_model_quantize_params { - int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() + int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() enum llama_ftype ftype; // quantize to this llama_ftype bool allow_requantize; // allow quantizing non-f32/f16 tensors bool quantize_output_tensor; // quantize output.weight @@ -310,21 +310,20 @@ extern "C" { LLAMA_API int64_t llama_time_us(void); - LLAMA_API int llama_max_devices (void); + LLAMA_API int32_t llama_max_devices(void); LLAMA_API bool llama_mmap_supported (void); LLAMA_API bool llama_mlock_supported(void); LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); - // TODO: become more consistent with returned int types across the API LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); - LLAMA_API int llama_n_vocab (const struct llama_model * model); - LLAMA_API int llama_n_ctx_train(const struct llama_model * model); - LLAMA_API int llama_n_embd (const struct llama_model * model); + LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); + LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); + LLAMA_API int32_t llama_n_embd (const struct llama_model * model); // Get the model's RoPE frequency scaling factor LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model); @@ -335,19 +334,19 @@ extern "C" { // - GGUF array values are not supported by these functions // Get metadata value as a string by key name - LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size); + LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size); // Get the number of metadata key/value pairs - LLAMA_API int llama_model_meta_count(const struct llama_model * model); + LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model); // Get metadata key name by index - LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size); + LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size); // Get metadata value as a string by index - LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size); + LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size); // Get a string describing the model type - LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size); + LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size); // Returns the total size of all the tensors in the model in bytes LLAMA_API uint64_t llama_model_size(const struct llama_model * model); @@ -359,7 +358,7 @@ extern "C" { LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name); // Returns 0 on success - LLAMA_API int llama_model_quantize( + LLAMA_API uint32_t llama_model_quantize( const char * fname_inp, const char * fname_out, const llama_model_quantize_params * params); @@ -370,20 +369,20 @@ extern "C" { // The model needs to be reloaded before applying a new adapter, otherwise the adapter // will be applied on top of the previous one // Returns 0 on success - LLAMA_API DEPRECATED(int llama_apply_lora_from_file( + LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file( struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, - int n_threads), + int32_t n_threads), "use llama_model_apply_lora_from_file instead"); - LLAMA_API int llama_model_apply_lora_from_file( + LLAMA_API int32_t llama_model_apply_lora_from_file( const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, - int n_threads); + int32_t n_threads); // // KV cache @@ -439,10 +438,10 @@ extern "C" { // Returns the number of tokens in the KV cache (slow, use only for debug) // If a KV cell has multiple sequences assigned to it, it will be counted multiple times - LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx); + LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx); // Returns the number of used KV cells (i.e. have at least one sequence assigned to them) - LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx); + LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx); // Clear the KV cache LLAMA_API void llama_kv_cache_clear( @@ -533,7 +532,7 @@ extern "C" { struct llama_context * ctx, llama_token * tokens, int32_t n_tokens, - int n_past), + int32_t n_past), "use llama_decode() instead"); // Same as llama_eval, but use float matrix input directly. @@ -542,7 +541,7 @@ extern "C" { struct llama_context * ctx, float * embd, int32_t n_tokens, - int n_past), + int32_t n_past), "use llama_decode() instead"); // Return batch for single sequence of tokens starting at pos_0 @@ -574,7 +573,7 @@ extern "C" { // 0 - success // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) // < 0 - error - LLAMA_API int llama_decode( + LLAMA_API int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch); @@ -614,10 +613,10 @@ extern "C" { LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line // Returns -1 if unknown, 1 for true or 0 for false. - LLAMA_API int llama_add_bos_token(const struct llama_model * model); + LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model); // Returns -1 if unknown, 1 for true or 0 for false. - LLAMA_API int llama_add_eos_token(const struct llama_model * model); + LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model); // codellama infill tokens LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix @@ -635,12 +634,12 @@ extern "C" { /// @return Returns a negative number on failure - the number of tokens that would have been returned /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext. /// Does not insert a leading space. - LLAMA_API int llama_tokenize( + LLAMA_API int32_t llama_tokenize( const struct llama_model * model, const char * text, - int text_len, + int32_t text_len, llama_token * tokens, - int n_max_tokens, + int32_t n_max_tokens, bool add_bos, bool special); @@ -648,11 +647,11 @@ extern "C" { // Uses the vocabulary in the provided context. // Does not write null terminator to the buffer. // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens. - LLAMA_API int llama_token_to_piece( + LLAMA_API int32_t llama_token_to_piece( const struct llama_model * model, llama_token token, char * buf, - int length); + int32_t length); // // Grammar @@ -704,7 +703,7 @@ extern "C" { LLAMA_API void llama_sample_top_k( struct llama_context * ctx, llama_token_data_array * candidates, - int k, + int32_t k, size_t min_keep); /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 @@ -763,7 +762,7 @@ extern "C" { llama_token_data_array * candidates, float tau, float eta, - int m, + int32_t m, float * mu); /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. @@ -836,8 +835,8 @@ extern "C" { llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, - int n_past, - int n_predict); + int32_t n_past, + int32_t n_predict); // Performance information LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx); From 540938f8904707dd74cb3be18495f853b312e72f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 2 Jan 2024 16:26:45 +0200 Subject: [PATCH 303/664] llama : llama_model_desc print number of experts --- llama.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 2e34cb395..3bb056dba 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9965,8 +9965,9 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3 } int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { - return snprintf(buf, buf_size, "%s %s %s", + return snprintf(buf, buf_size, "%s %s%s %s", llama_model_arch_name(model->arch).c_str(), + model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } From 0ef3ca2ac62016c0c545de1c89dc2e3e130f4a99 Mon Sep 17 00:00:00 2001 From: Phil H <5756783+phiharri@users.noreply.github.com> Date: Tue, 2 Jan 2024 15:48:49 +0000 Subject: [PATCH 304/664] server : add token counts to html footer (#4738) * server: add token counts to stats * server: generate hpp --------- Co-authored-by: phiharri --- examples/server/completion.js.hpp | 693 ++--- examples/server/index.html.hpp | 4591 +++++++++++++++-------------- examples/server/index.js.hpp | 3693 +++++++++++------------ examples/server/public/index.html | 4 +- 4 files changed, 4529 insertions(+), 4452 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index f0a071a69..fe5f81228 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -74,355 +74,376 @@ unsigned char 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0x2c, 0x55, + 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x64, 0x75, + 0x63, 0x65, 0x72, 0x2c, 0x4e, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, + 0x65, 0x52, 0x65, 0x66, 0x2c, 0x58, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, + 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x5a, 0x74, 0x20, + 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, + 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x45, 0x74, 0x20, 0x61, 0x73, + 0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a }; -unsigned int index_js_len = 22472; +unsigned int index_js_len = 22800; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 07d779d20..b059c75f2 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -427,7 +427,7 @@ } if (data.timings) { - llamaStats.value = data.timings; + llamaStats.value = data; } } @@ -880,7 +880,7 @@ } return html` - ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second + ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second ` } From f3f62f0d835d559e80714bbeb05d03125574e3dd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 2 Jan 2024 21:07:47 +0200 Subject: [PATCH 305/664] metal : optimize ggml_mul_mat_id (faster Mixtral PP) (#4725) * ggml : disable fast-math for Metal (cmake build only) ggml-ci * metal : fix Metal API debug warnings * cmake : add -fno-inline for Metal build (#4545) * metal : fix API debug warnings * metal : fix compile warnings * metal : use uint64_t for strides * cmake : rename option to LLAMA_METAL_SHADER_DEBUG * metal : fix mat-vec Q8_0 kernel for BS > 1 * metal : normalize mat-vec kernel signatures * cmake : respect LLAMA_QKK_64 option * metal : fix mat-vec Q4_K kernel for QK_K == 64 * metal : optimizing ggml_mul_mat_id (wip) * metal : minor fix * metal : opt mul_mm_id --- ggml-metal.m | 31 ++++--- ggml-metal.metal | 205 +++++++++++++++++++++++++++++++++++++++-------- 2 files changed, 190 insertions(+), 46 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index cd9d00456..7a369b55e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1657,6 +1657,10 @@ void ggml_metal_graph_compute( } }; + if (ggml_is_quantized(src0t)) { + GGML_ASSERT(ne00 >= nth0*nth1); + } + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1715,6 +1719,9 @@ void ggml_metal_graph_compute( // TODO: make this more general GGML_ASSERT(n_as <= 8); + // max size of the src1ids array in the kernel stack + GGML_ASSERT(ne11 <= 512); + struct ggml_tensor * src2 = gf->nodes[i]->src[2]; const int64_t ne20 = src2 ? src2->ne[0] : 0; @@ -1732,9 +1739,6 @@ void ggml_metal_graph_compute( GGML_ASSERT(!ggml_is_transposed(src2)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(ne20 % 32 == 0); - // !!!!!!!!! TODO: this assert is probably required but not sure! - //GGML_ASSERT(ne20 >= 64); GGML_ASSERT(src1t == GGML_TYPE_F32); const uint r2 = ne12/ne22; @@ -1742,22 +1746,22 @@ void ggml_metal_graph_compute( // find the break-even point where the matrix-matrix kernel becomes more efficient compared // to the matrix-vector kernel - int ne11_mm_min = 1; + int ne11_mm_min = n_as; const int idx = ((int32_t *) dst->op_params)[0]; // batch size GGML_ASSERT(ne01 == ne11); - const int64_t _ne1 = 1; // kernel_mul_mm_impl needs a reference in constant memory - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel // !!! // TODO: for now, always use mat-vec kernels until we figure out how to improve the // indirect matrix multiplication // !!! - if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && _ne1 > ne11_mm_min) { + if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && + ne20 % 32 == 0 && ne20 >= 64 && + ne11 > ne11_mm_min) { switch (src2->type) { case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32]; break; case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32]; break; @@ -1787,7 +1791,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11]; [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12]; [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:14]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; [encoder setBytes:&r2 length:sizeof(r2) atIndex:16]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; @@ -1805,8 +1809,7 @@ void ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - // TODO: processing one row at a time (ne11 -> 1) is not efficient - [encoder dispatchThreadgroups:MTLSizeMake( (_ne1 + 31)/32, (ne21 + 63)/64, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; } else { int nth0 = 32; int nth1 = 1; @@ -1889,11 +1892,17 @@ void ggml_metal_graph_compute( } break; default: { - GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); + GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); GGML_ASSERT(false && "not implemented"); } }; + if (ggml_is_quantized(src2t)) { + GGML_ASSERT(ne20 >= nth0*nth1); + } + + const int64_t _ne1 = 1; // kernels needs a reference in constant memory + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; diff --git a/ggml-metal.metal b/ggml-metal.metal index 1d5b8f6f4..9aa7b502a 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -846,7 +846,7 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre #define N_SIMDGROUP 2 // number of SIMD groups in a thread group //Note: This is a template, but strictly speaking it only applies to // quantizations where the block size is 32. It also does not -// giard against the number of rows not being divisible by +// guard against the number of rows not being divisible by // N_DST, so this is another explicit assumption of the implementation. template void mul_vec_q_n_f32_impl( @@ -3973,6 +3973,131 @@ void kernel_mul_mm_impl(device const uchar * src0, } } +// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in src1ids +template +void kernel_mul_mm_id_impl( + device const uchar * src0, + device const uchar * src1, + thread short * src1ids, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne02, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + int64_t ne1, + constant uint & r2, + constant uint & r3, + threadgroup uchar * shared_memory, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + threadgroup half * sa = (threadgroup half *)(shared_memory); + threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); + + const uint r0 = tgpig.y; + const uint r1 = tgpig.x; + const uint im = tgpig.z; + + if (r1 * BLOCK_SIZE_N >= ne1) return; + + // if this block is of 64x32 shape or smaller + short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M; + short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N; + + // a thread shouldn't load data outside of the matrix + short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1; + short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1; + + simdgroup_half8x8 ma[4]; + simdgroup_float8x8 mb[2]; + simdgroup_float8x8 c_res[8]; + for (int i = 0; i < 8; i++){ + c_res[i] = make_filled_simdgroup_matrix(0.f); + } + + short il = (tiitg % THREAD_PER_ROW); + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + uint offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02); + ushort offset1 = il/nl; + + device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1; + device const float * y = (device const float *)(src1 + + nb12 * im + + nb11 * src1ids[r1 * BLOCK_SIZE_N + thread_col] + + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); + + for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { + // load data and store to threadgroup memory + half4x4 temp_a; + dequantize_func(x, il, temp_a); + threadgroup_barrier(mem_flags::mem_threadgroup); + + for (int i = 0; i < 16; i++) { + *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \ + + (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \ + + (tiitg / THREAD_PER_ROW) % 8 + (i & 7) * 8) = temp_a[i/4][i%4]; + } + + *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y); + + il = (il + 2 < nl) ? il + 2 : il % 2; + x = (il < 2) ? x + (2+nl-1)/nl : x; + y += BLOCK_SIZE_K; + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // load matrices from threadgroup memory and conduct outer products + threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); + threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); + + for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { + for (int i = 0; i < 4; i++) { + simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i); + } + simdgroup_barrier(mem_flags::mem_none); + for (int i = 0; i < 2; i++) { + simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i); + } + + lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; + lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE; + + for (int i = 0; i < 8; i++){ + simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]); + } + } + } + + { + threadgroup_barrier(mem_flags::mem_threadgroup); + threadgroup float * temp_str = ((threadgroup float *)shared_memory) \ + + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; + for (int i = 0; i < 8; i++) { + simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + device float * C = dst + (BLOCK_SIZE_M * r0) + im*ne1*ne0; + if (sgitg == 0) { + for (int i = 0; i < n_rows; i++) { + for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { + *(C + i + src1ids[j + r1*BLOCK_SIZE_N] * ne0) = *(temp_str + i + j * BLOCK_SIZE_M); + } + } + } + } +} + template kernel void kernel_mul_mm(device const uchar * src0, device const uchar * src1, @@ -4019,7 +4144,7 @@ template( - src0[id], - src1 + bid*nb11, - (device float *) (dst + bid*nb1), + for (int64_t i1 = 0; i1 < ne1; i1++) { + if (((device int32_t *) (ids + i1*nbi1))[idx] == id) { + src1ids[_ne1++] = i1; + } + } + + kernel_mul_mm_id_impl( + src0s[id], + src1, + src1ids, + dst, ne00, ne02, nb01, @@ -4069,7 +4204,7 @@ kernel void kernel_mul_mm_id( nb11, nb12, ne0, - ne1, + _ne1, r2, r3, shared_memory, @@ -4158,7 +4293,7 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4471,7 +4606,7 @@ kernel void kernel_mul_mv_id_q4_0_f32( kernel void kernel_mul_mv_id_q4_1_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4515,7 +4650,7 @@ kernel void kernel_mul_mv_id_q4_1_f32( mul_vec_q_n_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4534,7 +4669,7 @@ kernel void kernel_mul_mv_id_q4_1_f32( kernel void kernel_mul_mv_id_q5_0_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4578,7 +4713,7 @@ kernel void kernel_mul_mv_id_q5_0_f32( mul_vec_q_n_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4597,7 +4732,7 @@ kernel void kernel_mul_mv_id_q5_0_f32( kernel void kernel_mul_mv_id_q5_1_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4641,7 +4776,7 @@ kernel void kernel_mul_mv_id_q5_1_f32( mul_vec_q_n_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4660,7 +4795,7 @@ kernel void kernel_mul_mv_id_q5_1_f32( kernel void kernel_mul_mv_id_q2_K_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4704,7 +4839,7 @@ kernel void kernel_mul_mv_id_q2_K_f32( kernel_mul_mv_q2_K_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4723,7 +4858,7 @@ kernel void kernel_mul_mv_id_q2_K_f32( kernel void kernel_mul_mv_id_q3_K_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4767,7 +4902,7 @@ kernel void kernel_mul_mv_id_q3_K_f32( kernel_mul_mv_q3_K_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4786,7 +4921,7 @@ kernel void kernel_mul_mv_id_q3_K_f32( kernel void kernel_mul_mv_id_q4_K_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4830,7 +4965,7 @@ kernel void kernel_mul_mv_id_q4_K_f32( kernel_mul_mv_q4_K_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4849,7 +4984,7 @@ kernel void kernel_mul_mv_id_q4_K_f32( kernel void kernel_mul_mv_id_q5_K_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4893,7 +5028,7 @@ kernel void kernel_mul_mv_id_q5_K_f32( kernel_mul_mv_q5_K_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, @@ -4912,7 +5047,7 @@ kernel void kernel_mul_mv_id_q5_K_f32( kernel void kernel_mul_mv_id_q6_K_f32( device const char * ids, device const char * src1, - device uchar * dst, + device float * dst, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -4956,7 +5091,7 @@ kernel void kernel_mul_mv_id_q6_K_f32( kernel_mul_mv_q6_K_f32_impl( src0[id], (device const float *) (src1 + bid*nb11), - (device float *) ( dst + bid*nb1), + dst + bid*ne0, ne00, ne01, ne02, From f2eb19bd8bc9f5730d6e05d7a52a9e19bf5ac099 Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Wed, 3 Jan 2024 03:43:19 -0500 Subject: [PATCH 306/664] server : throw an error when `slot unavailable` (#4741) --- examples/server/public/completion.js | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index 6e2b99565..baaec1d60 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -95,6 +95,15 @@ export async function* llama(prompt, params = {}, config = {}) { break; } } + if (result.error) { + result.error = JSON.parse(result.error); + if (result.error.content.includes('slot unavailable')) { + // Throw an error to be caught by upstream callers + throw new Error('slot unavailable'); + } else { + console.error(`llama.cpp error: ${result.error.content}`); + } + } if (result.error) { result.error = JSON.parse(result.error); console.error(`llama.cpp error: ${result.error.content}`); From 5f66ebca9c41a17385341da4b553a8eb5f07edee Mon Sep 17 00:00:00 2001 From: Guillaume Wenzek Date: Fri, 29 Dec 2023 18:07:03 +0100 Subject: [PATCH 307/664] ggml : extend ggml_get_rows, ggml_repeat, ggml_concat (ggml/639) * add more int ops * ggml_compute_forward_dup_bytes * add tests * PR comments * tests : minor indentations --------- Co-authored-by: Georgi Gerganov --- ggml.c | 166 ++++++++++++++++++++++++++++++++++++- tests/test-backend-ops.cpp | 42 ++++++++-- 2 files changed, 198 insertions(+), 10 deletions(-) diff --git a/ggml.c b/ggml.c index bcec200f6..b124f14cc 100644 --- a/ggml.c +++ b/ggml.c @@ -4766,8 +4766,11 @@ struct ggml_tensor * ggml_get_rows( } // TODO: implement non F32 return - //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); - struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0], b->ne[1], b->ne[2]); + enum ggml_type type = GGML_TYPE_F32; + if (a->type == GGML_TYPE_I32) { + type = a->type; + } + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]); result->op = GGML_OP_GET_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6938,14 +6941,165 @@ static void ggml_compute_forward_dup_f32( } } +// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. +static void ggml_compute_forward_dup_bytes( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(src0->type == dst->type); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { + ggml_compute_forward_dup_same_cont(params, src0, dst); + return; + } + + GGML_TENSOR_UNARY_OP_LOCALS; + + const size_t type_size = ggml_type_size(src0->type); + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == type_size && nb0 == type_size) { + // copy by rows + const size_t rs = ne00 * type_size; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + size_t id = 0; + char * dst_ptr = (char *) dst->data; + const size_t rs = ne00 * type_size; + + if (nb00 == type_size) { + // src0 is contigous on first dimension, copy by rows + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, type_size); + + id += type_size; + } + } + id += rs * (ne01 - ir1); + } + } + } + + return; + } + + // dst counters + + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, type_size); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } +} + static void ggml_compute_forward_dup( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { - if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { - ggml_compute_forward_dup_same_cont(params, src0, dst); + if (src0->type == dst->type) { + ggml_compute_forward_dup_bytes(params, src0, dst); return; } + switch (src0->type) { case GGML_TYPE_F16: { @@ -8404,10 +8558,12 @@ static void ggml_compute_forward_repeat( struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: + case GGML_TYPE_I16: { ggml_compute_forward_repeat_f16(params, src0, dst); } break; case GGML_TYPE_F32: + case GGML_TYPE_I32: { ggml_compute_forward_repeat_f32(params, src0, dst); } break; @@ -8550,6 +8706,7 @@ static void ggml_compute_forward_concat( struct ggml_tensor* dst) { switch (src0->type) { case GGML_TYPE_F32: + case GGML_TYPE_I32: { ggml_compute_forward_concat_f32(params, src0, src1, dst); } break; @@ -10674,6 +10831,7 @@ static void ggml_compute_forward_get_rows( ggml_compute_forward_get_rows_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: + case GGML_TYPE_I32: { ggml_compute_forward_get_rows_f32(params, src0, src1, dst); } break; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index eff063b2d..44412cb94 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -58,6 +58,9 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m int64_t hist[16]; ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); + } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { + // This is going to create some weird integers though. + ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor)); } else { GGML_ASSERT(false); } @@ -87,8 +90,13 @@ static std::vector tensor_to_float(const ggml_tensor * t) { tv.push_back(*(float *) &buf[i]); } else if (t->type == GGML_TYPE_I32) { tv.push_back((float)*(int32_t *) &buf[i]); + } else if (t->type == GGML_TYPE_I16) { + tv.push_back((float)*(int16_t *) &buf[i]); + } else if (t->type == GGML_TYPE_I8) { + tv.push_back((float)*(int8_t *) &buf[i]); } else if (quantized) { - tt.to_float(&buf[i], vq.data(), bs); + std::vector vq(ggml_blck_size(t->type)); + tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type)); tv.insert(tv.end(), vq.begin(), vq.end()); } else { GGML_ASSERT(false); @@ -661,17 +669,26 @@ struct test_repeat : public test_case { struct test_dup : public test_case { const ggml_type type; const std::array ne; + const std::array permute; + bool _use_permute; std::string vars() override { - return VARS_TO_STR2(type, ne); + std::string v = VARS_TO_STR2(type, ne); + if (_use_permute) v += "," + VAR_TO_STR(permute); + return v; } test_dup(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 1}) - : type(type), ne(ne) {} + std::array ne = {10, 10, 10, 1}, + std::array permute = {0, 0, 0, 0}) + : type(type), ne(ne), permute(permute), + _use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data()); + if (_use_permute) { + src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]); + } ggml_tensor * out = ggml_dup(ctx, src); return out; } @@ -1450,14 +1467,26 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } } + for (int b : {1, 7}) { + for (bool v : {false, true}) { + test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v)); + } + } test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2})); + test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1})); + test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2})); - test_cases.emplace_back(new test_dup()); + test_cases.emplace_back(new test_dup(GGML_TYPE_F32)); + test_cases.emplace_back(new test_dup(GGML_TYPE_F16)); + test_cases.emplace_back(new test_dup(GGML_TYPE_I32)); + test_cases.emplace_back(new test_dup(GGML_TYPE_I16)); + test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3})); for (ggml_type type : all_types) { test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1})); @@ -1565,7 +1594,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_alibi()); test_cases.emplace_back(new test_im2col()); - test_cases.emplace_back(new test_concat()); + test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); + test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) { test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order)); From ab62fc3e5520f5a143c36cb23c269f11aa4dafd6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 3 Jan 2024 11:25:54 +0200 Subject: [PATCH 308/664] scripts : fix sync order + metal sed --- scripts/sync-ggml-am.sh | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 91478f177..248cf1023 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -27,7 +27,7 @@ echo "Syncing ggml changes since commit $lc" cd $SRC_GGML git log --oneline $lc..HEAD -git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits +git log --oneline $lc..HEAD --reverse | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits if [ ! -s $SRC_LLAMA/ggml-commits ]; then rm -v $SRC_LLAMA/ggml-commits @@ -87,7 +87,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-impl.h -> ggml-impl.h # src/ggml-metal.h -> ggml-metal.h # src/ggml-metal.m -> ggml-metal.m - # src/ggml-metal.metal -> ggml-metal.metal # src/ggml-mpi.h -> ggml-mpi.h # src/ggml-mpi.c -> ggml-mpi.c # src/ggml-opencl.cpp -> ggml-opencl.cpp @@ -114,7 +113,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \ -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \ -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \ - -e 's/src\/ggml-metal\.metal/ggml-metal.metal/g' \ -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \ -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \ -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \ From 289313716ff7ccf6aee284f686a0fe8cbc7714af Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 3 Jan 2024 11:35:46 +0200 Subject: [PATCH 309/664] metal : add kernel_get_rows_i32 ggml-ci --- ggml-metal.m | 4 ++++ ggml-metal.metal | 29 +++++++++++++++++++++++++++++ 2 files changed, 33 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 7a369b55e..7aa92c14c 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -87,6 +87,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(get_rows_q4_K); GGML_METAL_DECL_KERNEL(get_rows_q5_K); GGML_METAL_DECL_KERNEL(get_rows_q6_K); + GGML_METAL_DECL_KERNEL(get_rows_i32); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(group_norm); GGML_METAL_DECL_KERNEL(norm); @@ -377,6 +378,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(get_rows_q4_K); GGML_METAL_ADD_KERNEL(get_rows_q5_K); GGML_METAL_ADD_KERNEL(get_rows_q6_K); + GGML_METAL_ADD_KERNEL(get_rows_i32); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(group_norm); GGML_METAL_ADD_KERNEL(norm); @@ -499,6 +501,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(get_rows_q4_K); GGML_METAL_DEL_KERNEL(get_rows_q5_K); GGML_METAL_DEL_KERNEL(get_rows_q6_K); + GGML_METAL_DEL_KERNEL(get_rows_i32); GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(group_norm); GGML_METAL_DEL_KERNEL(norm); @@ -1978,6 +1981,7 @@ void ggml_metal_graph_compute( case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break; case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break; + case GGML_TYPE_I32: [encoder setComputePipelineState:ctx->pipeline_get_rows_i32]; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index 9aa7b502a..a7d3f9efa 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -3829,6 +3829,35 @@ kernel void kernel_get_rows_f16( } } +kernel void kernel_get_rows_i32( + device const void * src0, + device const char * src1, + device int32_t * dst, + constant int64_t & ne00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb1, + constant uint64_t & nb2, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint3 tptg [[threads_per_threadgroup]]) { + const int64_t i10 = tgpig.x; + const int64_t i11 = tgpig.y; + + const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0]; + + const int64_t i02 = i11; + + for (int ind = tiitg; ind < ne00; ind += tptg.x) { + ((device int32_t *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] = + ((device int32_t *) ((device char *) src0 + r*nb01 + i02*nb02))[ind]; + } +} + + #define BLOCK_SIZE_M 64 // 8 simdgroup matrices from matrix A #define BLOCK_SIZE_N 32 // 4 simdgroup matrices from matrix B #define BLOCK_SIZE_K 32 From 75e3fd85814c367b55aea11e7bb38cb7b82c6aa0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 3 Jan 2024 11:37:44 +0200 Subject: [PATCH 310/664] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 5b6a440f7..2105a8df2 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -df098ea908764cba4a4889a1cbe7b026b2d31a14 +5b6f3aeba051be8926cb921b8ba529ff990608bf From d55356d3baa58a6c3a9171cb67a67094b9aa9dff Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 3 Jan 2024 13:01:44 +0200 Subject: [PATCH 311/664] cuda : mark I16 and I32 ops as unsupported ggml-ci --- ggml-cuda.cu | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 8c2712308..2e759d43e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -10039,14 +10039,22 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten } return false; } break; + case GGML_OP_DUP: + case GGML_OP_REPEAT: + case GGML_OP_CONCAT: + { + ggml_type src0_type = op->src[0]->type; + if (src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16) { + return true; + } + return false; + } break; case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NORM: - case GGML_OP_REPEAT: - case GGML_OP_DUP: case GGML_OP_ADD: case GGML_OP_MUL: case GGML_OP_DIV: @@ -10063,7 +10071,6 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: - case GGML_OP_CONCAT: case GGML_OP_GROUP_NORM: case GGML_OP_UPSCALE: case GGML_OP_PAD: From 7bed7eba359b0fa8e575345dc5467a46b4ba509f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 3 Jan 2024 14:18:46 +0200 Subject: [PATCH 312/664] cuda : simplify expression Co-authored-by: slaren --- ggml-cuda.cu | 5 +---- scripts/sync-ggml.last | 2 +- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2e759d43e..52d3cc6a6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -10044,10 +10044,7 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - if (src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16) { - return true; - } - return false; + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; case GGML_OP_NONE: case GGML_OP_RESHAPE: diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 2105a8df2..354246a26 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -5b6f3aeba051be8926cb921b8ba529ff990608bf +3fd01e00e40583ccd4b393a7c6502d6a4455a1d5 From ece9a45e8ffb73ad461c792720c2fec28b0137bc Mon Sep 17 00:00:00 2001 From: Ashraful Islam Date: Wed, 3 Jan 2024 11:30:02 -0600 Subject: [PATCH 313/664] swift : update Package.swift to use ggml as dependency (#4691) * updates the package.swift to use ggml as dependency * changes the ggml package url src to ggerganov --- Package.swift | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/Package.swift b/Package.swift index 18d610d69..e33a4ff46 100644 --- a/Package.swift +++ b/Package.swift @@ -13,21 +13,17 @@ let package = Package( products: [ .library(name: "llama", targets: ["llama"]), ], + dependencies: [ + .package(url: "https://github.com/ggerganov/ggml.git", .branch("master")) + ], targets: [ .target( name: "llama", + dependencies: ["ggml"], path: ".", exclude: [], sources: [ - "ggml.c", "llama.cpp", - "ggml-alloc.c", - "ggml-backend.c", - "ggml-quants.c", - "ggml-metal.m", - ], - resources: [ - .process("ggml-metal.metal") ], publicHeadersPath: "spm-headers", cSettings: [ From cb1e2818e0e12ec99f7236ec5d4f3ffd8bcc2f4a Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 3 Jan 2024 18:53:40 +0100 Subject: [PATCH 314/664] train : fix typo in overlapping-samples help msg (#4758) This commit fixes a typo in the help message for the --overlapping-samples option. Signed-off-by: Daniel Bevenius --- common/train.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/train.cpp b/common/train.cpp index dcf9614e4..e6f2f7a2f 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1107,7 +1107,7 @@ void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train fprintf(stderr, " --sample-start STR Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\n", params->sample_start.c_str()); fprintf(stderr, " --include-sample-start Include the sample start in the samples. (default off)\n"); fprintf(stderr, " --escape process sample start escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stderr, " --overlapping-samples Samples my overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n"); + fprintf(stderr, " --overlapping-samples Samples may overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n"); fprintf(stderr, " --fill-with-next-samples Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\n"); fprintf(stderr, " --separate-with-eos When fill-with-next-samples, insert end-of-sequence token between samples.%s\n", params->separate_with_eos ? " (default)" : ""); fprintf(stderr, " --separate-with-bos When fill-with-next-samples, insert begin-of-sequence token between samples.%s\n", params->separate_with_bos ? " (default)" : ""); From 46cea79e1f32499bb24b9fab12123cd386e96728 Mon Sep 17 00:00:00 2001 From: singularity <12184989+singularity-s0@users.noreply.github.com> Date: Thu, 4 Jan 2024 15:58:16 +0800 Subject: [PATCH 315/664] llama.swiftui : fix build of ggml.metallib (#4754) * metal: fix metal backend init failure in swiftui * metal: build ggml.metallib instead of copy src * llama.swift : remove debug flags from metallib build --------- Co-authored-by: Georgi Gerganov --- .../llama.swiftui.xcodeproj/project.pbxproj | 21 +++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index 2e6159928..7bf4489a2 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -9,7 +9,6 @@ /* Begin PBXBuildFile section */ 542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; }; 5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; }; - 542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; }; 542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; }; 542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; }; 542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; }; @@ -24,8 +23,25 @@ 8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; }; 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; }; 8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; }; + F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; }; /* End PBXBuildFile section */ +/* Begin PBXBuildRule section */ + F1FE20DB2B465C2100B45541 /* PBXBuildRule */ = { + isa = PBXBuildRule; + compilerSpec = com.apple.compilers.proxy.script; + fileType = sourcecode.metal; + inputFiles = ( + ); + isEditable = 1; + outputFiles = ( + "${DERIVED_FILES_DIR}/ggml-metal.air", + "${DERIVED_FILES_DIR}/ggml.metallib", + ); + script = "# metal\nxcrun metal -c \"${INPUT_FILE_PATH}\" -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\nxcrun metallib -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE%-metal}.metallib\" \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\n"; + }; +/* End PBXBuildRule section */ + /* Begin PBXFileReference section */ 542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = ""; }; 542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = ""; }; @@ -190,6 +206,7 @@ 8A1C83712AC328BD0096AF73 /* Resources */, ); buildRules = ( + F1FE20DB2B465C2100B45541 /* PBXBuildRule */, ); dependencies = ( ); @@ -241,7 +258,7 @@ isa = PBXResourcesBuildPhase; buildActionMask = 2147483647; files = ( - 542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */, + F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */, 8A3F84242AC4C891005E2EE8 /* models in Resources */, 8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */, 8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */, From dc891b7f7a23158d54f9383790b92c79cc5906c1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 4 Jan 2024 10:12:26 +0200 Subject: [PATCH 316/664] ggml : include stdlib.h before intrin.h (#4736) --- ggml-impl.h | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-impl.h b/ggml-impl.h index 1f5610a86..2faced080 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -5,6 +5,7 @@ // GGML internal header #include +#include // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/ #include #include #include // memcpy From e5804313a1edaf00726ed0b96ecced07accbf50c Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Thu, 4 Jan 2024 03:17:09 -0500 Subject: [PATCH 317/664] server : fix options in README.md (#4765) * fix examples/server/README.md * minor : fix whitespace --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 718a7e064..243e66991 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -168,6 +168,12 @@ node index.js `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) + + `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) + + `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) + *Result JSON:* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. @@ -198,12 +204,6 @@ node index.js `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) - `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) - - `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) - - `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) - - **POST** `/tokenize`: Tokenize a given text. *Options:* From 3c0b585561d74a56977cf3a3844535ecc9e37972 Mon Sep 17 00:00:00 2001 From: singularity <12184989+singularity-s0@users.noreply.github.com> Date: Thu, 4 Jan 2024 16:22:38 +0800 Subject: [PATCH 318/664] llama.swiftui : support loading custom model from file picker (#4767) * swiftui: support load model from file picker * swiftui: remove trailing whitespace --- .../llama.swiftui.xcodeproj/project.pbxproj | 4 ++ .../llama.swiftui/UI/ContentView.swift | 2 + .../llama.swiftui/UI/LoadCustomButton.swift | 44 +++++++++++++++++++ 3 files changed, 50 insertions(+) create mode 100644 examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index 7bf4489a2..a70750a22 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -23,6 +23,7 @@ 8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; }; 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; }; 8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; }; + F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; }; F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; }; /* End PBXBuildFile section */ @@ -68,6 +69,7 @@ 8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = ""; }; 8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = ""; }; 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = ""; }; + F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = ""; }; /* End PBXFileReference section */ /* Begin PBXFrameworksBuildPhase section */ @@ -182,6 +184,7 @@ children = ( 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */, 8A1C83782AC328BD0096AF73 /* ContentView.swift */, + F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */, ); path = UI; sourceTree = ""; @@ -274,6 +277,7 @@ files = ( 542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */, 549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */, + F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */, 542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */, 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */, 542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */, diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index 147e0c63b..7c81ea256 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -103,6 +103,8 @@ struct ContentView: View { ContentView.cleanupModelCaches() llamaState.cacheCleared = true } + + LoadCustomButton(llamaState: llamaState) } .padding(.top, 4) .font(.system(size: 12)) diff --git a/examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift b/examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift new file mode 100644 index 000000000..4315dbe4f --- /dev/null +++ b/examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift @@ -0,0 +1,44 @@ +import SwiftUI +import UniformTypeIdentifiers + +struct LoadCustomButton: View { + @ObservedObject private var llamaState: LlamaState + @State private var showFileImporter = false + + init(llamaState: LlamaState) { + self.llamaState = llamaState + } + + var body: some View { + VStack { + Button(action: { + showFileImporter = true + }) { + Text("Load Custom Model") + } + } + .fileImporter( + isPresented: $showFileImporter, + allowedContentTypes: [UTType(filenameExtension: "gguf", conformingTo: .data)!], + allowsMultipleSelection: false + ) { result in + switch result { + case .success(let files): + files.forEach { file in + let gotAccess = file.startAccessingSecurityScopedResource() + if !gotAccess { return } + + do { + try llamaState.loadModel(modelUrl: file.absoluteURL) + } catch let err { + print("Error: \(err.localizedDescription)") + } + + file.stopAccessingSecurityScopedResource() + } + case .failure(let error): + print(error) + } + } + } +} From a91928014fcf51fe297823fcff0788de4f14206e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 4 Jan 2024 09:43:23 +0100 Subject: [PATCH 319/664] Print backend name on test-backend-ops failure (#4751) --- tests/test-backend-ops.cpp | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 44412cb94..b79de7a7d 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -392,15 +392,21 @@ struct test_case { struct callback_userdata { bool ok; double max_err; + ggml_backend_t backend1; + ggml_backend_t backend2; }; callback_userdata ud { true, max_nmse_err(), + backend1, + backend2 }; auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool { callback_userdata * ud = (callback_userdata *) user_data; + const char * bn1 = ggml_backend_name(ud->backend1); + const char * bn2 = ggml_backend_name(ud->backend2); if (t1->op == GGML_OP_NONE) { // sentinels must be unchanged @@ -422,7 +428,7 @@ struct test_case { for (size_t i = 0; i < f1.size(); i++) { // check for nans if (std::isnan(f1[i]) || std::isnan(f2[i])) { - printf("[%s] NaN at index %zu (%f %f) ", ggml_op_desc(t1), i, f1[i], f2[i]); + printf("[%s] NaN at index %zu (%s=%f %s=%f) ", ggml_op_desc(t1), i, bn1, f1[i], bn2, f2[i]); ud->ok = false; return true; } @@ -430,12 +436,12 @@ struct test_case { if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) { if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) { if (std::signbit(f1[i]) != std::signbit(f2[i])) { - printf("[%s] inf sign mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]); + printf("[%s] inf sign mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]); ud->ok = false; return true; } } else { - printf("[%s] inf mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]); + printf("[%s] inf mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]); ud->ok = false; return true; } From 012cf349aec8ffb47c9def5dc018240fa3721e8b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 4 Jan 2024 19:56:33 +0200 Subject: [PATCH 320/664] server : send token probs for "stream == false" (#4714) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e45ea809a..d1469fb08 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1265,7 +1265,7 @@ struct llama_server_context { std::vector probs_output = {}; const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); - size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); + size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); if (probs_pos < probs_stop_pos) { @@ -1325,7 +1325,7 @@ struct llama_server_context { probs = std::vector( slot.generated_token_probs.begin(), - slot.generated_token_probs.begin() + slot.sent_token_probs_index); + slot.generated_token_probs.end()); } res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); } From b3a7c20b5c035250257d2b62851c379b159c899a Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 4 Jan 2024 20:45:37 +0100 Subject: [PATCH 321/664] finetune : remove unused includes (#4756) This commit removes unused includes from finetune.cpp. Signed-off-by: Daniel Bevenius --- examples/finetune/finetune.cpp | 6 ------ 1 file changed, 6 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index e0520f64c..eaca42fc1 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -3,15 +3,9 @@ #include "llama.h" #include "common.h" #include "train.h" -#include #include -#include -#include #include -#include #include -#include -#include #include #include From 3681f22443d917e7328456b69c276d6927dafeec Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 5 Jan 2024 15:11:10 +0200 Subject: [PATCH 322/664] examples : add few-shot translation example (#4783) --- examples/base-translate.sh | 56 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100755 examples/base-translate.sh diff --git a/examples/base-translate.sh b/examples/base-translate.sh new file mode 100755 index 000000000..50fba025c --- /dev/null +++ b/examples/base-translate.sh @@ -0,0 +1,56 @@ +#!/bin/bash +# +# Few-shot translation example. +# Requires a base model (i.e. no fine-tuned or instruct models). +# +# Usage: +# +# cd llama.cpp +# make -j +# +# ./examples/base-translate.sh "" +# + +if [ $# -ne 2 ]; then + echo "Usage: ./base-translate.sh \"\"" + exit 1 +fi + +ftmp="__llama.cpp_example_tmp__.txt" +trap "rm -f $ftmp" EXIT + +echo "Translate from English to French: + +=== + +sea otter, peppermint, plush girafe: + +sea otter => loutre de mer +peppermint => menthe poivrée +plush girafe => girafe peluche + +=== + +violin + +violin => violon + +=== + +phone, computer, mouse, keyboard: + +phone => téléphone +computer => ordinateur +mouse => souris +keyboard => clavier + +=== +" > $ftmp + +echo "$2 +" >> $ftmp + +model=$1 + +# generate the most likely continuation, run on the CPU until the string "===" is found +./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -ngl 0 -r "===" From c1d7cb28d3fed97fbc95fc3c43f0c5e2113e546c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 5 Jan 2024 15:18:21 +0200 Subject: [PATCH 323/664] ggml : do not sched_yield when calling BLAS (#4761) * ggml : do not sched_yield when calling BLAS ggml-ci * ggml : fix do_yield logic ggml-ci * ggml : simplify do_yield logic ggml-ci --- ggml.c | 41 ++++++++++++++--------------------------- 1 file changed, 14 insertions(+), 27 deletions(-) diff --git a/ggml.c b/ggml.c index b124f14cc..62f0f18ef 100644 --- a/ggml.c +++ b/ggml.c @@ -9704,10 +9704,10 @@ static void ggml_compute_forward_group_norm( #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // helper function to determine if it is better to use BLAS or not // for large matrices, BLAS is faster -static bool ggml_compute_forward_mul_mat_use_blas( - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { +static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + //const int64_t ne00 = src0->ne[0]; //const int64_t ne01 = src0->ne[1]; @@ -9787,7 +9787,7 @@ static void ggml_compute_forward_mul_mat( #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { + if (ggml_compute_forward_mul_mat_use_blas(dst)) { if (params->ith != 0) { return; } @@ -16301,24 +16301,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { //n_tasks = MIN(n_threads, MAX(1, nr0/128)); //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); - -#if defined(GGML_USE_CUBLAS) - if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning - } -#elif defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning - } -#endif -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning - } -#endif } break; case GGML_OP_MUL_MAT_ID: { @@ -16491,6 +16473,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { state->shared->node_n += 1; return (thread_ret_t) GGML_EXIT_ABORTED; } + if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { // all other threads are finished and spinning // do finalize and init here so we don't have synchronize again @@ -16556,14 +16539,18 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } else { // wait for other threads to finish const int last = node_n; + + const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT; + while (true) { // TODO: this sched_yield can have significant impact on the performance - either positive or negative // depending on the workload and the operating system. // since it is not clear what is the best approach, it should potentially become user-configurable // ref: https://github.com/ggerganov/ggml/issues/291 -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - sched_yield(); -#endif + // UPD: adding the do_yield flag seems to resolve the issue universally + if (do_yield) { + sched_yield(); + } node_n = atomic_load(&state->shared->node_n); if (node_n != last) break; @@ -16642,7 +16629,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { + if (ggml_compute_forward_mul_mat_use_blas(node)) { if (node->src[0]->type != GGML_TYPE_F32) { // here we need memory just for single 2D matrix from src0 cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]); From 1bf681f90ef4cf37b36e6d604d3e30fc57eda650 Mon Sep 17 00:00:00 2001 From: Finn Voorhees Date: Wed, 3 Jan 2024 08:39:43 -0500 Subject: [PATCH 324/664] ggml : add error handling to graph_compute (whisper/1714) --- ggml-backend-impl.h | 2 +- ggml-backend.c | 10 +++++++--- ggml-backend.h | 2 +- ggml-cuda.cu | 4 +++- ggml-metal.h | 2 +- ggml-metal.m | 9 +++++---- 6 files changed, 18 insertions(+), 11 deletions(-) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 05859935a..ca21b4743 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -90,7 +90,7 @@ extern "C" { void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan - void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); diff --git a/ggml-backend.c b/ggml-backend.c index 2c3752067..53e741cb8 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -195,11 +195,14 @@ void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_ ggml_backend_synchronize(backend); } -void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - backend->iface.graph_compute(backend, cgraph); +bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + if (!backend->iface.graph_compute(backend, cgraph)) { + return false; + } // TODO: optional sync ggml_backend_synchronize(backend); + return true; } bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -597,7 +600,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac GGML_UNUSED(backend); } -static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); @@ -611,6 +614,7 @@ static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c cplan.work_data = cpu_ctx->work_data; ggml_graph_compute(cgraph, &cplan); + return true; } static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { diff --git a/ggml-backend.h b/ggml-backend.h index a9d2fddd7..85ff67b0e 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -58,7 +58,7 @@ extern "C" { GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API void ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 52d3cc6a6..10c21615e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -9910,7 +9910,7 @@ static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_ba UNUSED(plan); } -static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -9967,6 +9967,8 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph } UNUSED(backend); + + return true; } static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { diff --git a/ggml-metal.h b/ggml-metal.h index b5e02b668..c4b7325da 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -87,7 +87,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx); // same as ggml_graph_compute but uses Metal // creates gf->n_threads command buffers in parallel -void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf); +bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf); // // backend API diff --git a/ggml-metal.m b/ggml-metal.m index 7aa92c14c..55cc1a872 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -977,7 +977,7 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { return false; } } -void ggml_metal_graph_compute( +bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @autoreleasepool { @@ -2405,10 +2405,11 @@ void ggml_metal_graph_compute( MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status]; if (status != MTLCommandBufferStatusCompleted) { GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); - GGML_ASSERT(false); + return false; } } + return true; } } @@ -2688,10 +2689,10 @@ static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggm UNUSED(backend); } -static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; - ggml_metal_graph_compute(metal_ctx, cgraph); + return ggml_metal_graph_compute(metal_ctx, cgraph); } static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { From d061bf9405cc5fd50792fb2dbdff9c9ea53d6bf9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 5 Jan 2024 15:36:04 +0200 Subject: [PATCH 325/664] ggml : fix q2_k bpw in comments (ggml/680) --- ggml-quants.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-quants.h b/ggml-quants.h index 70c12c274..62c1df6cb 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -70,7 +70,7 @@ static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block s // 2-bit quantization // weight is represented as x = a * q + b // 16 blocks of 16 elements each -// Effectively 2.5625 bits per weight +// Effectively 2.625 bits per weight typedef struct { uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits uint8_t qs[QK_K/4]; // quants From 91d38876dfa10332359ac671b62353aeceb448d3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 5 Jan 2024 16:30:52 +0200 Subject: [PATCH 326/664] metal : switch back to default.metallib (ggml/681) ggml-ci --- CMakeLists.txt | 10 ++++++---- .../llama.swiftui.xcodeproj/project.pbxproj | 19 +------------------ ggml-metal.m | 6 +++--- scripts/sync-ggml.last | 2 +- 4 files changed, 11 insertions(+), 26 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 57ae4c2df..ce237cf45 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -177,27 +177,29 @@ if (LLAMA_METAL) if (LLAMA_METAL_SHADER_DEBUG) # custom command to do the following: # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air - # xcrun -sdk macosx metallib ggml-metal.air -o ggml.metallib + # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib # # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works # disabling fast math is needed in order to pass tests/test-backend-ops # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 + # note: unfortunately, we have to call it default.metallib instead of ggml.metallib + # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 set(XC_FLAGS -fno-fast-math -fno-inline -g) if (LLAMA_QKK_64) set(XC_FLAGS ${XC_FLAGS} -DQK_K=64) endif() add_custom_command( - OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib + OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air - COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib + COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib DEPENDS ggml-metal.metal COMMENT "Compiling Metal kernels" ) add_custom_target( ggml-metal ALL - DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib + DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib ) endif() diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index a70750a22..14b93f26c 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -23,26 +23,10 @@ 8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; }; 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; }; 8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; }; - F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; }; F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; }; + F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; }; /* End PBXBuildFile section */ -/* Begin PBXBuildRule section */ - F1FE20DB2B465C2100B45541 /* PBXBuildRule */ = { - isa = PBXBuildRule; - compilerSpec = com.apple.compilers.proxy.script; - fileType = sourcecode.metal; - inputFiles = ( - ); - isEditable = 1; - outputFiles = ( - "${DERIVED_FILES_DIR}/ggml-metal.air", - "${DERIVED_FILES_DIR}/ggml.metallib", - ); - script = "# metal\nxcrun metal -c \"${INPUT_FILE_PATH}\" -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\nxcrun metallib -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE%-metal}.metallib\" \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\n"; - }; -/* End PBXBuildRule section */ - /* Begin PBXFileReference section */ 542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = ""; }; 542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = ""; }; @@ -209,7 +193,6 @@ 8A1C83712AC328BD0096AF73 /* Resources */, ); buildRules = ( - F1FE20DB2B465C2100B45541 /* PBXBuildRule */, ); dependencies = ( ); diff --git a/ggml-metal.m b/ggml-metal.m index 55cc1a872..fbbdcd8c4 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -258,14 +258,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; #endif NSError * error = nil; - NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"]; + NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"]; if (libPath != nil) { // pre-compiled library found NSURL * libURL = [NSURL fileURLWithPath:libPath]; GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]); ctx->library = [ctx->device newLibraryWithURL:libURL error:&error]; } else { - GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__); + GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); NSString * sourcePath; NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; @@ -295,7 +295,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { #endif // try to disable fast-math // NOTE: this seems to have no effect whatsoever - // instead, in order to disable fast-math, we have to build ggml.metallib from the command line + // instead, in order to disable fast-math, we have to build default.metallib from the command line // using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air // and go through the "pre-compiled library found" path above //[options setFastMathEnabled:false]; diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 354246a26..fe7f3202f 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -3fd01e00e40583ccd4b393a7c6502d6a4455a1d5 +f96711108d55bdbbd277e6be07204dce6a94fb93 From be36bb946a6336238e92706464de6a30495fe825 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Sat, 6 Jan 2024 01:02:44 +0900 Subject: [PATCH 327/664] flake.nix : fix typo (#4700) betwen -> between --- .devops/nix/package.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 5f2a7c9f4..43bdbd755 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -9,7 +9,7 @@ git, python3, mpi, - openblas, # TODO: Use the generic `blas` so users could switch betwen alternative implementations + openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations cudaPackages, darwin, rocmPackages, From eec22a1c6378d9a013943cbddb4330c0da621442 Mon Sep 17 00:00:00 2001 From: a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com> Date: Fri, 5 Jan 2024 08:04:40 -0800 Subject: [PATCH 328/664] cmake : check for openblas64 (#4134) openblas v0.3.22 64-bit pkg-config file is named openblas64.pc https://github.com/OpenMathLib/OpenBLAS/issues/3790 --- CMakeLists.txt | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ce237cf45..668669c6d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -230,7 +230,11 @@ if (LLAMA_BLAS) if (${LLAMA_BLAS_VENDOR} MATCHES "Generic") pkg_check_modules(DepBLAS REQUIRED blas) elseif (${LLAMA_BLAS_VENDOR} MATCHES "OpenBLAS") - pkg_check_modules(DepBLAS REQUIRED openblas) + # As of openblas v0.3.22, the 64-bit is named openblas64.pc + pkg_check_modules(DepBLAS openblas64) + if (NOT DepBLAS_FOUND) + pkg_check_modules(DepBLAS REQUIRED openblas) + endif() elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME") pkg_check_modules(DepBLAS REQUIRED blis) elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS") From 96e80dabc6e73ff68b09b68947b1fc25883c5094 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 6 Jan 2024 11:40:24 +0200 Subject: [PATCH 329/664] examples : improve base-translate.sh script (#4783) --- examples/base-translate.sh | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/examples/base-translate.sh b/examples/base-translate.sh index 50fba025c..00dedd0df 100755 --- a/examples/base-translate.sh +++ b/examples/base-translate.sh @@ -8,14 +8,19 @@ # cd llama.cpp # make -j # -# ./examples/base-translate.sh "" +# ./examples/base-translate.sh "" [extra-main-args] # -if [ $# -ne 2 ]; then - echo "Usage: ./base-translate.sh \"\"" +if [ $# -lt 2 ]; then + echo "Usage: ./base-translate.sh \"\" [extra-main-args]" exit 1 fi +eargs="" +if [ $# -gt 2 ]; then + eargs="${@:3}" +fi + ftmp="__llama.cpp_example_tmp__.txt" trap "rm -f $ftmp" EXIT @@ -52,5 +57,5 @@ echo "$2 model=$1 -# generate the most likely continuation, run on the CPU until the string "===" is found -./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -ngl 0 -r "===" +# generate the most likely continuation until the string "===" is found +./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs From c75ca5d96f902564cbbbdd7f5cade80d53c288bb Mon Sep 17 00:00:00 2001 From: Daniel Illescas Romero Date: Sat, 6 Jan 2024 16:12:59 +0100 Subject: [PATCH 330/664] llama.swiftui : use correct pointer for llama_token_eos (#4797) --- examples/llama.swiftui/llama.cpp.swift/LibLlama.swift | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 66244382f..8696b493c 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -161,7 +161,7 @@ actor LlamaContext { new_token_id = llama_sample_token_greedy(context, &candidates_p) } - if new_token_id == llama_token_eos(context) || n_cur == n_len { + if new_token_id == llama_token_eos(model) || n_cur == n_len { print("\n") let new_token_str = String(cString: temporary_invalid_cchars + [0]) temporary_invalid_cchars.removeAll() From 67984921a70a7e680a24494aeb7575a66e90685d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 7 Jan 2024 08:45:26 +0200 Subject: [PATCH 331/664] server : fix n_predict check (#4798) --- examples/server/server.cpp | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d1469fb08..6c7fcd176 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -447,8 +447,14 @@ struct llama_client_slot } bool has_budget(gpt_params &global_params) { + if (params.n_predict == -1 && global_params.n_predict == -1) + { + return true; // limitless + } + n_remaining = -1; - if(params.n_predict != -1) + + if (params.n_predict != -1) { n_remaining = params.n_predict - n_decoded; } @@ -456,7 +462,8 @@ struct llama_client_slot { n_remaining = global_params.n_predict - n_decoded; } - return n_remaining > 0 || n_remaining == -1; // no budget || limitless + + return n_remaining > 0; // no budget } bool available() const { @@ -1102,7 +1109,7 @@ struct llama_server_context } // check the limits - if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params)) + if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) { slot.stopped_limit = true; slot.has_next_token = false; @@ -1703,7 +1710,6 @@ struct llama_server_context llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); - slot.n_decoded += 1; slot.n_past += 1; } @@ -1921,6 +1927,7 @@ struct llama_server_context llama_sampling_accept(slot.ctx_sampling, ctx, id, true); + slot.n_decoded += 1; if (slot.n_decoded == 1) { slot.t_start_genereration = ggml_time_us(); From 63ee677efd92060b14894b984597c62e3742b8da Mon Sep 17 00:00:00 2001 From: Konstantin Zhuravlyov Date: Sun, 7 Jan 2024 01:52:42 -0500 Subject: [PATCH 332/664] ggml : use __builtin_amdgcn_sudot4 in __dp4a for gfx11 (#4787) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 10c21615e..54b266be4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -183,7 +183,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) { static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) c = __builtin_amdgcn_sdot4(a, b, c, false); -#elif defined(__gfx1100__) +#elif defined(RDNA3) c = __builtin_amdgcn_sudot4( true, a, true, b, c, false); #elif defined(__gfx1010__) || defined(__gfx900__) int tmp1; From 3418c03ecc149fd657527ebb06776239b60a3f3b Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Sun, 7 Jan 2024 08:46:55 +0100 Subject: [PATCH 333/664] llama.swiftui : add visionOS target (#4805) --- .../llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index 14b93f26c..9b1a9787b 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -420,11 +420,13 @@ MARKETING_VERSION = 1.0; PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift"; PRODUCT_NAME = "$(TARGET_NAME)"; + SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator"; + SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO; SWIFT_EMIT_LOC_STRINGS = YES; SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h"; SWIFT_OPTIMIZATION_LEVEL = "-Onone"; SWIFT_VERSION = 5.0; - TARGETED_DEVICE_FAMILY = "1,2"; + TARGETED_DEVICE_FAMILY = "1,2,7"; }; name = Debug; }; @@ -453,10 +455,12 @@ MARKETING_VERSION = 1.0; PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift"; PRODUCT_NAME = "$(TARGET_NAME)"; + SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator"; + SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO; SWIFT_EMIT_LOC_STRINGS = YES; SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h"; SWIFT_VERSION = 5.0; - TARGETED_DEVICE_FAMILY = "1,2"; + TARGETED_DEVICE_FAMILY = "1,2,7"; }; name = Release; }; From d117d4dc5dadb46831036bfa4d6e5e8c86babaf1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 7 Jan 2024 09:50:31 +0200 Subject: [PATCH 334/664] llama : print tensor meta for debugging --- llama.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 3bb056dba..06db40303 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2180,7 +2180,11 @@ struct llama_model_loader { type_max = type; } - // LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str()); + // TODO: make runtime configurable +#if 0 + struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str()); +#endif } switch (type_max) { From 72d8407b3696dd1293bd233b6db392be108bc377 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Sun, 7 Jan 2024 09:20:50 +0100 Subject: [PATCH 335/664] llama.swiftui : use llama.cpp as SPM package (#4804) --- .../llama.cpp.swift/LibLlama.swift | 5 +- .../llama.cpp.swift/bridging-header.h | 5 -- .../llama.swiftui.xcodeproj/project.pbxproj | 80 +++---------------- .../AccentColor.colorset/Contents.json | 11 --- .../Preview Assets.xcassets/Contents.json | 6 -- 5 files changed, 13 insertions(+), 94 deletions(-) delete mode 100644 examples/llama.swiftui/llama.cpp.swift/bridging-header.h delete mode 100644 examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json delete mode 100644 examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 8696b493c..fc79fd346 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -1,8 +1,5 @@ import Foundation - -// To use this in your own project, add llama.cpp as a swift package dependency -// and uncomment this import line. -// import llama +import llama enum LlamaError: Error { case couldNotInitializeContext diff --git a/examples/llama.swiftui/llama.cpp.swift/bridging-header.h b/examples/llama.swiftui/llama.cpp.swift/bridging-header.h deleted file mode 100644 index 6cd72c979..000000000 --- a/examples/llama.swiftui/llama.cpp.swift/bridging-header.h +++ /dev/null @@ -1,5 +0,0 @@ -// -// Use this file to import your target's public headers that you would like to expose to Swift. -// - -#import "llama.h" diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index 9b1a9787b..a8848a49f 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -7,52 +7,31 @@ objects = { /* Begin PBXBuildFile section */ - 542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; }; - 5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; }; - 542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; }; - 542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; }; - 542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; }; 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; 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}; 8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = ""; }; 8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = ""; }; 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = ""; }; + DF2D2FE72B4A59BE00FCB72D /* llama.cpp */ = {isa = PBXFileReference; lastKnownFileType = wrapper; name = llama.cpp; path = ../..; sourceTree = ""; }; F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = ""; }; /* End PBXFileReference section */ @@ -61,6 +40,7 @@ isa = PBXFrameworksBuildPhase; buildActionMask = 2147483647; files = ( + DF810E132B4A5BA200301144 /* llama in Frameworks */, 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */, 8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */, ); @@ -69,30 +49,10 @@ /* End PBXFrameworksBuildPhase section */ /* Begin PBXGroup section */ - 8A08D1F62AC7383900FE6CD4 /* llama.cpp */ = { - isa = PBXGroup; - children = ( - 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */, - 542376092B0D9C40008E6A1C /* ggml-backend.h */, - 542376062B0D9BEA008E6A1C /* ggml-quants.h */, - 542376072B0D9BFB008E6A1C /* ggml-quants.c */, - 549479C82AC9E10B00E0F78B /* ggml-metal.metal */, - 549479C62AC9E0F200E0F78B /* ggml-metal.h */, - 549479C52AC9E0F200E0F78B /* ggml-metal.m */, - 542EA09B2AC8723900A8AEE9 /* ggml.c */, - 542EA09C2AC8723900A8AEE9 /* ggml.h */, - 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */, - 542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */, - 542EA0A12AC8729100A8AEE9 /* llama.cpp */, - 542EA0A22AC8729100A8AEE9 /* llama.h */, - ); - name = llama.cpp; - sourceTree = ""; - }; 8A1C836A2AC328BD0096AF73 = { isa = PBXGroup; children = ( - 8A08D1F62AC7383900FE6CD4 /* llama.cpp */, + DF2D2FE72B4A59BE00FCB72D /* llama.cpp */, 8A907F312AC7134E006146EA /* llama.cpp.swift */, 8A3F84232AC4C891005E2EE8 /* models */, 8A1C83752AC328BD0096AF73 /* llama.swiftui */, @@ -117,19 +77,10 @@ 8A9F7C4A2AC332BF008AE1EA /* UI */, 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */, 8A1C837A2AC328BE0096AF73 /* Assets.xcassets */, - 8A1C837C2AC328BE0096AF73 /* Preview Content */, ); path = llama.swiftui; sourceTree = ""; }; - 8A1C837C2AC328BE0096AF73 /* Preview Content */ = { - isa = PBXGroup; - children = ( - 8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */, - ); - path = "Preview Content"; - sourceTree = ""; - }; 8A39BE082AC7601000BFEB40 /* Frameworks */ = { isa = PBXGroup; children = ( @@ -157,7 +108,6 @@ 8A907F312AC7134E006146EA /* llama.cpp.swift */ = { isa = PBXGroup; children = ( - 8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */, 8A907F322AC7134E006146EA /* LibLlama.swift */, ); path = llama.cpp.swift; @@ -198,6 +148,7 @@ ); name = llama.swiftui; packageProductDependencies = ( + DF810E122B4A5BA200301144 /* llama */, ); productName = llama.swiftui; productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */; @@ -244,9 +195,7 @@ isa = PBXResourcesBuildPhase; buildActionMask = 2147483647; files = ( - F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */, 8A3F84242AC4C891005E2EE8 /* models in Resources */, - 8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */, 8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */, ); runOnlyForDeploymentPostprocessing = 0; @@ -258,18 +207,12 @@ isa = PBXSourcesBuildPhase; buildActionMask = 2147483647; files = ( - 542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */, - 549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */, F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */, - 542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */, 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */, - 542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */, 8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */, 8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */, 8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */, 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */, - 542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */, - 5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -399,11 +342,9 @@ isa = XCBuildConfiguration; buildSettings = { ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon; - ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor; CLANG_ENABLE_MODULES = YES; CODE_SIGN_STYLE = Automatic; CURRENT_PROJECT_VERSION = 1; - DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\""; DEVELOPMENT_TEAM = STLSG3FG8Q; ENABLE_PREVIEWS = YES; GENERATE_INFOPLIST_FILE = YES; @@ -423,7 +364,6 @@ SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator"; SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO; SWIFT_EMIT_LOC_STRINGS = YES; - SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h"; SWIFT_OPTIMIZATION_LEVEL = "-Onone"; SWIFT_VERSION = 5.0; TARGETED_DEVICE_FAMILY = "1,2,7"; @@ -434,11 +374,9 @@ isa = XCBuildConfiguration; buildSettings = { ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon; - ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor; CLANG_ENABLE_MODULES = YES; CODE_SIGN_STYLE = Automatic; CURRENT_PROJECT_VERSION = 1; - DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\""; DEVELOPMENT_TEAM = STLSG3FG8Q; ENABLE_PREVIEWS = YES; GENERATE_INFOPLIST_FILE = YES; @@ -458,7 +396,6 @@ SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator"; SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO; SWIFT_EMIT_LOC_STRINGS = YES; - SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h"; SWIFT_VERSION = 5.0; TARGETED_DEVICE_FAMILY = "1,2,7"; }; @@ -486,6 +423,13 @@ defaultConfigurationName = Release; }; /* End XCConfigurationList section */ + +/* Begin XCSwiftPackageProductDependency section */ + DF810E122B4A5BA200301144 /* llama */ = { + isa = XCSwiftPackageProductDependency; + productName = llama; + }; +/* End XCSwiftPackageProductDependency section */ }; rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */; } diff --git a/examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json b/examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json deleted file mode 100644 index eb8789700..000000000 --- a/examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "colors" : [ - { - "idiom" : "universal" - } - ], - "info" : { - "author" : "xcode", - "version" : 1 - } -} diff --git a/examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json b/examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json deleted file mode 100644 index 73c00596a..000000000 --- a/examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "info" : { - "author" : "xcode", - "version" : 1 - } -} From 3c36213df850a2353e95572b3636797c79b7c815 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 7 Jan 2024 11:21:53 +0200 Subject: [PATCH 336/664] llama : remove redundant GQA check (#4796) --- llama.cpp | 8 -------- 1 file changed, 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index 06db40303..021e79a8f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4776,7 +4776,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4900,7 +4899,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * pos; @@ -5001,7 +4999,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); const int64_t n_rot = n_embd_head_k / 2; @@ -5215,7 +5212,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5308,7 +5304,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5404,7 +5399,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5731,7 +5725,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * attn_norm_output; @@ -5955,7 +5948,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_gqa == n_embd); struct ggml_tensor * cur; struct ggml_tensor * pos; From 9dede37d812604897496dd9d276ae9fbe13d1042 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 7 Jan 2024 14:29:36 +0200 Subject: [PATCH 337/664] llama : remove unused vars (#4796) --- llama.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 021e79a8f..91aa3f8e7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4997,7 +4997,6 @@ struct llm_build_context { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_embd_head = hparams.n_embd_head_v; - const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); const int64_t n_rot = n_embd_head_k / 2; @@ -5210,7 +5209,6 @@ struct llm_build_context { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_embd_head = hparams.n_embd_head_v; - const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); struct ggml_tensor * cur; From d5a410e8556191672465f7ff58682ea2474038b0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 7 Jan 2024 17:24:08 +0100 Subject: [PATCH 338/664] CUDA: fixed redundant value dequantization (#4809) --- ggml-cuda.cu | 35 +++++++++++++++++++++++------------ 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 54b266be4..2df64b111 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1872,14 +1872,6 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs, v.y = x[ib + iqs + 1]; } -static __device__ void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const float * x = (const float *) vx; - - // automatic half -> float type cast if dfloat == float - v.x = x[ib + iqs + 0]; - v.y = x[ib + iqs + 1]; -} - static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) { const int ix = blockDim.x*blockIdx.x + threadIdx.x; @@ -1983,7 +1975,7 @@ static __global__ void k_get_rows_float( template static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { - const int i = blockDim.x*blockIdx.x + 2*threadIdx.x; + const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x); if (i >= k) { return; @@ -2002,6 +1994,19 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __ y[iybs + iqs + y_offset] = v.y; } +template +static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + + const src_t * x = (src_t *) vx; + + y[i] = x[i]; +} + // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q @@ -5609,7 +5614,7 @@ static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, con template static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; + const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE); dequantize_block<<>>(vx, y, k); } @@ -5659,6 +5664,12 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu #endif } +template +static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; + convert_unary<<>>(vx, y, k); +} + static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: @@ -5682,7 +5693,7 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { case GGML_TYPE_Q6_K: return dequantize_row_q6_K_cuda; case GGML_TYPE_F32: - return dequantize_block_cuda<1, 1, convert_f32>; + return convert_unary_cuda; default: return nullptr; } @@ -5711,7 +5722,7 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { case GGML_TYPE_Q6_K: return dequantize_row_q6_K_cuda; case GGML_TYPE_F16: - return dequantize_block_cuda<1, 1, convert_f16>; + return convert_unary_cuda; default: return nullptr; } From 226460cc0d5b185bc6685fb76f418fd9418d7add Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 7 Jan 2024 17:59:01 +0100 Subject: [PATCH 339/664] llama-bench : add no-kv-offload parameter (#4812) --- examples/llama-bench/llama-bench.cpp | 34 +++++++++++++++++++++++++--- 1 file changed, 31 insertions(+), 3 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 6617c050d..7f7186cde 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -138,6 +138,7 @@ struct cmd_params { std::vector n_threads; std::vector n_gpu_layers; std::vector main_gpu; + std::vector no_kv_offload; std::vector mul_mat_q; std::vector> tensor_split; int reps; @@ -155,6 +156,7 @@ static const cmd_params cmd_params_defaults = { /* n_threads */ {get_num_physical_cores()}, /* n_gpu_layers */ {99}, /* main_gpu */ {0}, + /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, /* tensor_split */ {{}}, /* reps */ 5, @@ -176,6 +178,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split \n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); @@ -309,6 +312,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } params.main_gpu = split(argv[i], split_delim); + } else if (arg == "-nkvo" || arg == "--no-kv-offload") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); } else if (arg == "-mmq" || arg == "--mul-mat-q") { if (++i >= argc) { invalid_param = true; @@ -383,6 +393,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } + if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } @@ -400,6 +411,7 @@ struct cmd_params_instance { int n_threads; int n_gpu_layers; int main_gpu; + bool no_kv_offload; bool mul_mat_q; std::array tensor_split; @@ -428,6 +440,7 @@ struct cmd_params_instance { cparams.type_k = type_k; cparams.type_v = type_v; cparams.mul_mat_q = mul_mat_q; + cparams.offload_kqv = !no_kv_offload; return cparams; } @@ -444,6 +457,7 @@ static std::vector get_cmd_params_instances_int(const cmd_p for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) for (const auto & mmq : params.mul_mat_q) + for (const auto & nkvo : params.no_kv_offload) for (const auto & nt : params.n_threads) { cmd_params_instance instance = { /* .model = */ m, @@ -455,6 +469,7 @@ static std::vector get_cmd_params_instances_int(const cmd_p /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, /* .main_gpu = */ mg, + /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, }; @@ -476,6 +491,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) for (const auto & mmq : params.mul_mat_q) + for (const auto & nkvo : params.no_kv_offload) for (const auto & nt : params.n_threads) { for (const auto & n_prompt : params.n_prompt) { if (n_prompt == 0) { @@ -491,6 +507,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, /* .main_gpu = */ mg, + /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, }; @@ -511,6 +528,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, /* .main_gpu = */ mg, + /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, }; @@ -559,6 +577,7 @@ struct test { ggml_type type_v; int n_gpu_layers; int main_gpu; + bool no_kv_offload; bool mul_mat_q; std::array tensor_split; int n_prompt; @@ -579,6 +598,7 @@ struct test { type_v = inst.type_v; n_gpu_layers = inst.n_gpu_layers; main_gpu = inst.main_gpu; + no_kv_offload = inst.no_kv_offload; mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; n_prompt = inst.n_prompt; @@ -640,7 +660,8 @@ struct test { "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", - "n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split", + "n_gpu_layers", "main_gpu", "no_kv_offload", + "mul_mat_q", "tensor_split", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -659,7 +680,7 @@ struct test { return INT; } if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" || - field == "f16_kv" || field == "mul_mat_q") { + field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -690,7 +711,8 @@ struct test { cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), - std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str, + std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload), + std::to_string(mul_mat_q), tensor_split_str, std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -851,6 +873,9 @@ struct markdown_printer : public printer { if (field == "mul_mat_q") { return "mmq"; } + if (field == "no_kv_offload") { + return "nkvo"; + } if (field == "tensor_split") { return "ts"; } @@ -885,6 +910,9 @@ struct markdown_printer : public printer { if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { fields.push_back("mul_mat_q"); } + if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { + fields.push_back("no_kv_offload"); + } if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.push_back("tensor_split"); } From b7e7982953f80a656e03feb5cfb17a17a173eb26 Mon Sep 17 00:00:00 2001 From: Lars Grammel Date: Sun, 7 Jan 2024 21:24:11 +0100 Subject: [PATCH 340/664] readme : add lgrammel/modelfusion JS/TS client for llama.cpp (#4814) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ca6d14e17..2f6e6ffee 100644 --- a/README.md +++ b/README.md @@ -118,6 +118,7 @@ as the main playground for developing new features for the [ggml](https://github - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) +- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) - Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) From b0034d93ce2949ce7d9c098ca02e56f66cd484e2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 8 Jan 2024 11:14:04 +0200 Subject: [PATCH 341/664] examples : add passkey test (#3856) * examples : add passkey test * passkey : better prints * passkey : select pass key pos from CLI * passkey : simplify n_past logic * make : add passkey target * passkey : add "self-extend"-like context extension (#4810) * llama : "self-extend"-like context extension * passkey : add comment * passkey : add readme --- .gitignore | 1 + Makefile | 5 +- examples/CMakeLists.txt | 1 + examples/batched/batched.cpp | 1 + examples/passkey/CMakeLists.txt | 5 + examples/passkey/README.md | 12 ++ examples/passkey/passkey.cpp | 296 ++++++++++++++++++++++++++++++++ llama.cpp | 34 ++++ llama.h | 7 + 9 files changed, 361 insertions(+), 1 deletion(-) create mode 100644 examples/passkey/CMakeLists.txt create mode 100644 examples/passkey/README.md create mode 100644 examples/passkey/passkey.cpp diff --git a/.gitignore b/.gitignore index def74a1e9..cf1b692e9 100644 --- a/.gitignore +++ b/.gitignore @@ -51,6 +51,7 @@ models-mnt /lookup /main /metal +/passkey /perplexity /q8dot /quantize diff --git a/Makefile b/Makefile index 28c6d79bc..4c7e175bf 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ BUILD_TARGETS = \ main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ - speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o + speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o # Binaries only useful for tests TEST_TARGETS = \ @@ -665,6 +665,9 @@ lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) +passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + ifdef LLAMA_METAL metal: examples/metal/metal.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 4cc13d6e9..0c71cbdf7 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -31,6 +31,7 @@ else() add_subdirectory(quantize-stats) add_subdirectory(save-load-state) add_subdirectory(simple) + add_subdirectory(passkey) add_subdirectory(speculative) add_subdirectory(lookahead) add_subdirectory(lookup) diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 22a4265df..b1775e0b0 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -69,6 +69,7 @@ int main(int argc, char ** argv) { std::vector tokens_list; tokens_list = ::llama_tokenize(model, params.prompt, true); + const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel; // initialize the context diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt new file mode 100644 index 000000000..3161bf3ef --- /dev/null +++ b/examples/passkey/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET passkey) +add_executable(${TARGET} passkey.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/passkey/README.md b/examples/passkey/README.md new file mode 100644 index 000000000..4a22bb559 --- /dev/null +++ b/examples/passkey/README.md @@ -0,0 +1,12 @@ +# llama.cpp/example/passkey + +See the following PRs for more info: + +- https://github.com/ggerganov/llama.cpp/pull/3856 +- https://github.com/ggerganov/llama.cpp/pull/4810 + +### Usage + +```bash +make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250 +``` diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp new file mode 100644 index 000000000..5c0022832 --- /dev/null +++ b/examples/passkey/passkey.cpp @@ -0,0 +1,296 @@ +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv) { + gpt_params params; + + if (argc == 1 || argv[1][0] == '-') { + printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]); + return 1 ; + } + + int seed = -1; + + int n_junk = 250; // number of times to repeat the junk text + int n_keep = 32; // number of tokens in the prompt prefix + int n_grp = 1; // if more than 1 - perform LongLM SelfExtend + int i_pos = -1; // position of the passkey in the junk text + + if (argc >= 2) { + params.model = argv[1]; + } + + if (argc >= 3) { + n_junk = std::stoi(argv[2]); + } + + if (argc >= 4) { + n_grp = std::stoi(argv[3]); + } + + if (argc >= 5) { + i_pos = std::stoi(argv[4]); + } + + if (argc >= 6) { + seed = std::stoi(argv[5]); + } + + if (seed == -1) { + seed = time(NULL); + } + + srand(seed); + + if (i_pos == -1) { + i_pos = rand() % n_junk; + } + + const std::string prompt_prefix = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there."; + const std::string prompt_suffix = " What is the pass key? The pass key is"; + + // generate junk text + params.prompt = prompt_prefix; + + const int passkey = rand() % 50000 + 1; + + for (int i = 0; i < n_junk; i++) { + if (i % n_junk == i_pos) { + params.prompt += " The pass key is " + std::to_string(passkey) + ". Remember it. " + std::to_string(passkey) + " is the pass key."; + } + + params.prompt += " The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again."; + } + + params.prompt += prompt_suffix; + + // init LLM + + llama_backend_init(params.numa); + + // initialize the model + + llama_model_params model_params = llama_model_default_params(); + + model_params.n_gpu_layers = 99; // offload all layers to the GPU + + llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + + if (model == NULL) { + fprintf(stderr , "%s: error: unable to load model\n" , __func__); + return 1; + } + + // initialize the context + + llama_context_params ctx_params = llama_context_default_params(); + + ctx_params.seed = seed; + ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; + ctx_params.n_batch = 512; + ctx_params.n_threads = params.n_threads; + ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + + GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp"); + + llama_context * ctx = llama_new_context_with_model(model, ctx_params); + + if (ctx == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + // tokenize the prompt + std::vector tokens_list; + tokens_list = ::llama_tokenize(ctx, params.prompt, true); + + // tokenize the prefix and use it as a sink + const int n_tokens_prefix = ::llama_tokenize(ctx, prompt_prefix, true).size(); + + const int n_tokens_all = tokens_list.size(); + + // we leave a margin of 16 tokens for the generated text - it should contain just the passkey + const int n_predict = 16; + + // total length of the sequences including the prompt + const int n_len = n_tokens_all + n_predict; + + const int n_ctx = llama_n_ctx(ctx) - n_keep; + const int n_kv_req = llama_n_ctx(ctx); + const int n_batch = ctx_params.n_batch; + const int n_batch_grp = ctx_params.n_batch/n_grp; + + LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch); + + // print the prompt token-by-token + + LOG_TEE("\n"); + LOG_TEE("prefix tokens: %d\n", n_tokens_prefix); + LOG_TEE("prompt tokens: %d\n", n_tokens_all); + //LOG_TEE("prompt: %s\n", params.prompt.c_str()); + + llama_batch batch = llama_batch_init(512, 0, 1); + + int n_past = 0; + + // fill the KV cache + for (int i = 0; i < n_ctx; i += n_batch) { + if (i > 0 && n_grp > 1) { + // if SelfExtend is enabled, we compress the position from the last batch by a factor of n_grp + const int ib = i/n_batch - 1; + const int bd = n_batch_grp*(n_grp - 1); + + llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch, n_past, ib*bd); + llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp); + + n_past -= bd; + } + + llama_batch_clear(batch); + + for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) { + llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); + } + + if (i + n_batch >= n_tokens_all) { + batch.logits[batch.n_tokens - 1] = true; + } + + if (llama_decode(ctx, batch) != 0) { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return 1; + } + + LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all)); + + if (i + n_batch >= n_tokens_all) { + break; + } + } + + for (int i = n_ctx; i < n_tokens_all; i += n_batch) { + const int n_discard = n_batch; + + LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard); + + llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); + llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + + n_past -= n_discard; + + llama_batch_clear(batch); + + for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) { + llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); + } + + if (i + n_batch >= n_tokens_all) { + batch.logits[batch.n_tokens - 1] = true; + } + + if (llama_decode(ctx, batch) != 0) { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return 1; + } + + LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all)); + } + + { + const int n_discard = n_past - n_ctx + n_predict; + + if (n_discard > 0) { + LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard); + + llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); + llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + + n_past -= n_discard; + } + } + + LOG_TEE("\n"); + LOG_TEE("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk); + LOG_TEE("\n"); + + // main loop + + int n_cur = n_tokens_all; + int n_decode = 0; + + LOG_TEE("%s", prompt_suffix.c_str()); + fflush(stdout); + + const auto t_main_start = ggml_time_us(); + + while (n_cur <= n_len) { + // sample the next token + { + auto n_vocab = llama_n_vocab(model); + auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1); + + std::vector candidates; + candidates.reserve(n_vocab); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + // sample the most likely token + const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); + + // is it an end of stream? + if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + LOG_TEE("\n"); + + break; + } + + LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str()); + fflush(stdout); + + n_decode += 1; + + // prepare the next batch + llama_batch_clear(batch); + + // push this new token for next evaluation + llama_batch_add(batch, new_token_id, n_past++, { 0 }, true); + } + + n_cur += 1; + + // evaluate the current batch with the transformer model + if (llama_decode(ctx, batch)) { + fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); + return 1; + } + } + + LOG_TEE("\n"); + + const auto t_main_end = ggml_time_us(); + + LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", + __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); + + llama_print_timings(ctx); + + fprintf(stderr, "\n"); + + llama_batch_free(batch); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp b/llama.cpp index 91aa3f8e7..63853d1c3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1903,6 +1903,28 @@ static void llama_kv_cache_seq_shift( cache.head = new_head != cache.size ? new_head : 0; } +static void llama_kv_cache_seq_div( + struct llama_kv_cache & cache, + llama_seq_id seq_id, + llama_pos p0, + llama_pos p1, + int d) { + if (p0 < 0) p0 = 0; + if (p1 < 0) p1 = std::numeric_limits::max(); + + for (uint32_t i = 0; i < cache.size; ++i) { + if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { + cache.has_shift = true; + + { + llama_pos p_old = cache.cells[i].pos; + cache.cells[i].pos /= d; + cache.cells[i].delta += cache.cells[i].pos - p_old; + } + } + } +} + // // model loading and saving // @@ -10140,9 +10162,21 @@ void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) { } void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { + if (delta == 0) { + return; + } + llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta); } +void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { + if (d == 1) { + return; + } + + llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d); +} + // Returns the *maximum* size of the state size_t llama_get_state_size(const struct llama_context * ctx) { // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state. diff --git a/llama.h b/llama.h index 461d4604a..5305de90b 100644 --- a/llama.h +++ b/llama.h @@ -484,6 +484,13 @@ extern "C" { llama_pos p1, llama_pos delta); + LLAMA_API void llama_kv_cache_seq_div( + struct llama_context * ctx, + llama_seq_id seq_id, + llama_pos p0, + llama_pos p1, + int d); + // // State / sessions // From 52531fdff88764282c1b233174721aab8347252d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 8 Jan 2024 11:18:32 +0200 Subject: [PATCH 342/664] main : add self-extend support (#4815) * examples : add passkey test * passkey : better prints * passkey : select pass key pos from CLI * passkey : simplify n_past logic * llama : "self-extend"-like context extension * passkey : add comment * main : add Self-Extend support * llama : add comment about llama_kv_cache_seq_div --- common/common.cpp | 18 +++++++++ common/common.h | 2 + examples/main/main.cpp | 87 ++++++++++++++++++++++++++++++------------ llama.h | 4 ++ 4 files changed, 87 insertions(+), 24 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index eacaee18e..6b4913a65 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -220,6 +220,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_ctx = std::stoi(argv[i]); + } else if (arg == "--grp-attn-n" || arg == "-gan") { + if (++i >= argc) { + invalid_param = true; + break; + } + + params.grp_attn_n = std::stoi(argv[i]); + } else if (arg == "--grp-attn-w" || arg == "-gaw") { + if (++i >= argc) { + invalid_param = true; + break; + } + + params.grp_attn_w = std::stoi(argv[i]); } else if (arg == "--rope-freq-base") { if (++i >= argc) { invalid_param = true; @@ -904,6 +918,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" Not recommended since this is both slower and uses more VRAM.\n"); #endif // GGML_USE_CUBLAS #endif + printf(" -gan N, --grp-attn-n N\n"); + printf(" group-attention factor (default: %d)\n", params.grp_attn_n); + printf(" -gat N, --grp-attn-w N\n"); + printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); printf(" --verbose-prompt print prompt before generation\n"); printf(" -dkvc, --dump-kv-cache\n"); printf(" verbose print of the KV cache\n"); diff --git a/common/common.h b/common/common.h index 9659aa045..e2bbfc258 100644 --- a/common/common.h +++ b/common/common.h @@ -62,6 +62,8 @@ struct gpt_params { int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_beams = 0; // if non-zero then use beam search of given width. + int32_t grp_attn_n = 1; // group-attention factor + int32_t grp_attn_w = 512; // group-attention width float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor diff --git a/examples/main/main.cpp b/examples/main/main.cpp index c096f110b..5ea67051f 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -439,6 +439,21 @@ int main(int argc, char ** argv) { LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str()); LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str()); LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); + + // group-attention state + // number of grouped KV tokens so far (used only if params.grp_attn_n > 1) + int ga_i = 0; + + const int ga_n = params.grp_attn_n; + const int ga_w = params.grp_attn_w; + + if (ga_n != 1) { + GGML_ASSERT(ga_n > 0 && "grp_attn_n must be positive"); // NOLINT + GGML_ASSERT(ga_w % ga_n == 0 && "grp_attn_w must be a multiple of grp_attn_n"); // NOLINT + //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of grp_attn_w"); // NOLINT + //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT + LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w); + } LOG_TEE("\n\n"); if (params.interactive) { @@ -500,37 +515,61 @@ int main(int argc, char ** argv) { fflush(stdout); } - // infinite text generation via context swapping - // if we run out of context: - // - take the n_keep first tokens from the original prompt (via n_past) - // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches - if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { - if (params.n_predict == -2) { - LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); - break; + if (ga_n == 1) { + // infinite text generation via context shifting + // if we run out of context: + // - take the n_keep first tokens from the original prompt (via n_past) + // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches + if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { + if (params.n_predict == -2) { + LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); + break; + } + + const int n_left = n_past - params.n_keep - 1; + const int n_discard = n_left/2; + + LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", + n_past, n_left, n_ctx, params.n_keep, n_discard); + + llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + + n_past -= n_discard; + + if (ctx_guidance) { + n_past_guidance -= n_discard; + } + + LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); + + LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); + + LOG("clear session path\n"); + path_session.clear(); } + } else { + // context extension via Self-Extend + while (n_past >= ga_i + ga_w) { + const int ib = (ga_n*ga_i)/ga_w; + const int bd = (ga_w/ga_n)*(ga_n - 1); + const int dd = (ga_w/ga_n) - ib*bd - ga_w; - const int n_left = n_past - params.n_keep - 1; - const int n_discard = n_left/2; + LOG("\n"); + LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd); + LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n); + LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd); - LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", - n_past, n_left, n_ctx, params.n_keep, n_discard); + llama_kv_cache_seq_shift(ctx, 0, ga_i, n_past, ib*bd); + llama_kv_cache_seq_div (ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); + llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd); - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + n_past -= bd; - n_past -= n_discard; + ga_i += ga_w/ga_n; - if (ctx_guidance) { - n_past_guidance -= n_discard; + LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i); } - - LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); - - LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); - - LOG("clear session path\n"); - path_session.clear(); } // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past) diff --git a/llama.h b/llama.h index 5305de90b..869ff0acf 100644 --- a/llama.h +++ b/llama.h @@ -484,6 +484,10 @@ extern "C" { llama_pos p1, llama_pos delta); + // Integer division of the positions by factor of `d > 1` + // If the KV cache is RoPEd, the KV data is updated accordingly + // p0 < 0 : [0, p1] + // p1 < 0 : [p0, inf) LLAMA_API void llama_kv_cache_seq_div( struct llama_context * ctx, llama_seq_id seq_id, From 42ea63c5a3da01d4a94e906d8565868012c79f4f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 8 Jan 2024 15:57:36 +0200 Subject: [PATCH 343/664] llama.swiftui : update readme --- examples/llama.swiftui/README.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/examples/llama.swiftui/README.md b/examples/llama.swiftui/README.md index fa68e6ed8..96cf743d4 100644 --- a/examples/llama.swiftui/README.md +++ b/examples/llama.swiftui/README.md @@ -1,7 +1,12 @@ -# llama.swiftui +# llama.cpp/examples/llama.swiftui -Local inference of llama.cpp on an iPhone. -So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well. +Local inference of llama.cpp on an iPhone. This is a sample app that can be used as a starting +point for more advanced projects. + +For usage instructions and performance stats, check the following discussion: https://github.com/ggerganov/llama.cpp/discussions/4508 + +![image](https://github.com/ggerganov/llama.cpp/assets/1991296/2b40284f-8421-47a2-b634-74eece09a299) + +Video demonstration: https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545 - From 668b31fc7d86245435ad6574e0e1126e734049e2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 8 Jan 2024 16:40:51 +0200 Subject: [PATCH 344/664] swift : exclude ggml-metal.metal from the package (#4822) --- Package.swift | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Package.swift b/Package.swift index e33a4ff46..583e2e276 100644 --- a/Package.swift +++ b/Package.swift @@ -21,7 +21,7 @@ let package = Package( name: "llama", dependencies: ["ggml"], path: ".", - exclude: [], + exclude: ["ggml-metal.metal"], sources: [ "llama.cpp", ], From dd5ae06405c5565b99889bdb3f168f4351252cfb Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 8 Jan 2024 16:02:32 +0100 Subject: [PATCH 345/664] SOTA 2-bit quants (#4773) * iq2_xxs: basics * iq2_xxs: scalar and AVX2 dot products Needed to change Q8_K to have quants in the -127...127 range, else the IQ2_XXS AVX implementation becomes very awkward. The alternative would have been to use Q8_0 instead. Perhaps I'll change later, for now this is what we have. * iq2_xxs: ARM_NEON dot product Somehow strangely slow (112 ms/token). * iq2_xxs: WIP Metal Dequantize works, something is still wrong with the dot product. * iq2_xxs: Metal dot product now works We have PP-512 = 475 t/s TG-128 = 47.3 t/s Not the greatest performance, but not complete garbage either. * iq2_xxs: slighty faster dot product TG-128 is now 48.4 t/s * iq2_xxs: slighty faster dot product TG-128 is now 50.9 t/s * iq2_xxs: even faster Metal dot product TG-128 is now 54.1 t/s. Strangely enough, putting the signs lookup table into shared memory has a bigger impact than the grid values being in shared memory. * iq2_xxs: dequantize CUDA kernel - fix conflict with master * iq2_xxs: quantized CUDA dot product (MMVQ) We get TG-128 = 153.1 t/s * iq2_xxs: slightly faster CUDA dot product TG-128 is now at 155.1 t/s. * iq2_xxs: add to llama ftype enum * iq2_xxs: fix MoE on Metal * Fix missing MMQ ops when on hipBLAS I had put the ggml_supports_mmq call at the wrong place. * Fix bug in qequantize_row_iq2_xxs The 0.25f factor was missing. Great detective work by @ggerganov! * Fixing tests * PR suggestion --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 205 +++++++++++++++++++++++ ggml-metal.m | 40 +++++ ggml-metal.metal | 314 ++++++++++++++++++++++++++++++++++++ ggml-quants.c | 294 ++++++++++++++++++++++++++++++++- ggml-quants.h | 12 ++ ggml.c | 26 +++ ggml.h | 3 + llama.cpp | 3 + llama.h | 1 + tests/test-quantize-fns.cpp | 5 + 10 files changed, 902 insertions(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2df64b111..e0ea890b1 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -477,6 +477,14 @@ typedef struct { } block_q6_K; static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding"); +#define QR2_XXS 8 +#define QI2_XXS (QK_K / (4*QR2_XXS)) +typedef struct { + half d; + uint16_t qs[QK_K/8]; +} block_iq2_xxs; +static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -1292,6 +1300,128 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t #endif } +static const __device__ uint64_t kgrid_iq2xxs[256] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, + 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, + 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, + 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, + 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, + 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, + 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, + 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, + 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, + 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, + 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, + 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, + 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, + 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, + 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, + 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, + 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, + 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, + 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, + 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, + 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, + 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, + 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, + 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, + 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, + 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, + 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, + 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, + 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, + 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, + 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, + 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, + 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, + 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, + 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, + 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, +}; + +static const __device__ uint8_t ksigns_iq2xs[128] = { + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, + 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, + 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, + 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, + 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, + 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, + 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, + 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, +}; + +static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; + +inline bool ggml_cuda_supports_mmq(enum ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + return true; + default: + return false; + } +} + +template +static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq2_xxs * x = (const block_iq2_xxs *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid = (const uint8_t *)(kgrid_iq2xxs + aux8[il]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); @@ -3825,6 +3955,55 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat( return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); } +static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if QK_K == 256 + const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; + +#if QR2_XXS == 8 + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = q2[2] | (q2[3] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(kgrid_iq2xxs + aux8[l]); + const uint8_t signs = ksigns_iq2xs[aux32 & 127]; + for (int j = 0; j < 8; ++j) { + sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * (float)bq8_1[ib32].ds.x * 0.25f; + return d * sumi; +#else + // iqs is 0...15 + const int ib32 = iqs/2; + const int il = iqs%2; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid1 = (const uint8_t *)(kgrid_iq2xxs + aux8[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(kgrid_iq2xxs + aux8[2*il+1]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * (float)bq8_1[ib32].ds.x * 0.25f; + const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; + const int8_t * q8 = bq8_1[ib32].qs + 16*il; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); + sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); + } + return d * (sumi1 + sumi2); +#endif +#else + assert(false); + return 0.f; +#endif +} + template static __device__ __forceinline__ void mul_mat_q( @@ -5664,6 +5843,12 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu #endif } +template +static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_xxs<<>>(vx, y); +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -5692,6 +5877,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_q5_K_cuda; case GGML_TYPE_Q6_K: return dequantize_row_q6_K_cuda; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_F32: return convert_unary_cuda; default: @@ -5721,6 +5908,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_q5_K_cuda; case GGML_TYPE_Q6_K: return dequantize_row_q6_K_cuda; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_F16: return convert_unary_cuda; default: @@ -5915,6 +6104,15 @@ static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * <<>>(vx, vy, dst, ncols, nrows); } +static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + static void ggml_mul_mat_q4_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { @@ -7407,6 +7605,7 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: return max_compute_capability >= CC_RDNA2 ? 128 : 64; default: GGML_ASSERT(false); @@ -7427,6 +7626,7 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: return max_compute_capability >= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: return 64; @@ -7477,6 +7677,9 @@ static void ggml_cuda_op_mul_mat_vec_q( case GGML_TYPE_Q6_K: mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); break; + case GGML_TYPE_IQ2_XXS: + mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; @@ -8693,6 +8896,8 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + use_mul_mat_q = use_mul_mat_q && ggml_cuda_supports_mmq(src0->type); + // debug helpers //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); diff --git a/ggml-metal.m b/ggml-metal.m index fbbdcd8c4..6c2a8d04e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -88,6 +88,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(get_rows_q5_K); GGML_METAL_DECL_KERNEL(get_rows_q6_K); GGML_METAL_DECL_KERNEL(get_rows_i32); + GGML_METAL_DECL_KERNEL(get_rows_iq2_xxs); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(group_norm); GGML_METAL_DECL_KERNEL(norm); @@ -106,6 +107,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mv_q4_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32); + GGML_METAL_DECL_KERNEL(mul_mv_iq2_xxs_f32); GGML_METAL_DECL_KERNEL(mul_mv_id_f32_f32); //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f16); GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32); @@ -121,6 +123,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mv_id_q4_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_id_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_id_q6_K_f32); + GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xxs_f32); GGML_METAL_DECL_KERNEL(mul_mm_f32_f32); GGML_METAL_DECL_KERNEL(mul_mm_f16_f32); GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32); @@ -133,6 +136,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32); + GGML_METAL_DECL_KERNEL(mul_mm_iq2_xxs_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_f32_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_f16_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_q4_0_f32); @@ -145,6 +149,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mm_id_q4_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_q6_K_f32); + GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xxs_f32); GGML_METAL_DECL_KERNEL(rope_f32); GGML_METAL_DECL_KERNEL(rope_f16); GGML_METAL_DECL_KERNEL(alibi_f32); @@ -379,6 +384,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(get_rows_q5_K); GGML_METAL_ADD_KERNEL(get_rows_q6_K); GGML_METAL_ADD_KERNEL(get_rows_i32); + GGML_METAL_ADD_KERNEL(get_rows_iq2_xxs); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(group_norm); GGML_METAL_ADD_KERNEL(norm); @@ -397,6 +403,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mv_q4_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32); + GGML_METAL_ADD_KERNEL(mul_mv_iq2_xxs_f32); GGML_METAL_ADD_KERNEL(mul_mv_id_f32_f32); //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f16); GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32); @@ -412,6 +419,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mv_id_q4_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_id_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_id_q6_K_f32); + GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xxs_f32); if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { GGML_METAL_ADD_KERNEL(mul_mm_f32_f32); GGML_METAL_ADD_KERNEL(mul_mm_f16_f32); @@ -425,6 +433,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32); + GGML_METAL_ADD_KERNEL(mul_mm_iq2_xxs_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_f32_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_f16_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_q4_0_f32); @@ -437,6 +446,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mm_id_q4_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_q6_K_f32); + GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xxs_f32); } GGML_METAL_ADD_KERNEL(rope_f32); GGML_METAL_ADD_KERNEL(rope_f16); @@ -502,6 +512,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(get_rows_q5_K); GGML_METAL_DEL_KERNEL(get_rows_q6_K); GGML_METAL_DEL_KERNEL(get_rows_i32); + GGML_METAL_DEL_KERNEL(get_rows_iq2_xxs); GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(group_norm); GGML_METAL_DEL_KERNEL(norm); @@ -520,6 +531,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mv_q4_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32); + GGML_METAL_DEL_KERNEL(mul_mv_iq2_xxs_f32); GGML_METAL_DEL_KERNEL(mul_mv_id_f32_f32); //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f16); GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32); @@ -535,6 +547,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mv_id_q4_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_id_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_id_q6_K_f32); + GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xxs_f32); if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { GGML_METAL_DEL_KERNEL(mul_mm_f32_f32); GGML_METAL_DEL_KERNEL(mul_mm_f16_f32); @@ -548,6 +561,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_iq2_xxs_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_f32_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_f16_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_q4_0_f32); @@ -560,6 +574,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mm_id_q4_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_q6_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xxs_f32); } GGML_METAL_DEL_KERNEL(rope_f32); GGML_METAL_DEL_KERNEL(rope_f16); @@ -1541,6 +1556,7 @@ bool ggml_metal_graph_compute( case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break; case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break; + case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xxs_f32]; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1653,6 +1669,12 @@ bool ggml_metal_graph_compute( nth1 = 32; [encoder setComputePipelineState:ctx->pipeline_mul_mv_q6_K_f32]; } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xxs_f32]; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1686,9 +1708,14 @@ bool ggml_metal_graph_compute( if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || + //src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src0t == GGML_TYPE_IQ2_XXS) { + [encoder setThreadgroupMemoryLength:(256*8+128) atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1778,6 +1805,7 @@ bool ggml_metal_graph_compute( case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_K_f32]; break; case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_K_f32]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q6_K_f32]; break; + case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xxs_f32]; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1893,6 +1921,12 @@ bool ggml_metal_graph_compute( nth1 = 32; [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q6_K_f32]; } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xxs_f32]; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1942,9 +1976,14 @@ bool ggml_metal_graph_compute( if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || + //src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src2t == GGML_TYPE_IQ2_XXS) { + [encoder setThreadgroupMemoryLength:(256*8+128) atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1982,6 +2021,7 @@ bool ggml_metal_graph_compute( case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break; case GGML_TYPE_I32: [encoder setComputePipelineState:ctx->pipeline_get_rows_i32]; break; + case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xxs]; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index a7d3f9efa..0cc535ac7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2446,6 +2446,12 @@ typedef struct { } block_q6_K; // 210 bytes / block +typedef struct { + half d; + uint16_t qs[QK_K/8]; +} block_iq2_xxs; +// 66 bytes / block for QK_K = 256, so 2.0625 bpw + //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -3468,6 +3474,221 @@ kernel void kernel_mul_mv_q6_K_f32( kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); } +// ======================= "True" 2-bit + +constexpr constant static uint64_t kgrid_iq2xxs[256] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, + 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, + 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, + 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, + 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, + 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, + 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, + 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, + 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, + 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, + 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, + 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, + 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, + 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, + 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, + 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, + 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, + 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, + 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, + 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, + 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, + 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, + 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, + 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, + 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, + 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, + 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, + 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, + 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, + 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, + 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, + 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, + 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, + 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, + 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, + 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, +}; + +constexpr constant static uint8_t ksigns_iq2xs[128] = { + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, + 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, + 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, + 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, + 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, + 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, + 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, + 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, +}; + +constexpr constant static uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; + +void kernel_mul_mv_iq2_xxs_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq2_xxs * x = (device const block_iq2_xxs *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[32]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + + threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; + threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); + { + int nval = 4; + int pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) values[pos + i] = kgrid_iq2xxs[pos + i]; + nval = 2; + pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + threadgroup_barrier(mem_flags::mem_threadgroup); + } + +#if QK_K == 256 + const int ix = tiisg; + + device const float * y4 = y + 32 * ix; + + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { + + for (int i = 0; i < 32; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq2_xxs * xr = x + ibl; + device const uint16_t * q2 = xr->qs + 4 * ib; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + const float db = dh[0]; + device const uint8_t * aux8 = (device const uint8_t *)q2; + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = db * (0.5f + (aux32 >> 28)); + + float sum = 0; + for (int l = 0; l < 4; ++l) { + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + aux8[l]); + const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; + for (int j = 0; j < 8; ++j) { + sum += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); + } + } + sumf[row] += d * sum; + + dh += nb*sizeof(block_iq2_xxs)/2; + q2 += nb*sizeof(block_iq2_xxs)/2; + } + + y4 += 32 * 32; + } +#else + // TODO +#endif + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + } + } +} + +[[host_name("kernel_mul_mv_iq2_xxs_f32")]] +kernel void kernel_mul_mv_iq2_xxs_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + //============================= templates and their specializations ============================= // NOTE: this is not dequantizing - we are simply fitting the template @@ -3739,6 +3960,31 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg } } +template +void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + // each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's. + device const uint16_t * q2 = xb->qs + 4*ib32; + const uint32_t aux32_g = q2[0] | (q2[1] << 16); + const uint32_t aux32_s = q2[2] | (q2[3] << 16); + thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; + const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; + constant uint8_t * grid = (constant uint8_t *)(kgrid_iq2xxs + aux8[2*il+0]); + uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; + for (int i = 0; i < 8; ++i) { + reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } + grid = (constant uint8_t *)(kgrid_iq2xxs + aux8[2*il+1]); + signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; + for (int i = 0; i < 8; ++i) { + reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } +} + template kernel void kernel_get_rows( device const void * src0, @@ -4278,6 +4524,7 @@ template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows; // // matrix-matrix multiplication @@ -4314,6 +4561,7 @@ template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; // // indirect matrix-matrix multiplication @@ -4362,6 +4610,7 @@ template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mat_mm_id_t kernel_mu template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; // // matrix-vector multiplication @@ -5134,3 +5383,68 @@ kernel void kernel_mul_mv_id_q6_K_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] +kernel void kernel_mul_mv_id_iq2_xxs_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq2_xxs_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-quants.c b/ggml-quants.c index 55a9496d1..fd127f2d1 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -2340,6 +2340,138 @@ size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * return (n/QK_K*sizeof(block_q6_K)); } +// ====================== "True" 2-bit (de)-quantization + +void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k) { + (void)x; + (void)y; + (void)k; + assert(k % QK_K == 0); + //fprintf(stderr, "=========================== %s: not implemented\n", __func__); +} + +static const uint64_t iq2xxs_grid[256] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, + 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, + 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, + 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, + 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, + 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, + 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, + 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, + 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, + 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, + 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, + 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, + 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, + 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, + 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, + 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, + 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, + 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, + 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, + 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, + 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, + 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, + 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, + 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, + 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, + 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, + 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, + 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, + 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, + 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, + 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, + 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, + 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, + 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, + 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, + 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, +}; + +static const uint8_t ksigns_iq2xs[128] = { + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, + 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, + 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, + 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, + 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, + 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, + 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, + 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, +}; +static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; + +void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + uint32_t aux32[2]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(aux32, x[i].qs + 4*ib32, 2*sizeof(uint32_t)); + const float db = d * (0.5f + (aux32[1] >> 28)) * 0.25f; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127]; + for (int j = 0; j < 8; ++j) { + y[j] = db * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); + } + y += 8; + } + } + } +} + +void quantize_row_iq2_xxs(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq2_xxs * restrict y = vy; + quantize_row_iq2_xxs_reference(x, y, k); +} + +size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist) { + assert(k % QK_K == 0); + (void)hist; // TODO: collect histograms + + for (int j = 0; j < n; j += k) { + block_iq2_xxs * restrict y = (block_iq2_xxs *)dst + j/QK_K; + quantize_row_iq2_xxs_reference(src + j, y, k); + } + return (n/QK_K*sizeof(block_iq2_xxs)); +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -2362,7 +2494,9 @@ void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict x += QK_K; continue; } - const float iscale = -128.f/max; + //const float iscale = -128.f/max; + // We need this change for IQ2_XXS, else the AVX implementation becomes very awkward + const float iscale = -127.f/max; for (int j = 0; j < QK_K; ++j) { int v = nearest_int(iscale*x[j]); y[i].qs[j] = MIN(127, v); @@ -7065,3 +7199,161 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri } #endif + +static const int8_t keven_signs_q2xs[1024] = { + 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, + 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, + 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, + 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, + 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, + 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, + 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, + 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, + 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, + 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, + 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, + 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, + 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, 1, + 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, + 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, + 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, + 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, + 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, + 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, + 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, + 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, + 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, + 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, + 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, + 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, + 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, + 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, + 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, + 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, + 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, + 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, + 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, +}; + +void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + + const block_iq2_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + int8x16x4_t q2u; + int8x16x4_t q2s; + int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 0])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 1]))); + q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 2])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 3]))); + q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 8])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 9]))); + q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[10])), vld1_s8((const void *)(iq2xxs_grid + aux8[11]))); + q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 7) & 127)))); + q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 21) & 127)))); + q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); + q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); + q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); + q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]), q2u.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]), q2u.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[1] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[3] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.25f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + uint32_t aux32[2]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(aux32, q2, 2*sizeof(uint32_t)); + q2 += 4; + const uint32_t ls = 2*(aux32[1] >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls; + } + sumf += d * bsum; + } + *s = 0.125f * sumf; +#endif +} diff --git a/ggml-quants.h b/ggml-quants.h index 62c1df6cb..8dd911d41 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -165,6 +165,14 @@ typedef struct { } block_q8_K; static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding"); +// (Almost) "true" 2-bit quantization. +// Due to the need to use blocks as per ggml dsign, it ends up using +// 2.0625 bpw because of the 16-bit scale for each block of 256. +typedef struct { + ggml_fp16_t d; + uint16_t qs[QK_K/8]; +} block_iq2_xxs; +static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); // Quantization void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); @@ -180,6 +188,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); +void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k); void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); @@ -194,6 +203,7 @@ void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); +void quantize_row_iq2_xxs(const float * restrict x, void * restrict y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); @@ -209,6 +219,7 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k); void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k); void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); +void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); @@ -222,3 +233,4 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); diff --git a/ggml.c b/ggml.c index 62f0f18ef..adb387100 100644 --- a/ggml.c +++ b/ggml.c @@ -573,6 +573,17 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, + [GGML_TYPE_IQ2_XXS] = { + .type_name = "iq2_xxs", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_xxs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, + .from_float = quantize_row_iq2_xxs, + .from_float_reference = (ggml_from_float_t) quantize_row_iq2_xxs_reference, + .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -2111,6 +2122,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break; case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break; case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break; + case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7436,6 +7448,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; @@ -7700,6 +7713,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: { ggml_compute_forward_add1_q_f32(params, src0, src1, dst); } break; @@ -7814,6 +7828,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: default: { GGML_ASSERT(false); @@ -10455,6 +10470,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: { ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); } break; @@ -10629,6 +10645,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: default: { GGML_ASSERT(false); @@ -10823,6 +10840,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; @@ -11459,6 +11477,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -11533,6 +11552,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -18648,6 +18668,12 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i block_q6_K * block = (block_q6_K*)dst + start / QK_K; result = ggml_quantize_q6_K(src + start, block, n, n, hist); } break; + case GGML_TYPE_IQ2_XXS: + { + GGML_ASSERT(start % QK_K == 0); + block_iq2_xxs * block = (block_iq2_xxs*)dst + start / QK_K; + result = ggml_quantize_iq2_xxs(src + start, block, n, n, hist); + } break; case GGML_TYPE_F16: { int elemsize = sizeof(ggml_fp16_t); diff --git a/ggml.h b/ggml.h index 64f4e45e8..c55e598b4 100644 --- a/ggml.h +++ b/ggml.h @@ -339,6 +339,7 @@ extern "C" { GGML_TYPE_Q5_K = 13, GGML_TYPE_Q6_K = 14, GGML_TYPE_Q8_K = 15, + GGML_TYPE_IQ2_XXS = 16, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -373,6 +374,7 @@ extern "C" { GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors }; // available tensor operations: @@ -2067,6 +2069,7 @@ extern "C" { GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist); diff --git a/llama.cpp b/llama.cpp index 63853d1c3..8e0717db9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2222,6 +2222,7 @@ struct llama_model_loader { case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break; case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break; case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; + case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -2593,6 +2594,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small"; case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw"; default: return "unknown, may not work"; } @@ -9038,6 +9040,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_Q5_K_S: case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } diff --git a/llama.h b/llama.h index 869ff0acf..c11075bbc 100644 --- a/llama.h +++ b/llama.h @@ -103,6 +103,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index a2459a286..cee712618 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -134,6 +134,11 @@ int main(int argc, char * argv[]) { continue; } + if ((ggml_type)i == GGML_TYPE_IQ2_XXS) { + printf("Skip %s due to missing quantization functionality\n", ggml_type_name((ggml_type) i)); + continue; + } + printf("Testing %s\n", ggml_type_name((ggml_type) i)); if (qfns.from_float && qfns.to_float) { From a9a8c5de3d2028701c239d821b220214fcaefbf1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 8 Jan 2024 20:25:17 +0200 Subject: [PATCH 346/664] readme : add link to SOTA models --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 2f6e6ffee..a0d86a6ef 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508 From 1fc2f265ff9377a37fd2c61eae9cd813a3491bea Mon Sep 17 00:00:00 2001 From: howlger Date: Mon, 8 Jan 2024 20:05:53 +0100 Subject: [PATCH 347/664] common : fix the short form of `--grp-attn-w`, not `-gat` (#4825) See https://github.com/ggerganov/llama.cpp/blob/master/common/common.cpp#L230C53-L230C57 --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 6b4913a65..4e89fe516 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -920,7 +920,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { #endif printf(" -gan N, --grp-attn-n N\n"); printf(" group-attention factor (default: %d)\n", params.grp_attn_n); - printf(" -gat N, --grp-attn-w N\n"); + printf(" -gaw N, --grp-attn-w N\n"); printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); printf(" --verbose-prompt print prompt before generation\n"); printf(" -dkvc, --dump-kv-cache\n"); From 8f900abfc09851e281bc9027e0ab2f16bf079b29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 9 Jan 2024 08:58:55 +0100 Subject: [PATCH 348/664] CUDA: faster softmax via shared memory + fp16 math (#4742) --- ggml-cuda.cu | 333 ++++++++++++++++++++++++++++++++++--- tests/test-backend-ops.cpp | 17 +- 2 files changed, 321 insertions(+), 29 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e0ea890b1..e26260a35 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -116,6 +116,7 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 #define CC_OFFSET_AMD 1000000 @@ -556,11 +557,12 @@ static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0}; struct cuda_device_capabilities { int cc; // compute capability + size_t smpb; // max. shared memory per block bool vmm; // virtual memory support size_t vmm_granularity; // granularity of virtual memory }; -static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, false, 0} }; +static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, false, 0} }; static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default @@ -593,6 +595,19 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { return a; } +static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { +#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + (void) a; + bad_arch(); +#else +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + } + return a; +#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +} + static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -601,6 +616,19 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { return x; } +static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { +#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + (void) x; + bad_arch(); +#else +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + } + return x; +#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +} + static __device__ __forceinline__ float op_repeat(const float a, const float b) { return b; GGML_UNUSED(a); @@ -5385,75 +5413,233 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; } -static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale) { +template +static __global__ void soft_max_f16(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL + const int ncols_data = ncols_template == 0 ? ncols_par : ncols_template; + const int ncols_smem = GGML_PAD(ncols_data, 2*WARP_SIZE)/2; + const int tid = threadIdx.x; const int rowx = blockIdx.x; const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension - const int block_size = blockDim.x; + const int block_size = block_size_template == 0 ? blockDim.x : block_size_template; const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - __shared__ float buf[CUDA_SOFT_MAX_BLOCK_SIZE/WARP_SIZE]; + extern __shared__ half data_soft_max_f16[]; + half * buf_iw = data_soft_max_f16 + 0; // shared memory buffer for inter-warp communication + // (shared memory) buffer to cache values between iterations: + half2 * vals = vals_smem ? (half2 *) (buf_iw + WARP_SIZE) : (half2 *) (dst + rowx*ncols_data); + // if the buffer is larger than max. shared memory per block, use dst as temp. buffer instead + // in that case col_smem == col_data must be enforced to avoid race conditions - float max_val = -INFINITY; + half2 max_val = make_half2(-INFINITY, -INFINITY); - for (int col = tid; col < ncols; col += block_size) { - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; - max_val = max(max_val, x[ix]*scale + (y ? y[iy] : 0.0f)); +#pragma unroll + for (int col0 = 0; col0 < ncols_smem; col0 += block_size) { + const int col_data = 2*col0 + 2*WARP_SIZE*warp_id + lane_id; + const int col_smem = vals_smem ? col0 + tid : col_data; + + const int ix = rowx*ncols_data + col_data; + const int iy = rowy*ncols_data + col_data; + + half2 val; + if (need_check && col_data + 0 >= ncols_data) { + val.x = -INFINITY; + } else { + val.x = x[ix + 0]*scale + (y ? y[iy + 0] : 0.0f); + } + if (need_check && col_data + WARP_SIZE >= ncols_data) { + val.y = -INFINITY; + } else { + val.y = x[ix + WARP_SIZE]*scale + (y ? y[iy + WARP_SIZE] : 0.0f); + } + if (!need_check || col_smem < (vals_smem ? ncols_smem : ncols_data)) { + vals[col_smem] = val; + } + max_val = __hmax2(max_val, val); } // find the max value in the block max_val = warp_reduce_max(max_val); if (block_size > WARP_SIZE) { if (warp_id == 0) { - buf[lane_id] = -INFINITY; + buf_iw[lane_id] = -INFINITY; } __syncthreads(); if (lane_id == 0) { - buf[warp_id] = max_val; + buf_iw[warp_id] = __hmax(max_val.x, max_val.y); } __syncthreads(); - max_val = buf[lane_id]; + max_val = __half2half2(buf_iw[lane_id]); max_val = warp_reduce_max(max_val); + } else { + max_val = __half2half2(__hmax(max_val.x, max_val.y)); } - float tmp = 0.f; + half2 tmp = make_half2(0.0f, 0.0f); // partial sums + +#pragma unroll + for (int col0 = 0; col0 < ncols_smem; col0 += block_size) { + const int col_smem = vals_smem ? col0 + tid : 2*col0 + 2*warp_id*WARP_SIZE + lane_id; + + if (ncols_template == 0 && col_smem >= (vals_smem ? ncols_smem : ncols_data)) { + break; + } + + const half2 val = h2exp(vals[col_smem] - max_val); - for (int col = tid; col < ncols; col += block_size) { - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; - const float val = expf((x[ix]*scale + (y ? y[iy] : 0.0f)) - max_val); tmp += val; - dst[ix] = val; + vals[col_smem] = val; } // find the sum of exps in the block tmp = warp_reduce_sum(tmp); if (block_size > WARP_SIZE) { if (warp_id == 0) { - buf[lane_id] = 0.f; + buf_iw[lane_id] = 0.0f; } __syncthreads(); if (lane_id == 0) { - buf[warp_id] = tmp; + buf_iw[warp_id] = tmp.x + tmp.y; } __syncthreads(); - tmp = buf[lane_id]; + tmp = __half2half2(buf_iw[lane_id]); + tmp = warp_reduce_sum(tmp); + } else { + tmp = __half2half2(tmp.x + tmp.y); + } + + const half2 inv_sum = make_half2(1.0f, 1.0f) / tmp; + +#pragma unroll + for (int col0 = 0; col0 < ncols_smem; col0 += block_size) { + const int col_data = 2*col0 + 2*WARP_SIZE*warp_id + lane_id; + const int col_smem = vals_smem ? col0 + tid : col_data; + + const int idst = rowx*ncols_data + col_data; + const half2 result = vals[col_smem] * inv_sum; + + if (need_check && col_data + 0 >= ncols_data) { + return; + } + dst[idst] = result.x; + + if (need_check && col_data + WARP_SIZE >= ncols_data) { + return; + } + + dst[idst + WARP_SIZE] = result.y; + } +#else + (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale; + bad_arch(); +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +} + +template +static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) { + const int ncols = ncols_template == 0 ? ncols_par : ncols_template; + + const int tid = threadIdx.x; + const int rowx = blockIdx.x; + const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension + + const int block_size = block_size_template == 0 ? blockDim.x : block_size_template; + + const int warp_id = threadIdx.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + + extern __shared__ float data_soft_max_f32[]; + float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication + // shared memory buffer to cache values between iterations: + float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols; + + float max_val = -INFINITY; + +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + break; + } + + const int ix = rowx*ncols + col; + const int iy = rowy*ncols + col; + + const float val = x[ix]*scale + (y ? y[iy] : 0.0f); + vals[col] = val; + max_val = max(max_val, val); + } + + // find the max value in the block + max_val = warp_reduce_max(max_val); + if (block_size > WARP_SIZE) { + if (warp_id == 0) { + buf_iw[lane_id] = -INFINITY; + } + __syncthreads(); + + if (lane_id == 0) { + buf_iw[warp_id] = max_val; + } + __syncthreads(); + + max_val = buf_iw[lane_id]; + max_val = warp_reduce_max(max_val); + } + + float tmp = 0.0f; // partial sum + +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + break; + } + + const float val = expf(vals[col] - max_val); + tmp += val; + vals[col] = val; + } + + // find the sum of exps in the block + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + if (warp_id == 0) { + buf_iw[lane_id] = 0.0f; + } + __syncthreads(); + + if (lane_id == 0) { + buf_iw[warp_id] = tmp; + } + __syncthreads(); + + tmp = buf_iw[lane_id]; tmp = warp_reduce_sum(tmp); } - const float inv_tmp = 1.f / tmp; + const float inv_sum = 1.0f / tmp; - for (int col = tid; col < ncols; col += block_size) { - const int i = rowx*ncols + col; - dst[i] *= inv_tmp; +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + return; + } + + const int idst = rowx*ncols + col; + dst[idst] = vals[col] * inv_sum; } } @@ -6752,12 +6938,90 @@ static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols diag_mask_inf_f32<<>>(x, dst, ncols_x, rows_per_channel, n_past); } +static void soft_max_f16_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) { + int nth = WARP_SIZE; + while (nth < ncols_x/2 && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; + const dim3 block_dims(nth, 1, 1); + const dim3 block_nums(nrows_x, 1, 1); + const size_t shmem = (GGML_PAD(ncols_x, 2*WARP_SIZE) + WARP_SIZE)*sizeof(half); + static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); + if (shmem <= g_device_caps[g_main_device].smpb) { + switch (ncols_x) { + case 32: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 64: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 128: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 256: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 512: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 1024: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 2048: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 4096: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + default: + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + } + } else { + const size_t shmem_low = WARP_SIZE*sizeof(half); + soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); + } +} + static void soft_max_f32_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) { int nth = WARP_SIZE; while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; const dim3 block_dims(nth, 1, 1); const dim3 block_nums(nrows_x, 1, 1); - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float); + static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); + if (shmem < g_device_caps[g_main_device].smpb) { + switch (ncols_x) { + case 32: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 64: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 128: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 256: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 512: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 1024: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 2048: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + case 4096: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + default: + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + break; + } + } else { + const size_t shmem_low = WARP_SIZE*sizeof(float); + soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + } } static void im2col_f32_f16_cuda(const float* x, half* dst, @@ -7072,6 +7336,7 @@ void ggml_init_cublas() { #else g_device_caps[id].cc = 100*prop.major + 10*prop.minor; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + g_device_caps[id].smpb = prop.sharedMemPerBlock; } for (int id = 0; id < g_device_count; ++id) { g_tensor_split[id] /= total_vram; @@ -8087,7 +8352,21 @@ static void ggml_cuda_op_soft_max( float scale = 1.0f; memcpy(&scale, dst->op_params, sizeof(float)); - soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + const bool use_f16_soft_max = false; +#else +#ifdef GGML_CUDA_F16 + const bool use_f16_soft_max = true; +#else + const bool use_f16_soft_max = false; +#endif // GGML_CUDA_F16 +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + + if (use_f16_soft_max) { + soft_max_f16_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); + } else { + soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); + } (void) dst; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b79de7a7d..7a60d7743 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -450,7 +450,7 @@ struct test_case { double err = nmse(f1.data(), f2.data(), f1.size()); if (err > ud->max_err) { - printf("[%s] NMSE = %f ", ggml_op_desc(t1), err); + printf("[%s] NMSE = %.9f > %.9f ", ggml_op_desc(t1), err, ud->max_err); //for (int i = 0; i < (int) f1.size(); i++) { // printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]); //} @@ -1449,6 +1449,7 @@ struct test_moe : public test_case { static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) { std::vector> test_cases; + std::default_random_engine rng(0); const ggml_type all_types[] = { GGML_TYPE_F32, GGML_TYPE_F16, @@ -1583,7 +1584,19 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 1}, 5)); test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 10}, 5)); - test_cases.emplace_back(new test_soft_max()); + std::uniform_int_distribution<> dist_ne1(1, 50); + int exponent = 1; + while (exponent < (1 << 17)) { + std::uniform_int_distribution<> dist_ne0(exponent, 2*exponent); + + for (int n = 0; n < 10; ++n) { + int64_t ne0 = dist_ne0(rng); + int64_t ne1 = dist_ne1(rng); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1})); + } + + exponent <<= 1; + } for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512)); // llama 7B From 18c2e1752c3b387689e9e73d7d8a1a3b1511ce23 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 9 Jan 2024 10:42:06 +0200 Subject: [PATCH 349/664] ggml : fix vld1q_s8_x4 32-bit compat (#4828) * ggml : fix vld1q_s8_x4 32-bit compat ggml-ci * ggml : fix 32-bit ARM compat (cont) ggml-ci --- ggml-quants.c | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index fd127f2d1..d497e6de9 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -7250,9 +7250,9 @@ void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * res uint32_t aux32[4]; const uint8_t * aux8 = (const uint8_t *)aux32; - int8x16x4_t q2u; - int8x16x4_t q2s; - int8x16x4_t q8b; + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -7261,7 +7261,7 @@ void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * res const int8_t * restrict q8 = y[i].qs; float sumf1 = 0, sumf2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = vld1q_s8_x4(q8); q8 += 64; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 0])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 1]))); q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 2])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 3]))); From 8c5833031857c9e9ada61948bae894ab9c785f86 Mon Sep 17 00:00:00 2001 From: Zsapi Date: Tue, 9 Jan 2024 10:12:43 +0100 Subject: [PATCH 350/664] server : add api-key flag to documentation (#4832) Document the api-key flag added to server in https://github.com/ggerganov/llama.cpp/pull/4441 --- examples/server/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/README.md b/examples/server/README.md index 243e66991..5d9829624 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -23,6 +23,7 @@ Command line options: - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. - `--path`: path from which to serve static files (default examples/server/public) +- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. - `--embedding`: Enable embedding extraction, Default: disabled. - `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) - `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) From 128de3585b0f58b1e562733448fc00109f23a95d Mon Sep 17 00:00:00 2001 From: Behnam M <58621210+ibehnam@users.noreply.github.com> Date: Tue, 9 Jan 2024 05:02:05 -0500 Subject: [PATCH 351/664] server : update readme about token probs (#4777) * updated server readme to reflect the gg/server-token-probs-4088 commit added explanation for the API's completion result which now includes `completion_probabilities`. Also added a JSON schema that shows the type/structure of `completion_probabilities`. * simplified the `completion_probabilities` JSON schema It's now easier to understand what the structure of `completion_probabilities` looks like. * minor : fix trailing whitespace --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 57 ++++++++++++++++++++++----------------- 1 file changed, 33 insertions(+), 24 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 5d9829624..d85a14f89 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -175,35 +175,44 @@ node index.js `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) - *Result JSON:* +### Result JSON: - Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. +* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) +- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model` +``` +{ + "content": "", + "probs": [ + { + "prob": float, + "tok_str": "" + }, + { + "prob": float, + "tok_str": "" + }, + ... + ] +}, +``` +Notice that each `probs` is an array of length `n_probs`. - `model`: The path to the model loaded with `-m` - - `prompt`: The provided `prompt` - - `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token - - `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered - - `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided - - `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word) - - `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second` - - `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`) - - `tokens_evaluated`: Number of tokens evaluated in total from the prompt - - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) +- `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. +- `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) +- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model` +- `model`: The path to the model loaded with `-m` +- `prompt`: The provided `prompt` +- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token +- `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered +- `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided +- `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word) +- `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second` +- `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`) +- `tokens_evaluated`: Number of tokens evaluated in total from the prompt +- `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) - **POST** `/tokenize`: Tokenize a given text. From d9653894dffbfd3a58616f31b0967b34faf6f611 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 9 Jan 2024 16:23:05 +0200 Subject: [PATCH 352/664] scripts : script to get Paul Graham essays in txt format (#4838) --- scripts/get-pg.sh | 47 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100755 scripts/get-pg.sh diff --git a/scripts/get-pg.sh b/scripts/get-pg.sh new file mode 100755 index 000000000..d516db46c --- /dev/null +++ b/scripts/get-pg.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +function usage { + echo "usage: $0" + exit 1 +} + +function has_cmd { + if ! [ -x "$(command -v $1)" ]; then + echo "error: $1 is not available" >&2 + exit 1 + fi +} + +# check for: curl, html2text, tail, sed, fmt +has_cmd curl +has_cmd html2text +has_cmd tail +has_cmd sed + +if [ $# -ne 1 ]; then + usage +fi + +n=$1 + +# get urls +urls="$(curl http://www.aaronsw.com/2002/feeds/pgessays.rss | grep html | sed -e "s/.*http/http/" | sed -e "s/html.*/html/" | head -n $n)" + +printf "urls:\n%s\n" "$urls" + +if [ -f pg.txt ]; then + rm pg.txt +fi + +for url in $urls; do + echo "processing $url" + + curl -L $url | html2text | tail -n +4 | sed -E "s/^[[:space:]]+//g" | fmt -w 80 >> pg.txt + + # don't flood the server + sleep 1 +done + +echo "done. data in pg.txt" + +exit 0 From 18adb4e9bb340b7b4565d8b6715b4449283e7641 Mon Sep 17 00:00:00 2001 From: iohub Date: Wed, 10 Jan 2024 00:45:54 +0800 Subject: [PATCH 353/664] readme : add 3rd party collama reference to UI list (#4840) Add a VSCode extension for llama.cpp reference to UI list --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index a0d86a6ef..866aa87b4 100644 --- a/README.md +++ b/README.md @@ -137,6 +137,7 @@ as the main playground for developing new features for the [ggml](https://github - [semperai/amica](https://github.com/semperai/amica) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) +- [iohub/collama](https://github.com/iohub/coLLaMA) --- From 9a818f7c42761984ac99e08e613cc20634f8410e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 9 Jan 2024 19:20:45 +0200 Subject: [PATCH 354/664] scripts : improve get-pg.sh (#4838) --- scripts/get-pg.sh | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/scripts/get-pg.sh b/scripts/get-pg.sh index d516db46c..b027793e1 100755 --- a/scripts/get-pg.sh +++ b/scripts/get-pg.sh @@ -2,6 +2,22 @@ function usage { echo "usage: $0" + echo "note: n is the number of essays to download" + echo "for specific n, the resulting pg.txt file will have the following number of tokens:" + echo "n | tokens" + echo "--- | ---" + echo "1 | 6230" + echo "2 | 23619" + echo "5 | 25859" + echo "10 | 36888" + echo "15 | 50188" + echo "20 | 59094" + echo "25 | 88764" + echo "30 | 103121" + echo "32 | 108338" + echo "35 | 113403" + echo "40 | 127699" + echo "45 | 135896" exit 1 } @@ -33,10 +49,17 @@ if [ -f pg.txt ]; then rm pg.txt fi +c=1 for url in $urls; do echo "processing $url" - curl -L $url | html2text | tail -n +4 | sed -E "s/^[[:space:]]+//g" | fmt -w 80 >> pg.txt + cc=$(printf "%03d" $c) + + curl -L $url | html2text | tail -n +4 | sed -E "s/^[[:space:]]+//g" | fmt -w 80 >> pg-$cc-one.txt + cat pg-$cc-one.txt >> pg.txt + + cp -v pg.txt pg-$cc-all.txt + c=$((c+1)) # don't flood the server sleep 1 From 4dccb38d9abab7f9f2d1f9a6977df4185d490132 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 9 Jan 2024 19:37:08 +0200 Subject: [PATCH 355/664] metal : improve dequantize precision to match CPU (#4836) ggml-ci --- ggml-metal.metal | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 0cc535ac7..229efb8b6 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -3841,8 +3841,8 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4]; int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) : (scale_2&kmask2) | ((scale_1&kmask1) << 4); - half dl = il<8 ? d_all * (dl_int - 32.h) : d_all * (dl_int / 16.h - 32.h); - const half ml = 4.h * dl; + float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f); + const float ml = 4.f * dl; il = (il/2) & 3; const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); @@ -3909,7 +3909,7 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg uint8_t ul = 1 << (il/2); il = il & 3; const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.h; + const float d = il < 2 ? xb->d : xb->d / 16.f; const float min = xb->dmin; const float dl = d * sc[0]; const float ml = min * sc[1]; @@ -3942,17 +3942,17 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg #if QK_K == 256 ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); qh = qh + 32*(il/8) + 16*(il&1); - half sc = scales[(il%2) + 2 * ((il/2))]; + float sc = scales[(il%2) + 2 * ((il/2))]; il = (il/2) & 3; #else ql = ql + 16 * (il&1); - half sc = scales[il]; + float sc = scales[il]; #endif const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; - const half coef = il>1 ? 1.f/16.h : 1.h; - const half ml = d_all * sc * 32.h; - const half dl = d_all * sc * coef; + const float coef = il>1 ? 1.f/16.f : 1.f; + const float ml = d_all * sc * 32.f; + const float dl = d_all * sc * coef; for (int i = 0; i < 16; ++i) { const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2)) : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4)); From 36e5a08b203542dca53cca4eaf172c5dc4bbc991 Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Tue, 9 Jan 2024 09:59:14 -0800 Subject: [PATCH 356/664] llava-cli : don't crash if --image flag is invalid (#4835) This change fixes an issue where supplying `--image missing-file` would result in a segfault due to a null pointer being dereferenced. This can result in distracting info being printed if robust crash analysis tools are being used. --- examples/llava/llava-cli.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 502b788b1..d94795fe3 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -243,6 +243,9 @@ int main(int argc, char ** argv) { } auto image_embed = load_image(ctx_llava, ¶ms); + if (!image_embed) { + return 1; + } // process the prompt process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); From 6efb8eb30e7025b168f3fda3ff83b9b386428ad6 Mon Sep 17 00:00:00 2001 From: Austin <77757836+teleprint-me@users.noreply.github.com> Date: Tue, 9 Jan 2024 13:46:46 -0500 Subject: [PATCH 357/664] convert.py : fix vanilla LLaMA model conversion (#4818) * Update Imports and Add Notes for Future Reference - Updated import statements in `convert.py`. - Added import for `AutoTokenizer` from `transformers` module. - Added conditional import for `gguf` from the local directory. - Added comments and notes for future reference. Additional Notes: - Noted removal of a redundant `TypeAlias` import. - Noted the removal of a `gguf` debug statement. - Commented on the presence of `ARCH` and `NDArray` definitions. - Commented on cleaning up and refactoring data type definitions. * Refine Model Hyperparameters and Params Class - Updated type annotations to use `Optional` for clarity. - Improved method names and attribute consistency. - Removed unnecessary variables for better code readability. Additional Notes: - Highlighted the use of `Optional` for clearer intent. - Ensured backward and forward compatibility. * Restore BpeVocab and SentencePieceVocab classes - Restored the BpeVocab class for handling BPE tokenization. - Restored the SentencePieceVocab class for SentencePiece tokenization. These classes are essential for maintaining the original behavior of the codebase. * refactor: Standardize vocabulary handling with HfVocab - Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes. - Updated initialization of HfVocab with local_files_only=True for AutoTokenizer. - Introduced optional parameter fname_added_tokens for flexible added token management. - Streamlined added token handling for clarity and conciseness. - Maintained special tokens and IDs, enhancing token management. - Simplified token processing methods for improved readability. - Added a placeholder for score computation with a default value of -1000.0. - Optimized newline token check for efficiency. - Updated __repr__ function for clarity in representation. - Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab. - Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection. This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability. * refactor: Enhance readability, functionality, and code quality - Improved code formatting and readability for better maintainability. - Refactored LazyUnpickler's CLASSES dictionary for clarity. - Added print statements and warnings in check_vocab_size for user feedback. - Removed find_vocab_file_path, as it's superseded by VocabFactory. - Preparatory changes for upcoming classes: OutputFile and VocabFactory. - Overall focus on code quality, error handling, and consistency. These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory. * refactor: Update OutputFile class for enhanced model vocabulary management - Restructured the constructor for improved readability. - Updated `add_meta_arch` method for flexible model name determination. - Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models. - Streamlined vocabulary extraction with `extract_vocabulary_from_model`. - Simplified vocabulary metadata addition using `add_meta_vocab`. - Refactored `add_tensor_info` for clarity and consistency. - Improved error handling for better user feedback. These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information. * feat: Introduce VocabFactory for flexible vocabulary management in model conversion - The VocabFactory class is added to facilitate modular vocabulary handling. - The constructor initializes a directory path and detects vocabulary-related files. - The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece). - _create_special_vocab generates special vocabularies, accommodating different types. - The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer. - Error handling and logging enhance debugging and user feedback. - The modular and flexible design simplifies vocabulary management and supports future extensions. The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process. * refactor: Improve code organization, argument parsing, and user interface - Renamed 'default_outfile' to 'default_output_file' for clarity. - Refactored argument parser setup into 'get_argument_parser' function. - Introduced descriptive comments for each argument in the parser. - Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing. - Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'. - Enhanced error handling to prevent overwriting input data in 'default_output_file'. - Made 'argv' in 'main' an optional parameter for flexibility. - Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency. These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability. * refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling - Renamed command-line arguments for clarity and consistency. - Improved path resolution and import adjustments for robustness. - Thoughtfully handled 'awq-path' and conditional logic for the weighted model. - Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading. - Strengthened error handling and user feedback for a more user-friendly experience. - Structured output file handling with clear conditions and defaults. - Streamlined and organized the 'main' function for better logic flow. - Passed 'sys.argv[1:]' to 'main' for adaptability and testability. These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development. * chore: Apply ruff formatting to convert.py Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com> * Revert to commit 0614c33 * chore: Apply flake8 formatting rules Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com> * refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness - Resolved an unreachable branch issue by reorganizing the conditional structure. - Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion. - Flattened the conditional logic for improved clarity and predictability of the function's behavior. These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality. * py : fix outfile and outtype * py : suggest hint for missing vocab size --------- Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com> Co-authored-by: Georgi Gerganov --- convert.py | 969 ++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 666 insertions(+), 303 deletions(-) diff --git a/convert.py b/convert.py index c3f3fc0a1..3b613eefc 100755 --- a/convert.py +++ b/convert.py @@ -17,29 +17,58 @@ import signal import struct import sys import time +import warnings import zipfile from abc import ABCMeta, abstractmethod -from collections import OrderedDict +from argparse import ArgumentParser from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, Optional, TypeVar, cast +from typing import ( + IO, + TYPE_CHECKING, + Any, + Callable, + Iterable, + Literal, + Optional, + Tuple, + TypeVar, +) import numpy as np from sentencepiece import SentencePieceProcessor -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) -import gguf +try: + from transformers import AutoTokenizer +except ModuleNotFoundError as e: + warnings.warn(f"Could not import AutoTokenizer from transformers: {e}") -if TYPE_CHECKING: - from typing import TypeAlias +# If NO_LOCAL_GGUF is not set, try to import gguf from the local gguf-py directory +if "NO_LOCAL_GGUF" not in os.environ: + # Use absolute path to the gguf-py directory + gguf_py_dir = str(Path(__file__).resolve().parent / "gguf-py") + print(gguf_py_dir) # NOTE: Remove this once path is verified after changes are completed + if gguf_py_dir not in sys.path: + sys.path.insert(1, gguf_py_dir) -if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): +# Import gguf module +try: + import gguf +except ModuleNotFoundError as e: + print(f"Could not import gguf: {e}") + sys.exit(1) + +if TYPE_CHECKING: # NOTE: This isn't necessary. + from typing import TypeAlias # This can technically be omitted. + +if hasattr(faulthandler, "register") and hasattr(signal, "SIGUSR1"): faulthandler.register(signal.SIGUSR1) -NDArray: TypeAlias = 'np.ndarray[Any, Any]' +# NOTE: n-dimensional arrays should be directly referenced +NDArray: TypeAlias = "np.ndarray[Any, Any]" +# Why is this here? LLAMA and GPT are technically the only compatible ARCHs. ARCH = gguf.MODEL_ARCH.LLAMA DEFAULT_CONCURRENCY = 8 @@ -49,6 +78,7 @@ DEFAULT_CONCURRENCY = 8 # +# TODO: Clean up and refactor data types @dataclass(frozen=True) class DataType: name: str @@ -153,65 +183,85 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { @dataclass class Params: - n_vocab: int - n_embd: int - n_layer: int - n_ctx: int - n_ff: int - n_head: int - n_head_kv: int - n_experts: int | None = None - n_experts_used: int | None = None - f_norm_eps: float | None = None + n_vocab: int + n_embd: int + n_layer: int + n_ctx: int + n_ff: int + n_head: int + n_head_kv: int + f_norm_eps: Optional[float] = None + n_experts: Optional[int] = None + n_experts_used: Optional[int] = None - rope_scaling_type: gguf.RopeScalingType | None = None - f_rope_freq_base: float | None = None - f_rope_scale: float | None = None - n_orig_ctx: int | None = None - rope_finetuned: bool | None = None + rope_scaling_type: Optional[gguf.RopeScalingType] = None + f_rope_freq_base: Optional[float] = None + f_rope_scale: Optional[float] = None + n_orig_ctx: Optional[int] = None + rope_finetuned: Optional[bool] = None - ftype: GGMLFileType | None = None + ftype: Optional[GGMLFileType] = None # path to the directory containing the model files - path_model: Path | None = None + path_model: Optional[Path] = None @staticmethod - def guessed(model: LazyModel) -> Params: + def guessed(model: LazyModel) -> "Params": # try transformer naming first - n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape + n_vocab, n_embd = ( + model["model.embed_tokens.weight"].shape + if "model.embed_tokens.weight" in model + else model["tok_embeddings.weight"].shape + ) # try transformer naming first if "model.layers.0.self_attn.q_proj.weight" in model: - n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model) - elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming - n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model) + n_layer = next( + i + for i in itertools.count() + if f"model.layers.{i}.self_attn.q_proj.weight" not in model + ) + elif ( + "model.layers.0.self_attn.W_pack.weight" in model + ): # next: try baichuan naming + n_layer = next( + i + for i in itertools.count() + if f"model.layers.{i}.self_attn.W_pack.weight" not in model + ) else: - n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model) + n_layer = next( + i + for i in itertools.count() + if f"layers.{i}.attention.wq.weight" not in model + ) if n_layer < 1: - raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n" - "Suggestion: provide 'config.json' of the model in the same directory containing model files.") + raise Exception( + "failed to guess 'n_layer'. This model is unknown or unsupported.\n" + "Suggestion: provide 'config.json' of the model in the same directory containing model files." + ) - n_head = n_embd // 128 # guessed - n_mult = 256 # guessed + n_head = n_embd // 128 # guessed + n_mult = 256 # guessed # TODO: verify this n_ff = int(2 * (4 * n_embd) / 3) n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult) return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_layer = n_layer, - n_ctx = -1, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head, - f_norm_eps = 1e-5, + n_vocab=n_vocab, + n_embd=n_embd, + n_layer=n_layer, + n_ctx=-1, + n_ff=n_ff, + n_head=n_head, + n_head_kv=n_head, + f_norm_eps=1e-5, ) @staticmethod - def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: + def load_transformers_config(model: LazyModel, config_path: Path) -> "Params": config = json.load(open(config_path)) rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None @@ -224,20 +274,22 @@ class Params: rope_scaling_type = gguf.RopeScalingType.LINEAR elif typ == "yarn": rope_scaling_type = gguf.RopeScalingType.YARN - n_orig_ctx = rope_scaling['original_max_position_embeddings'] - rope_finetuned = rope_scaling['finetuned'] + n_orig_ctx = rope_scaling["original_max_position_embeddings"] + rope_finetuned = rope_scaling["finetuned"] else: - raise NotImplementedError(f'Unknown rope scaling type: {typ}') + raise NotImplementedError(f"Unknown rope scaling type: {typ}") if "max_sequence_length" in config: n_ctx = config["max_sequence_length"] elif "max_position_embeddings" in config: n_ctx = config["max_position_embeddings"] else: - raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n" - "Suggestion: provide 'config.json' of the model in the same directory containing model files.") + raise Exception( + "failed to guess 'n_ctx'. This model is unknown or unsupported.\n" + "Suggestion: provide 'config.json' of the model in the same directory containing model files." + ) - n_experts = None + n_experts = None n_experts_used = None if "num_local_experts" in config: @@ -245,30 +297,30 @@ class Params: n_experts_used = config["num_experts_per_tok"] return Params( - n_vocab = config["vocab_size"], - n_embd = config["hidden_size"], - n_layer = config["num_hidden_layers"], - n_ctx = n_ctx, - n_ff = config["intermediate_size"], - n_head = (n_head := config["num_attention_heads"]), - n_head_kv = config.get("num_key_value_heads", n_head), - n_experts = n_experts, - n_experts_used = n_experts_used, - f_norm_eps = config["rms_norm_eps"], - f_rope_freq_base = config.get("rope_theta"), - rope_scaling_type = rope_scaling_type, - f_rope_scale = f_rope_scale, - n_orig_ctx = n_orig_ctx, - rope_finetuned = rope_finetuned, + n_vocab=config["vocab_size"], + n_embd=config["hidden_size"], + n_layer=config["num_hidden_layers"], + n_ctx=n_ctx, + n_ff=config["intermediate_size"], + n_head=(n_head := config["num_attention_heads"]), + n_head_kv=config.get("num_key_value_heads", n_head), + n_experts=n_experts, + n_experts_used=n_experts_used, + f_norm_eps=config["rms_norm_eps"], + f_rope_freq_base=config.get("rope_theta"), + rope_scaling_type=rope_scaling_type, + f_rope_scale=f_rope_scale, + n_orig_ctx=n_orig_ctx, + rope_finetuned=rope_finetuned, ) # LLaMA v2 70B params.json # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1} @staticmethod - def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: + def load_torch_params(model: LazyModel, config_path: Path) -> "Params": config = json.load(open(config_path)) - n_experts = None + n_experts = None n_experts_used = None f_rope_freq_base = None @@ -291,129 +343,249 @@ class Params: if config.get("moe"): n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0] - n_experts = config["moe"]["num_experts"] + n_experts = config["moe"]["num_experts"] n_experts_used = config["moe"]["num_experts_per_tok"] f_rope_freq_base = 1e6 return Params( - n_vocab = model["tok_embeddings.weight"].shape[0], - n_embd = config["dim"], - n_layer = config["n_layers"], - n_ctx = n_ctx, - n_ff = n_ff, - n_head = (n_head := config["n_heads"]), - n_head_kv = config.get("n_kv_heads", n_head), - n_experts = n_experts, - n_experts_used = n_experts_used, - f_norm_eps = config["norm_eps"], - f_rope_freq_base = config.get("rope_theta", f_rope_freq_base), + n_vocab=config.get("vocab_size", model["tok_embeddings.weight"].shape[0]), + n_embd=config["dim"], + n_layer=config["n_layers"], + n_ctx=n_ctx, + n_ff=n_ff, + n_head=(n_head := config["n_heads"]), + n_head_kv=config.get("n_kv_heads", n_head), + n_experts=n_experts, + n_experts_used=n_experts_used, + f_norm_eps=config["norm_eps"], + f_rope_freq_base=config.get("rope_theta", f_rope_freq_base), ) @staticmethod - def load(model_plus: ModelPlus) -> Params: - hf_config_path = model_plus.paths[0].parent / "config.json" + def load(model_plus: ModelPlus) -> "Params": + hf_config_path = model_plus.paths[0].parent / "config.json" orig_config_path = model_plus.paths[0].parent / "params.json" if hf_config_path.exists(): - params = Params.loadHFTransformerJson(model_plus.model, hf_config_path) + params = Params.load_transformers_config(model_plus.model, hf_config_path) elif orig_config_path.exists(): - params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path) - elif model_plus.format != 'none': + params = Params.load_torch_params(model_plus.model, orig_config_path) + elif model_plus.format != "none": params = Params.guessed(model_plus.model) else: - raise ValueError('Cannot guess params when model format is none') + raise ValueError("Cannot guess params when model format is none") params.path_model = model_plus.paths[0].parent return params -class VocabLoader: - def __init__(self, params: Params, fname_tokenizer: Path) -> None: - try: - from transformers import AutoTokenizer - except ImportError as e: - raise ImportError( - "To use VocabLoader, please install the `transformers` package. " - "You can install it with `pip install transformers`." - ) from e +class BpeVocab: # GPT + def __init__( + self, fname_tokenizer: Path, fname_added_tokens: Optional[Path] + ) -> None: + self.bpe_tokenizer = json.loads( + open(str(fname_tokenizer), encoding="utf-8").read() + ) + added_tokens: dict[str, int] + if fname_added_tokens is not None: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. + added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) + else: + # Fall back to trying to find the added tokens in tokenizer.json + tokenizer_json_file = fname_tokenizer.parent / "tokenizer.json" + if not tokenizer_json_file.is_file(): + added_tokens = {} + else: + tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8")) + added_tokens = dict( + (item["content"], item["id"]) + for item in tokenizer_json.get("added_tokens", []) + # Added tokens here can be duplicates of the main vocabulary. + if item["content"] not in self.bpe_tokenizer + ) - try: - self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), trust_remote_code=True) - except ValueError: - self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), use_fast=False, trust_remote_code=True) + vocab_size: int = len(self.bpe_tokenizer) + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + expected_end_id = vocab_size + len(actual_ids) - 1 + raise Exception( + f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}" + ) - self.added_tokens_dict: OrderedDict[str, int] = OrderedDict() + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base: int = vocab_size + self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + self.fname_added_tokens = fname_added_tokens - for tok, tokidx in sorted(self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]): - if tokidx >= params.n_vocab or tokidx < self.tokenizer.vocab_size: - continue + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.bpe_tokenizer + reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()} - self.added_tokens_dict[tok] = tokidx + for i, _ in enumerate(tokenizer): + yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL - self.unk_token_id: int = self.tokenizer.unk_token_id - self.specials: dict[str, int] = { + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.CONTROL + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.bpe_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class SentencePieceVocab: # LlaMa + def __init__( + self, fname_tokenizer: Path, fname_added_tokens: Optional[Path] + ) -> None: + self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) + added_tokens: dict[str, int] + if fname_added_tokens is not None: + added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) + else: + added_tokens = {} + + vocab_size: int = self.sentencepiece_tokenizer.vocab_size() + + new_tokens = { + id: piece for piece, id in added_tokens.items() if id >= vocab_size + } + expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) + actual_new_ids = sorted(new_tokens.keys()) + + if expected_new_ids != actual_new_ids: + raise ValueError( + f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}" + ) + + # Token pieces that were added to the base vocabulary. + self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + self.fname_added_tokens = fname_added_tokens + + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + piece = tokenizer.id_to_piece(i) + text: bytes = piece.encode("utf-8") + score: float = tokenizer.get_score(i) + + toktype = gguf.TokenType.NORMAL + if tokenizer.is_unknown(i): + toktype = gguf.TokenType.UNKNOWN + if tokenizer.is_control(i): + toktype = gguf.TokenType.CONTROL + + # NOTE: I think added_tokens are user defined. + # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto + # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED + + if tokenizer.is_unused(i): + toktype = gguf.TokenType.UNUSED + if tokenizer.is_byte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class HfVocab: + def __init__( + self, + fname_tokenizer: Path, + fname_added_tokens: Optional[Path] = None, + ) -> None: + print("fname_tokenizer:", fname_tokenizer) + # Allow the tokenizer to default to slow or fast versions. + # Explicitly set tokenizer to use local paths. + self.tokenizer = AutoTokenizer.from_pretrained( + fname_tokenizer, + cache_dir=fname_tokenizer, + local_files_only=True, + ) + + # Initialize lists and dictionaries for added tokens + self.added_tokens_list = [] + self.added_tokens_dict = dict() + self.added_tokens_ids = set() + + # Process added tokens + for tok, tokidx in sorted( + self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] + ): + # Only consider added tokens that are not in the base vocabulary + if tokidx >= self.tokenizer.vocab_size: + self.added_tokens_list.append(tok) + self.added_tokens_dict[tok] = tokidx + self.added_tokens_ids.add(tokidx) + + # Store special tokens and their IDs + self.specials = { tok: self.tokenizer.get_vocab()[tok] for tok in self.tokenizer.all_special_tokens } - self.special_ids: set[int] = set(self.tokenizer.all_special_ids) - self.reverse_vocab = {id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()} - self.vocab_size_base: int = self.tokenizer.vocab_size - self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_dict) - self.fname_tokenizer: Path = fname_tokenizer + self.special_ids = set(self.tokenizer.all_special_ids) - vocab_file = "tokenizer.model" - path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file) - if path_candidate is not None: - self.spm = SentencePieceProcessor(str(path_candidate)) - print(self.spm.vocab_size(), self.vocab_size_base) - else: - self.spm = None + # Set vocabulary sizes + self.vocab_size_base = self.tokenizer.vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - added_tokens_ids = set(self.added_tokens_dict.values()) + self.fname_tokenizer = fname_tokenizer + self.fname_added_tokens = fname_added_tokens - for i in range(self.vocab_size_base): - if i in added_tokens_ids: + def hf_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = { + id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() + } + + for token_id in range(self.vocab_size_base): + # Skip processing added tokens here + if token_id in self.added_tokens_ids: continue - text = self.reverse_vocab[i].encode("utf-8") - yield text, self.get_token_score(i), self.get_token_type(i) + # Convert token text to bytes + token_text = reverse_vocab[token_id].encode("utf-8") - def get_token_type(self, token_id: int) -> gguf.TokenType: - toktype = gguf.TokenType.NORMAL + # Yield token text, score, and type + yield token_text, self.get_token_score(token_id), self.get_token_type( + token_id, self.special_ids # Reuse already stored special IDs + ) - if self.spm is not None and token_id < self.spm.vocab_size(): - if self.spm.is_unknown(token_id): - toktype = gguf.TokenType.UNKNOWN - if self.spm.is_control(token_id): - toktype = gguf.TokenType.CONTROL - if self.spm.is_unused(token_id): - toktype = gguf.TokenType.UNUSED - if self.spm.is_byte(token_id): - toktype = gguf.TokenType.BYTE - else: - token = self.reverse_vocab[token_id] - if token_id == self.unk_token_id: - toktype = gguf.TokenType.UNKNOWN - elif token_id in self.special_ids: - toktype = gguf.TokenType.CONTROL - elif len(token) == 6 and token.startswith("<0x") and token.endswith(">"): - toktype = gguf.TokenType.BYTE - - return toktype + def get_token_type(self, token_id: int, special_ids: set) -> gguf.TokenType: + # Determine token type based on whether it's a special token + return ( + gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL + ) def get_token_score(self, token_id: int) -> float: - if self.spm is not None and token_id < self.spm.vocab_size(): - return cast(float, self.spm.get_score(token_id)) - return 0.0 + # Placeholder for actual logic to determine the token's score + # This needs to be implemented based on specific requirements + return -1000.0 # Default score def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - - for text in self.added_tokens_dict: + for text in self.added_tokens_list: if text in self.specials: - - toktype = self.get_token_type(self.specials[text]) + toktype = self.get_token_type(self.specials[text], self.special_ids) score = self.get_token_score(self.specials[text]) else: @@ -422,45 +594,18 @@ class VocabLoader: yield text.encode("utf-8"), score, toktype - def has_newline_token(self) -> bool: - return '<0x0A>' in self.tokenizer.vocab or '\n' in self.tokenizer.vocab + def has_newline_token(self): + return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.hf_tokens() yield from self.added_tokens() - def get_vocab_type(self) -> str: - path_candidates = [] - vocab_file = "tokenizer.model" - path_candidates.append(vocab_file) - path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file) - if path_candidate is not None: - return "llama" - - vocab_file = "vocab.json" - path_candidates.append(vocab_file) - path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file) - if path_candidate is not None: - return "gpt2" - - vocab_file = "tokenizer.json" - path_candidates.append(vocab_file) - path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file) - if path_candidate: - if not self.has_newline_token(): - return "gpt2" - return "llama" - - raise FileNotFoundError( - f"Could not find {path_candidates} in {self.fname_tokenizer} or its parent; " - "if it's in another directory, pass the directory as --vocab-dir" - ) - def __repr__(self) -> str: - return f"" + return f"" -Vocab: TypeAlias = 'VocabLoader' +Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab" # @@ -724,13 +869,17 @@ class LazyUnpickler(pickle.Unpickler): CLASSES: dict[tuple[str, str], Any] = { # getattr used here as a workaround for mypy not being smart enough to determine # the staticmethods have a __func__ attribute. - ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), - ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'), - ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), - ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), - ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), - ('torch', 'IntStorage'): LazyStorageKind(DT_I32), - ('torch', 'Tensor'): LazyTensor, + ("torch._tensor", "_rebuild_from_type_v2"): getattr( + rebuild_from_type_v2, "__func__" + ), + ("torch._utils", "_rebuild_tensor_v2"): getattr( + lazy_rebuild_tensor_v2, "__func__" + ), + ("torch", "BFloat16Storage"): LazyStorageKind(DT_BF16), + ("torch", "HalfStorage"): LazyStorageKind(DT_F16), + ("torch", "FloatStorage"): LazyStorageKind(DT_F32), + ("torch", "IntStorage"): LazyStorageKind(DT_I32), + ("torch", "Tensor"): LazyTensor, } def find_class(self, module: str, name: str) -> Any: @@ -839,32 +988,43 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None: - if params.n_vocab != vocab.vocab_size: - if params.n_vocab == vocab.vocab_size: - print("Ignoring added_tokens.json since model matches vocab size without it.") - vocab.added_tokens_dict = OrderedDict() - vocab.vocab_size = vocab.vocab_size - return + # Handle special case where the model's vocab size is not set + if params.n_vocab == -1: + raise ValueError( + f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?" + ) - if pad_vocab and params.n_vocab > vocab.vocab_size: - pad_count = params.n_vocab - vocab.vocab_size - print(f'Padding vocab with {pad_count} token(s) - through ') - for i in range(1, (params.n_vocab - vocab.vocab_size) + 1): - vocab.added_tokens_dict[f''] = -1 - vocab.vocab_size = params.n_vocab - return - msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}" - msg += f" has {vocab.vocab_size})." - if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20: - msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." - if vocab.vocab_size < params.n_vocab: - msg += " Possibly try using the --padvocab option." - raise Exception(msg) + # Check for a vocab size mismatch + if params.n_vocab == vocab.vocab_size: + print("Ignoring added_tokens.json since model matches vocab size without it.") + return + + if pad_vocab and params.n_vocab > vocab.vocab_size: + pad_count = params.n_vocab - vocab.vocab_size + print( + f"Padding vocab with {pad_count} token(s) - through " + ) + for i in range(1, pad_count + 1): + vocab.added_tokens_dict[f""] = -1 + vocab.vocab_size = params.n_vocab + return + + msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer} has {vocab.vocab_size})." + if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20: + msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." + if vocab.vocab_size < params.n_vocab: + msg += " Add the --pad-vocab option and try again." + + raise Exception(msg) class OutputFile: - def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None: - self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess) + def __init__( + self, fname_out: Path, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE + ) -> None: + self.gguf = gguf.GGUFWriter( + fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess + ) def add_meta_arch(self, params: Params) -> None: name = "LLaMA" @@ -873,16 +1033,21 @@ class OutputFile: if params.n_ctx == 4096: name = "LLaMA v2" elif params.path_model is not None: - name = str(params.path_model.parent).split('/')[-1] + name = str(params.path_model.parent).split("/")[-1] - self.gguf.add_name (name) - self.gguf.add_context_length (params.n_ctx) - self.gguf.add_embedding_length (params.n_embd) - self.gguf.add_block_count (params.n_layer) - self.gguf.add_feed_forward_length (params.n_ff) + self.gguf.add_name(name) + self.gguf.add_context_length(params.n_ctx) + self.gguf.add_embedding_length(params.n_embd) + self.gguf.add_block_count(params.n_layer) + self.gguf.add_feed_forward_length(params.n_ff) self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) - self.gguf.add_head_count (params.n_head) - self.gguf.add_head_count_kv (params.n_head_kv) + self.gguf.add_head_count(params.n_head) + self.gguf.add_head_count_kv(params.n_head_kv) + + if params.f_norm_eps is None: + raise ValueError("f_norm_eps is None") + + self.gguf.add_layer_norm_rms_eps(params.f_norm_eps) if params.n_experts: self.gguf.add_expert_count(params.n_experts) @@ -890,11 +1055,6 @@ class OutputFile: if params.n_experts_used: self.gguf.add_expert_used_count(params.n_experts_used) - if params.f_norm_eps: - self.gguf.add_layer_norm_rms_eps(params.f_norm_eps) - else: - raise ValueError('f_norm_eps is None') - if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) @@ -912,18 +1072,44 @@ class OutputFile: if params.ftype is not None: self.gguf.add_file_type(params.ftype) - def add_meta_vocab(self, vocab: Vocab) -> None: + def handle_tokenizer_model(self, vocab: Vocab) -> str: + # Map the vocab types to the supported tokenizer models + tokenizer_model = { + SentencePieceVocab: "llama", + HfVocab: "llama", + BpeVocab: "gpt2", + }.get(type(vocab)) + + # Block if vocab type is not predefined + if tokenizer_model is None: + raise ValueError("Unknown vocab type: Not supported") + + return tokenizer_model + + def extract_vocabulary_from_model(self, vocab: Vocab) -> Tuple[list, list, list]: tokens = [] scores = [] toktypes = [] + # NOTE: `all_tokens` returns the base vocabulary and added tokens for text, score, toktype in vocab.all_tokens(): tokens.append(text) scores.append(score) toktypes.append(toktype) - vocab_type = vocab.get_vocab_type() - self.gguf.add_tokenizer_model(vocab_type) + return tokens, scores, toktypes + + def add_meta_vocab(self, vocab: Vocab) -> None: + # Handle the tokenizer model + tokenizer_model = self.handle_tokenizer_model(vocab) + + # Ensure that tokenizer_model is added to the GGUF model + self.gguf.add_tokenizer_model(tokenizer_model) + + # Extract model vocabulary for model conversion + tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab) + + # Add extracted token information for model conversion self.gguf.add_token_list(tokens) self.gguf.add_token_scores(scores) self.gguf.add_token_types(toktypes) @@ -933,10 +1119,14 @@ class OutputFile: def add_tensor_info(self, name: str, tensor: LazyTensor) -> None: n_elements = int(np.prod(tensor.shape)) - raw_dtype = getattr(tensor.data_type, 'ggml_type', None) - data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype + raw_dtype = getattr(tensor.data_type, "ggml_type", None) + data_type = ( + getattr(tensor.data_type, "quantized_type", None) or tensor.data_type.dtype + ) data_nbytes = tensor.data_type.elements_to_bytes(n_elements) - self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype) + self.gguf.add_tensor_info( + name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype + ) def write_meta(self) -> None: self.gguf.write_header_to_file() @@ -950,11 +1140,14 @@ class OutputFile: @staticmethod def write_vocab_only( - fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, + fname_out: Path, + params: Params, + vocab: Vocab, + svocab: gguf.SpecialVocab, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, ) -> None: - check_vocab_size(params, vocab, pad_vocab = pad_vocab) + check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) @@ -982,12 +1175,17 @@ class OutputFile: @staticmethod def write_all( - fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, + fname_out: Path, + ftype: GGMLFileType, + params: Params, + model: LazyModel, + vocab: Vocab, + svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, ) -> None: - check_vocab_size(params, vocab, pad_vocab = pad_vocab) + check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) @@ -1004,18 +1202,30 @@ class OutputFile: of.write_tensor_info() # tensor data - ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency) + ndarrays_inner = bounded_parallel_map( + OutputFile.do_item, model.items(), concurrency=concurrency + ) if ftype == GGMLFileType.MostlyQ8_0: - ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True) + ndarrays = bounded_parallel_map( + OutputFile.maybe_do_quantize, + ndarrays_inner, + concurrency=concurrency, + max_workers=concurrency, + use_processpool_executor=True, + ) else: ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) start = time.time() - for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): + for i, ((name, lazy_tensor), ndarray) in enumerate( + zip(model.items(), ndarrays) + ): elapsed = time.time() - start - size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) + size = " x ".join(f"{dim:6d}" for dim in lazy_tensor.shape) padi = len(str(len(model))) - print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}") + print( + f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" + ) of.gguf.write_tensor_data(ndarray) of.close() @@ -1145,30 +1355,95 @@ def load_some_model(path: Path) -> ModelPlus: return model_plus -def find_vocab_file_path(path: Path, vocab_file: str) -> Optional[Path]: - path2 = path / vocab_file - # Use `.parent` instead of /.. to handle the symlink case better. - path3 = path.parent / vocab_file +class VocabFactory: + def __init__(self, path: Path): + self.path = path + self.files = { + "tokenizer.model": None, + "vocab.json": None, + "tokenizer.json": None, + } + self._detect_files() - if path2.exists(): - return path2 - if path3.exists(): - return path3 + def _detect_files(self): + for file in self.files.keys(): + file_path = self.path / file + parent_file_path = self.path.parent / file + if file_path.exists(): + self.files[file] = file_path + elif parent_file_path.exists(): + self.files[file] = parent_file_path - return None + def _select_file(self, vocabtype: Optional[str]) -> Path: + if vocabtype in ["spm", "bpe"]: + # For SentencePiece and BPE, return specific files as before + file_key = "tokenizer.model" if vocabtype == "spm" else "vocab.json" + if self.files[file_key]: + return self.files[file_key] + else: + raise FileNotFoundError(f"{vocabtype} {file_key} not found.") + elif vocabtype == "hfft": + # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file + return self.path + else: + raise ValueError(f"Unsupported vocabulary type {vocabtype}") + + def _create_special_vocab( + self, + vocab: Vocab, + vocabtype: str, + model_parent_path: Path, + ) -> gguf.SpecialVocab: + load_merges = vocabtype == "bpe" + n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None + return gguf.SpecialVocab( + model_parent_path, + load_merges=load_merges, + special_token_types=None, # Predetermined or passed as a parameter + n_vocab=n_vocab, + ) + + def load_vocab( + self, vocabtype: str, model_parent_path: Path + ) -> Tuple[Vocab, gguf.SpecialVocab]: + path = self._select_file(vocabtype) + print(f"Loading vocab file '{path}', type '{vocabtype}'") + + added_tokens_path = path.parent / "added_tokens.json" + if vocabtype == "bpe": + vocab = BpeVocab( + path, added_tokens_path if added_tokens_path.exists() else None + ) + elif vocabtype == "spm": + vocab = SentencePieceVocab( + path, added_tokens_path if added_tokens_path.exists() else None + ) + elif vocabtype == "hfft": + vocab = HfVocab( + path, added_tokens_path if added_tokens_path.exists() else None + ) + else: + raise ValueError(f"Unsupported vocabulary type {vocabtype}") + special_vocab = self._create_special_vocab( + vocab, + vocabtype, + model_parent_path, + ) + return vocab, special_vocab -def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: +def default_output_file(model_paths: list[Path], file_type: GGMLFileType) -> Path: namestr = { - GGMLFileType.AllF32: "f32", + GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", - GGMLFileType.MostlyQ8_0:"q8_0", + GGMLFileType.MostlyQ8_0: "q8_0", }[file_type] ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" if ret in model_paths: sys.stderr.write( f"Error: Default output path ({ret}) would overwrite the input. " - "Please explicitly specify a path using --outfile.\n") + "Please explicitly specify a path using --outfile.\n" + ) sys.exit(1) return ret @@ -1178,32 +1453,111 @@ def do_dump_model(model_plus: ModelPlus) -> None: print(f"model_plus.format = {model_plus.format!r}") print(f"model_plus.vocab = {model_plus.vocab!r}") for name, lazy_tensor in model_plus.model.items(): - print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") + print( + f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}" + ) -def main(args_in: list[str] | None = None) -> None: +def get_argument_parser() -> ArgumentParser: output_choices = ["f32", "f16"] if np.uint32(1) == np.uint32(1).newbyteorder("<"): # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") - parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) - parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") - parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") - parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") - parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") - parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY) - parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") - parser.add_argument("--padvocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") - args = parser.parse_args(args_in) + parser = argparse.ArgumentParser( + description="Convert a LLaMa model to a GGML compatible file" + ) + + parser.add_argument( + "model", + type=Path, + help="Directory containing the model file or the model file itself (*.pth, *.pt, *.bin)", + ) + + parser.add_argument( + "--awq-path", + type=Path, + help="Path to the Activation-aware Weight Quantization cache file", + default=None, + ) + + parser.add_argument( + "--dump", + action="store_true", + help="Display the model content without converting it", + ) + + parser.add_argument( + "--dump-single", + action="store_true", + help="Display the content of a single model file without conversion", + ) + + parser.add_argument( + "--vocab-only", + action="store_true", + help="Extract and output only the vocabulary", + ) + + parser.add_argument( + "--outtype", + choices=output_choices, + help="Output format - note: q8_0 may be very slow (default: f16 or f32 based on input)", + ) + + parser.add_argument( + "--vocab-dir", + type=Path, + help="Directory containing the tokenizer.model, if separate from the model file", + ) + + parser.add_argument( + "--vocab-type", + choices=["spm", "bpe", "hfft"], # hfft: Hugging Face Fast Tokenizer + default="spm", + help="The vocabulary format used to define the tokenizer model (default: spm)", + ) + + parser.add_argument( + "--pad-vocab", + action="store_true", + help="Add padding tokens when the model's vocabulary size exceeds the tokenizer metadata", + ) + + parser.add_argument( + "--outfile", + type=Path, + help="Specify the path for the output file (default is based on input)", + ) + + parser.add_argument( + "--ctx", type=int, help="Model training context (default is based on input)" + ) + + parser.add_argument( + "--concurrency", + type=int, + help=f"Concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", + default=DEFAULT_CONCURRENCY, + ) + + parser.add_argument( + "--big-endian", + action="store_true", + help="Indicate that the model is executed on a big-endian machine", + ) + + return parser + + +def main(argv: Optional[list[str]] = None) -> None: + parser = get_argument_parser() + args = parser.parse_args(argv) + if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + sys.path.insert(1, str(Path(__file__).resolve().parent / "awq-py")) from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" if tmp_model_path.is_dir(): print(f"{tmp_model_path} exists as a weighted model.") @@ -1222,22 +1576,27 @@ def main(args_in: list[str] | None = None) -> None: if not args.vocab_only: model_plus = load_some_model(args.model) else: - model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) + model_plus = ModelPlus( + model={}, paths=[args.model / "dummy"], format="none", vocab=None + ) if args.dump: do_dump_model(model_plus) return + endianess = gguf.GGUFEndian.LITTLE - if args.bigendian: + if args.big_endian: endianess = gguf.GGUFEndian.BIG params = Params.load(model_plus) if params.n_ctx == -1: if args.ctx is None: - raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n" - "Please specify one with --ctx:\n" - " - LLaMA v1: --ctx 2048\n" - " - LLaMA v2: --ctx 4096\n") + raise Exception( + "The model doesn't have a context size, and you didn't specify one with --ctx\n" + "Please specify one with --ctx:\n" + " - LLaMA v1: --ctx 2048\n" + " - LLaMA v2: --ctx 4096\n" + ) params.n_ctx = args.ctx if args.outtype: @@ -1249,47 +1608,51 @@ def main(args_in: list[str] | None = None) -> None: print(f"params = {params}") - vocab: Vocab + model_parent_path = model_plus.paths[0].parent + vocab_path = Path(args.vocab_dir or args.model or model_parent_path) + vocab_factory = VocabFactory(vocab_path) + vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path) + if args.vocab_only: if not args.outfile: raise ValueError("need --outfile if using --vocab-only") - # FIXME: Try to respect vocab_dir somehow? - vocab = VocabLoader(params, args.vocab_dir or args.model) - special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, - load_merges = True, - n_vocab = vocab.vocab_size) outfile = args.outfile - OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, - endianess = endianess, pad_vocab = args.padvocab) + OutputFile.write_vocab_only( + outfile, + params, + vocab, + special_vocab, + endianess=endianess, + pad_vocab=args.pad_vocab, + ) print(f"Wrote {outfile}") return if model_plus.vocab is not None and args.vocab_dir is None: vocab = model_plus.vocab - else: - vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent - vocab = VocabLoader(params, vocab_dir) - # FIXME: Try to respect vocab_dir somehow? - print(f"Vocab info: {vocab}") - special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, - load_merges = True, - n_vocab = vocab.vocab_size) - - print(f"Special vocab info: {special_vocab}") - model = model_plus.model - model = convert_model_names(model, params) - ftype = pick_output_type(model, args.outtype) - model = convert_to_output_type(model, ftype) - outfile = args.outfile or default_outfile(model_plus.paths, ftype) + model = model_plus.model + model = convert_model_names(model, params) + ftype = pick_output_type(model, args.outtype) + model = convert_to_output_type(model, ftype) + outfile = args.outfile or default_output_file(model_plus.paths, ftype) params.ftype = ftype print(f"Writing {outfile}, format {ftype}") - OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, - concurrency = args.concurrency, endianess = endianess, pad_vocab = args.padvocab) + OutputFile.write_all( + outfile, + ftype, + params, + model, + vocab, + special_vocab, + concurrency=args.concurrency, + endianess=endianess, + pad_vocab=args.pad_vocab, + ) print(f"Wrote {outfile}") -if __name__ == '__main__': - main() +if __name__ == "__main__": + main(sys.argv[1:]) # Exclude the first element (script name) from sys.argv From 4f56458d34cb13dcbf69aca650e9bf77d5497e6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 10 Jan 2024 01:04:33 +0100 Subject: [PATCH 358/664] Python script to compare commits with llama-bench (#4844) --- scripts/compare-llama-bench.py | 356 +++++++++++++++++++++++++++++++++ 1 file changed, 356 insertions(+) create mode 100755 scripts/compare-llama-bench.py diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py new file mode 100755 index 000000000..bc1714487 --- /dev/null +++ b/scripts/compare-llama-bench.py @@ -0,0 +1,356 @@ +#!/usr/bin/env python3 + +import argparse +import heapq +import sys +import os +from glob import glob +import sqlite3 + +try: + import git + from tabulate import tabulate +except ImportError: + print("ERROR: the following Python libraries are required: GitPython, tabulate.") + sys.exit(1) + +# Properties by which to differentiate results per commit: +KEY_PROPERTIES = [ + "cuda", "opencl", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", + "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", + "n_gpu_layers", "main_gpu", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" +] + +# Properties that are boolean and are converted to Yes/No for the table: +BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"] + +# Header names for the table: +PRETTY_NAMES = { + "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS", + "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", + "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters", + "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", + "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO", + "mul_mat_q": "MMQ", "tensor_split": "Tensor split" +} + +DEFAULT_SHOW = ["model_type"] # Always show these properties by default. +DEFAULT_HIDE = ["model_filename"] # Always hide these properties by default. +GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "] # Strip prefixes for smaller tables. + +DESCRIPTION = """Creates tables from llama-bench data written to an SQLite database. Example usage (Linux): + +$ git checkout master +$ make clean && make llama-bench +$ ./llama-bench -o sql | sqlite3 llama-bench.sqlite +$ git checkout some_branch +$ make clean && make llama-bench +$ ./llama-bench -o sql | sqlite3 llama-bench.sqlite +$ ./scripts/compare-llama-bench.py + +Performance numbers from multiple runs per commit are averaged WITHOUT being weighted by the --repetitions parameter of llama-bench. +""" + +parser = argparse.ArgumentParser( + description=DESCRIPTION, formatter_class=argparse.RawDescriptionHelpFormatter) +help_b = ( + "The baseline commit to compare performance to. " + "Accepts either a branch name, tag name, or commit hash. " + "Defaults to latest master commit with data." +) +parser.add_argument("-b", "--baseline", help=help_b) +help_c = ( + "The commit whose performance is to be compared to the baseline. " + "Accepts either a branch name, tag name, or commit hash. " + "Defaults to the non-master commit for which llama-bench was run most recently." +) +parser.add_argument("-c", "--compare", help=help_c) +help_i = ( + "Input SQLite file for comparing commits. " + "Defaults to 'llama-bench.sqlite' in the current working directory. " + "If no such file is found and there is exactly one .sqlite file in the current directory, " + "that file is instead used as input." +) +parser.add_argument("-i", "--input", help=help_i) +help_o = ( + "Output format for the table. " + "Defaults to 'pipe' (GitHub compatible). " + "Also supports e.g. 'latex' or 'mediawiki'. " + "See tabulate documentation for full list." +) +parser.add_argument("-o", "--output", help=help_o, default="pipe") +help_s = ( + "Columns to add to the table. " + "Accepts a comma-separated list of values. " + f"Legal values: {', '.join(KEY_PROPERTIES[:-2])}. " + "Defaults to model name (model_type) and CPU and/or GPU name (cpu_info, gpu_info) " + "plus any column where not all data points are the same. " + "If the columns are manually specified, then the results for each unique combination of the " + "specified values are averaged WITHOUT weighing by the --repetitions parameter of llama-bench." +) +parser.add_argument("-s", "--show", help=help_s) + +known_args, unknown_args = parser.parse_known_args() + +if unknown_args: + print(f"ERROR: Received unknown args: {unknown_args}.") + print() + parser.print_help() + sys.exit(1) + +input_file = known_args.input +if input_file is None and os.path.exists("./llama-bench.sqlite"): + input_file = "llama-bench.sqlite" +if input_file is None: + sqlite_files = glob("*.sqlite") + if len(sqlite_files) == 1: + input_file = sqlite_files[0] + +if input_file is None: + print("ERROR: Cannot find a suitable input file, please provide one.") + print() + parser.print_help() + sys.exit(1) + +connection = sqlite3.connect(input_file) +cursor = connection.cursor() +builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall() + +try: + repo = git.Repo(".", search_parent_directories=True) +except git.exc.InvalidGitRepositoryError: + repo = None + + +def find_parent_in_data(commit): + """Helper function to find the most recent parent measured in number of commits for which there is data.""" + heap = [(0, commit)] + seen_hexsha8 = set() + while heap: + depth, current_commit = heapq.heappop(heap) + current_hexsha8 = commit.hexsha[:8] + if (current_hexsha8,) in builds: + return current_hexsha8 + for parent in commit.parents: + parent_hexsha8 = parent.hexsha[:8] + if parent_hexsha8 not in seen_hexsha8: + seen_hexsha8.add(parent_hexsha8) + heapq.heappush(heap, (depth + 1, parent)) + return None + + +def get_all_parent_hexsha8s(commit): + """Helper function to recursively get hexsha8 values for all parents of a commit.""" + unvisited = [commit] + visited = [] + + while unvisited: + current_commit = unvisited.pop(0) + visited.append(current_commit.hexsha[:8]) + for parent in current_commit.parents: + if parent.hexsha[:8] not in visited: + unvisited.append(parent) + + return visited + + +def get_commit_name(hexsha8): + """Helper function to find a human-readable name for a commit if possible.""" + if repo is None: + return hexsha8 + for h in repo.heads: + if h.commit.hexsha[:8] == hexsha8: + return h.name + for t in repo.tags: + if t.commit.hexsha[:8] == hexsha8: + return t.name + return hexsha8 + + +def get_commit_hexsha8(name): + """Helper function to search for a commit given a human-readable name.""" + if repo is None: + return None + for h in repo.heads: + if h.name == name: + return h.commit.hexsha[:8] + for t in repo.tags: + if t.name == name: + return t.commit.hexsha[:8] + return None + + +hexsha8_baseline = name_baseline = None + +# If the user specified a baseline, try to find a commit for it: +if known_args.baseline is not None: + if (known_args.baseline,) in builds: + hexsha8_baseline = known_args.baseline + if hexsha8_baseline is None: + hexsha8_baseline = get_commit_hexsha8(known_args.baseline) + name_baseline = known_args.baseline + if hexsha8_baseline is None: + print(f"ERROR: cannot find data for baseline={known_args.baseline}.") + sys.exit(1) +# Otherwise, search for the most recent parent of master for which there is data: +elif repo is not None: + hexsha8_baseline = find_parent_in_data(repo.heads.master.commit) + + if hexsha8_baseline is None: + print("ERROR: No baseline was provided and did not find data for any master branch commits.") + print() + parser.print_help() + sys.exit(1) +else: + print( + "ERROR: No baseline was provided and the current working directory " + "is not part of a git repository from which a baseline could be inferred." + ) + print() + parser.print_help() + sys.exit(1) + + +name_baseline = get_commit_name(hexsha8_baseline) + +hexsha8_compare = name_compare = None + +# If the user has specified a compare value, try to find a corresponding commit: +if known_args.compare is not None: + if (known_args.compare,) in builds: + hexsha8_compare = known_args.compare + if hexsha8_compare is None: + hexsha8_compare = get_commit_hexsha8(known_args.compare) + name_compare = known_args.compare + if hexsha8_compare is None: + print(f"ERROR: cannot find data for baseline={known_args.compare}.") + sys.exit(1) +# Otherwise, search for the commit for llama-bench was most recently run +# and that is not a parent of master: +elif repo is not None: + hexsha8s_master = get_all_parent_hexsha8s(repo.heads.master.commit) + builds_timestamp = cursor.execute( + "SELECT build_commit, test_time FROM test ORDER BY test_time;").fetchall() + for (hexsha8, _) in reversed(builds_timestamp): + if hexsha8 not in hexsha8s_master: + hexsha8_compare = hexsha8 + break + + if hexsha8_compare is None: + print("ERROR: No compare target was provided and did not find data for any non-master commits.") + print() + parser.print_help() + sys.exit(1) +else: + print( + "ERROR: No compare target was provided and the current working directory " + "is not part of a git repository from which a compare target could be inferred." + ) + print() + parser.print_help() + sys.exit(1) + +name_compare = get_commit_name(hexsha8_compare) + + +def get_rows(properties): + """ + Helper function that gets table rows for some list of properties. + Rows are created by combining those where all provided properties are equal. + The resulting rows are then grouped by the provided properties and the t/s values are averaged. + The returned rows are unique in terms of property combinations. + """ + select_string = ", ".join( + [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"]) + equal_string = " AND ".join( + [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [ + f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"] + ) + group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt"]) + query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} " + f"GROUP BY {group_order_string} ORDER BY {group_order_string};") + return cursor.execute(query).fetchall() + + +# If the user provided columns to group the results by, use them: +if known_args.show is not None: + show = known_args.show.split(",") + unknown_cols = [] + for prop in show: + if prop not in KEY_PROPERTIES[:-2]: # Last two values are n_prompt, n_gen. + unknown_cols.append(prop) + if unknown_cols: + print(f"ERROR: Unknown values for --show: {', '.join(unknown_cols)}") + print() + parser.print_usage() + sys.exit(1) + rows_show = get_rows(show) +# Otherwise, select those columns where the values are not all the same: +else: + rows_full = get_rows(KEY_PROPERTIES) + properties_different = [] + for i, kp_i in enumerate(KEY_PROPERTIES): + if kp_i in DEFAULT_SHOW or kp_i == "n_prompt" or kp_i == "n_gen": + continue + for row_full in rows_full: + if row_full[i] != rows_full[0][i]: + properties_different.append(kp_i) + break + + show = [] + # Show CPU and/or GPU by default even if the hardware for all results is the same: + if "gpu_blas" not in properties_different and "n_gpu_layers" not in properties_different: + gpu_blas = bool(rows_full[0][KEY_PROPERTIES.index("gpu_blas")]) + ngl = int(rows_full[0][KEY_PROPERTIES.index("n_gpu_layers")]) + + if not gpu_blas or ngl != 99 and "cpu_info" not in properties_different: + show.append("cpu_info") + if gpu_blas and "gpu_info" not in properties_different: + show.append("gpu_info") + + show += DEFAULT_SHOW + show += properties_different + for prop in DEFAULT_HIDE: + try: + show.remove(prop) + except ValueError: + pass + rows_show = get_rows(show) + +table = [] +for row in rows_show: + n_prompt = int(row[-4]) + n_gen = int(row[-3]) + assert n_prompt == 0 or n_gen == 0 + test_name = f"tg{n_gen}" if n_prompt == 0 else f"pp{n_prompt}" + # Regular columns test name avg t/s values Speedup + # VVVVVVVVVVVVV VVVVVVVVV VVVVVVVVVVVVVV VVVVVVV + table.append(list(row[:-4]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])]) + +# Some a-posteriori fixes to make the table contents prettier: +for bool_property in BOOL_PROPERTIES: + if bool_property in show: + ip = show.index(bool_property) + for row_table in table: + row_table[ip] = "Yes" if int(row_table[ip]) == 1 else "No" + +if "model_size" in show: + ip = show.index("model_size") + for row_table in table: + row_table[ip] = float(row_table[ip]) / 1024 ** 3 + +if "gpu_info" in show: + ip = show.index("gpu_info") + for gns in GPU_NAME_STRIP: + for row_table in table: + row_table[ip] = row_table[ip].replace(gns, "") + +headers = [PRETTY_NAMES[p] for p in show] +headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"] + +print(tabulate( + table, + headers=headers, + floatfmt=".2f", + tablefmt=known_args.output +)) From d34633d8db6c2e400355de4862cd699154ecc73f Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Wed, 10 Jan 2024 14:37:09 +0100 Subject: [PATCH 359/664] clip : support more quantization types (#4846) Uses ggml functions instead of hardcoded names and adds support to quantize into the modern Q-K variants. This is just the bare minimum to get k-types working - a more refined choice of types would be needed to get best quality on low quantizations. I ran a few tests, it doesn't break anything I could notice and a Q6_K ViT works almost as well as Q8_0 but 3 times the inference speed. --- examples/llava/clip.cpp | 62 ++++++++++++++++------------------------- 1 file changed, 24 insertions(+), 38 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index cfb79e789..2ae8853d3 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -126,24 +126,7 @@ static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::str } static std::string get_ftype(int ftype) { - switch (ftype) { - case 0: - return "f32"; - case 1: - return "f16"; - case 2: - return "q4_0"; - case 3: - return "q4_1"; - case 6: - return "q5_0"; - case 7: - return "q5_1"; - case 8: - return "q8_0"; - default: - throw std::runtime_error(format("%s: Unrecognized file type: %d\n", __func__, ftype)); - } + return ggml_type_name(static_cast(ftype)); } // @@ -533,6 +516,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { buffer_size += n_tensors * 128 /* CLIP PADDING */; clip_ctx * new_clip = new clip_ctx; + #ifdef GGML_USE_CUBLAS new_clip->backend = ggml_backend_cuda_init(0); printf("%s: CLIP using CUDA backend\n", __func__); @@ -543,6 +527,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("%s: CLIP using Metal backend\n", __func__); #endif + if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); printf("%s: CLIP using CPU backend\n", __func__); @@ -931,26 +916,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i ggml_type type = GGML_TYPE_Q4_1; - switch (itype) { - case 2: - type = GGML_TYPE_Q4_0; - break; - case 3: - type = GGML_TYPE_Q4_1; - break; - case 6: - type = GGML_TYPE_Q5_0; - break; - case 7: - type = GGML_TYPE_Q5_1; - break; - case 8: - type = GGML_TYPE_Q8_0; - break; - default: - fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); - return false; - }; + assert(itype < GGML_TYPE_COUNT); + type = static_cast(itype); auto * ctx_clip = clip_model_load(fname_inp, 2); @@ -1010,6 +977,10 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i if (quantize) { new_type = type; + if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) { + new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type + // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); + } const size_t n_elms = ggml_nelements(cur); float * f32_data; @@ -1054,6 +1025,21 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i case GGML_TYPE_Q8_0: { new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); } break; + case GGML_TYPE_Q2_K: { + new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q3_K: { + new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q4_K: { + new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q5_K: { + new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q6_K: { + new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); + } break; default: { fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type); return false; From 329ff615699d32f596d4ebf8baba654c30064e0d Mon Sep 17 00:00:00 2001 From: Austin <77757836+teleprint-me@users.noreply.github.com> Date: Wed, 10 Jan 2024 08:39:09 -0500 Subject: [PATCH 360/664] llama : recognize 1B phi models (#4847) This update categorizes models with 24 layers as MODEL_1B, ensuring compatibility with different Phi model variants without impacting existing Phi-2 model functionality. --- llama.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/llama.cpp b/llama.cpp index 8e0717db9..0f09d0c2b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2829,6 +2829,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); switch (hparams.n_layer) { + case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; default: model.type = e_model::MODEL_UNKNOWN; } From 57d016ba2d46a6e22517a31a75cebb48f9e234b6 Mon Sep 17 00:00:00 2001 From: Brian Date: Thu, 11 Jan 2024 01:09:53 +1100 Subject: [PATCH 361/664] llama : add additional suffixes for model params (#4834) * llm_load_print_meta: Add additional suffixs for model params * Update llama.cpp model param log remove unneeded comments and convert from > to >= --- llama.cpp | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 0f09d0c2b..e1f1932ba 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3146,7 +3146,15 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); - LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9); + if (ml.n_elements >= 1e12) { + LLAMA_LOG_INFO("%s: model params = %.2f T\n", __func__, ml.n_elements*1e-12); + } else if (ml.n_elements >= 1e9) { + LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9); + } else if (ml.n_elements >= 1e6) { + LLAMA_LOG_INFO("%s: model params = %.2f M\n", __func__, ml.n_elements*1e-6); + } else { + LLAMA_LOG_INFO("%s: model params = %.2f K\n", __func__, ml.n_elements*1e-3); + } if (ml.n_bytes < GiB) { LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements); } else { From cd108e641dbdedd8c5641c4cec1762f751f38136 Mon Sep 17 00:00:00 2001 From: Behnam M <58621210+ibehnam@users.noreply.github.com> Date: Wed, 10 Jan 2024 14:56:05 -0500 Subject: [PATCH 362/664] server : add a `/health` endpoint (#4860) * added /health endpoint to the server * added comments on the additional /health endpoint * Better handling of server state When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value. * initialized server_state * fixed a typo * starting http server before initializing the model * Update server.cpp * Update server.cpp * fixes * fixes * fixes * made ServerState atomic and turned two-line spaces into one-line --- examples/server/server.cpp | 199 +++++++++++++++++++++---------------- 1 file changed, 113 insertions(+), 86 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6c7fcd176..1cca634d5 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -26,6 +26,7 @@ #include #include #include +#include #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 @@ -146,6 +147,12 @@ static std::vector base64_decode(const std::string & encoded_string) // parallel // +enum ServerState { + LOADING_MODEL, // Server is starting up, model not fully loaded yet + READY, // Server is ready and model is loaded + ERROR // An error occurred, load_model failed +}; + enum task_type { COMPLETION_TASK, CANCEL_TASK @@ -2453,7 +2460,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } - static std::string random_string() { static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); @@ -2790,15 +2796,117 @@ int main(int argc, char **argv) {"system_info", llama_print_system_info()}, }); - // load the model - if (!llama.load_model(params)) + httplib::Server svr; + + std::atomic server_state{LOADING_MODEL}; + + svr.set_default_headers({{"Server", "llama.cpp"}, + {"Access-Control-Allow-Origin", "*"}, + {"Access-Control-Allow-Headers", "content-type"}}); + + svr.Get("/health", [&](const httplib::Request&, httplib::Response& res) { + ServerState current_state = server_state.load(); + switch(current_state) { + case READY: + res.set_content(R"({"status": "ok"})", "application/json"); + res.status = 200; // HTTP OK + break; + case LOADING_MODEL: + res.set_content(R"({"status": "loading model"})", "application/json"); + res.status = 503; // HTTP Service Unavailable + break; + case ERROR: + res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); + res.status = 500; // HTTP Internal Server Error + break; + } + }); + + svr.set_logger(log_server_request); + + svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) + { + const char fmt[] = "500 Internal Server Error\n%s"; + char buf[BUFSIZ]; + try + { + std::rethrow_exception(std::move(ep)); + } + catch (std::exception &e) + { + snprintf(buf, sizeof(buf), fmt, e.what()); + } + catch (...) + { + snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); + } + res.set_content(buf, "text/plain; charset=utf-8"); + res.status = 500; + }); + + svr.set_error_handler([](const httplib::Request &, httplib::Response &res) + { + if (res.status == 401) + { + res.set_content("Unauthorized", "text/plain; charset=utf-8"); + } + if (res.status == 400) + { + res.set_content("Invalid request", "text/plain; charset=utf-8"); + } + else if (res.status == 404) + { + res.set_content("File Not Found", "text/plain; charset=utf-8"); + res.status = 404; + } + }); + + // set timeouts and change hostname and port + svr.set_read_timeout (sparams.read_timeout); + svr.set_write_timeout(sparams.write_timeout); + + if (!svr.bind_to_port(sparams.hostname, sparams.port)) { + fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } - llama.initialize(); + // Set the base directory for serving static files + svr.set_base_dir(sparams.public_path); - httplib::Server svr; + // to make it ctrl+clickable: + LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); + + std::unordered_map log_data; + log_data["hostname"] = sparams.hostname; + log_data["port"] = std::to_string(sparams.port); + + if (!sparams.api_key.empty()) { + log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4); + } + + LOG_INFO("HTTP server listening", log_data); + // run the HTTP server in a thread - see comment below + std::thread t([&]() + { + if (!svr.listen_after_bind()) + { + server_state.store(ERROR); + return 1; + } + + return 0; + }); + + // load the model + if (!llama.load_model(params)) + { + server_state.store(ERROR); + return 1; + } else { + llama.initialize(); + server_state.store(READY); + } // Middleware for API key validation auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { @@ -2826,10 +2934,6 @@ int main(int argc, char **argv) return false; }; - svr.set_default_headers({{"Server", "llama.cpp"}, - {"Access-Control-Allow-Origin", "*"}, - {"Access-Control-Allow-Headers", "content-type"}}); - // this is only called if no index.html is found in the public --path svr.Get("/", [](const httplib::Request &, httplib::Response &res) { @@ -2937,8 +3041,6 @@ int main(int argc, char **argv) } }); - - svr.Get("/v1/models", [¶ms](const httplib::Request&, httplib::Response& res) { std::time_t t = std::time(0); @@ -3157,81 +3259,6 @@ int main(int argc, char **argv) return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); - svr.set_logger(log_server_request); - - svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) - { - const char fmt[] = "500 Internal Server Error\n%s"; - char buf[BUFSIZ]; - try - { - std::rethrow_exception(std::move(ep)); - } - catch (std::exception &e) - { - snprintf(buf, sizeof(buf), fmt, e.what()); - } - catch (...) - { - snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); - } - res.set_content(buf, "text/plain; charset=utf-8"); - res.status = 500; - }); - - svr.set_error_handler([](const httplib::Request &, httplib::Response &res) - { - if (res.status == 401) - { - res.set_content("Unauthorized", "text/plain; charset=utf-8"); - } - if (res.status == 400) - { - res.set_content("Invalid request", "text/plain; charset=utf-8"); - } - else if (res.status == 404) - { - res.set_content("File Not Found", "text/plain; charset=utf-8"); - res.status = 404; - } - }); - - // set timeouts and change hostname and port - svr.set_read_timeout (sparams.read_timeout); - svr.set_write_timeout(sparams.write_timeout); - - if (!svr.bind_to_port(sparams.hostname, sparams.port)) - { - fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); - return 1; - } - - // Set the base directory for serving static files - svr.set_base_dir(sparams.public_path); - - // to make it ctrl+clickable: - LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); - - std::unordered_map log_data; - log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); - - if (!sparams.api_key.empty()) { - log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4); - } - - LOG_INFO("HTTP server listening", log_data); - // run the HTTP server in a thread - see comment below - std::thread t([&]() - { - if (!svr.listen_after_bind()) - { - return 1; - } - - return 0; - }); - // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? // "Bus error: 10" - this is on macOS, it does not crash on Linux //std::thread t2([&]() From 5c1980d8d4c4e0c0af77359f81cc44d90b3f250b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 09:10:34 +0200 Subject: [PATCH 363/664] server : fix build + rename enums (#4870) --- examples/server/server.cpp | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1cca634d5..4a0714997 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -147,15 +147,15 @@ static std::vector base64_decode(const std::string & encoded_string) // parallel // -enum ServerState { - LOADING_MODEL, // Server is starting up, model not fully loaded yet - READY, // Server is ready and model is loaded - ERROR // An error occurred, load_model failed +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded + SERVER_STATE_ERROR // An error occurred, load_model failed }; enum task_type { - COMPLETION_TASK, - CANCEL_TASK + TASK_TYPE_COMPLETION, + TASK_TYPE_CANCEL, }; struct task_server { @@ -1402,7 +1402,7 @@ struct llama_server_context task.data = std::move(data); task.infill_mode = infill; task.embedding_mode = embedding; - task.type = COMPLETION_TASK; + task.type = TASK_TYPE_COMPLETION; task.multitask_id = multitask_id; // when a completion task's prompt array is not a singleton, we split it into multiple requests @@ -1524,7 +1524,7 @@ struct llama_server_context std::unique_lock lock(mutex_tasks); task_server task; task.id = id_gen++; - task.type = CANCEL_TASK; + task.type = TASK_TYPE_CANCEL; task.target_id = task_id; queue_tasks.push_back(task); condition_tasks.notify_one(); @@ -1560,7 +1560,7 @@ struct llama_server_context queue_tasks.erase(queue_tasks.begin()); switch (task.type) { - case COMPLETION_TASK: { + case TASK_TYPE_COMPLETION: { llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); if (slot == nullptr) { @@ -1589,7 +1589,7 @@ struct llama_server_context break; } } break; - case CANCEL_TASK: { // release slot linked with the task id + case TASK_TYPE_CANCEL: { // release slot linked with the task id for (auto & slot : slots) { if (slot.task_id == task.target_id) @@ -2798,24 +2798,24 @@ int main(int argc, char **argv) httplib::Server svr; - std::atomic server_state{LOADING_MODEL}; + std::atomic state{SERVER_STATE_LOADING_MODEL}; svr.set_default_headers({{"Server", "llama.cpp"}, {"Access-Control-Allow-Origin", "*"}, {"Access-Control-Allow-Headers", "content-type"}}); svr.Get("/health", [&](const httplib::Request&, httplib::Response& res) { - ServerState current_state = server_state.load(); + server_state current_state = state.load(); switch(current_state) { - case READY: + case SERVER_STATE_READY: res.set_content(R"({"status": "ok"})", "application/json"); res.status = 200; // HTTP OK break; - case LOADING_MODEL: + case SERVER_STATE_LOADING_MODEL: res.set_content(R"({"status": "loading model"})", "application/json"); res.status = 503; // HTTP Service Unavailable break; - case ERROR: + case SERVER_STATE_ERROR: res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); res.status = 500; // HTTP Internal Server Error break; @@ -2891,7 +2891,7 @@ int main(int argc, char **argv) { if (!svr.listen_after_bind()) { - server_state.store(ERROR); + state.store(SERVER_STATE_ERROR); return 1; } @@ -2901,11 +2901,11 @@ int main(int argc, char **argv) // load the model if (!llama.load_model(params)) { - server_state.store(ERROR); + state.store(SERVER_STATE_ERROR); return 1; } else { llama.initialize(); - server_state.store(READY); + state.store(SERVER_STATE_READY); } // Middleware for API key validation From 7a9f75c38b5e62fe27b8a5a3ed823b4a3714024b Mon Sep 17 00:00:00 2001 From: Behnam M <58621210+ibehnam@users.noreply.github.com> Date: Thu, 11 Jan 2024 02:12:05 -0500 Subject: [PATCH 364/664] server : update readme to document the new `/health` endpoint (#4866) * added /health endpoint to the server * added comments on the additional /health endpoint * Better handling of server state When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value. * initialized server_state * fixed a typo * starting http server before initializing the model * Update server.cpp * Update server.cpp * fixes * fixes * fixes * made ServerState atomic and turned two-line spaces into one-line * updated `server` readme to document the `/health` endpoint too --- examples/server/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index d85a14f89..dc27e72b9 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -110,6 +110,10 @@ node index.js ``` ## API Endpoints +- **GET** `/health`: Returns the current state of the server: + - `{"status": "loading model"}` if the model is still being loaded. + - `{"status": "error"}` if the model failed to load. + - `{"status": "ok"}` if the model is successfully loaded and the server is ready for further requests mentioned below. - **POST** `/completion`: Given a `prompt`, it returns the predicted completion. From f34432ca1e0b288129390c1db8296a82aaf1e632 Mon Sep 17 00:00:00 2001 From: Erik Scholz Date: Fri, 5 Jan 2024 16:00:00 +0100 Subject: [PATCH 365/664] fix : cuda order of synchronization when setting a buffer (ggml/679) * fix : cuda order of synchronization when setting a buffer * also sync before memcpy --------- Co-authored-by: slaren --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e26260a35..900f7ba4a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -10184,8 +10184,8 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice)); + CUDA_CHECK(cudaDeviceSynchronize()); } static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { From c910e3c28a1caee8cb1398143d582dd9ab697e68 Mon Sep 17 00:00:00 2001 From: Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com> Date: Tue, 9 Jan 2024 11:16:37 -0500 Subject: [PATCH 366/664] Fix execlp call (ggml/689) NULL can be an integer constant expression with the value zero, in this case the behavior would be undefined because of an incorrect type being passed to the variable arguments. --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index adb387100..4a0ec4c44 100644 --- a/ggml.c +++ b/ggml.c @@ -132,7 +132,7 @@ void ggml_print_backtrace(void) { "-ex", "bt -frame-info source-and-location", "-ex", "detach", "-ex", "quit", - NULL); + (char *) NULL); } else { waitpid(pid, NULL, 0); } From e739de790921e6abbc8c70398303cacd74913f61 Mon Sep 17 00:00:00 2001 From: leejet Date: Wed, 10 Jan 2024 21:13:42 +0800 Subject: [PATCH 367/664] ggml : change GGML_MAX_NAME at compile time (ggml/682) * change GGML_MAX_NAME to 128 * allow controlling the value of GGML_MAX_NAME through external macro definitions --- ggml.h | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml.h b/ggml.h index c55e598b4..b6cc85952 100644 --- a/ggml.h +++ b/ggml.h @@ -218,7 +218,9 @@ #define GGML_MAX_PARAMS 2048 #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 +#ifndef GGML_MAX_NAME #define GGML_MAX_NAME 64 +#endif #define GGML_MAX_OP_PARAMS 64 #define GGML_DEFAULT_N_THREADS 4 #define GGML_DEFAULT_GRAPH_SIZE 2048 From 5362e43962e84d61e20b91f34991d7ccaef4a7d5 Mon Sep 17 00:00:00 2001 From: Jack Mousseau Date: Wed, 10 Jan 2024 06:19:19 -0800 Subject: [PATCH 368/664] metal : wrap each operation in debug group (ggml/690) --- ggml-metal.m | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 6c2a8d04e..161906824 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1067,6 +1067,8 @@ bool ggml_metal_graph_compute( GGML_ASSERT(!"unsupported op"); } + [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst)]]; + const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; const int64_t ne02 = src0 ? src0->ne[2] : 0; @@ -2423,6 +2425,8 @@ bool ggml_metal_graph_compute( GGML_ASSERT(false); } } + + [encoder popDebugGroup]; } if (encoder != nil) { From f85a973aa139ae6f37e8b8e1966f1d278b5e0372 Mon Sep 17 00:00:00 2001 From: Timothy Cronin <40186632+4imothy@users.noreply.github.com> Date: Thu, 11 Jan 2024 02:27:48 -0500 Subject: [PATCH 369/664] ggml : remove ggml_cpy_inplace and ggml_cont_inplace (ggml/693) --- ggml.c | 30 ++++++++---------------------- ggml.h | 11 ----------- 2 files changed, 8 insertions(+), 33 deletions(-) diff --git a/ggml.c b/ggml.c index 4a0ec4c44..9c42a45e3 100644 --- a/ggml.c +++ b/ggml.c @@ -4311,13 +4311,13 @@ struct ggml_tensor * ggml_set_2d_inplace( static struct ggml_tensor * ggml_cpy_impl( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b, - bool inplace) { + struct ggml_tensor * b) { GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; - if (!inplace && (a->grad || b->grad)) { + if (a->grad || b->grad) { + // inplace is false and either one have a grad is_node = true; } @@ -4341,29 +4341,21 @@ struct ggml_tensor * ggml_cpy( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { - return ggml_cpy_impl(ctx, a, b, false); -} - -struct ggml_tensor * ggml_cpy_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - return ggml_cpy_impl(ctx, a, b, true); + return ggml_cpy_impl(ctx, a, b); } // ggml_cont static struct ggml_tensor * ggml_cont_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - bool inplace) { + struct ggml_tensor * a) { bool is_node = false; - if (!inplace && a->grad) { + if (a->grad) { is_node = true; } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); ggml_format_name(result, "%s (cont)", a->name); result->op = GGML_OP_CONT; @@ -4376,13 +4368,7 @@ static struct ggml_tensor * ggml_cont_impl( struct ggml_tensor * ggml_cont( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_cont_impl(ctx, a, false); -} - -struct ggml_tensor * ggml_cont_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a) { - return ggml_cont_impl(ctx, a, true); + return ggml_cont_impl(ctx, a); } // make contiguous, with new shape diff --git a/ggml.h b/ggml.h index b6cc85952..127dcef1d 100644 --- a/ggml.h +++ b/ggml.h @@ -1163,22 +1163,11 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); - // a -> b, in-place, return view(b) - GGML_API struct ggml_tensor * ggml_cpy_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - // make contiguous GGML_API struct ggml_tensor * ggml_cont( struct ggml_context * ctx, struct ggml_tensor * a); - // make contiguous, in-place - GGML_API struct ggml_tensor * ggml_cont_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a); - // make contiguous, with new shape GGML_API struct ggml_tensor * ggml_cont_1d( struct ggml_context * ctx, From 3267c2abc72e34608224408ace3c048831050f97 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 09:34:59 +0200 Subject: [PATCH 370/664] metal : fix deprecation warning (ggml/690) --- ggml-metal.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-metal.m b/ggml-metal.m index 161906824..82d68cd1b 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1067,7 +1067,7 @@ bool ggml_metal_graph_compute( GGML_ASSERT(!"unsupported op"); } - [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst)]]; + [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; From 64802ec00d6383784a9dacf616095eaced16c3c3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 09:39:08 +0200 Subject: [PATCH 371/664] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index fe7f3202f..3e2c579d5 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -f96711108d55bdbbd277e6be07204dce6a94fb93 +979cc23b345006504cfc1f67c0fdf627805e3319 From 2a7c94db5fb67b2f8882d2d16a11bf5d8d12d397 Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Thu, 11 Jan 2024 14:31:52 +0000 Subject: [PATCH 372/664] metal : put encoder debug group behind a define (#4873) --- ggml-metal.m | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 82d68cd1b..9698e5a79 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1067,7 +1067,9 @@ bool ggml_metal_graph_compute( GGML_ASSERT(!"unsupported op"); } +#ifndef GGML_METAL_NDEBUG [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; +#endif const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; @@ -2426,7 +2428,9 @@ bool ggml_metal_graph_compute( } } +#ifndef GGML_METAL_NDEBUG [encoder popDebugGroup]; +#endif } if (encoder != nil) { From 2f043328e3116724d15b915b5c6078e2df860a69 Mon Sep 17 00:00:00 2001 From: Isaac McFadyen Date: Thu, 11 Jan 2024 09:33:26 -0500 Subject: [PATCH 373/664] server : fix typo in model name (#4876) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 4a0714997..860e4e9ae 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2515,7 +2515,7 @@ json oaicompat_completion_params_parse( // // https://platform.openai.com/docs/api-reference/chat/create llama_sampling_params default_sparams; - llama_params["model"] = json_value(body, "model", std::string("uknown")); + llama_params["model"] = json_value(body, "model", std::string("unknown")); llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); llama_params["temperature"] = json_value(body, "temperature", 0.0); From 43f76bf1c362c067fce46bb8dcda0b64af8a9533 Mon Sep 17 00:00:00 2001 From: pudepiedj Date: Thu, 11 Jan 2024 16:14:52 +0000 Subject: [PATCH 374/664] main : print total token count and tokens consumed so far (#4874) * Token count changes * Add show token count * Updating before PR * Two requested changes * Move param def posn --- common/common.cpp | 8 ++++++++ common/common.h | 2 +- examples/main/main.cpp | 6 +++++- llama.cpp | 2 +- 4 files changed, 15 insertions(+), 3 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 4e89fe516..bfcd6d4df 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -630,6 +630,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.ppl_stride = std::stoi(argv[i]); + } else if (arg == "-stc" || arg == "--show_token_count") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.token_interval = std::stoi(argv[i]); } else if (arg == "--ppl-output-type") { if (++i >= argc) { invalid_param = true; @@ -944,6 +950,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" -stc N --show_token_count N\n"); + printf(" show consumed tokens every N tokens\n"); printf("\n"); #ifndef LOG_DISABLE_LOGS log_print_usage(); diff --git a/common/common.h b/common/common.h index e2bbfc258..a295e88b0 100644 --- a/common/common.h +++ b/common/common.h @@ -64,6 +64,7 @@ struct gpt_params { int32_t n_beams = 0; // if non-zero then use beam search of given width. int32_t grp_attn_n = 1; // group-attention factor int32_t grp_attn_w = 512; // group-attention width + int32_t token_interval = 512; // show token count every 512 tokens float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor @@ -242,4 +243,3 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); - diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 5ea67051f..1f35febbd 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -500,7 +500,7 @@ int main(int argc, char ** argv) { while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (!embd.empty()) { - // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via + // Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via // --prompt or --file which uses the same value. int max_embd_size = n_ctx - 4; @@ -650,6 +650,10 @@ int main(int argc, char ** argv) { n_past += n_eval; LOG("n_past = %d\n", n_past); + // Display total tokens alongside total time + if (n_past % params.token_interval == 0) { + printf("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); + } } if (!embd.empty() && !path_session.empty()) { diff --git a/llama.cpp b/llama.cpp index e1f1932ba..aaadfa444 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10921,7 +10921,7 @@ void llama_print_timings(struct llama_context * ctx) { __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval); LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval); - LLAMA_LOG_INFO("%s: total time = %10.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms)); + LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (timings.t_end_ms - timings.t_start_ms), (timings.n_p_eval + timings.n_eval)); } void llama_reset_timings(struct llama_context * ctx) { From d8d90aa343c22fe01429d3540e47ded87e9dcb9d Mon Sep 17 00:00:00 2001 From: Someone Date: Thu, 11 Jan 2024 17:22:34 +0000 Subject: [PATCH 375/664] ci: nix-flake-update: new token with pr permissions (#4879) * ci: nix-flake-update: new token with pr permissions --------- Co-authored-by: Georgi Gerganov --- .github/workflows/nix-flake-update.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml index fa9360841..3a6a96e26 100644 --- a/.github/workflows/nix-flake-update.yml +++ b/.github/workflows/nix-flake-update.yml @@ -19,4 +19,4 @@ jobs: pr-labels: | nix pr-reviewers: philiptaron,SomeoneSerge - token: ${{ secrets.GITHUB_TOKEN }} + token: ${{ secrets.FLAKE_TOKEN }} From eab67950068e4b125007d027232c47d2a5831cd0 Mon Sep 17 00:00:00 2001 From: Behnam M <58621210+ibehnam@users.noreply.github.com> Date: Thu, 11 Jan 2024 12:41:39 -0500 Subject: [PATCH 376/664] server : add `LOG_INFO` when model is successfully loaded (#4881) * added /health endpoint to the server * added comments on the additional /health endpoint * Better handling of server state When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value. * initialized server_state * fixed a typo * starting http server before initializing the model * Update server.cpp * Update server.cpp * fixes * fixes * fixes * made ServerState atomic and turned two-line spaces into one-line * updated `server` readme to document the `/health` endpoint too * used LOG_INFO after successful model loading --- examples/server/server.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 860e4e9ae..51a4b689f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2906,6 +2906,7 @@ int main(int argc, char **argv) } else { llama.initialize(); state.store(SERVER_STATE_READY); + LOG_INFO("model loaded", {}); } // Middleware for API key validation From 27379455c38cb13f24de92dbd6fcdd04eeb1b9d9 Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Thu, 11 Jan 2024 12:51:17 -0500 Subject: [PATCH 377/664] server : support for multiple api keys (#4864) * server: added support for multiple api keys, added loading api keys from file * minor: fix whitespace * added file error handling to --api-key-file, changed code to better reflect current style * server: update README.md for --api-key-file --------- Co-authored-by: Michael Coppola --- examples/server/README.md | 3 ++- examples/server/server.cpp | 36 ++++++++++++++++++++++++++++++------ 2 files changed, 32 insertions(+), 7 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index dc27e72b9..fd3034b99 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -23,7 +23,8 @@ Command line options: - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. - `--path`: path from which to serve static files (default examples/server/public) -- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. +- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. +- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s. - `--embedding`: Enable embedding extraction, Default: disabled. - `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) - `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 51a4b689f..345004fa1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -39,7 +39,7 @@ using json = nlohmann::json; struct server_params { std::string hostname = "127.0.0.1"; - std::string api_key; + std::vector api_keys; std::string public_path = "examples/server/public"; int32_t port = 8080; int32_t read_timeout = 600; @@ -2021,6 +2021,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); + printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); @@ -2081,7 +2082,28 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - sparams.api_key = argv[i]; + sparams.api_keys.push_back(argv[i]); + } + else if (arg == "--api-key-file") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::ifstream key_file(argv[i]); + if (!key_file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + break; + } + std::string key; + while (std::getline(key_file, key)) { + if (key.size() > 0) { + sparams.api_keys.push_back(key); + } + } + key_file.close(); } else if (arg == "--timeout" || arg == "-to") { @@ -2881,8 +2903,10 @@ int main(int argc, char **argv) log_data["hostname"] = sparams.hostname; log_data["port"] = std::to_string(sparams.port); - if (!sparams.api_key.empty()) { - log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4); + if (sparams.api_keys.size() == 1) { + log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4); + } else if (sparams.api_keys.size() > 1) { + log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; } LOG_INFO("HTTP server listening", log_data); @@ -2912,7 +2936,7 @@ int main(int argc, char **argv) // Middleware for API key validation auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { // If API key is not set, skip validation - if (sparams.api_key.empty()) { + if (sparams.api_keys.empty()) { return true; } @@ -2921,7 +2945,7 @@ int main(int argc, char **argv) std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); - if (received_api_key == sparams.api_key) { + if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { return true; // API key is valid } } From 4330bd83feb39683de4bd7a34cfcf672ff8ac3e4 Mon Sep 17 00:00:00 2001 From: Laura Date: Thu, 11 Jan 2024 19:02:48 +0100 Subject: [PATCH 378/664] server : implement credentialed CORS (#4514) * Implement credentialed CORS according to MDN * Fix syntax error * Move validate_api_key up so it is defined before its first usage --- examples/server/server.cpp | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 345004fa1..031824e14 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2822,9 +2822,15 @@ int main(int argc, char **argv) std::atomic state{SERVER_STATE_LOADING_MODEL}; - svr.set_default_headers({{"Server", "llama.cpp"}, - {"Access-Control-Allow-Origin", "*"}, - {"Access-Control-Allow-Headers", "content-type"}}); + svr.set_default_headers({{"Server", "llama.cpp"}}); + + // CORS preflight + svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + res.set_header("Access-Control-Allow-Credentials", "true"); + res.set_header("Access-Control-Allow-Methods", "POST"); + res.set_header("Access-Control-Allow-Headers", "*"); + }); svr.Get("/health", [&](const httplib::Request&, httplib::Response& res) { server_state current_state = state.load(); @@ -2987,9 +2993,9 @@ int main(int argc, char **argv) return false; }); - svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) + svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res) { - res.set_header("Access-Control-Allow-Origin", "*"); + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", llama.name_user.c_str() }, { "assistant_name", llama.name_assistant.c_str() } @@ -2999,6 +3005,7 @@ int main(int argc, char **argv) svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; } @@ -3066,8 +3073,9 @@ int main(int argc, char **argv) } }); - svr.Get("/v1/models", [¶ms](const httplib::Request&, httplib::Response& res) + svr.Get("/v1/models", [¶ms](const httplib::Request& req, httplib::Response& res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); std::time_t t = std::time(0); json models = { @@ -3085,9 +3093,11 @@ int main(int argc, char **argv) res.set_content(models.dump(), "application/json; charset=utf-8"); }); + // TODO: add mount point without "/v1" prefix -- how? svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; } @@ -3161,6 +3171,7 @@ int main(int argc, char **argv) svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; } @@ -3233,6 +3244,7 @@ int main(int argc, char **argv) svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); std::vector tokens; if (body.count("content") != 0) @@ -3245,6 +3257,7 @@ int main(int argc, char **argv) svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); std::string content; if (body.count("tokens") != 0) @@ -3259,6 +3272,7 @@ int main(int argc, char **argv) svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); json prompt; if (body.count("content") != 0) From 3ba5b8ca8e6181a5c712c5b77595a29f1d3e2b97 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 21:31:31 +0200 Subject: [PATCH 379/664] swift : pin ggml commit + remove ggml.h from spm-headers (#4878) ggml-ci --- Package.swift | 2 +- spm-headers/ggml.h | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) delete mode 120000 spm-headers/ggml.h diff --git a/Package.swift b/Package.swift index 583e2e276..59191da45 100644 --- a/Package.swift +++ b/Package.swift @@ -14,7 +14,7 @@ let package = Package( .library(name: "llama", targets: ["llama"]), ], dependencies: [ - .package(url: "https://github.com/ggerganov/ggml.git", .branch("master")) + .package(url: "https://github.com/ggerganov/ggml.git", .revision("979cc23b345006504cfc1f67c0fdf627805e3319")) ], targets: [ .target( diff --git a/spm-headers/ggml.h b/spm-headers/ggml.h deleted file mode 120000 index 39215298f..000000000 --- a/spm-headers/ggml.h +++ /dev/null @@ -1 +0,0 @@ -../ggml.h \ No newline at end of file From 49662cbed3e95f5976c070b85b9fd53fd577038d Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 11 Jan 2024 20:39:39 +0100 Subject: [PATCH 380/664] ggml : SOTA 2-bit quants (add IQ2_XS) (#4856) * iq2_xs: basics * iq2_xs: this should have been in the basics * iq2_xs: CUDA and scalar CPU works * iq2_xs: WIP Metal * iq2_xs: Metal now works * iq2_xs: working, but dog slow, ARM_NEON dot product * iq2_xs: better ARM_NEON dot product We are now at 19.5 t/s for TG-128 and 61 t/s for PP-512 when running on the CPU. * iq2_xs: AVX2 dot product - 19.5 t/s * iq2_xs: faster AVX2 dit product 21.4 t/s for TG-128, 59.2 t/s for PP-512. The latter is 2x compared to the previous version. * iq2_xs: had forgotten to delete iq2-data.h * Add llama enum for IQ2_XS --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 232 +++++++++++++++++++++- ggml-metal.m | 42 +++- ggml-metal.metal | 378 +++++++++++++++++++++++++++++++++++- ggml-quants.c | 360 +++++++++++++++++++++++++++++++++- ggml-quants.h | 12 ++ ggml.c | 30 ++- ggml.h | 3 + llama.cpp | 3 + llama.h | 1 + tests/test-quantize-fns.cpp | 5 +- 10 files changed, 1038 insertions(+), 28 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 900f7ba4a..dd19699f6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -486,6 +486,15 @@ typedef struct { } block_iq2_xxs; static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); +#define QR2_XS 8 +#define QI2_XS (QK_K / (4*QR2_XS)) +typedef struct { + half d; + uint16_t qs[QK_K/8]; + uint8_t scales[QK_K/32]; +} block_iq2_xs; +static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -1328,7 +1337,7 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t #endif } -static const __device__ uint64_t kgrid_iq2xxs[256] = { +static const __device__ uint64_t iq2xxs_grid[256] = { 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, @@ -1395,6 +1404,137 @@ static const __device__ uint64_t kgrid_iq2xxs[256] = { 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, }; +static const __device__ uint64_t iq2xs_grid[512] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, + 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, + 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, + 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, + 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, + 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, + 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, + 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, + 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, + 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, + 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, + 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, + 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, + 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, + 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, + 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, + 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, + 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, + 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, + 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, + 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, + 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, + 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, + 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, + 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, + 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, + 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, + 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, + 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, + 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, + 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, + 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, + 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, + 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, + 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, + 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, + 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, + 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, + 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, + 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, + 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, + 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, + 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, + 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, + 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, + 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, + 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, + 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, + 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, + 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, + 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, + 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, + 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, + 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, + 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, + 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, + 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, + 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, + 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, + 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, + 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, + 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, + 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, + 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, + 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, + 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, + 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, + 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, + 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, + 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, + 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, + 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, + 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, + 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, + 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, + 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, + 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, + 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, + 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, + 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, + 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, + 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, + 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, + 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, + 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, + 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, + 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, + 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, + 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, + 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, + 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, + 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, + 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, + 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, + 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, + 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, + 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, + 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, + 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, + 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, + 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, + 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, + 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, + 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, +}; + static const __device__ uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -1439,7 +1579,7 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint16_t * q2 = x[i].qs + 4*ib; const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid = (const uint8_t *)(kgrid_iq2xxs + aux8[il]); + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); const uint32_t aux32 = q2[2] | (q2[3] << 16); const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; @@ -1450,6 +1590,28 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds } +template +static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq2_xs * x = (const block_iq2_xs *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); @@ -3996,7 +4158,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( uint32_t aux32 = q2[2] | (q2[3] << 16); int sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(kgrid_iq2xxs + aux8[l]); + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); const uint8_t signs = ksigns_iq2xs[aux32 & 127]; for (int j = 0; j < 8; ++j) { sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); @@ -4012,8 +4174,8 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( const int il = iqs%2; const uint16_t * q2 = bq2->qs + 4*ib32; const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid1 = (const uint8_t *)(kgrid_iq2xxs + aux8[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(kgrid_iq2xxs + aux8[2*il+1]); + const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); const uint32_t aux32 = q2[2] | (q2[3] << 16); const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * (float)bq8_1[ib32].ds.x * 0.25f; const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; @@ -4032,6 +4194,42 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( #endif } +static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if QK_K == 256 + const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; + + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi2 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + const float d = (float)bq2->d * (float)bq8_1[ib32].ds.x * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + assert(false); + return 0.f; +#endif +} + template static __device__ __forceinline__ void mul_mat_q( @@ -6035,6 +6233,12 @@ static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq2_xxs<<>>(vx, y); } +template +static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_xs<<>>(vx, y); +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -6065,6 +6269,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_q6_K_cuda; case GGML_TYPE_IQ2_XXS: return dequantize_row_iq2_xxs_cuda; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_cuda; case GGML_TYPE_F32: return convert_unary_cuda; default: @@ -6096,6 +6302,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_q6_K_cuda; case GGML_TYPE_IQ2_XXS: return dequantize_row_iq2_xxs_cuda; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_cuda; case GGML_TYPE_F16: return convert_unary_cuda; default: @@ -6299,6 +6507,15 @@ static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, floa <<>>(vx, vy, dst, ncols, nrows); } +static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + static void ggml_mul_mat_q4_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { @@ -7871,6 +8088,7 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: return max_compute_capability >= CC_RDNA2 ? 128 : 64; default: GGML_ASSERT(false); @@ -7892,6 +8110,7 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: return max_compute_capability >= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: return 64; @@ -7945,6 +8164,9 @@ static void ggml_cuda_op_mul_mat_vec_q( case GGML_TYPE_IQ2_XXS: mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); break; + case GGML_TYPE_IQ2_XS: + mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; diff --git a/ggml-metal.m b/ggml-metal.m index 9698e5a79..6e5594432 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -89,6 +89,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(get_rows_q6_K); GGML_METAL_DECL_KERNEL(get_rows_i32); GGML_METAL_DECL_KERNEL(get_rows_iq2_xxs); + GGML_METAL_DECL_KERNEL(get_rows_iq2_xs); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(group_norm); GGML_METAL_DECL_KERNEL(norm); @@ -108,6 +109,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_iq2_xxs_f32); + GGML_METAL_DECL_KERNEL(mul_mv_iq2_xs_f32); GGML_METAL_DECL_KERNEL(mul_mv_id_f32_f32); //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f16); GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32); @@ -124,6 +126,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mv_id_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_id_q6_K_f32); GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xxs_f32); + GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xs_f32); GGML_METAL_DECL_KERNEL(mul_mm_f32_f32); GGML_METAL_DECL_KERNEL(mul_mm_f16_f32); GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32); @@ -137,6 +140,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_iq2_xxs_f32); + GGML_METAL_DECL_KERNEL(mul_mm_iq2_xs_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_f32_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_f16_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_q4_0_f32); @@ -150,6 +154,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(mul_mm_id_q5_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_q6_K_f32); GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xxs_f32); + GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xs_f32); GGML_METAL_DECL_KERNEL(rope_f32); GGML_METAL_DECL_KERNEL(rope_f16); GGML_METAL_DECL_KERNEL(alibi_f32); @@ -385,6 +390,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(get_rows_q6_K); GGML_METAL_ADD_KERNEL(get_rows_i32); GGML_METAL_ADD_KERNEL(get_rows_iq2_xxs); + GGML_METAL_ADD_KERNEL(get_rows_iq2_xs); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(group_norm); GGML_METAL_ADD_KERNEL(norm); @@ -404,6 +410,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_iq2_xxs_f32); + GGML_METAL_ADD_KERNEL(mul_mv_iq2_xs_f32); GGML_METAL_ADD_KERNEL(mul_mv_id_f32_f32); //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f16); GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32); @@ -420,6 +427,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mv_id_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_id_q6_K_f32); GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xxs_f32); + GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xs_f32); if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { GGML_METAL_ADD_KERNEL(mul_mm_f32_f32); GGML_METAL_ADD_KERNEL(mul_mm_f16_f32); @@ -434,6 +442,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_iq2_xxs_f32); + GGML_METAL_ADD_KERNEL(mul_mm_iq2_xs_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_f32_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_f16_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_q4_0_f32); @@ -447,6 +456,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul_mm_id_q5_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_q6_K_f32); GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xxs_f32); + GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xs_f32); } GGML_METAL_ADD_KERNEL(rope_f32); GGML_METAL_ADD_KERNEL(rope_f16); @@ -513,6 +523,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(get_rows_q6_K); GGML_METAL_DEL_KERNEL(get_rows_i32); GGML_METAL_DEL_KERNEL(get_rows_iq2_xxs); + GGML_METAL_DEL_KERNEL(get_rows_iq2_xs); GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(group_norm); GGML_METAL_DEL_KERNEL(norm); @@ -532,6 +543,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_iq2_xxs_f32); + GGML_METAL_DEL_KERNEL(mul_mv_iq2_xs_f32); GGML_METAL_DEL_KERNEL(mul_mv_id_f32_f32); //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f16); GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32); @@ -548,6 +560,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mv_id_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_id_q6_K_f32); GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xxs_f32); + GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xs_f32); if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { GGML_METAL_DEL_KERNEL(mul_mm_f32_f32); GGML_METAL_DEL_KERNEL(mul_mm_f16_f32); @@ -562,6 +575,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_iq2_xxs_f32); + GGML_METAL_DEL_KERNEL(mul_mm_iq2_xs_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_f32_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_f16_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_q4_0_f32); @@ -575,6 +589,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul_mm_id_q5_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_q6_K_f32); GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xxs_f32); + GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xs_f32); } GGML_METAL_DEL_KERNEL(rope_f32); GGML_METAL_DEL_KERNEL(rope_f16); @@ -1561,6 +1576,7 @@ bool ggml_metal_graph_compute( case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break; case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xxs_f32]; break; + case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xs_f32]; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1679,6 +1695,12 @@ bool ggml_metal_graph_compute( nth1 = 16; [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xxs_f32]; } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xs_f32]; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1712,12 +1734,12 @@ bool ggml_metal_graph_compute( if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || - //src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src0t == GGML_TYPE_IQ2_XXS) { - [encoder setThreadgroupMemoryLength:(256*8+128) atIndex:0]; + else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { + const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q4_K) { @@ -1810,6 +1832,7 @@ bool ggml_metal_graph_compute( case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_K_f32]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q6_K_f32]; break; case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xxs_f32]; break; + case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xs_f32]; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1931,6 +1954,12 @@ bool ggml_metal_graph_compute( nth1 = 16; [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xxs_f32]; } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xs_f32]; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1980,12 +2009,12 @@ bool ggml_metal_graph_compute( if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || - //src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src2t == GGML_TYPE_IQ2_XXS) { - [encoder setThreadgroupMemoryLength:(256*8+128) atIndex:0]; + else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { + const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_Q4_K) { @@ -2026,6 +2055,7 @@ bool ggml_metal_graph_compute( case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break; case GGML_TYPE_I32: [encoder setComputePipelineState:ctx->pipeline_get_rows_i32]; break; case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xxs]; break; + case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xs]; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index 229efb8b6..029578dc5 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2452,6 +2452,13 @@ typedef struct { } block_iq2_xxs; // 66 bytes / block for QK_K = 256, so 2.0625 bpw +typedef struct { + half d; + uint16_t qs[QK_K/8]; + uint8_t scales[QK_K/32]; +} block_iq2_xs; +// 74 bytes / block for QK_K = 256, so 2.3125 bpw + //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -3476,7 +3483,7 @@ kernel void kernel_mul_mv_q6_K_f32( // ======================= "True" 2-bit -constexpr constant static uint64_t kgrid_iq2xxs[256] = { +constexpr constant static uint64_t iq2xxs_grid[256] = { 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, @@ -3543,6 +3550,137 @@ constexpr constant static uint64_t kgrid_iq2xxs[256] = { 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, }; +constexpr constant static uint64_t iq2xs_grid[512] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, + 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, + 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, + 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, + 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, + 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, + 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, + 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, + 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, + 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, + 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, + 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, + 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, + 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, + 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, + 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, + 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, + 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, + 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, + 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, + 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, + 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, + 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, + 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, + 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, + 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, + 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, + 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, + 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, + 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, + 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, + 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, + 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, + 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, + 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, + 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, + 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, + 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, + 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, + 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, + 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, + 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, + 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, + 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, + 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, + 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, + 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, + 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, + 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, + 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, + 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, + 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, + 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, + 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, + 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, + 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, + 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, + 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, + 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, + 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, + 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, + 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, + 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, + 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, + 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, + 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, + 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, + 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, + 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, + 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, + 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, + 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, + 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, + 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, + 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, + 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, + 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, + 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, + 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, + 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, + 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, + 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, + 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, + 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, + 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, + 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, + 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, + 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, + 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, + 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, + 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, + 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, + 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, + 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, + 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, + 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, + 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, + 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, + 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, + 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, + 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, + 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, + 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, + 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, +}; + constexpr constant static uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -3600,7 +3738,7 @@ void kernel_mul_mv_iq2_xxs_f32_impl( { int nval = 4; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = kgrid_iq2xxs[pos + i]; + for (int i = 0; i < nval; ++i) values[pos + i] = iq2xxs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; @@ -3689,6 +3827,149 @@ kernel void kernel_mul_mv_iq2_xxs_f32( kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +void kernel_mul_mv_iq2_xs_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq2_xs * x = (device const block_iq2_xs *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[32]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + + threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; + threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 512); + { + int nval = 8; + int pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) values[pos + i] = iq2xs_grid[pos + i]; + nval = 2; + pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + threadgroup_barrier(mem_flags::mem_threadgroup); + } + +#if QK_K == 256 + const int ix = tiisg; + + device const float * y4 = y + 32 * ix; + + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { + + for (int i = 0; i < 32; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq2_xs * xr = x + ibl; + device const uint16_t * q2 = xr->qs + 4 * ib; + device const uint8_t * sc = xr->scales + ib; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + const float db = dh[0]; + const uint8_t ls1 = sc[0] & 0xf; + const uint8_t ls2 = sc[0] >> 4; + const float d1 = db * (0.5f + ls1); + const float d2 = db * (0.5f + ls2); + + float sum1 = 0, sum2 = 0; + for (int l = 0; l < 2; ++l) { + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); + const uint8_t signs = shared_signs[(q2[l] >> 9)]; + for (int j = 0; j < 8; ++j) { + sum1 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); + } + } + for (int l = 2; l < 4; ++l) { + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); + const uint8_t signs = shared_signs[(q2[l] >> 9)]; + for (int j = 0; j < 8; ++j) { + sum2 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); + } + } + sumf[row] += d1 * sum1 + d2 * sum2; + + dh += nb*sizeof(block_iq2_xs)/2; + q2 += nb*sizeof(block_iq2_xs)/2; + sc += nb*sizeof(block_iq2_xs); + } + + y4 += 32 * 32; + } +#else + // TODO +#endif + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + } + } +} + +[[host_name("kernel_mul_mv_iq2_xs_f32")]] +kernel void kernel_mul_mv_iq2_xs_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + //============================= templates and their specializations ============================= // NOTE: this is not dequantizing - we are simply fitting the template @@ -3973,18 +4254,39 @@ void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x const uint32_t aux32_s = q2[2] | (q2[3] << 16); thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(kgrid_iq2xxs + aux8[2*il+0]); + constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]); uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; for (int i = 0; i < 8; ++i) { reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); } - grid = (constant uint8_t *)(kgrid_iq2xxs + aux8[2*il+1]); + grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]); signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; for (int i = 0; i < 8; ++i) { reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); } } +template +void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint16_t * q2 = xb->qs + 4*ib32; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511)); + uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9]; + for (int i = 0; i < 8; ++i) { + reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } + grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511)); + signs = ksigns_iq2xs[q2[2*il+1] >> 9]; + for (int i = 0; i < 8; ++i) { + reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } +} + template kernel void kernel_get_rows( device const void * src0, @@ -4525,6 +4827,7 @@ template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; // // matrix-matrix multiplication @@ -4562,6 +4865,7 @@ template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; // // indirect matrix-matrix multiplication @@ -4611,6 +4915,7 @@ template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mu template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; // // matrix-vector multiplication @@ -5448,3 +5753,68 @@ kernel void kernel_mul_mv_id_iq2_xxs_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq2_xs_f32")]] +kernel void kernel_mul_mv_id_iq2_xs_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq2_xs_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-quants.c b/ggml-quants.c index d497e6de9..a24b4b244 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -2342,15 +2342,7 @@ size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * // ====================== "True" 2-bit (de)-quantization -void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k) { - (void)x; - (void)y; - (void)k; - assert(k % QK_K == 0); - //fprintf(stderr, "=========================== %s: not implemented\n", __func__); -} - -static const uint64_t iq2xxs_grid[256] = { +static const uint64_t iq2xxs_grid[256] = { 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, @@ -2417,6 +2409,137 @@ static const uint64_t iq2xxs_grid[256] = { 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, }; +static const uint64_t iq2xs_grid[512] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, + 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, + 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, + 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, + 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, + 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, + 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, + 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, + 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, + 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, + 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, + 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, + 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, + 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, + 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, + 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, + 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, + 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, + 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, + 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, + 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, + 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, + 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, + 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, + 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, + 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, + 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, + 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, + 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, + 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, + 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, + 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, + 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, + 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, + 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, + 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, + 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, + 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, + 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, + 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, + 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, + 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, + 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, + 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, + 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, + 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, + 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, + 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, + 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, + 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, + 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, + 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, + 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, + 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, + 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, + 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, + 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, + 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, + 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, + 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, + 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, + 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, + 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, + 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, + 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, + 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, + 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, + 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, + 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, + 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, + 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, + 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, + 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, + 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, + 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, + 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, + 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, + 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, + 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, + 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, + 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, + 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, + 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, + 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, + 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, + 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, + 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, + 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, + 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, + 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, + 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, + 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, + 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, + 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, + 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, + 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, + 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, + 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, + 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, + 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, + 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, + 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, + 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, + 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, +}; + static const uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -2427,8 +2550,17 @@ static const uint8_t ksigns_iq2xs[128] = { 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, }; + static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; +void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k) { + (void)x; + (void)y; + (void)k; + assert(k % QK_K == 0); + //fprintf(stderr, "=========================== %s: not implemented\n", __func__); +} + void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2472,6 +2604,58 @@ size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_ return (n/QK_K*sizeof(block_iq2_xxs)); } +// ====================== 2.3125 bpw (de)-quantization + +void quantize_row_iq2_xs_reference(const float * restrict x, block_iq2_xs * restrict y, int k) { + (void)x; + (void)y; + (void)k; + assert(k % QK_K == 0); + //fprintf(stderr, "=========================== %s: not implemented\n", __func__); +} + +void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + float db[2]; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + db[0] = d * (0.5f + (x[i].scales[ib32] & 0xf)) * 0.25f; + db[1] = d * (0.5f + (x[i].scales[ib32] >> 4)) * 0.25f; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (x[i].qs[4*ib32 + l] & 511)); + const uint8_t signs = ksigns_iq2xs[x[i].qs[4*ib32 + l] >> 9]; + for (int j = 0; j < 8; ++j) { + y[j] = db[l/2] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); + } + y += 8; + } + } + } +} + +void quantize_row_iq2_xs(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq2_xs * restrict y = vy; + quantize_row_iq2_xs_reference(x, y, k); +} + +size_t ggml_quantize_iq2_xs(const float * src, void * dst, int n, int k, int64_t * hist) { + assert(k % QK_K == 0); + (void)hist; // TODO: collect histograms + + for (int j = 0; j < n; j += k) { + block_iq2_xs * restrict y = (block_iq2_xs *)dst + j/QK_K; + quantize_row_iq2_xs_reference(src + j, y, k); + } + return (n/QK_K*sizeof(block_iq2_xs)); +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -7357,3 +7541,161 @@ void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * res *s = 0.125f * sumf; #endif } + +void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + + const block_iq2_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + int8x16x4_t q2u; + int8x16x4_t q2s; + int8x16x4_t q8b; + + int32x4x4_t scales32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const uint8x8_t scales8 = vld1_u8(x[i].scales); + const uint8x8_t scales_l = vand_u8(scales8, vdup_n_u8(0xf)); + const uint8x8_t scales_h = vshr_n_u8(scales8, 4); + uint8x16_t scales = vcombine_u8(vzip1_u8(scales_l, scales_h), vzip2_u8(scales_l, scales_h)); + scales = vaddq_u8(vshlq_n_u8(scales, 1), vdupq_n_u8(1)); + const uint16x8_t scales1 = vmovl_u8(vget_low_u8(scales)); + const uint16x8_t scales2 = vmovl_u8(vget_high_u8(scales)); + scales32.val[0] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales1))); + scales32.val[1] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales1))); + scales32.val[2] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales2))); + scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); + int32x4_t sumi = vdupq_n_s32(0); + for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { + q8b = vld1q_s8_x4(q8); q8 += 64; + q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); + q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); + q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); + q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[6] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[7] & 511)))); + q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[0] >> 9))), vld1_s8((const void *)(signs64 + (q2[1] >> 9)))); + q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[2] >> 9))), vld1_s8((const void *)(signs64 + (q2[3] >> 9)))); + q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[4] >> 9))), vld1_s8((const void *)(signs64 + (q2[5] >> 9)))); + q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[6] >> 9))), vld1_s8((const void *)(signs64 + (q2[7] >> 9)))); + q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); + q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); + q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); + q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); + const int32x4_t p1 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[3], q8b.val[3]); + const int32x4_t p = vpaddq_s32(vpaddq_s32(p1, p2), vpaddq_s32(p3, p4)); + sumi = vmlaq_s32(sumi, p, scales32.val[ib64]); + q2 += 8; + } + sumf += d*vaddvq_s32(sumi); + } + *s = 0.125f * sumf; + +#elif defined(__AVX2__) + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + const __m128i m511 = _mm_set1_epi16(511); + const __m128i m127 = _mm_set1_epi16(127); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m128i aux_gindex, aux_sindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + const uint16_t * sindex = (const uint16_t *)&aux_sindex; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = _mm_set1_epi64x(aux64); + stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); + const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m128i q2_data = _mm_loadu_si128((const __m128i*)q2); q2 += 8; + aux_gindex = _mm_and_si128(q2_data, m511); + aux_sindex = _mm_and_si128(_mm_srli_epi16(q2_data, 9), m127); + const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]], iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]], iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]); + const __m256i s2_1 = _mm256_set_epi64x(signs64[sindex[3]], signs64[sindex[2]], signs64[sindex[1]], signs64[sindex[0]]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[sindex[7]], signs64[sindex[6]], signs64[sindex[5]], signs64[sindex[4]]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + + const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1))); + + sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2)); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1; + const uint16_t ls2 = 2*(sc[ib32] >> 4) + 1; + int32_t sumi = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls1; + sumi = 0; + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls2; + q2 += 4; + } + sumf += d * bsum; + } + *s = 0.125f * sumf; +#endif +} diff --git a/ggml-quants.h b/ggml-quants.h index 8dd911d41..df5e7ae80 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -174,6 +174,14 @@ typedef struct { } block_iq2_xxs; static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); +// 2.3125 bpw quants +typedef struct { + ggml_fp16_t d; + uint16_t qs[QK_K/8]; + uint8_t scales[QK_K/32]; +} block_iq2_xs; +static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); + // Quantization void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k); @@ -189,6 +197,7 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k); +void quantize_row_iq2_xs_reference (const float * restrict x, block_iq2_xs * restrict y, int k); void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); @@ -204,6 +213,7 @@ void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); void quantize_row_iq2_xxs(const float * restrict x, void * restrict y, int k); +void quantize_row_iq2_xs (const float * restrict x, void * restrict y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); @@ -220,6 +230,7 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k); void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k); +void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); @@ -234,3 +245,4 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); diff --git a/ggml.c b/ggml.c index 9c42a45e3..d2a8c0478 100644 --- a/ggml.c +++ b/ggml.c @@ -584,6 +584,17 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, + [GGML_TYPE_IQ2_XS] = { + .type_name = "iq2_xs", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_xs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, + .from_float = quantize_row_iq2_xs, + .from_float_reference = (ggml_from_float_t) quantize_row_iq2_xs_reference, + .vec_dot = ggml_vec_dot_iq2_xs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -2123,6 +2134,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break; case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break; case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; + case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7435,6 +7447,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; @@ -7700,6 +7713,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: { ggml_compute_forward_add1_q_f32(params, src0, src1, dst); } break; @@ -7815,6 +7829,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: default: { GGML_ASSERT(false); @@ -10457,6 +10472,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: { ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); } break; @@ -10632,6 +10648,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: default: { GGML_ASSERT(false); @@ -10827,6 +10844,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; @@ -11464,6 +11482,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -11539,6 +11558,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -18660,6 +18680,12 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i block_iq2_xxs * block = (block_iq2_xxs*)dst + start / QK_K; result = ggml_quantize_iq2_xxs(src + start, block, n, n, hist); } break; + case GGML_TYPE_IQ2_XS: + { + GGML_ASSERT(start % QK_K == 0); + block_iq2_xs * block = (block_iq2_xs*)dst + start / QK_K; + result = ggml_quantize_iq2_xs(src + start, block, n, n, hist); + } break; case GGML_TYPE_F16: { int elemsize = sizeof(ggml_fp16_t); @@ -19015,8 +19041,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p (int64_t) info->ne[3]; if (ne % ggml_blck_size(info->type) != 0) { - fprintf(stderr, "%s: tensor '%s' number of elements (%" PRId64 ") is not a multiple of block size (%d)\n", - __func__, info->name.data, ne, ggml_blck_size(info->type)); + fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%d)\n", + __func__, info->name.data, (int)info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type)); fclose(file); gguf_free(ctx); return NULL; diff --git a/ggml.h b/ggml.h index 127dcef1d..93b42a27d 100644 --- a/ggml.h +++ b/ggml.h @@ -342,6 +342,7 @@ extern "C" { GGML_TYPE_Q6_K = 14, GGML_TYPE_Q8_K = 15, GGML_TYPE_IQ2_XXS = 16, + GGML_TYPE_IQ2_XS = 17, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -377,6 +378,7 @@ extern "C" { GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors }; // available tensor operations: @@ -2061,6 +2063,7 @@ extern "C" { GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_iq2_xs (const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist); diff --git a/llama.cpp b/llama.cpp index aaadfa444..bd219d49c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2223,6 +2223,7 @@ struct llama_model_loader { case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break; case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break; + case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -2595,6 +2596,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; default: return "unknown, may not work"; } @@ -9050,6 +9052,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_XS :quantized_type = GGML_TYPE_IQ2_XS; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } diff --git a/llama.h b/llama.h index c11075bbc..6fde113ff 100644 --- a/llama.h +++ b/llama.h @@ -104,6 +104,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index cee712618..31a78c632 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -134,8 +134,9 @@ int main(int argc, char * argv[]) { continue; } - if ((ggml_type)i == GGML_TYPE_IQ2_XXS) { - printf("Skip %s due to missing quantization functionality\n", ggml_type_name((ggml_type) i)); + const ggml_type ei = (ggml_type)i; + if (ei == GGML_TYPE_IQ2_XXS || ei == GGML_TYPE_IQ2_XS) { + printf("Skip %s due to missing quantization functionality\n", ggml_type_name(ei)); continue; } From 469e75d0a35b08de549a4fd87f082ca7a8a539ba Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 11 Jan 2024 20:43:15 +0100 Subject: [PATCH 381/664] llama : restore intended k-quants mixes for MoE models (#4872) * Restore intended k-quants quantization mixes for MoE models * Update Q2_K_S values in the quantize tool Still using LLaMA-v1 PPL values in the quant description today does not make much sense. But let's leave this update for another PR. --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- examples/quantize/quantize.cpp | 1 + llama.cpp | 24 +++++++++++++++--------- llama.h | 1 + 3 files changed, 17 insertions(+), 9 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index d27ea5e91..f878f6911 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -18,6 +18,7 @@ static const std::vector QUANT_OPTIONS = { { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", }, { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, + { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", }, diff --git a/llama.cpp b/llama.cpp index bd219d49c..d39ff94c7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2586,7 +2586,8 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0"; // K-quants - case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K"; + case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small"; case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small"; case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large"; @@ -8955,10 +8956,13 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty // TODO: explore better strategies new_type = GGML_TYPE_Q8_0; } - } else if (name.find("ffn_down.weight") != std::string::npos) { + } else if (name.find("ffn_down") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) { + if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q4_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { - new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K + new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q5_K : arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; } @@ -8967,14 +8971,14 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { if (arch == LLM_ARCH_FALCON) { - new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K : + new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q6_K : use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } else { if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; } } else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < 4) { + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) { new_type = GGML_TYPE_Q5_K; } ++qs.i_feed_forward_w2; @@ -8992,9 +8996,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K; } - else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; - } + // IK: let's remove this, else Q2_K is almost the same as Q3_K_S + //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) { + // if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; + //} // This can be used to reduce the size of the Q5_K_S model. // The associated PPL increase is fully in line with the size reduction //else { @@ -9043,6 +9048,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // K-quants case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; + case LLAMA_FTYPE_MOSTLY_Q2_K_S: quantized_type = GGML_TYPE_Q2_K; break; case LLAMA_FTYPE_MOSTLY_Q3_K_S: case LLAMA_FTYPE_MOSTLY_Q3_K_M: case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; @@ -9101,7 +9107,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) { ++qs.n_attention_wv; } - else if (name.find("ffn_down.weight") != std::string::npos) { + else if (name.find("ffn_down") != std::string::npos) { ++qs.n_feed_forward_w2; } } diff --git a/llama.h b/llama.h index 6fde113ff..43d41b8f6 100644 --- a/llama.h +++ b/llama.h @@ -105,6 +105,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; From b0377875488b33f7114138687d828da1de61775d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 21:58:28 +0200 Subject: [PATCH 382/664] swift : track ggml release branch (#4867) --- Package.swift | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Package.swift b/Package.swift index 59191da45..37524edee 100644 --- a/Package.swift +++ b/Package.swift @@ -14,7 +14,7 @@ let package = Package( .library(name: "llama", targets: ["llama"]), ], dependencies: [ - .package(url: "https://github.com/ggerganov/ggml.git", .revision("979cc23b345006504cfc1f67c0fdf627805e3319")) + .package(url: "https://github.com/ggerganov/ggml.git", .branch("release")) ], targets: [ .target( From 3ca63b4538dfc78aaec88cd2c3e3f8417c1924e3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 22:43:05 +0200 Subject: [PATCH 383/664] main : disable token count by default (#4874) --- common/common.cpp | 6 +++--- common/common.h | 2 +- examples/main/main.cpp | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index bfcd6d4df..287e8bd5a 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -630,7 +630,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.ppl_stride = std::stoi(argv[i]); - } else if (arg == "-stc" || arg == "--show_token_count") { + } else if (arg == "-stc" || arg == "--show-token-count") { if (++i >= argc) { invalid_param = true; break; @@ -950,8 +950,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -stc N --show_token_count N\n"); - printf(" show consumed tokens every N tokens\n"); + printf(" -stc N --show-token-count N\n"); + printf(" show consumed tokens every N tokens (default: %d)\n", params.token_interval); printf("\n"); #ifndef LOG_DISABLE_LOGS log_print_usage(); diff --git a/common/common.h b/common/common.h index a295e88b0..82d23cf54 100644 --- a/common/common.h +++ b/common/common.h @@ -64,7 +64,7 @@ struct gpt_params { int32_t n_beams = 0; // if non-zero then use beam search of given width. int32_t grp_attn_n = 1; // group-attention factor int32_t grp_attn_w = 512; // group-attention width - int32_t token_interval = 512; // show token count every 512 tokens + int32_t token_interval = -1; // show token count every 512 tokens (-1 = disabled) float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 1f35febbd..6953d107c 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -651,8 +651,8 @@ int main(int argc, char ** argv) { LOG("n_past = %d\n", n_past); // Display total tokens alongside total time - if (n_past % params.token_interval == 0) { - printf("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); + if (params.token_interval > 0 && n_past % params.token_interval == 0) { + LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); } } From 7edefbd79cc6dea96640edc54c6b94b2b2496d8b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 22:46:26 +0200 Subject: [PATCH 384/664] main : better name for variable n_print (#4874) --- common/common.cpp | 8 ++++---- common/common.h | 2 +- examples/main/main.cpp | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 287e8bd5a..b2cb0e257 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -630,12 +630,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.ppl_stride = std::stoi(argv[i]); - } else if (arg == "-stc" || arg == "--show-token-count") { + } else if (arg == "-ptc" || arg == "--print-token-count") { if (++i >= argc) { invalid_param = true; break; } - params.token_interval = std::stoi(argv[i]); + params.n_print = std::stoi(argv[i]); } else if (arg == "--ppl-output-type") { if (++i >= argc) { invalid_param = true; @@ -950,8 +950,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -stc N --show-token-count N\n"); - printf(" show consumed tokens every N tokens (default: %d)\n", params.token_interval); + printf(" -stc N --print-token-count N\n"); + printf(" print token count every N tokens (default: %d)\n", params.n_print); printf("\n"); #ifndef LOG_DISABLE_LOGS log_print_usage(); diff --git a/common/common.h b/common/common.h index 82d23cf54..1359e76ab 100644 --- a/common/common.h +++ b/common/common.h @@ -64,7 +64,7 @@ struct gpt_params { int32_t n_beams = 0; // if non-zero then use beam search of given width. int32_t grp_attn_n = 1; // group-attention factor int32_t grp_attn_w = 512; // group-attention width - int32_t token_interval = -1; // show token count every 512 tokens (-1 = disabled) + int32_t n_print = -1; // print token count every n tokens (-1 = disabled) float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 6953d107c..c53b29978 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -651,7 +651,7 @@ int main(int argc, char ** argv) { LOG("n_past = %d\n", n_past); // Display total tokens alongside total time - if (params.token_interval > 0 && n_past % params.token_interval == 0) { + if (params.n_print > 0 && n_past % params.n_print == 0) { LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); } } From 1d118386fea031f01550f8cd47a5c86296e5333f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 11 Jan 2024 23:23:49 +0200 Subject: [PATCH 385/664] server : fix infill when prompt is empty (#4833) --- examples/server/server.cpp | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 031824e14..1d30a15a6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1406,7 +1406,7 @@ struct llama_server_context task.multitask_id = multitask_id; // when a completion task's prompt array is not a singleton, we split it into multiple requests - if (task.data.at("prompt").size() > 1) + if (task.data.count("prompt") && task.data.at("prompt").size() > 1) { lock.unlock(); // entering new func scope return split_multiprompt_task(task); @@ -1577,9 +1577,9 @@ struct llama_server_context slot->reset(); - slot->infill = task.infill_mode; - slot->embedding = task.embedding_mode; - slot->task_id = task.id; + slot->infill = task.infill_mode; + slot->embedding = task.embedding_mode; + slot->task_id = task.id; slot->multitask_id = task.multitask_id; if (!launch_slot_with_data(slot, task.data)) @@ -1731,7 +1731,8 @@ struct llama_server_context const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()) || !slot.images.empty(); // empty prompt passed -> release the slot and send empty response - if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt) + // note: infill mode allows empty prompt + if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill) { slot.release(); slot.print_timings(); @@ -2609,8 +2610,8 @@ static json format_final_response_oaicompat(const json &request, const task_resu {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, {"usage", json{{"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, + {"prompt_tokens", num_prompt_tokens}, + {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, {"id", gen_chatcmplid()}}; if (server_verbose) { From 326b418b59b6d48d854c4461a2303e8ac0a311e6 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 12 Jan 2024 06:59:57 +0100 Subject: [PATCH 386/664] Importance Matrix calculation (#4861) * imatrix: 1st version * imatrix: WIP * Cleanup * Update examples/imatrix/imatrix.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- Makefile | 5 +- examples/CMakeLists.txt | 1 + examples/imatrix/CMakeLists.txt | 5 + examples/imatrix/imatrix.cpp | 380 ++++++++++++++++++++++++++++++++ ggml.c | 14 ++ ggml.h | 6 + 6 files changed, 410 insertions(+), 1 deletion(-) create mode 100644 examples/imatrix/CMakeLists.txt create mode 100644 examples/imatrix/imatrix.cpp diff --git a/Makefile b/Makefile index 4c7e175bf..05fe9a0f6 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,6 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ - main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ + main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o @@ -614,6 +614,9 @@ quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml. perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) +imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 0c71cbdf7..fa127a3aa 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -36,6 +36,7 @@ else() add_subdirectory(lookahead) add_subdirectory(lookup) add_subdirectory(train-text-from-scratch) + add_subdirectory(imatrix) if (LLAMA_METAL) add_subdirectory(metal) endif() diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt new file mode 100644 index 000000000..d688a1620 --- /dev/null +++ b/examples/imatrix/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET imatrix) +add_executable(${TARGET} imatrix.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp new file mode 100644 index 000000000..1461bc963 --- /dev/null +++ b/examples/imatrix/imatrix.cpp @@ -0,0 +1,380 @@ +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +struct Stats { + std::vector values; + int ncall = 0; +}; + +struct StatParams { + std::string ofile = "imatrix.dat"; + int n_output_frequency = 10; + int verbosity = 1; + bool collect_output_weight = false; +}; + +class IMatrixCollector { +public: + IMatrixCollector() = default; + void set_parameters(StatParams&& params) { m_params = std::move(params); } + void collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1); + void save_imatrix() const; +private: + std::unordered_map m_stats; + StatParams m_params; + std::mutex m_mutex; + int m_last_call = 0; +}; + +void IMatrixCollector::collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) { + if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return; + if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return; + std::lock_guard lock(m_mutex); + auto& e = m_stats[src0->name]; + if (e.values.empty()) { + e.values.resize(src1->ne[0], 0); + } + else if (e.values.size() != (size_t)src1->ne[0]) { + fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]); + exit(1); //GGML_ASSERT(false); + } + ++e.ncall; + if (m_params.verbosity > 1) { + printf("%s[%d]: %s, %d x %d, %d\n",__func__,m_last_call,src0->name,(int)src1->ne[0],(int)src1->ne[1],(int)src1->type); + } + for (int row = 0; row < (int)src1->ne[1]; ++row) { + const float * x = (const float *)src1->data + row * src1->ne[0]; + for (int j = 0; j < (int)src1->ne[0]; ++j) { + e.values[j] += x[j]*x[j]; + } + } + if (e.ncall > m_last_call) { + m_last_call = e.ncall; + if (m_last_call % m_params.n_output_frequency == 0) { + save_imatrix(); + } + } +} + +void IMatrixCollector::save_imatrix() const { + const char * fname = m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(); + std::ofstream out(fname, std::ios::binary); + int n_entries = m_stats.size(); + out.write((const char*)&n_entries, sizeof(n_entries)); + for (auto& p : m_stats) { + int len = p.first.size(); + out.write((const char*)&len, sizeof(len)); + out.write(p.first.c_str(), len); + out.write((const char*)&p.second.ncall, sizeof(p.second.ncall)); + int nval = p.second.values.size(); + out.write((const char*)&nval, sizeof(nval)); + if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float)); + } + if (m_params.verbosity > 0) { + fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname); + } +} + +static IMatrixCollector g_collector; + +static void ik_collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) { + g_collector.collect_imatrix(src0, src1); +} + + +struct results_log_softmax { + double log_softmax; + float logit; + float prob; +}; + +static std::vector softmax(const std::vector& logits) { + std::vector probs(logits.size()); + float max_logit = logits[0]; + for (float v : logits) { + max_logit = std::max(max_logit, v); + } + double sum_exp = 0.0; + for (size_t i = 0; i < logits.size(); i++) { + // Subtract the maximum logit value from the current logit value for numerical stability + const float logit = logits[i] - max_logit; + const float exp_logit = expf(logit); + sum_exp += exp_logit; + probs[i] = exp_logit; + } + for (size_t i = 0; i < probs.size(); i++) { + probs[i] /= sum_exp; + } + return probs; +} + +static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { + float max_logit = logits[0]; + for (int i = 1; i < n_vocab; ++i) { + max_logit = std::max(max_logit, logits[i]); + } + double sum_exp = 0.0; + for (int i = 0; i < n_vocab; ++i) { + sum_exp += expf(logits[i] - max_logit); + } + return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp}; +} + +static void process_logits( + int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, + double & nll, double & nll2, float * logit_history, float * prob_history +) { + std::mutex mutex; + int counter = 0; + auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { + double local_nll = 0; + double local_nll2 = 0; + while (true) { + std::unique_lock lock(mutex); + int i = counter++; + if (i >= n_token) { + nll += local_nll; nll2 += local_nll2; + break; + } + lock.unlock(); + const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]); + const double v = -results.log_softmax; + local_nll += v; + local_nll2 += v*v; + + logit_history[i] = results.logit; + prob_history[i] = results.prob; + } + }; + for (auto & w : workers) { + w = std::thread(compute); + } + compute(); + for (auto & w : workers) { + w.join(); + } +} + +static bool compute_imatrix(llama_context * ctx, const gpt_params & params) { + + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const int n_ctx = llama_n_ctx(ctx); + + auto tim1 = std::chrono::high_resolution_clock::now(); + fprintf(stderr, "%s: tokenizing the input ..\n", __func__); + + std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + + auto tim2 = std::chrono::high_resolution_clock::now(); + fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + + if (int(tokens.size()) < 2*n_ctx) { + fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx, + n_ctx); + fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + return false; + } + + std::vector logit_history; + logit_history.resize(tokens.size()); + + std::vector prob_history; + prob_history.resize(tokens.size()); + + const int n_chunk_max = tokens.size() / n_ctx; + + const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); + const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_batch = params.n_batch; + + int count = 0; + double nll = 0.0; + double nll2 = 0.0; + + fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch); + + std::vector workers(std::thread::hardware_concurrency() - 1); + + for (int i = 0; i < n_chunk; ++i) { + const int start = i * n_ctx; + const int end = start + n_ctx; + + const int num_batches = (n_ctx + n_batch - 1) / n_batch; + + std::vector logits; + + const auto t_start = std::chrono::high_resolution_clock::now(); + + // clear the KV cache + llama_kv_cache_clear(ctx); + + for (int j = 0; j < num_batches; ++j) { + const int batch_start = start + j * n_batch; + const int batch_size = std::min(end - batch_start, n_batch); + + // save original token and restore it after eval + const auto token_org = tokens[batch_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + } + + if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + + // restore the original token in case it was set to BOS + tokens[batch_start] = token_org; + + const auto * batch_logits = llama_get_logits(ctx); + logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + } + + const auto t_end = std::chrono::high_resolution_clock::now(); + + if (i == 0) { + const float t_total = std::chrono::duration(t_end - t_start).count(); + fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total * n_chunk); + if (total_seconds >= 60*60) { + fprintf(stderr, "%d hours ", total_seconds / (60*60)); + total_seconds = total_seconds % (60*60); + } + fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); + } + + const int first = n_ctx/2; + process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, + workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); + count += n_ctx - first - 1; + + printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); + fflush(stdout); + } + printf("\n"); + + nll2 /= count; + nll /= count; + const double ppl = exp(nll); + nll2 -= nll * nll; + if (nll2 > 0) { + nll2 = sqrt(nll2/(count-1)); + printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); + } else { + printf("Unexpected negative standard deviation of log(prob)\n"); + } + + return true; +} + +int main(int argc, char ** argv) { + + StatParams sparams; + std::vector args; + args.push_back(argv[0]); + int iarg = 1; + for (; iarg < argc-1; ++iarg) { + std::string arg{argv[iarg]}; + if (arg == "-o" || arg == "--output-file") { + sparams.ofile = argv[++iarg]; + } + else if (arg == "-ofreq" || arg == "--output-frequency") { + sparams.n_output_frequency = std::stoi(argv[++iarg]); + } + else if (arg == "-ow" || arg == "--output-weight") { + sparams.collect_output_weight = std::stoi(argv[++iarg]); + } + else if (arg == "--verbosity") { + sparams.verbosity = std::stoi(argv[++iarg]); + } else { + args.push_back(argv[iarg]); + } + } + if (iarg < argc) { + args.push_back(argv[iarg]); + } + + gpt_params params; + params.n_batch = 512; + if (!gpt_params_parse(args.size(), args.data(), params)) { + return 1; + } + + g_collector.set_parameters(std::move(sparams)); + + ggml_set_imatrix_collection(ik_collect_imatrix); + + params.logits_all = true; + params.n_batch = std::min(params.n_batch, params.n_ctx); + + print_build_info(); + + if (params.seed == LLAMA_DEFAULT_SEED) { + params.seed = time(NULL); + } + + fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); + + std::mt19937 rng(params.seed); + if (params.random_prompt) { + params.prompt = gpt_random_prompt(rng); + } + + llama_backend_init(params.numa); + + llama_model * model; + llama_context * ctx; + + // load the model and apply lora adapter, if any + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == NULL) { + fprintf(stderr, "%s: error: unable to load model\n", __func__); + return 1; + } + + const int n_ctx_train = llama_n_ctx_train(model); + if (params.n_ctx > n_ctx_train) { + fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, params.n_ctx); + } + + // print system information + { + fprintf(stderr, "\n"); + fprintf(stderr, "%s\n", get_system_info(params).c_str()); + } + + bool OK = compute_imatrix(ctx, params); + if (!OK) { + return 1; + } + + g_collector.save_imatrix(); + + llama_print_timings(ctx); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + return 0; +} diff --git a/ggml.c b/ggml.c index d2a8c0478..f5caeba08 100644 --- a/ggml.c +++ b/ggml.c @@ -394,6 +394,12 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y); static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y); +ggml_collect_imatrix_t g_imatrix_collect = NULL; + +void ggml_set_imatrix_collection(ggml_collect_imatrix_t imatrix_collect) { + g_imatrix_collect = imatrix_collect; +} + static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { [GGML_TYPE_I8] = { .type_name = "i8", @@ -9763,6 +9769,10 @@ static void ggml_compute_forward_mul_mat( const int ith = params->ith; const int nth = params->nth; + if (ith == 1 && g_imatrix_collect) { + g_imatrix_collect(src0, src1); + } + const enum ggml_type type = src0->type; const bool src1_cont = ggml_is_contiguous(src1); @@ -10066,6 +10076,10 @@ static void ggml_compute_forward_mul_mat_id( const struct ggml_tensor * src0_cur = dst->src[cur_a + 2]; + if (ith == 1 && g_imatrix_collect) { + g_imatrix_collect(src0_cur, src1); + } + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); diff --git a/ggml.h b/ggml.h index 93b42a27d..4c2ff6c66 100644 --- a/ggml.h +++ b/ggml.h @@ -2067,6 +2067,12 @@ extern "C" { GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist); + // + // Importance matrix + // + typedef void(*ggml_collect_imatrix_t)(const struct ggml_tensor * src0, const struct ggml_tensor * src1); + GGML_API void ggml_set_imatrix_collection(ggml_collect_imatrix_t imatrix_collect); + // // gguf // From f445c0e68cf8e1faca0b2aa8dfb9d48231cec301 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 13:01:56 +0200 Subject: [PATCH 387/664] llama : fix llm_build_k_shift to use correct n_rot (#4889) * llama : fix llm_build_k_shift to use correct n_rot ggml-ci * llama : always use hparams.n_rot for ggml_rope_custom ggml-ci * convert : fix persimmon conversion to write correct n_rot --- common/common.cpp | 3 ++ convert-hf-to-gguf.py | 9 ++++- gguf-py/gguf/tensor_mapping.py | 7 ++++ llama.cpp | 65 +++++++++++++++++----------------- 4 files changed, 51 insertions(+), 33 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index b2cb0e257..3aefed01d 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1055,6 +1055,9 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & } static ggml_type kv_cache_type_from_str(const std::string & s) { + if (s == "f32") { + return GGML_TYPE_F32; + } if (s == "f16") { return GGML_TYPE_F16; } diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 203eaf64b..813aeeed6 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -817,10 +817,17 @@ class PersimmonModel(Model): hidden_size = self.hparams["hidden_size"] self.gguf_writer.add_name('persimmon-8b-chat') + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hidden_size) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + + # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller + # than the head size? + # ref: https://github.com/ggerganov/llama.cpp/pull/4889 + #self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) + self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 80c1d5449..24a089037 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -57,6 +57,7 @@ class TensorNameMap: "transformer.norm_f", # mpt "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon + "model.final_layernorm", # persimmon "lm_head.ln", # phi2 ), @@ -98,6 +99,7 @@ class TensorNameMap: "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + "model.layers.{bid}.self_attn.query_key_value", # persimmon "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 ), @@ -141,6 +143,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "model.layers.{bid}.self_attn.dense", # persimmon "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo @@ -184,6 +187,7 @@ class TensorNameMap: "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon + "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 @@ -225,6 +229,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo @@ -237,10 +242,12 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", + "model.layers.{bid}.self_attn.q_layernorm", # persimmon ), MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", + "model.layers.{bid}.self_attn.k_layernorm", # persimmon ), MODEL_TENSOR.ROPE_FREQS: ( diff --git a/llama.cpp b/llama.cpp index d39ff94c7..0bab95563 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4104,7 +4104,6 @@ static void llm_build_k_shift( struct ggml_cgraph * graph, llm_rope_type type, int64_t n_ctx, - int n_rot, float freq_base, float freq_scale, const llm_build_cb & cb) { @@ -4112,14 +4111,13 @@ static void llm_build_k_shift( const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head_k = hparams.n_embd_head_k; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int32_t n_rot = hparams.n_rot; const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; const float ext_factor = cparams.yarn_ext_factor; const float attn_factor = cparams.yarn_attn_factor; const float beta_fast = cparams.yarn_beta_fast; const float beta_slow = cparams.yarn_beta_slow; - GGML_ASSERT(n_embd_head_k % n_rot == 0); - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx); cb(K_shift, "K_shift", -1); @@ -4523,7 +4521,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4561,14 +4559,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -4691,6 +4689,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4708,7 +4707,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4734,12 +4733,12 @@ struct llm_build_context { case MODEL_7B: Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); break; @@ -4812,6 +4811,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4829,7 +4829,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4870,13 +4870,13 @@ struct llm_build_context { // using mode = 2 for neox mode Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -5033,9 +5033,8 @@ struct llm_build_context { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - - const int64_t n_rot = n_embd_head_k / 2; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head/2 == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5052,7 +5051,7 @@ struct llm_build_context { cb(KQ_mask, "KQ_mask", -1); if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5112,7 +5111,7 @@ struct llm_build_context { // RoPE the first n_rot of q/k, pass the other half, and concat. struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, + ctx0, tmpq, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpq) * n_embd_head, ggml_element_size(tmpq) * n_embd_head * n_head, 0 @@ -5120,7 +5119,7 @@ struct llm_build_context { cb(qrot, "qrot", il); struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, + ctx0, tmpk, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, 0 @@ -5129,29 +5128,29 @@ struct llm_build_context { // get the second half of tmpq, e.g tmpq[n_rot:, :, :] struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, + ctx0, tmpq, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpq) * n_embd_head, ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * n_rot + ggml_element_size(tmpq) * hparams.n_rot ); cb(qpass, "qpass", il); struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, + ctx0, tmpk, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * n_rot + ggml_element_size(tmpk) * hparams.n_rot ); cb(kpass, "kpass", il); struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx, + ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(qrotated, "qrotated", il); struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx, + ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(krotated, "krotated", il); @@ -5531,6 +5530,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5548,7 +5548,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5661,7 +5661,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5693,13 +5693,13 @@ struct llm_build_context { // using mode = 2 for neox mode Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -5778,7 +5778,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5874,6 +5874,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5891,7 +5892,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5917,13 +5918,13 @@ struct llm_build_context { cb(Vcur, "Vcur", il); Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + ctx0, ggml_reshape_3d(ctx0, Qcur, hparams.n_rot, n_head, n_tokens), inp_pos, n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + ctx0, ggml_reshape_3d(ctx0, Kcur, hparams.n_rot, n_head_kv, n_tokens), inp_pos, n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); From 2d00741e12c5db4a33dfccd1125f5de4adec9a5b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 13:03:38 +0200 Subject: [PATCH 388/664] py : fix lint (#4889) --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 813aeeed6..a1c79fd47 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -825,7 +825,7 @@ class PersimmonModel(Model): # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller # than the head size? # ref: https://github.com/ggerganov/llama.cpp/pull/4889 - #self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) self.gguf_writer.add_head_count(head_count) From 4315a94366708828f949f9db89d2a8d99b634459 Mon Sep 17 00:00:00 2001 From: howlger Date: Fri, 12 Jan 2024 12:05:32 +0100 Subject: [PATCH 389/664] common : streamline the formatting of help (#4890) * common : streamline the formatting of help - Separate alternative parameters by a comma - Do not indent `--version` differently * Update common/common.cpp --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 3aefed01d..062a8b4de 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -818,7 +818,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf("\n"); printf("options:\n"); printf(" -h, --help show this help message and exit\n"); - printf(" --version show version and build info\n"); + printf(" --version show version and build info\n"); printf(" -i, --interactive run in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); @@ -915,7 +915,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" number of layers to store in VRAM\n"); printf(" -ngld N, --n-gpu-layers-draft N\n"); printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" -ts SPLIT, --tensor-split SPLIT\n"); printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); #ifdef GGML_USE_CUBLAS @@ -950,7 +950,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -stc N --print-token-count N\n"); + printf(" -ptc N, --print-token-count N\n"); printf(" print token count every N tokens (default: %d)\n", params.n_print); printf("\n"); #ifndef LOG_DISABLE_LOGS From 3cabe80630c7eeb57713cd02249053a8cf6894fa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 13:10:19 +0200 Subject: [PATCH 390/664] llama : fix typo "imp_embd" -> "inp_embd" --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 0bab95563..29f8873f6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5040,7 +5040,7 @@ struct llm_build_context { struct ggml_tensor * inpL; inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); - cb(inpL, "imp_embd", -1); + cb(inpL, "inp_embd", -1); // inp_pos - contains the positions struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); From 1b280c9fffd682b6924010a4437f0275f2921fa9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 12 Jan 2024 12:30:41 +0100 Subject: [PATCH 391/664] CUDA: fix softmax compile for old CUDA versions (#4862) --- ggml-cuda.cu | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index dd19699f6..a345b0c4a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -116,6 +116,8 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) + #define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 @@ -605,16 +607,16 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { } static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) - (void) a; - bad_arch(); -#else +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); } return a; -#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#else + (void) a; + bad_arch(); +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } static __device__ __forceinline__ float warp_reduce_max(float x) { @@ -626,16 +628,16 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { } static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) - (void) x; - bad_arch(); -#else +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); } return x; -#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#else + (void) x; + bad_arch(); +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX } static __device__ __forceinline__ float op_repeat(const float a, const float b) { @@ -5613,7 +5615,7 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int template static __global__ void soft_max_f16(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX const int ncols_data = ncols_template == 0 ? ncols_par : ncols_template; const int ncols_smem = GGML_PAD(ncols_data, 2*WARP_SIZE)/2; @@ -5738,7 +5740,7 @@ static __global__ void soft_max_f16(const float * x, const float * y, float * ds #else (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale; bad_arch(); -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX } template @@ -8574,15 +8576,15 @@ static void ggml_cuda_op_soft_max( float scale = 1.0f; memcpy(&scale, dst->op_params, sizeof(float)); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - const bool use_f16_soft_max = false; -#else +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION >= CUDART_HMAX #ifdef GGML_CUDA_F16 const bool use_f16_soft_max = true; #else const bool use_f16_soft_max = false; #endif // GGML_CUDA_F16 -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#else + const bool use_f16_soft_max = false; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && CUDART_VERSION >= CUDART_HMAX if (use_f16_soft_max) { soft_max_f16_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); From 5537d9d36bfdb4379555431f574d3d78ce6e7955 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 14:33:21 +0200 Subject: [PATCH 392/664] gitignore : imatrix --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index cf1b692e9..fba207045 100644 --- a/.gitignore +++ b/.gitignore @@ -43,6 +43,7 @@ models-mnt /embedding /gguf /gguf-llama-simple +/imatrix /infill /libllama.so /llama-bench From e790eef21ce659f5c16d59f8a5c8dcf6cde0692a Mon Sep 17 00:00:00 2001 From: Zay <95888118+isaiahbjork@users.noreply.github.com> Date: Fri, 12 Jan 2024 05:48:00 -0700 Subject: [PATCH 393/664] llama.swiftui : update models layout (#4826) * Updated Models Layout - Added a models drawer - Added downloading directly from Hugging Face - Load custom models from local folder - Delete models by swiping left * trimmed trailing white space * Updated Models Layout --- .../llama.swiftui.xcodeproj/project.pbxproj | 8 +- .../llama.swiftui/Models/LlamaState.swift | 89 ++++++++ .../llama.swiftui/UI/ContentView.swift | 213 +++++++++--------- .../llama.swiftui/UI/DownloadButton.swift | 2 + .../llama.swiftui/UI/InputButton.swift | 131 +++++++++++ 5 files changed, 338 insertions(+), 105 deletions(-) create mode 100644 examples/llama.swiftui/llama.swiftui/UI/InputButton.swift diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index a8848a49f..3950b9e9d 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -8,6 +8,7 @@ /* Begin PBXBuildFile section */ 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; }; + 79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; }; 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; }; 8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; }; 8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; }; @@ -22,6 +23,7 @@ /* Begin PBXFileReference section */ 549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; }; + 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = InputButton.swift; sourceTree = ""; }; 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = ""; }; 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; }; 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = ""; }; @@ -119,6 +121,7 @@ 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */, 8A1C83782AC328BD0096AF73 /* ContentView.swift */, F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */, + 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */, ); path = UI; sourceTree = ""; @@ -213,6 +216,7 @@ 8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */, 8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */, 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */, + 79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -345,7 +349,7 @@ CLANG_ENABLE_MODULES = YES; CODE_SIGN_STYLE = Automatic; CURRENT_PROJECT_VERSION = 1; - DEVELOPMENT_TEAM = STLSG3FG8Q; + DEVELOPMENT_TEAM = K5UQJPP73A; ENABLE_PREVIEWS = YES; GENERATE_INFOPLIST_FILE = YES; INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES; @@ -377,7 +381,7 @@ CLANG_ENABLE_MODULES = YES; CODE_SIGN_STYLE = Automatic; CURRENT_PROJECT_VERSION = 1; - DEVELOPMENT_TEAM = STLSG3FG8Q; + DEVELOPMENT_TEAM = K5UQJPP73A; ENABLE_PREVIEWS = YES; GENERATE_INFOPLIST_FILE = YES; INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES; diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift index 17cb5b9dd..5bde18917 100644 --- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift +++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift @@ -1,9 +1,19 @@ import Foundation +struct Model: Identifiable { + var id = UUID() + var name: String + var url: String + var filename: String + var status: String? +} + @MainActor class LlamaState: ObservableObject { @Published var messageLog = "" @Published var cacheCleared = false + @Published var downloadedModels: [Model] = [] + @Published var undownloadedModels: [Model] = [] let NS_PER_S = 1_000_000_000.0 private var llamaContext: LlamaContext? @@ -13,23 +23,102 @@ class LlamaState: ObservableObject { } init() { + loadModelsFromDisk() + loadDefaultModels() + } + + private func loadModelsFromDisk() { + do { + let documentsURL = getDocumentsDirectory() + let modelURLs = try FileManager.default.contentsOfDirectory(at: documentsURL, includingPropertiesForKeys: nil, options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants]) + for modelURL in modelURLs { + let modelName = modelURL.deletingPathExtension().lastPathComponent + downloadedModels.append(Model(name: modelName, url: "", filename: modelURL.lastPathComponent, status: "downloaded")) + } + } catch { + print("Error loading models from disk: \(error)") + } + } + + private func loadDefaultModels() { do { try loadModel(modelUrl: defaultModelUrl) } catch { messageLog += "Error!\n" } + + for model in defaultModels { + let fileURL = getDocumentsDirectory().appendingPathComponent(model.filename) + if FileManager.default.fileExists(atPath: fileURL.path) { + + } else { + var undownloadedModel = model + undownloadedModel.status = "download" + undownloadedModels.append(undownloadedModel) + } + } } + func getDocumentsDirectory() -> URL { + let paths = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask) + return paths[0] + } + private let defaultModels: [Model] = [ + Model(name: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",url: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf", status: "download"), + Model( + name: "TinyLlama-1.1B Chat (Q8_0, 1.1 GiB)", + url: "https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q8_0.gguf?download=true", + filename: "tinyllama-1.1b-chat-v1.0.Q8_0.gguf", status: "download" + ), + + Model( + name: "TinyLlama-1.1B (F16, 2.2 GiB)", + url: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", + filename: "tinyllama-1.1b-f16.gguf", status: "download" + ), + + Model( + name: "Phi-2.7B (Q4_0, 1.6 GiB)", + url: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true", + filename: "phi-2-q4_0.gguf", status: "download" + ), + + Model( + name: "Phi-2.7B (Q8_0, 2.8 GiB)", + url: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true", + filename: "phi-2-q8_0.gguf", status: "download" + ), + + Model( + name: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)", + url: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true", + filename: "mistral-7b-v0.1.Q4_0.gguf", status: "download" + ), + Model( + name: "OpenHermes-2.5-Mistral-7B (Q3_K_M, 3.52 GiB)", + url: "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q3_K_M.gguf?download=true", + filename: "openhermes-2.5-mistral-7b.Q3_K_M.gguf", status: "download" + ) + ] func loadModel(modelUrl: URL?) throws { if let modelUrl { messageLog += "Loading model...\n" llamaContext = try LlamaContext.create_context(path: modelUrl.path()) messageLog += "Loaded model \(modelUrl.lastPathComponent)\n" + + // Assuming that the model is successfully loaded, update the downloaded models + updateDownloadedModels(modelName: modelUrl.lastPathComponent, status: "downloaded") } else { messageLog += "Load a model from the list below\n" } } + + private func updateDownloadedModels(modelName: String, status: String) { + undownloadedModels.removeAll { $0.name == modelName } + } + + func complete(text: String) async { guard let llamaContext else { return diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index 7c81ea256..30c2dc431 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -2,115 +2,57 @@ import SwiftUI struct ContentView: View { @StateObject var llamaState = LlamaState() - @State private var multiLineText = "" - - private static func cleanupModelCaches() { - // Delete all models (*.gguf) - let fileManager = FileManager.default - let documentsUrl = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0] - do { - let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil) - for fileURL in fileURLs { - if fileURL.pathExtension == "gguf" { - try fileManager.removeItem(at: fileURL) - } - } - } catch { - print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)") - } - } + @State private var showingHelp = false // To track if Help Sheet should be shown var body: some View { - VStack { - ScrollView(.vertical, showsIndicators: true) { - Text(llamaState.messageLog) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) + NavigationView { + VStack { + ScrollView(.vertical, showsIndicators: true) { + Text(llamaState.messageLog) + .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) + .padding() + .onTapGesture { + UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil) + } + } + + TextEditor(text: $multiLineText) + .frame(height: 80) + .padding() + .border(Color.gray, width: 0.5) + + HStack { + Button("Send") { + sendText() + } + + Button("Bench") { + bench() + } + + Button("Clear") { + clear() + } + + Button("Copy") { + UIPasteboard.general.string = llamaState.messageLog + } + } + .buttonStyle(.bordered) .padding() - .onTapGesture { - UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil) - } - } - TextEditor(text: $multiLineText) - .frame(height: 80) + NavigationLink(destination: DrawerView(llamaState: llamaState)) { + Text("View Models") + } .padding() - .border(Color.gray, width: 0.5) - HStack { - Button("Send") { - sendText() - } - - Button("Bench") { - bench() - } - - Button("Clear") { - clear() - } - - Button("Copy") { - UIPasteboard.general.string = llamaState.messageLog - } - }.buttonStyle(.bordered) - - VStack(alignment: .leading) { - DownloadButton( - llamaState: llamaState, - modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)", - modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true", - filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "TinyLlama-1.1B (Q8_0, 1.1 GiB)", - modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true", - filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "TinyLlama-1.1B (F16, 2.2 GiB)", - modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", - filename: "tinyllama-1.1b-f16.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "Phi-2.7B (Q4_0, 1.6 GiB)", - modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true", - filename: "phi-2-q4_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "Phi-2.7B (Q8_0, 2.8 GiB)", - modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true", - filename: "phi-2-q8_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)", - modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true", - filename: "mistral-7b-v0.1.Q4_0.gguf" - ) - - Button("Clear downloaded models") { - ContentView.cleanupModelCaches() - llamaState.cacheCleared = true - } - - LoadCustomButton(llamaState: llamaState) } - .padding(.top, 4) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) + .padding() + .navigationBarTitle("Model Settings", displayMode: .inline) + } - .padding() } func sendText() { @@ -131,8 +73,73 @@ struct ContentView: View { await llamaState.clear() } } + struct DrawerView: View { + + @ObservedObject var llamaState: LlamaState + @State private var showingHelp = false + func delete(at offsets: IndexSet) { + offsets.forEach { offset in + let model = llamaState.downloadedModels[offset] + let fileURL = getDocumentsDirectory().appendingPathComponent(model.filename) + do { + try FileManager.default.removeItem(at: fileURL) + } catch { + print("Error deleting file: \(error)") + } + } + + // Remove models from downloadedModels array + llamaState.downloadedModels.remove(atOffsets: offsets) + } + + func getDocumentsDirectory() -> URL { + let paths = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask) + return paths[0] + } + var body: some View { + List { + Section(header: Text("Download Models From Hugging Face")) { + HStack { + InputButton(llamaState: llamaState) + } + } + Section(header: Text("Downloaded Models")) { + ForEach(llamaState.downloadedModels) { model in + DownloadButton(llamaState: llamaState, modelName: model.name, modelUrl: model.url, filename: model.filename) + } + .onDelete(perform: delete) + } + Section(header: Text("Default Models")) { + ForEach(llamaState.undownloadedModels) { model in + DownloadButton(llamaState: llamaState, modelName: model.name, modelUrl: model.url, filename: model.filename) + } + } + + } + .listStyle(GroupedListStyle()) + .navigationBarTitle("Model Settings", displayMode: .inline).toolbar { + ToolbarItem(placement: .navigationBarTrailing) { + Button("Help") { + showingHelp = true + } + } + }.sheet(isPresented: $showingHelp) { // Sheet for help modal + VStack(alignment: .leading) { + VStack(alignment: .leading) { + Text("1. Make sure the model is in GGUF Format") + .padding() + Text("2. Copy the download link of the quantized model") + .padding() + } + Spacer() + } + } + } + } } -//#Preview { -// ContentView() -//} +struct ContentView_Previews: PreviewProvider { + static var previews: some View { + ContentView() + } +} diff --git a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift index c9f322ca1..4584d6eaa 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift @@ -53,6 +53,8 @@ struct DownloadButton: View { llamaState.cacheCleared = false + let model = Model(name: modelName, url: modelUrl, filename: filename, status: "downloaded") + llamaState.downloadedModels.append(model) status = "downloaded" } } catch let err { diff --git a/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift b/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift new file mode 100644 index 000000000..c5ffbad4e --- /dev/null +++ b/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift @@ -0,0 +1,131 @@ +import SwiftUI + +struct InputButton: View { + @ObservedObject var llamaState: LlamaState + @State private var inputLink: String = "" + @State private var status: String = "download" + @State private var filename: String = "" + + @State private var downloadTask: URLSessionDownloadTask? + @State private var progress = 0.0 + @State private var observation: NSKeyValueObservation? + + private static func extractModelInfo(from link: String) -> (modelName: String, filename: String)? { + guard let url = URL(string: link), + let lastPathComponent = url.lastPathComponent.components(separatedBy: ".").first, + let modelName = lastPathComponent.components(separatedBy: "-").dropLast().joined(separator: "-").removingPercentEncoding, + let filename = lastPathComponent.removingPercentEncoding else { + return nil + } + + return (modelName, filename) + } + + private static func getFileURL(filename: String) -> URL { + FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename) + } + + private func download() { + guard let extractedInfo = InputButton.extractModelInfo(from: inputLink) else { + // Handle invalid link or extraction failure + return + } + + let (modelName, filename) = extractedInfo + self.filename = filename // Set the state variable + + status = "downloading" + print("Downloading model \(modelName) from \(inputLink)") + guard let url = URL(string: inputLink) else { return } + let fileURL = InputButton.getFileURL(filename: filename) + + downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in + if let error = error { + print("Error: \(error.localizedDescription)") + return + } + + guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else { + print("Server error!") + return + } + + do { + if let temporaryURL = temporaryURL { + try FileManager.default.copyItem(at: temporaryURL, to: fileURL) + print("Writing to \(filename) completed") + + llamaState.cacheCleared = false + + let model = Model(name: modelName, url: self.inputLink, filename: filename, status: "downloaded") + llamaState.downloadedModels.append(model) + status = "downloaded" + } + } catch let err { + print("Error: \(err.localizedDescription)") + } + } + + observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in + self.progress = progress.fractionCompleted + } + + downloadTask?.resume() + } + + var body: some View { + VStack { + HStack { + TextField("Paste Quantized Download Link", text: $inputLink) + .textFieldStyle(RoundedBorderTextFieldStyle()) + + Button(action: { + downloadTask?.cancel() + status = "download" + }) { + Text("Cancel") + } + } + + if status == "download" { + Button(action: download) { + Text("Download Custom Model") + } + } else if status == "downloading" { + Button(action: { + downloadTask?.cancel() + status = "download" + }) { + Text("Downloading \(Int(progress * 100))%") + } + } else if status == "downloaded" { + Button(action: { + let fileURL = InputButton.getFileURL(filename: self.filename) + if !FileManager.default.fileExists(atPath: fileURL.path) { + download() + return + } + do { + try llamaState.loadModel(modelUrl: fileURL) + } catch let err { + print("Error: \(err.localizedDescription)") + } + }) { + Text("Load Custom Model") + } + } else { + Text("Unknown status") + } + } + .onDisappear() { + downloadTask?.cancel() + } + .onChange(of: llamaState.cacheCleared) { newValue in + if newValue { + downloadTask?.cancel() + let fileURL = InputButton.getFileURL(filename: self.filename) + status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download" + } + } + } +} From 930f907d3ece1eb5b0a1ec5e209983a66dcbfa68 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 12 Jan 2024 18:54:53 +0100 Subject: [PATCH 394/664] export-lora : use LLAMA_FILE_MAGIC_GGLA (#4894) This commit replaces the magic number used in export-lora.cpp with the one defined in llama.h, which is indirectly included via common.h. Signed-off-by: Daniel Bevenius --- examples/export-lora/export-lora.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 58fbe204d..4cd5d99bb 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -245,9 +245,8 @@ static struct lora_data * load_lora(struct lora_info * info) { params_ggml.no_alloc = true; result->ctx = ggml_init(params_ggml); - uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla' uint32_t magic = file.read_u32(); - if (magic != LLAMA_FILE_MAGIC_LORA) { + if (magic != LLAMA_FILE_MAGIC_GGLA) { die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str()); } uint32_t version = file.read_u32(); From 584d674be622fbf1578694ada6e62eebedbfd377 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 20:54:12 +0200 Subject: [PATCH 395/664] llama : remove redundant assert for StableLM (#4901) --- llama.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 29f8873f6..ce413f605 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5530,7 +5530,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; From e7e4df031b9e29d4b55a4e0b0295187f6b213db1 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 12 Jan 2024 20:07:38 +0100 Subject: [PATCH 396/664] llama : ggml-backend integration (#4766) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov Co-authored-by: Johannes Gäßler --- common/common.cpp | 65 +- common/common.h | 1 + examples/batched-bench/batched-bench.cpp | 3 + examples/llama-bench/llama-bench.cpp | 146 +- examples/server/server.cpp | 40 +- ggml-alloc.c | 34 +- ggml-alloc.h | 4 +- ggml-backend-impl.h | 38 +- ggml-backend.c | 693 ++++--- ggml-backend.h | 60 +- ggml-cuda.cu | 907 +++++---- ggml-cuda.h | 26 +- ggml-impl.h | 2 + ggml-metal.m | 55 +- ggml-opencl.cpp | 335 +++- ggml-opencl.h | 16 +- ggml.c | 30 +- ggml.h | 9 +- llama.cpp | 2320 +++++++++------------- llama.h | 18 +- tests/test-backend-ops.cpp | 26 +- 21 files changed, 2533 insertions(+), 2295 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 062a8b4de..322b9f91e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -543,9 +543,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD params.n_gpu_layers = std::stoi(argv[i]); -#else +#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); #endif @@ -554,9 +553,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD params.n_gpu_layers_draft = std::stoi(argv[i]); -#else +#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); #endif @@ -565,25 +563,44 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifdef GGML_USE_CUBLAS params.main_gpu = std::stoi(argv[i]); -#else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n"); -#endif +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the main GPU has no effect.\n"); +#endif // GGML_USE_CUBLAS + } else if (arg == "--split-mode" || arg == "-sm") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string arg_next = argv[i]; + if (arg_next == "none") { + params.split_mode = LLAMA_SPLIT_NONE; + } else if (arg_next == "layer") { + params.split_mode = LLAMA_SPLIT_LAYER; + } else if (arg_next == "row") { + params.split_mode = LLAMA_SPLIT_ROW; + } else { + invalid_param = true; + break; + } +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUBLAS } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; break; } -#ifdef GGML_USE_CUBLAS std::string arg_next = argv[i]; // split string by , and / const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); - + if (split_arg.size() >= LLAMA_MAX_DEVICES) { + invalid_param = true; + break; + } for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { if (i < split_arg.size()) { params.tensor_split[i] = std::stof(split_arg[i]); @@ -591,14 +608,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.tensor_split[i] = 0.0f; } } -#else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n"); -#endif // GGML_USE_CUBLAS - } else if (arg == "--no-mul-mat-q" || arg == "-nommq") { -#ifdef GGML_USE_CUBLAS - params.mul_mat_q = false; -#else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n"); +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS } else if (arg == "--no-mmap") { params.use_mmap = false; @@ -915,14 +926,15 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" number of layers to store in VRAM\n"); printf(" -ngld N, --n-gpu-layers-draft N\n"); printf(" number of layers to store in VRAM for the draft model\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); -#ifdef GGML_USE_CUBLAS - printf(" -nommq, --no-mul-mat-q\n"); - printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n"); - printf(" Not recommended since this is both slower and uses more VRAM.\n"); -#endif // GGML_USE_CUBLAS + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); #endif printf(" -gan N, --grp-attn-n N\n"); printf(" group-attention factor (default: %d)\n", params.grp_attn_n); @@ -1041,6 +1053,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & mparams.n_gpu_layers = params.n_gpu_layers; } mparams.main_gpu = params.main_gpu; + mparams.split_mode = params.split_mode; mparams.tensor_split = params.tensor_split; mparams.use_mmap = params.use_mmap; mparams.use_mlock = params.use_mlock; diff --git a/common/common.h b/common/common.h index 1359e76ab..f29be5b5a 100644 --- a/common/common.h +++ b/common/common.h @@ -59,6 +59,7 @@ struct gpt_params { float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_beams = 0; // if non-zero then use beam search of given width. diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 57596ed98..7924db267 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -88,7 +88,10 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); + const std::vector t_split (LLAMA_MAX_DEVICES, 0.0f); + model_params.n_gpu_layers = n_gpu_layers; + model_params.tensor_split = t_split.data(); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 7f7186cde..97325b5bd 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -128,6 +128,25 @@ static std::string get_gpu_info() { // command line params enum output_formats {CSV, JSON, MARKDOWN, SQL}; +static const char * output_format_str(output_formats format) { + switch (format) { + case CSV: return "csv"; + case JSON: return "json"; + case MARKDOWN: return "md"; + case SQL: return "sql"; + default: GGML_ASSERT(!"invalid output format"); + } +} + +static const char * split_mode_str(llama_split_mode mode) { + switch (mode) { + case LLAMA_SPLIT_NONE: return "none"; + case LLAMA_SPLIT_LAYER: return "layer"; + case LLAMA_SPLIT_ROW: return "row"; + default: GGML_ASSERT(!"invalid split mode"); + } +} + struct cmd_params { std::vector model; std::vector n_prompt; @@ -137,6 +156,7 @@ struct cmd_params { std::vector type_v; std::vector n_threads; std::vector n_gpu_layers; + std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; std::vector mul_mat_q; @@ -155,6 +175,7 @@ static const cmd_params cmd_params_defaults = { /* type_v */ {GGML_TYPE_F16}, /* n_threads */ {get_num_physical_cores()}, /* n_gpu_layers */ {99}, + /* split_mode */ {LLAMA_SPLIT_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, @@ -169,21 +190,22 @@ static void print_usage(int /* argc */, char ** argv) { printf("\n"); printf("options:\n"); printf(" -h, --help\n"); - printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); - printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ctk , --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv , --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); - printf(" -ts, --tensor_split \n"); - printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); - printf(" -o, --output (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql"); - printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ctk , --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv , --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); + printf(" -ts, --tensor_split (default: 0)\n"); + printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); + printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); + printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); printf("\n"); printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); } @@ -306,6 +328,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (arg == "-sm" || arg == "--split-mode") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + std::vector modes; + for (const auto & m : p) { + llama_split_mode mode; + if (m == "none") { + mode = LLAMA_SPLIT_NONE; + } else if (m == "layer") { + mode = LLAMA_SPLIT_LAYER; + } else if (m == "row") { + mode = LLAMA_SPLIT_ROW; + } else { + invalid_param = true; + break; + } + modes.push_back(mode); + } + params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); } else if (arg == "-mg" || arg == "--main-gpu") { if (++i >= argc) { invalid_param = true; @@ -392,6 +436,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } + if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } @@ -410,6 +455,7 @@ struct cmd_params_instance { ggml_type type_v; int n_threads; int n_gpu_layers; + llama_split_mode split_mode; int main_gpu; bool no_kv_offload; bool mul_mat_q; @@ -419,6 +465,7 @@ struct cmd_params_instance { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; + mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); @@ -428,6 +475,7 @@ struct cmd_params_instance { bool equal_mparams(const cmd_params_instance & other) const { return model == other.model && n_gpu_layers == other.n_gpu_layers && + split_mode == other.split_mode && main_gpu == other.main_gpu && tensor_split == other.tensor_split; } @@ -446,45 +494,13 @@ struct cmd_params_instance { } }; -static std::vector get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) { - std::vector instances; - - for (const auto & m : params.model) - for (const auto & nl : params.n_gpu_layers) - for (const auto & mg : params.main_gpu) - for (const auto & ts : params.tensor_split) - for (const auto & nb : params.n_batch) - for (const auto & tk : params.type_k) - for (const auto & tv : params.type_v) - for (const auto & mmq : params.mul_mat_q) - for (const auto & nkvo : params.no_kv_offload) - for (const auto & nt : params.n_threads) { - cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ n_prompt, - /* .n_gen = */ n_gen, - /* .n_batch = */ nb, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .n_gpu_layers = */ nl, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, - /* .tensor_split = */ ts, - }; - instances.push_back(instance); - } - return instances; -} - static std::vector get_cmd_params_instances(const cmd_params & params) { std::vector instances; -#if 1 // this ordering minimizes the number of times that each model needs to be reloaded for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) + for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) for (const auto & nb : params.n_batch) @@ -506,6 +522,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, @@ -527,6 +544,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, @@ -535,24 +553,6 @@ static std::vector get_cmd_params_instances(const cmd_param instances.push_back(instance); } } -#else - // this ordering separates the prompt and generation tests - for (const auto & n_prompt : params.n_prompt) { - if (n_prompt == 0) { - continue; - } - auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt); - instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end()); - } - - for (const auto & n_gen : params.n_gen) { - if (n_gen == 0) { - continue; - } - auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0); - instances.insert(instances.end(), instances_gen.begin(), instances_gen.end()); - } -#endif return instances; } @@ -576,6 +576,7 @@ struct test { ggml_type type_k; ggml_type type_v; int n_gpu_layers; + llama_split_mode split_mode; int main_gpu; bool no_kv_offload; bool mul_mat_q; @@ -597,6 +598,7 @@ struct test { type_k = inst.type_k; type_v = inst.type_v; n_gpu_layers = inst.n_gpu_layers; + split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; mul_mat_q = inst.mul_mat_q; @@ -660,7 +662,8 @@ struct test { "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", - "n_gpu_layers", "main_gpu", "no_kv_offload", + "n_gpu_layers", "split_mode", + "main_gpu", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", @@ -711,7 +714,8 @@ struct test { cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), - std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload), + std::to_string(n_gpu_layers), split_mode_str(split_mode), + std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(mul_mat_q), tensor_split_str, std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), @@ -867,6 +871,9 @@ struct markdown_printer : public printer { if (field == "n_gpu_layers") { return "ngl"; } + if (field == "split_mode") { + return "sm"; + } if (field == "n_threads") { return "threads"; } @@ -907,6 +914,9 @@ struct markdown_printer : public printer { if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) { fields.push_back("main_gpu"); } + if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { + fields.push_back("split_mode"); + } if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { fields.push_back("mul_mat_q"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1d30a15a6..c1ab8f9dc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2005,12 +2005,15 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); printf(" number of layers to store in VRAM\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); - printf(" -nommq, --no-mul-mat-q\n"); - printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n"); - printf(" Not recommended since this is both slower and uses more VRAM.\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); #endif printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); @@ -2253,6 +2256,33 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); #endif + } + else if (arg == "--split-mode" || arg == "-sm") + { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string arg_next = argv[i]; + if (arg_next == "none") + { + params.split_mode = LLAMA_SPLIT_NONE; + } + else if (arg_next == "layer") + { + params.split_mode = LLAMA_SPLIT_LAYER; + } + else if (arg_next == "row") + { + params.split_mode = LLAMA_SPLIT_ROW; + } + else { + invalid_param = true; + break; + } +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUBLAS } else if (arg == "--tensor-split" || arg == "-ts") { diff --git a/ggml-alloc.c b/ggml-alloc.c index a27dd54b0..89b85d348 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -102,8 +102,6 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { } } - AT_PRINTF("block %d\n", best_fit_block); - if (best_fit_block == -1) { // the last block is our last resort struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1]; @@ -117,6 +115,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { return; } } + struct free_block * block = &alloc->free_blocks[best_fit_block]; void * addr = block->addr; block->addr = (char*)block->addr + size; @@ -129,6 +128,8 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { } } + AT_PRINTF("block %d, addr %p\n", best_fit_block, addr); + tensor->data = addr; tensor->buffer = alloc->buffer; if (!alloc->measure) { @@ -229,6 +230,7 @@ void ggml_tallocr_reset(ggml_tallocr_t alloc) { alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows } else { alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; + ggml_backend_buffer_reset(alloc->buffer); } } @@ -263,9 +265,9 @@ ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) { return alloc; } -ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { +ggml_tallocr_t ggml_tallocr_new_measure_from_buft(struct ggml_backend_buffer_type * buft) { // create a backend buffer to get the correct tensor allocation sizes - ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, 1); + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, 1); // TODO: move alloc initialization to a common ggml_tallocr_new_impl function ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); @@ -275,13 +277,22 @@ ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backe return alloc; } -ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { - ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, size); +ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { + return ggml_tallocr_new_measure_from_buft(ggml_backend_get_default_buffer_type(backend)); +} + +ggml_tallocr_t ggml_tallocr_new_from_buft(struct ggml_backend_buffer_type * buft, size_t size) { + // create a backend buffer to get the correct tensor allocation sizes + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); alloc->buffer_owned = true; return alloc; } +ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { + return ggml_tallocr_new_from_buft(ggml_backend_get_default_buffer_type(backend), size); +} + ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) { ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); @@ -779,10 +790,21 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte if (nbytes == 0) { // all the tensors in the context are already allocated +#ifndef NDEBUG + fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__); +#endif return NULL; } ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes); + if (buffer == NULL) { + // failed to allocate buffer +#ifndef NDEBUG + fprintf(stderr, "%s: failed to allocate buffer\n", __func__); +#endif + return NULL; + } + ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer); for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { diff --git a/ggml-alloc.h b/ggml-alloc.h index 64a412468..4e5997521 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -52,8 +52,10 @@ typedef struct ggml_tallocr * ggml_tallocr_t; GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment); GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment); -GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_tallocr_t ggml_tallocr_new_from_buft(struct ggml_backend_buffer_type * buft, size_t size); GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer +GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_buft(struct ggml_backend_buffer_type * buft); GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend); GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc); diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index ca21b4743..1db32901f 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -16,9 +16,10 @@ extern "C" { typedef void * ggml_backend_buffer_type_context_t; struct ggml_backend_buffer_type_i { + const char * (*get_name) (ggml_backend_buffer_type_t buft); ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment - size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding + size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend // check if tensor data is in host memory // should be equivalent to supports_backend(buft, ggml_backend_cpu_init()) @@ -34,16 +35,15 @@ extern "C" { typedef void * ggml_backend_buffer_context_t; struct ggml_backend_buffer_i { - void (*free_buffer) (ggml_backend_buffer_t buffer); - //void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras - void * (*get_base) (ggml_backend_buffer_t buffer); - void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - // (optional) copy tensor between different buffer-type, allow for single-copy tranfers - void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); + const char * (*get_name) (ggml_backend_buffer_t buffer); + void (*free_buffer)(ggml_backend_buffer_t buffer); + void * (*get_base) (ggml_backend_buffer_t buffer); + void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer + void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); + void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras }; struct ggml_backend_buffer { @@ -51,6 +51,7 @@ extern "C" { ggml_backend_buffer_type_t buft; ggml_backend_buffer_context_t context; size_t size; + enum ggml_backend_buffer_usage usage; }; ggml_backend_buffer_t ggml_backend_buffer_init( @@ -59,6 +60,8 @@ extern "C" { ggml_backend_buffer_context_t context, size_t size); + // do not use directly, use ggml_backend_tensor_copy instead + bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst); // // Backend @@ -74,22 +77,20 @@ extern "C" { // buffer allocation ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend); - // (optional) asynchroneous tensor data access + // (optional) asynchronous tensor data access void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); - // (optional) asynchroneous tensor copy - void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - + // (optional) complete all pending operations void (*synchronize)(ggml_backend_t backend); // compute graph with a plan - ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); + ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - // compute graph without a plan + // compute graph without a plan (async) bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation @@ -102,7 +103,6 @@ extern "C" { ggml_backend_context_t context; }; - // // Backend registry // diff --git a/ggml-backend.c b/ggml-backend.c index 53e741cb8..4c2d8b0b2 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -15,6 +15,10 @@ // backend buffer type +const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name(buft); +} + ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { return buft->iface.alloc_buffer(buft, size); } @@ -58,11 +62,16 @@ ggml_backend_buffer_t ggml_backend_buffer_init( /* .buft = */ buft, /* .context = */ context, /* .size = */ size, + /* .usage = */ GGML_BACKEND_BUFFER_USAGE_ANY }; return buffer; } +const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name(buffer); +} + void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { if (buffer == NULL) { return; @@ -94,11 +103,11 @@ void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_t } size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) { - return ggml_backend_buft_get_alignment(ggml_backend_buffer_type(buffer)); + return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer)); } size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { - return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type(buffer), tensor); + return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); } void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -106,13 +115,31 @@ void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { } bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { - return ggml_backend_buft_is_host(ggml_backend_buffer_type(buffer)); + return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer)); } -ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer) { +void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { + buffer->usage = usage; +} + +ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) { return buffer->buft; } +void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) { + if (buffer->iface.reset) { + buffer->iface.reset(buffer); + } +} + +bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) { + ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer; + if (dst_buf->iface.cpy_tensor) { + return src->buffer->iface.cpy_tensor(dst_buf, src, dst); + } + return false; +} + // backend const char * ggml_backend_name(ggml_backend_t backend) { @@ -146,30 +173,42 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - backend->iface.set_tensor_async(backend, tensor, data, offset, size); + if (backend->iface.set_tensor_async == NULL) { + ggml_backend_tensor_set(tensor, data, offset, size); + } else { + backend->iface.set_tensor_async(backend, tensor, data, offset, size); + } } void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - backend->iface.get_tensor_async(backend, tensor, data, offset, size); + if (backend->iface.get_tensor_async == NULL) { + ggml_backend_tensor_get(tensor, data, offset, size); + } else { + backend->iface.get_tensor_async(backend, tensor, data, offset, size); + } } void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set"); + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - tensor->buffer->iface.set_tensor(tensor->buffer, tensor, data, offset, size); + tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size); } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - tensor->buffer->iface.get_tensor(tensor->buffer, tensor, data, offset, size); + tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size); } void ggml_backend_synchronize(ggml_backend_t backend) { @@ -190,19 +229,10 @@ void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_pla void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { backend->iface.graph_plan_compute(backend, plan); - - // TODO: optional sync - ggml_backend_synchronize(backend); } bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - if (!backend->iface.graph_compute(backend, cgraph)) { - return false; - } - - // TODO: optional sync - ggml_backend_synchronize(backend); - return true; + return backend->iface.graph_compute(backend, cgraph); } bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -227,28 +257,20 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml } void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) { - //printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]); - //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]); GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); - // fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); - if (src == dst) { return; } - // TODO: allow backends to support copy to/from same backend - - if (dst->buffer->iface.cpy_tensor_from != NULL) { - dst->buffer->iface.cpy_tensor_from(dst->buffer, src, dst); - } else if (src->buffer->iface.cpy_tensor_to != NULL) { - src->buffer->iface.cpy_tensor_to(src->buffer, src, dst); - } else { - // shouldn't be hit when copying from/to CPU - #ifndef NDEBUG - fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to " - "are implemented for %s and %s, falling back to get/set\n", src->name, dst->name); - #endif + if (ggml_backend_buffer_is_host(src->buffer)) { + ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); + } else if (ggml_backend_buffer_is_host(dst->buffer)) { + ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); + } else if (!ggml_backend_buffer_copy_tensor(src, dst)) { +#ifndef NDEBUG + fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer)); +#endif size_t nbytes = ggml_nbytes(src); void * data = malloc(nbytes); ggml_backend_tensor_get(src, data, 0, nbytes); @@ -257,6 +279,31 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst } } +void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { + GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); + + if (src == dst) { + return; + } + + if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) { + if (backend->iface.cpy_tensor_async != NULL) { + if (backend->iface.cpy_tensor_async(backend, src, dst)) { + return; + } + } + } + + size_t nbytes = ggml_nbytes(src); + if (ggml_backend_buffer_is_host(src->buffer)) { + ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes); + } + else { + ggml_backend_tensor_copy(src, dst); + } +} + + // backend registry #define GGML_MAX_BACKENDS_REG 16 @@ -392,6 +439,12 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) { // backend CPU +static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { + return "CPU"; + + GGML_UNUSED(buffer); +} + static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { return (void *)buffer->context; } @@ -412,14 +465,12 @@ static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, con GGML_UNUSED(buffer); } -static void ggml_backend_cpu_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); +static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; GGML_UNUSED(buffer); } @@ -429,30 +480,38 @@ static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static struct ggml_backend_buffer_i cpu_backend_buffer_i = { + /* .get_name = */ ggml_backend_cpu_buffer_name, /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, /* .get_base = */ ggml_backend_cpu_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_cpu_buffer_cpy_tensor_to, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, }; // for buffers from ptr, free is not called static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { + /* .get_name = */ ggml_backend_cpu_buffer_name, /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed /* .get_base = */ ggml_backend_cpu_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_cpu_buffer_cpy_tensor_to, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, }; static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 +static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU"; + + GGML_UNUSED(buft); +} + static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? @@ -483,6 +542,7 @@ static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes @@ -501,6 +561,18 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { #include +static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_HBM"; + + GGML_UNUSED(buft); +} + +static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { + return "CPU_HBM"; + + GGML_UNUSED(buf); +} + static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { hbw_free(buffer->context); } @@ -514,17 +586,18 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_ return NULL; } - // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name; buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; return buffer; } -ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type() { +ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes @@ -568,7 +641,7 @@ struct ggml_backend_plan_cpu { struct ggml_cgraph cgraph; }; -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); @@ -634,8 +707,7 @@ static struct ggml_backend_i cpu_backend_i = { /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, + /* .cpy_tensor_async = */ NULL, /* .synchronize = */ NULL, /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, @@ -661,7 +733,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { } bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_cpu_name; + return backend && backend->iface.get_name == ggml_backend_cpu_name; } void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { @@ -685,7 +757,7 @@ static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user // scheduler -#define GGML_MAX_BACKENDS 4 +#define GGML_MAX_BACKENDS 16 #define GGML_MAX_SPLITS 256 #define GGML_MAX_SPLIT_INPUTS 16 @@ -695,21 +767,29 @@ struct ggml_backend_sched_split { int i_end; struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; int n_inputs; + // graph view of this split struct ggml_cgraph graph; }; struct ggml_backend_sched { + bool is_reset; // true if the scheduler has been reset since the last graph split + int n_backends; ggml_backend_t backends[GGML_MAX_BACKENDS]; + ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS]; ggml_tallocr_t tallocs[GGML_MAX_BACKENDS]; ggml_gallocr_t galloc; + // hash keys of the nodes in the graph struct ggml_hash_set hash_set; - ggml_tallocr_t * node_talloc; // [hash_set.size] - struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS] + // hash values (arrays of [hash_set.size]) + ggml_tallocr_t * node_talloc; // tallocr assigned to each node (indirectly this is the backend) + struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // copies of each node for each destination backend + // copy of the graph with modified inputs struct ggml_cgraph * graph; + struct ggml_backend_sched_split splits[GGML_MAX_SPLITS]; int n_splits; @@ -750,14 +830,22 @@ static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) return INT_MAX; } -static ggml_backend_t get_buffer_backend(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { +static ggml_tallocr_t sched_allocr_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { if (buffer == NULL) { return NULL; } + + // check if this is already allocate in a allocr buffer (from user manual allocations) + for (int i = 0; i < sched->n_backends; i++) { + if (ggml_tallocr_get_buffer(sched->tallocs[i]) == buffer) { + return sched->tallocs[i]; + } + } + // find highest prio backend that supports the buffer type for (int i = 0; i < sched->n_backends; i++) { if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) { - return sched->backends[i]; + return sched->tallocs[i]; } } GGML_ASSERT(false && "tensor buffer type not supported by any backend"); @@ -767,7 +855,6 @@ static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_talloc if (allocr == NULL) { return NULL; } - // find highest prio backend that supports the buffer type for (int i = 0; i < sched->n_backends; i++) { if (sched->tallocs[i] == allocr) { return sched->backends[i]; @@ -777,7 +864,7 @@ static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_talloc } #if 0 -static char causes[GGML_DEFAULT_GRAPH_SIZE*8 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove +static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) #define GET_CAUSE(node) causes[hash_id(node)] #else @@ -786,45 +873,37 @@ static char causes[GGML_DEFAULT_GRAPH_SIZE*8 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_IN #endif // returns the backend that should be used for the node based on the current locations -static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { - // if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there - // ie. kv cache updates - // note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend. +static ggml_tallocr_t sched_allocr_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { + // assign pre-allocated nodes to their backend // dst - ggml_backend_t cur_backend = get_buffer_backend(sched, node->buffer); - if (cur_backend != NULL) { + ggml_tallocr_t cur_allocr = sched_allocr_from_buffer(sched, node->buffer); + if (cur_allocr != NULL) { SET_CAUSE(node, "1.dst"); - return cur_backend; + return cur_allocr; } - // view_src - if (node->view_src != NULL && get_buffer_backend(sched, node->view_src->buffer) != NULL) { - SET_CAUSE(node, "1.vsrc"); - return get_buffer_backend(sched, node->view_src->buffer); + if (node->view_src != NULL) { + cur_allocr = sched_allocr_from_buffer(sched, node->view_src->buffer); + if (cur_allocr != NULL) { + SET_CAUSE(node, "1.vsrc"); + return cur_allocr; + } } - - // src - int cur_prio = INT_MAX; - size_t cur_size = 0; - + // assign nodes that use weights to the backend of the weights for (int i = 0; i < GGML_MAX_SRC; i++) { const struct ggml_tensor * src = node->src[i]; if (src == NULL) { break; } - ggml_backend_t src_backend = get_buffer_backend(sched, src->buffer); - if (src_backend != NULL) { - int src_prio = sched_backend_prio(sched, src_backend); - size_t src_size = ggml_nbytes(src); - if (src_prio < cur_prio && src_size >= cur_size) { - cur_prio = src_prio; - cur_size = src_size; - cur_backend = src_backend; - SET_CAUSE(node, "1.src%d", i); - } + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + ggml_tallocr_t src_allocr = sched_allocr_from_buffer(sched, src->buffer); + // operations with weights are always run on the same backend as the weights + SET_CAUSE(node, "1.wgt%d", i); + return src_allocr; } } - return cur_backend; + + return NULL; } static char * fmt_size(size_t size) { @@ -857,7 +936,7 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra } ggml_tallocr_t node_allocr = node_allocr(node); ggml_backend_t node_backend = node_allocr ? get_allocr_backend(sched, node_allocr) : NULL; // FIXME: - fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name, + fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", GET_CAUSE(node)); for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; @@ -866,7 +945,7 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra } ggml_tallocr_t src_allocr = node_allocr(src); ggml_backend_t src_backend = src_allocr ? get_allocr_backend(sched, src_allocr) : NULL; - fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name, + fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } fprintf(stderr, "\n"); @@ -882,15 +961,17 @@ static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, co return dup; } + +//#define DEBUG_PASS1 +//#define DEBUG_PASS2 +//#define DEBUG_PASS3 +//#define DEBUG_PASS4 + // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend -// TODO: merge passes static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - // reset state - size_t hash_size = sched->hash_set.size; - memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); - memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); - memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); + // reset splits sched->n_splits = 0; + sched->is_reset = false; struct ggml_init_params params = { /* .mem_size = */ sizeof(sched->context_buffer), @@ -898,26 +979,22 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g /* .no_alloc = */ true }; - if (sched->ctx != NULL) { - ggml_free(sched->ctx); - } + ggml_free(sched->ctx); sched->ctx = ggml_init(params); + if (sched->ctx == NULL) { + fprintf(stderr, "%s: failed to initialize context\n", __func__); + GGML_ASSERT(false); + } - // pass 1: assign backends to ops with allocated inputs + // pass 1: assign backends to ops with pre-allocated inputs for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; if (node_allocr(leaf) != NULL) { // do not overwrite user assignments continue; } - ggml_backend_t leaf_backend = get_buffer_backend(sched, leaf->buffer); - if (leaf_backend == NULL && leaf->view_src != NULL) { - leaf_backend = get_buffer_backend(sched, leaf->view_src->buffer); - } - if (leaf_backend != NULL) { - node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend); - } + node_allocr(leaf) = sched_allocr_from_cur(sched, leaf); } for (int i = 0; i < graph->n_nodes; i++) { @@ -926,50 +1003,102 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // do not overwrite user assignments continue; } - ggml_backend_t node_backend = sched_backend_from_cur(sched, node); - if (node_backend != NULL) { - node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend); + node_allocr(node) = sched_allocr_from_cur(sched, node); + // src + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + if (node_allocr(src) == NULL) { + node_allocr(src) = sched_allocr_from_cur(sched, src); + } } } - //printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#ifdef DEBUG_PASS1 + fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif - // pass 2: assign backends to ops from current assignments - // TODO: - // - reuse sched_backend_from_cur - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr == NULL) { - int cur_prio = INT_MAX; - size_t cur_size = 0; - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - break; - } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != NULL) { - int src_prio = sched_allocr_prio(sched, src_allocr); - size_t src_size = ggml_nbytes(src); - if (src_prio < cur_prio && src_size >= cur_size) { - cur_prio = src_prio; - cur_size = src_size; - node_allocr = src_allocr; - SET_CAUSE(node, "2.src%d", j); - } - } + // pass 2: expand current backend assignments + // assign the same backend to adjacent nodes + // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend) + // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops + + // pass 2.1 expand gpu up + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = graph->n_nodes - 1; i >= 0; i--) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; } + ggml_tallocr_t node_allocr = node_allocr(node); if (node_allocr != NULL) { - node_allocr(node) = node_allocr; + if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) { + // skip cpu (lowest prio backend) + cur_allocr = NULL; + } else { + cur_allocr = node_allocr; + } + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.1"); } } } - //printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); - // pass 3: assign backends to remaining src from dst (should only be leafs) + // pass 2.2 expand gpu down + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr != NULL) { + if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) { + // skip cpu (lowest prio backend) + cur_allocr = NULL; + } else { + cur_allocr = node_allocr; + } + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.2"); + } + } + } + + // pass 2.3 expand rest up + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = graph->n_nodes - 1; i >= 0; i--) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr != NULL) { + cur_allocr = node_allocr; + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.3"); + } + } + } +#ifdef DEBUG_PASS2 + fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif + + // pass 3: assign backends to remaining src from dst and view_src for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); + ggml_tallocr_t cur_allocr = node_allocr(node); + if (node->view_src != NULL && cur_allocr == NULL) { + cur_allocr = node_allocr(node) = node_allocr(node->view_src); + SET_CAUSE(node, "3.vsrc"); + } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { @@ -977,81 +1106,105 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g } ggml_tallocr_t src_allocr = node_allocr(src); if (src_allocr == NULL) { - node_allocr(src) = node_allocr; + if (src->view_src != NULL) { + // views are always on the same backend as the source + node_allocr(src) = node_allocr(src->view_src); + SET_CAUSE(src, "3.vsrc"); + } else { + node_allocr(src) = cur_allocr; + SET_CAUSE(src, "3.cur"); + } } } } - //printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#ifdef DEBUG_PASS3 + fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif // pass 4: split graph, find tensors that need to be copied - // TODO: - // - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost - // find first backend - int cur_split = 0; - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - if (node->view_src == NULL) { - sched->splits[0].tallocr = node_allocr(node); - break; - } - } - sched->splits[0].i_start = 0; - sched->splits[0].n_inputs = 0; - memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK - ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; - size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - - if (ggml_is_view_op(node->op)) { - continue; - } - - ggml_tallocr_t node_allocr = node_allocr(node); - - if (node_allocr != cur_allocr) { - sched->splits[cur_split].i_end = i; - cur_split++; - GGML_ASSERT(cur_split < GGML_MAX_SPLITS); - sched->splits[cur_split].tallocr = node_allocr; - sched->splits[cur_split].i_start = i; - sched->splits[cur_split].n_inputs = 0; - memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK - cur_allocr = node_allocr; - cur_backend_id = sched_allocr_prio(sched, cur_allocr); - } - - // find inputs that are not on the same backend - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { + { + int cur_split = 0; + // find the backend of the first split, skipping view ops + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (!ggml_is_view_op(node->op)) { + sched->splits[0].tallocr = node_allocr(node); break; } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != node_allocr) { - int n_inputs = sched->splits[cur_split].n_inputs++; - GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); - sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src; + } + sched->splits[0].i_start = 0; + sched->splits[0].n_inputs = 0; + memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK + ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; + size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; - // create copies - size_t id = hash_id(src); - if (sched->node_copies[id][cur_backend_id] == NULL) { - struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); - sched->node_copies[id][cur_backend_id] = tensor_copy; - node_allocr(tensor_copy) = cur_allocr; - ggml_backend_t backend = get_allocr_backend(sched, cur_allocr); - ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + if (ggml_is_view_op(node->op)) { + continue; + } + + ggml_tallocr_t node_allocr = node_allocr(node); + + if (node_allocr != cur_allocr) { + sched->splits[cur_split].i_end = i; + cur_split++; + GGML_ASSERT(cur_split < GGML_MAX_SPLITS); + sched->splits[cur_split].tallocr = node_allocr; + sched->splits[cur_split].i_start = i; + sched->splits[cur_split].n_inputs = 0; + cur_allocr = node_allocr; + cur_backend_id = sched_allocr_prio(sched, cur_allocr); + } + + // find inputs that are not on the same backend + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + GGML_ASSERT(src_allocr != NULL); // all inputs should be assigned by now + if (src_allocr != node_allocr) { + // check if the input is already in the split + bool found = false; + for (int k = 0; k < sched->splits[cur_split].n_inputs; k++) { + if (sched->splits[cur_split].inputs[k] == src) { + found = true; + break; + } + } + + if (!found) { + int n_inputs = sched->splits[cur_split].n_inputs++; + //printf("split %d input %d: %s (%s)\n", cur_split, n_inputs, src->name, ggml_backend_name(get_allocr_backend(sched, src_allocr))); + GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); + sched->splits[cur_split].inputs[n_inputs] = src; + } + + // create a copy of the input in the split's backend + size_t id = hash_id(src); + if (sched->node_copies[id][cur_backend_id] == NULL) { + ggml_backend_t backend = get_allocr_backend(sched, cur_allocr); + struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + + sched->node_copies[id][cur_backend_id] = tensor_copy; + node_allocr(tensor_copy) = cur_allocr; + SET_CAUSE(tensor_copy, "4.cpy"); + } + node->src[j] = sched->node_copies[id][cur_backend_id]; } - node->src[j] = sched->node_copies[id][cur_backend_id]; } } + sched->splits[cur_split].i_end = graph->n_nodes; + sched->n_splits = cur_split + 1; } - sched->splits[cur_split].i_end = graph->n_nodes; - sched->n_splits = cur_split + 1; +#ifdef DEBUG_PASS4 + fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif - //fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout); - -#if 1 +#ifndef NDEBUG // sanity check: all sources should have the same backend as the node for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -1059,6 +1212,11 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g if (node_allocr == NULL) { fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); } + if (node->view_src != NULL && node_allocr != node_allocr(node->view_src)) { + fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n", + node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL", + node->view_src->name, node_allocr(node->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(node->view_src))) : "NULL"); + } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { @@ -1070,8 +1228,14 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL", j, src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL"); } + if (src->view_src != NULL && src_allocr != node_allocr(src->view_src)) { + fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n", + src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL", + src->view_src->name, node_allocr(src->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(src->view_src))) : "NULL"); + } } } + fflush(stderr); #endif // create copies of the graph for each split @@ -1085,6 +1249,8 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)]; + // add a dependency to the input source so that it is not freed before the copy is done + GGML_ASSERT(input_cpy->src[0] == NULL || input_cpy->src[0] == input); input_cpy->src[0] = input; graph_copy->nodes[graph_copy->n_nodes++] = input_cpy; } @@ -1119,24 +1285,16 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { uint64_t copy_start_us = ggml_time_us(); for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_backend_prio(sched, split_backend)]; - if (input->buffer == NULL) { - if (input->view_src == NULL) { - fprintf(stderr, "input %s has no buffer and no view_src\n", input->name); - exit(1); - } - // FIXME: may need to use the sched buffer instead - ggml_backend_view_init(input->view_src->buffer, input); - } - if (input_cpy->buffer == NULL) { - fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name); - exit(1); - } - //GGML_ASSERT(input->buffer->backend != input_cpy->buffer->backend); - //GGML_ASSERT(input_cpy->buffer->backend == split_backend); - ggml_backend_tensor_copy(input, input_cpy); + struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][split_backend_id]; + + GGML_ASSERT(input->buffer != NULL); + GGML_ASSERT(input_cpy->buffer != NULL); + + // TODO: avoid this copy if it was already copied in a previous split, and the input didn't change + // this is important to avoid copying constants such as KQ_mask and inp_pos multiple times + ggml_backend_tensor_copy_async(split_backend, input, input_cpy); } - // ggml_backend_synchronize(split_backend); + //ggml_backend_synchronize(split_backend); // necessary to measure copy time int64_t copy_end_us = ggml_time_us(); copy_us[split_backend_id] += copy_end_us - copy_start_us; @@ -1148,7 +1306,7 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { uint64_t compute_start_us = ggml_time_us(); ggml_backend_graph_compute(split_backend, &split->graph); - // ggml_backend_synchronize(split_backend); + //ggml_backend_synchronize(split_backend); // necessary to measure compute time uint64_t compute_end_us = ggml_time_us(); compute_us[split_backend_id] += compute_end_us - compute_start_us; } @@ -1168,26 +1326,41 @@ static void sched_reset(ggml_backend_sched_t sched) { for (int i = 0; i < sched->n_backends; i++) { ggml_tallocr_reset(sched->tallocs[i]); } + // reset state for the next run + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); + memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); + memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); + + sched->is_reset = true; } -ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) { +ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { + GGML_ASSERT(n_backends > 0); GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS); - struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched)); - memset(sched, 0, sizeof(struct ggml_backend_sched)); + struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); + + // initialize hash table + sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + sched->node_talloc = calloc(sizeof(sched->node_talloc[0]) * sched->hash_set.size, 1); + sched->node_copies = calloc(sizeof(sched->node_copies[0]) * sched->hash_set.size, 1); sched->n_backends = n_backends; for (int i = 0; i < n_backends; i++) { sched->backends[i] = backends[i]; + sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]); } sched->galloc = ggml_gallocr_new(); // init measure allocs for each backend for (int i = 0; i < n_backends; i++) { - sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]); + sched->tallocs[i] = ggml_tallocr_new_measure_from_buft(sched->bufts[i]); } + sched_reset(sched); + return sched; } @@ -1199,6 +1372,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { ggml_tallocr_free(sched->tallocs[i]); } ggml_gallocr_free(sched->galloc); + ggml_free(sched->ctx); free(sched->hash_set.keys); free(sched->node_talloc); free(sched->node_copies); @@ -1206,12 +1380,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { } void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { - // initialize hash tables - size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS; - sched->hash_set.size = hash_size; - sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size); - sched->node_talloc = malloc(sizeof(sched->node_talloc[0]) * hash_size); - sched->node_copies = malloc(sizeof(sched->node_copies[0]) * hash_size); + GGML_ASSERT(ggml_tallocr_is_measure(sched->tallocs[0])); // can only be initialized once sched_split_graph(sched, measure_graph); sched_alloc_splits(sched); @@ -1220,28 +1389,41 @@ void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgr for (int i = 0; i < sched->n_backends; i++) { size_t size = ggml_tallocr_max_size(sched->tallocs[i]); ggml_tallocr_free(sched->tallocs[i]); - sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size); + sched->tallocs[i] = ggml_tallocr_new_from_buft(sched->bufts[i], size); } sched_reset(sched); } void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + + if (!sched->is_reset) { + sched_reset(sched); + } sched_split_graph(sched, graph); sched_alloc_splits(sched); sched_compute_splits(sched); +} + +void ggml_backend_sched_reset(ggml_backend_sched_t sched) { sched_reset(sched); } +int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) { + return sched->n_splits; +} + ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) { int backend_index = sched_backend_prio(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); return sched->tallocs[backend_index]; } ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) { int backend_index = sched_backend_prio(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); return ggml_tallocr_get_buffer(sched->tallocs[backend_index]); } @@ -1251,10 +1433,19 @@ void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml node_allocr(node) = sched->tallocs[backend_index]; } +ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { + ggml_tallocr_t allocr = node_allocr(node); + if (allocr == NULL) { + return NULL; + } + return get_allocr_backend(sched, allocr); +} + // utils + void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { GGML_ASSERT(tensor->buffer == NULL); - //GGML_ASSERT(tensor->data == NULL); // views of pre-allocted tensors may have the data set, but still need to be initialized + //GGML_ASSERT(tensor->data == NULL); // views of pre-allocated tensors may have the data set in ggml_new_tensor, but still need to be initialized by the backend GGML_ASSERT(tensor->view_src != NULL); GGML_ASSERT(tensor->view_src->buffer != NULL); GGML_ASSERT(tensor->view_src->data != NULL); @@ -1320,6 +1511,7 @@ static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor struct ggml_tensor * dst = node_copies[id]; if (dst->view_src != NULL) { + graph_init_tensor(hash_set, node_copies, node_init, src->view_src); ggml_backend_view_init(dst->view_src->buffer, dst); } else { @@ -1353,6 +1545,21 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s struct ggml_context * ctx_allocated = ggml_init(params); struct ggml_context * ctx_unallocated = ggml_init(params); + if (ctx_allocated == NULL || ctx_unallocated == NULL) { + fprintf(stderr, "failed to allocate context for graph copy\n"); + free(hash_set.keys); + free(node_copies); + free(node_init); + ggml_free(ctx_allocated); + ggml_free(ctx_unallocated); + return (struct ggml_backend_graph_copy) { + /* .buffer = */ NULL, + /* .ctx_allocated = */ NULL, + /* .ctx_unallocated = */ NULL, + /* .graph = */ NULL, + }; + } + // dup nodes for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -1361,6 +1568,20 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s // allocate nodes ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend); + if (buffer == NULL) { + fprintf(stderr, "failed to allocate buffer for graph copy\n"); + free(hash_set.keys); + free(node_copies); + free(node_init); + ggml_free(ctx_allocated); + ggml_free(ctx_unallocated); + return (struct ggml_backend_graph_copy) { + /* .buffer = */ NULL, + /* .ctx_allocated = */ NULL, + /* .ctx_unallocated = */ NULL, + /* .graph = */ NULL, + }; + } //printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024); @@ -1397,8 +1618,12 @@ void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) { ggml_free(copy.ctx_unallocated); } -void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) { +bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) { struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph); + if (copy.buffer == NULL) { + return false; + } + struct ggml_cgraph * g1 = graph; struct ggml_cgraph * g2 = copy.graph; @@ -1428,4 +1653,6 @@ void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t } ggml_backend_graph_copy_free(copy); + + return true; } diff --git a/ggml-backend.h b/ggml-backend.h index 85ff67b0e..4eb244af1 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -17,22 +17,31 @@ extern "C" { // // buffer type - GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size); - GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); - GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); - GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); - GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); + GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); + GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); + GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); + GGML_API size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); + GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); + GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); // buffer - GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); - GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); - GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); - GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer); + enum ggml_backend_buffer_usage { + GGML_BACKEND_BUFFER_USAGE_ANY = 0, + GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1, + }; + + GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); + GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); + GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer); // // Backend @@ -140,23 +149,24 @@ extern "C" { typedef struct ggml_backend_sched * ggml_backend_sched_t; // Initialize a backend scheduler - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends); - - GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); - + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph - GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + // Get the number of splits of the last graph + GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend); GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); - // Allocate a graph on the backend scheduler - GGML_API void ggml_backend_sched_graph_compute( - ggml_backend_sched_t sched, - struct ggml_cgraph * graph); + // Allocate and compute graph on the backend scheduler + GGML_API void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + // Reset all assignments and allocators - must be called before using the sched allocators to allocate inputs + GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // // Utils @@ -176,7 +186,7 @@ extern "C" { typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data); // Compare the output of two backends - GGML_API void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data); + GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data); // Tensor initialization GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index a345b0c4a..2db50437c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8,8 +8,13 @@ #include #include #include +#include #include - +#include +#include +#include "ggml-cuda.h" +#include "ggml.h" +#include "ggml-backend-impl.h" #if defined(GGML_USE_HIPBLAS) #include @@ -77,6 +82,7 @@ #define cudaMemcpyKind hipMemcpyKind #define cudaMemset hipMemset #define cudaMemsetAsync hipMemsetAsync +#define cudaMemGetInfo hipMemGetInfo #define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize #define cudaSetDevice hipSetDevice #define cudaStreamCreateWithFlags hipStreamCreateWithFlags @@ -112,10 +118,6 @@ #endif // defined(GGML_USE_HIPBLAS) -#include "ggml-cuda.h" -#include "ggml.h" -#include "ggml-backend-impl.h" - #define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) #define CC_PASCAL 600 @@ -564,7 +566,7 @@ static void ggml_cuda_set_device(const int device) { static int g_device_count = -1; static int g_main_device = 0; -static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0}; +static std::array g_default_tensor_split = {}; struct cuda_device_capabilities { int cc; // compute capability @@ -575,10 +577,6 @@ struct cuda_device_capabilities { static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, false, 0} }; -static void * g_scratch_buffer = nullptr; -static size_t g_scratch_size = 0; // disabled by default -static size_t g_scratch_offset = 0; - static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; [[noreturn]] @@ -7548,8 +7546,9 @@ void ggml_init_cublas() { CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); - g_tensor_split[id] = total_vram; + g_default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; + #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) g_device_caps[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; #else @@ -7558,7 +7557,7 @@ void ggml_init_cublas() { g_device_caps[id].smpb = prop.sharedMemPerBlock; } for (int id = 0; id < g_device_count; ++id) { - g_tensor_split[id] /= total_vram; + g_default_tensor_split[id] /= total_vram; } for (int id = 0; id < g_device_count; ++id) { @@ -7582,30 +7581,6 @@ void ggml_init_cublas() { } } -void ggml_cuda_set_tensor_split(const float * tensor_split) { - if (tensor_split == nullptr) { - return; - } - bool all_zero = true; - for (int i = 0; i < g_device_count; ++i) { - if (tensor_split[i] != 0.0f) { - all_zero = false; - break; - } - } - if (all_zero) { - return; - } - float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] /= split_sum; - } -} - void * ggml_cuda_host_malloc(size_t size) { if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { return nullptr; @@ -8057,11 +8032,11 @@ static void ggml_cuda_op_mul_mat_q( (void) src1_ddf_i; } -static int64_t get_row_rounding(ggml_type type) { +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; for (int id = 0; id < g_device_count; ++id) { - if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { + if (tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { if (min_compute_capability > g_device_caps[id].cc) { min_compute_capability = g_device_caps[id].cc; } @@ -8122,6 +8097,21 @@ static int64_t get_row_rounding(ggml_type type) { #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) } +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + + if (id == g_device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + static void ggml_cuda_op_mul_mat_vec_q( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, @@ -8739,6 +8729,11 @@ static void ggml_cuda_set_peer_access(const int n_tokens) { peer_access_enabled = enable_peer_access; } +// FIXME: move this somewhere else +struct ggml_backend_cuda_split_buffer_type_context { + std::array tensor_split; +}; + static void ggml_cuda_op_mul_mat( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op, const bool convert_src1_to_q8_1) { @@ -8790,6 +8785,14 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); + std::array tensor_split; + if (split) { + // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_GPU_SPLIT check + // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; + tensor_split = buft_ctx->tensor_split; + } + struct dev_data { cuda_pool_alloc src0_dd_alloc; cuda_pool_alloc src1_ddf_alloc; @@ -8817,17 +8820,17 @@ static void ggml_cuda_op_mul_mat( // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes if (split) { - const int64_t rounding = get_row_rounding(src0->type); + const int64_t rounding = get_row_rounding(src0->type, tensor_split); if (id != 0) { - dev[id].row_low = ne01*g_tensor_split[id]; + dev[id].row_low = ne01*tensor_split[id]; if (dev[id].row_low < ne01) { dev[id].row_low -= dev[id].row_low % rounding; } } if (id != g_device_count - 1) { - dev[id].row_high = ne01*g_tensor_split[id + 1]; + dev[id].row_high = ne01*tensor_split[id + 1]; if (dev[id].row_high < ne01) { dev[id].row_high -= dev[id].row_high % rounding; } @@ -9373,10 +9376,17 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; - for (int id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - min_compute_capability = g_device_caps[id].cc; + + if (split) { + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; + auto & tensor_split = buft_ctx->tensor_split; + for (int id = 0; id < g_device_count; ++id) { + if (min_compute_capability > g_device_caps[id].cc && tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { + min_compute_capability = g_device_caps[id].cc; + } } + } else { + min_compute_capability = g_device_caps[g_main_device].cc; } #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) @@ -9415,7 +9425,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { @@ -9877,247 +9887,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { - const int64_t nrows = ggml_nrows(tensor); - - const int64_t ne0 = tensor->ne[0]; - - const size_t nb1 = tensor->nb[1]; - - ggml_backend_type backend = tensor->backend; - ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - - for (int id = 0; id < g_device_count; ++id) { - if (backend == GGML_BACKEND_GPU && id != g_main_device) { - continue; - } - - ggml_cuda_set_device(id); - - int64_t row_low, row_high; - if (backend == GGML_BACKEND_GPU) { - row_low = 0; - row_high = nrows; - } else if (backend == GGML_BACKEND_GPU_SPLIT) { - const int64_t rounding = get_row_rounding(tensor->type); - - row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; - row_low -= row_low % rounding; - - if (id == g_device_count - 1) { - row_high = nrows; - } else { - row_high = nrows*g_tensor_split[id + 1]; - row_high -= row_high % rounding; - } - } else { - GGML_ASSERT(false); - } - if (row_low == row_high) { - continue; - } - - int64_t nrows_split = row_high - row_low; - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf; - CUDA_CHECK(cudaMalloc(&buf, size)); - char * buf_host = (char *)data + offset_split; - - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); - } - - CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice)); - - extra->data_device[id] = buf; - - if (backend == GGML_BACKEND_GPU_SPLIT) { - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); - } - } - } - - tensor->extra = extra; -} - -void ggml_cuda_free_data(struct ggml_tensor * tensor) { - if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { - return; - } - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - - for (int id = 0; id < g_device_count; ++id) { - ggml_cuda_set_device(id); - if (extra->data_device[id] != nullptr) { - CUDA_CHECK(cudaFree(extra->data_device[id])); - } - - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); - } - } - } - - delete extra; -} - -static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr; -static size_t g_temp_tensor_extra_index = 0; - -static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { - if (g_temp_tensor_extras == nullptr) { - g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; - } - - size_t alloc_index = g_temp_tensor_extra_index; - g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES; - ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; - memset(extra, 0, sizeof(*extra)); - - return extra; -} - -static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) { - if (scratch && g_scratch_size == 0) { - return; - } - - tensor->backend = GGML_BACKEND_GPU; - - // recursively assign CUDA buffers until a compute tensor is found - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) { - const ggml_op src0_op = tensor->src[0]->op; - if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) { - ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc); - } - } - if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) { - ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc); - } - - if (scratch && no_alloc) { - return; - } - - ggml_tensor_extra_gpu * extra; - - const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) || - tensor->op == GGML_OP_VIEW || - force_inplace; - const size_t size = ggml_nbytes(tensor); - - ggml_cuda_set_device(g_main_device); - if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - size_t offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&offset, tensor->op_params, sizeof(size_t)); - } - extra = ggml_cuda_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = src0_ddc + offset; - } else if (tensor->op == GGML_OP_CPY) { - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; - void * src1_ddv = src1_extra->data_device[g_main_device]; - extra = ggml_cuda_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = src1_ddv; - } else if (scratch) { - GGML_ASSERT(size <= g_scratch_size); - if (g_scratch_offset + size > g_scratch_size) { - g_scratch_offset = 0; - } - - char * data = (char *) g_scratch_buffer; - if (data == nullptr) { - CUDA_CHECK(cudaMalloc(&data, g_scratch_size)); - g_scratch_buffer = data; - } - extra = ggml_cuda_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = data + g_scratch_offset; - - g_scratch_offset += size; - - GGML_ASSERT(g_scratch_offset <= g_scratch_size); - } else { // allocate new buffers outside of scratch - void * data; - CUDA_CHECK(cudaMalloc(&data, size)); - CUDA_CHECK(cudaMemset(data, 0, size)); - extra = new ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - extra->data_device[g_main_device] = data; - } - - tensor->extra = extra; -} - -void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) { - if (g_scratch_size == 0) { - return; - } - if (g_scratch_buffer == nullptr) { - ggml_cuda_set_device(g_main_device); - CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size)); - } - - ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra(); - - const bool inplace = tensor->view_src != nullptr; - - if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - size_t view_offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&view_offset, tensor->op_params, sizeof(size_t)); - } - extra->data_device[g_main_device] = src0_ddc + view_offset; - } else { - extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset; - } - - tensor->extra = extra; -} - -void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - GGML_ASSERT(ggml_is_contiguous(tensor)); - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_cuda_set_device(g_main_device); - CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice)); -} - -void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, true, false, false); -} - -void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, true, false, true); -} - -void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, false, false, false); -} - -void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, false, true, false); -} - -void ggml_cuda_set_main_device(const int main_device) { +static void ggml_cuda_set_main_device(const int main_device) { if (main_device >= g_device_count) { fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", main_device, g_device_count, g_main_device); @@ -10126,30 +9896,12 @@ void ggml_cuda_set_main_device(const int main_device) { if (g_main_device != main_device && g_device_count > 1) { g_main_device = main_device; - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device)); - fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name); + //cudaDeviceProp prop; + //CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device)); + //fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name); } } -void ggml_cuda_set_scratch_size(const size_t scratch_size) { - // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously - // it still won't always work as expected, but it's better than nothing - if (scratch_size > g_scratch_size) { - ggml_cuda_free_scratch(); - } - g_scratch_size = std::max(g_scratch_size, scratch_size); -} - -void ggml_cuda_free_scratch() { - if (g_scratch_buffer == nullptr) { - return; - } - - CUDA_CHECK(cudaFree(g_scratch_buffer)); - g_scratch_buffer = nullptr; -} - bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { if (!g_cublas_loaded) return false; @@ -10328,21 +10080,31 @@ void ggml_cuda_get_device_description(int device, char * description, size_t des #define UNUSED GGML_UNUSED +struct ggml_backend_cuda_context { + int device; + std::string name; +}; + // cuda buffer -struct ggml_backend_buffer_context_cuda { +struct ggml_backend_cuda_buffer_context { int device; void * dev_ptr = nullptr; ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; size_t temp_tensor_extra_index = 0; + std::string name; - ggml_backend_buffer_context_cuda(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} + ggml_backend_cuda_buffer_context(int device, void * dev_ptr) : + device(device), dev_ptr(dev_ptr), + name(GGML_CUDA_NAME + std::to_string(device)) { + } - ~ggml_backend_buffer_context_cuda() { + ~ggml_backend_cuda_buffer_context() { delete[] temp_tensor_extras; } ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { + // TODO: remove GGML_CUDA_MAX_NODES, allocate dynamically and reuse in backend_buffer_reset if (temp_tensor_extras == nullptr) { temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; } @@ -10356,19 +10118,28 @@ struct ggml_backend_buffer_context_cuda { } }; +static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; +} + static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; CUDA_CHECK(cudaFree(ctx->dev_ptr)); delete ctx; } static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->dev_ptr; } static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { assert(tensor->view_src->buffer->buft == buffer->buft); @@ -10397,14 +10168,12 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[ctx->device][0])); } } - - UNUSED(buffer); } static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); @@ -10415,49 +10184,82 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost)); + CUDA_CHECK(cudaDeviceSynchronize()); +} + +static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_backend_buffer_is_cuda(src->buffer)) { + ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; + ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + + ggml_cuda_set_device(src_ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + ggml_cuda_set_device(dst_ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaMemcpy((char *)dst->data, (const char *)src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice)); + CUDA_CHECK(cudaDeviceSynchronize()); + + return true; + } + return false; } static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size)); + CUDA_CHECK(cudaDeviceSynchronize()); } -static struct ggml_backend_buffer_i cuda_backend_buffer_interface = { +static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { + /* .get_name = */ ggml_backend_cuda_buffer_get_name, /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, /* .get_base = */ ggml_backend_cuda_buffer_get_base, /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, /* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor, /* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor, - /* .cpy_tensor_from = */ NULL, - /* .cpy_tensor_to = */ NULL, + /* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor, /* .clear = */ ggml_backend_cuda_buffer_clear, + /* .reset = */ NULL, }; // cuda buffer type -static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - int device = (int) (intptr_t) buft->context; +struct ggml_backend_cuda_buffer_type_context { + int device; + std::string name; +}; - ggml_cuda_set_device(device); +static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + + ggml_cuda_set_device(buft_ctx->device); size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 void * dev_ptr; - CUDA_CHECK(cudaMalloc(&dev_ptr, size)); + cudaError_t err = cudaMalloc(&dev_ptr, size); + if (err != cudaSuccess) { + fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); + return nullptr; + } - ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda(device, dev_ptr); + ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr); - return ggml_backend_buffer_init(buft, cuda_backend_buffer_interface, ctx, size); + return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); } static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { @@ -10466,7 +10268,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_ty UNUSED(buft); } -static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, ggml_tensor * tensor) { +static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); int64_t nrows_split = row_high - row_low; @@ -10487,21 +10289,32 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t } static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_cuda(backend); + if (!ggml_backend_is_cuda(backend)) { + return false; + } - UNUSED(buft); + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + + return buft_ctx->device == cuda_ctx->device; } static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { + /* .get_name = */ ggml_backend_cuda_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, - /* .is_host = */ nullptr, + /* .is_host = */ NULL, }; ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { - static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; + // FIXME: this is not thread safe + if (device >= ggml_backend_cuda_get_device_count()) { + return nullptr; + } + + static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; static bool ggml_backend_cuda_buffer_type_initialized = false; @@ -10509,7 +10322,7 @@ ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) { ggml_backend_cuda_buffer_types[i] = { /* .iface = */ ggml_backend_cuda_buffer_type_interface, - /* .context = */ (ggml_backend_buffer_type_context_t) (intptr_t) i, + /* .context = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)}, }; } ggml_backend_cuda_buffer_type_initialized = true; @@ -10518,8 +10331,306 @@ ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { return &ggml_backend_cuda_buffer_types[device]; } +// cuda split buffer + +struct ggml_backend_cuda_split_buffer_context { + ~ggml_backend_cuda_split_buffer_context() { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int id = 0; id < g_device_count; ++id) { + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + if (extra->events[id][is] != nullptr) { + CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); + } + } + if (extra->data_device[id] != nullptr) { + CUDA_CHECK(cudaFree(extra->data_device[id])); + } + } + delete extra; + } + } + + std::vector tensor_extras; +}; + +static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_CUDA_NAME "_Split"; + + UNUSED(buffer); +} + +// unused at the moment +//static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { +// return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; +//} + +static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; + delete ctx; +} + +static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + UNUSED(buffer); +} + +static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_cuda_set_device(id); + char * buf; + CUDA_CHECK(cudaMalloc(&buf, size)); + + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); + } + + extra->data_device[id] = buf; + + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); + } + } + tensor->backend = GGML_BACKEND_GPU_SPLIT; + tensor->extra = extra; +} + +static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + CUDA_CHECK(cudaMemcpy(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice)); + } +} + +static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + CUDA_CHECK(cudaMemcpy(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost)); + } +} + +static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + UNUSED(buffer); + UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { + /* .get_name = */ ggml_backend_cuda_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_cuda_split_buffer_clear, + /* .reset = */ NULL, +}; + +// cuda split buffer type + +static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_CUDA_NAME "_Split"; + + UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); +} + +static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + + UNUSED(buft); +} + +static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_cuda(backend); + + UNUSED(buft); +} + +static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_cuda_split_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cuda_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, + /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, +}; + +ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = g_default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < g_device_count; ++i) { + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < g_device_count; ++i) { + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, + /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + // host buffer type +static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_CUDA_NAME "_Host"; + + UNUSED(buft); +} + +static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { + return GGML_CUDA_NAME "_Host"; + + UNUSED(buffer); +} + static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_cuda_host_free(buffer->context); } @@ -10532,9 +10643,9 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); } - // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_cuda_host_buffer_name; buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; return buffer; @@ -10543,6 +10654,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { /* .iface = */ { + /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, @@ -10557,31 +10669,27 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { // backend -struct ggml_backend_context_cuda { - int device; -}; - static const char * ggml_backend_cuda_name(ggml_backend_t backend) { - return GGML_CUDA_NAME; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - UNUSED(backend); + return cuda_ctx->name.c_str(); } static void ggml_backend_cuda_free(ggml_backend_t backend) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; delete cuda_ctx; delete backend; } static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; return ggml_backend_cuda_buffer_type(cuda_ctx->device); } static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -10590,7 +10698,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens } static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -10598,39 +10706,27 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); } +static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + + if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx->device][0])); + return true; + } + + return false; +} + static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0])); UNUSED(backend); } -static ggml_backend_graph_plan_t ggml_backend_cuda_graph_plan_create(ggml_backend_t backend, ggml_cgraph * cgraph) { - GGML_ASSERT(!"not implemented"); - - return nullptr; - - UNUSED(backend); - UNUSED(cgraph); -} - -static void ggml_backend_cuda_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); -} - -static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); -} - static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -10640,53 +10736,31 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; + } - assert(node->backend == GGML_BACKEND_GPU); +#ifndef NDEBUG + assert(node->backend == GGML_BACKEND_GPU || node->backend == GGML_BACKEND_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_GPU); + assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); assert(node->src[j]->extra != nullptr); } } +#endif bool ok = ggml_cuda_compute_forward(¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); - -#if 0 - if (node->type == GGML_TYPE_F32) { - cudaDeviceSynchronize(); - std::vector tmp(ggml_nelements(node), 0.0f); - cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost); - printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op), - ggml_type_name(node->src[0]->type), - node->src[1] ? ggml_type_name(node->src[1]->type) : "none", - node->src[0]->name, - node->src[1] ? node->src[1]->name : "none"); - double sum = 0.0; - double sq_sum = 0.0; - for (int i = 0; i < ggml_nelements(node); i++) { - printf("%f ", tmp[i]); - sum += tmp[i]; - sq_sum += tmp[i]*tmp[i]; - } - printf("\n"); - printf("sum: %f, ", sum); - printf("sq_sum: %f\n", sq_sum); - } -#endif } - UNUSED(backend); - return true; } @@ -10801,18 +10875,17 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten UNUSED(backend); } -static ggml_backend_i cuda_backend_i = { +static ggml_backend_i ggml_backend_cuda_interface = { /* .get_name = */ ggml_backend_cuda_name, /* .free = */ ggml_backend_cuda_free, /* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async, /* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, + /* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async, /* .synchronize = */ ggml_backend_cuda_synchronize, - /* .graph_plan_create = */ ggml_backend_cuda_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_cuda_graph_plan_free, - /* .graph_plan_compute = */ ggml_backend_cuda_graph_plan_compute, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, /* .supports_op = */ ggml_backend_cuda_supports_op, }; @@ -10828,12 +10901,13 @@ ggml_backend_t ggml_backend_cuda_init(int device) { // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_cuda_set_main_device(device); - ggml_backend_context_cuda * ctx = new ggml_backend_context_cuda { - /* .device = */ device + ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context { + /* .device = */ device, + /* .name = */ GGML_CUDA_NAME + std::to_string(device), }; ggml_backend_t cuda_backend = new ggml_backend { - /* .interface = */ cuda_backend_i, + /* .interface = */ ggml_backend_cuda_interface, /* .context = */ ctx }; @@ -10841,9 +10915,24 @@ ggml_backend_t ggml_backend_cuda_init(int device) { } bool ggml_backend_is_cuda(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_cuda_name; + return backend && backend->iface.get_name == ggml_backend_cuda_name; } +int ggml_backend_cuda_get_device_count() { + return ggml_cuda_get_device_count(); +} + +void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { + ggml_cuda_get_device_description(device, description, description_size); +} + +void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { + ggml_cuda_set_device(device); + + CUDA_CHECK(cudaMemGetInfo(free, total)); +} + +// backend registry static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data); return cuda_backend; diff --git a/ggml-cuda.h b/ggml-cuda.h index cdb0c0c41..d19cbf3fd 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -27,22 +27,6 @@ GGML_API void * ggml_cuda_host_malloc(size_t size); GGML_API void ggml_cuda_host_free(void * ptr); GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split); -GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor); -GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor); - -GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor); -GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor); -GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor); - -GGML_API void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor); -GGML_API void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset); -GGML_API void ggml_cuda_copy_to_device(struct ggml_tensor * tensor); - -GGML_API void ggml_cuda_set_main_device(int main_device); -GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q); -GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size); -GGML_API void ggml_cuda_free_scratch(void); GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); GGML_API int ggml_cuda_get_device_count(void); @@ -52,13 +36,17 @@ GGML_API void ggml_cuda_get_device_description(int device, char * description, GGML_API ggml_backend_t ggml_backend_cuda_init(int device); GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); -GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend); GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); - -// pinned host buffer for use with CPU backend for faster copies between CPU and GPU +// split tensor buffer that splits matrices by rows across multiple devices +GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); +// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); +GGML_API int ggml_backend_cuda_get_device_count(void); +GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); + #ifdef __cplusplus } #endif diff --git a/ggml-impl.h b/ggml-impl.h index 2faced080..2c58075ac 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -228,6 +228,8 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { #define GGML_HASHTABLE_FULL ((size_t)-1) #define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2) +struct ggml_hash_set ggml_hash_set_new(size_t size); + bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key); // returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted diff --git a/ggml-metal.m b/ggml-metal.m index 6e5594432..c03624073 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2520,10 +2520,10 @@ static void ggml_backend_metal_free_device(void) { } } -static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { - struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; +static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { + return "Metal"; - return ctx->all_data; + UNUSED(buffer); } static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { @@ -2541,6 +2541,12 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) free(ctx); } +static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { + struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; + + return ctx->all_data; +} + static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); @@ -2553,14 +2559,12 @@ static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, c UNUSED(buffer); } -static void ggml_backend_metal_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); - - UNUSED(buffer); -} - -static void ggml_backend_metal_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); +static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; UNUSED(buffer); } @@ -2572,18 +2576,25 @@ static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_ } static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { + /* .get_name = */ ggml_backend_metal_buffer_get_name, /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_tensor = */ NULL, /* .set_tensor = */ ggml_backend_metal_buffer_set_tensor, /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, - /* .cpy_tensor_from = */ ggml_backend_metal_buffer_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_metal_buffer_cpy_tensor_to, + /* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor, /* .clear = */ ggml_backend_metal_buffer_clear, + /* .reset = */ NULL, }; // default buffer type +static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "Metal"; + + UNUSED(buft); +} + static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); @@ -2656,6 +2667,7 @@ static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t bu ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = { /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes @@ -2679,6 +2691,14 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz ctx->n_buffers = 0; const size_t size_page = sysconf(_SC_PAGESIZE); + + // page-align the data ptr + { + const uintptr_t offs = (uintptr_t) data % size_page; + data = (void *) ((char *) data - offs); + size += offs; + } + size_t size_aligned = size; if ((size_aligned % size_page) != 0) { size_aligned += (size_page - (size_aligned % size_page)); @@ -2779,14 +2799,13 @@ static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct UNUSED(backend); } -static struct ggml_backend_i metal_backend_i = { +static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, /* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, + /* .cpy_tensor_async = */ NULL, /* .synchronize = */ NULL, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, @@ -2805,7 +2824,7 @@ ggml_backend_t ggml_backend_metal_init(void) { ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend)); *metal_backend = (struct ggml_backend) { - /* .interface = */ metal_backend_i, + /* .interface = */ ggml_backend_metal_i, /* .context = */ ctx, }; @@ -2813,7 +2832,7 @@ ggml_backend_t ggml_backend_metal_init(void) { } bool ggml_backend_is_metal(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_metal_name; + return backend && backend->iface.get_name == ggml_backend_metal_name; } void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 496f9cdca..2bb93638f 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1,5 +1,6 @@ #include "ggml.h" #include "ggml-opencl.h" +#include "ggml-backend-impl.h" #include #include @@ -10,7 +11,7 @@ #include #include -#define CL_TARGET_OPENCL_VERSION 110 +#define CL_TARGET_OPENCL_VERSION 120 #include #if defined(_MSC_VER) @@ -929,6 +930,12 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co } void ggml_cl_init(void) { + static bool initialized = false; + if (initialized) { + return; + } + initialized = true; + cl_int err; struct cl_device; @@ -1483,8 +1490,8 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr } else { d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); } - cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); + cl_mem d_Y = src1->backend == GGML_BACKEND_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); + cl_mem d_D = dst->backend == GGML_BACKEND_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); size_t x_offset = 0; @@ -1501,7 +1508,9 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { // copy src1 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); + if (src1->backend == GGML_BACKEND_CPU) { + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); + } CL_CHECK(clFinish(queue)); @@ -1522,8 +1531,10 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr } // copy dst to host - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); + if (dst->backend == GGML_BACKEND_CPU) { + float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); + CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); + } } } } @@ -1532,8 +1543,12 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr if (src0->backend != GGML_BACKEND_GPU) { ggml_cl_pool_free(d_X, x_size); } - ggml_cl_pool_free(d_Y, y_size); - ggml_cl_pool_free(d_D, d_size); + if (src1->backend != GGML_BACKEND_GPU) { + ggml_cl_pool_free(d_Y, y_size); + } + if (dst->backend != GGML_BACKEND_GPU) { + ggml_cl_pool_free(d_D, d_size); + } } static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) { @@ -1598,6 +1613,8 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); } + // FIXME: convert on device + for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { // convert src1 to fp16 // TODO: use multiple threads @@ -1643,11 +1660,13 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr } // copy dst to host, then convert to float - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); - - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - - ggml_fp16_to_fp32_row(tmp, d, d_ne); + if (dst->backend == GGML_BACKEND_CPU) { + CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); + float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); + ggml_fp16_to_fp32_row(tmp, d, d_ne); + } else { + // FIXME: convert dst to fp32 on device + } } } } @@ -1801,7 +1820,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * } -bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) { const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; @@ -1895,3 +1914,291 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) { tensor->extra = dst; GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); } + +// ggml-backend + +// buffer + +struct ggml_backend_opencl_buffer_context { + ~ggml_backend_opencl_buffer_context() { + if (buffer) { + clReleaseMemObject(buffer); + } + for (auto * sub_buffer : sub_buffers) { + clReleaseMemObject(sub_buffer); + } + } + + cl_mem buffer; + std::vector sub_buffers; +}; + +static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000; + +static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) { + return "OpenCL"; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + delete ctx; +} + +static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) { + return cl_ptr_base; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + if (tensor->view_src != NULL && tensor->view_offs == 0) { + tensor->extra = tensor->view_src->extra; + } else { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)}; + cl_int err; + cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); + CL_CHECK(err); + ctx->sub_buffers.push_back(sub_buffer); + tensor->extra = sub_buffer; + } + tensor->backend = GGML_BACKEND_GPU; +} + +static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + cl_mem tensor_buffer = (cl_mem) tensor->extra; + CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); + CL_CHECK(clFinish(queue)); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + cl_mem tensor_buffer = (cl_mem) tensor->extra; + CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); + CL_CHECK(clFinish(queue)); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL)); + CL_CHECK(clFinish(queue)); +} + +static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + for (auto * sub_buffer : ctx->sub_buffers) { + clReleaseMemObject(sub_buffer); + } + ctx->sub_buffers.clear(); +} + +static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = { + /* .get_name = */ ggml_backend_opencl_buffer_get_name, + /* .free_buffer = */ ggml_backend_opencl_buffer_free_buffer, + /* .get_base = */ ggml_backend_opencl_buffer_get_base, + /* .init_tensor = */ ggml_backend_opencl_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_opencl_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_opencl_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_opencl_buffer_clear, + /* .reset = */ ggml_backend_opencl_buffer_reset, +}; + +// buffer type + +static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) { + return "OpenCL"; + + GGML_UNUSED(buffer_type); +} + +static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) { + ggml_cl_init(); + + cl_int err; + cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err); + if (err != CL_SUCCESS) { + fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0); + return nullptr; + } + + ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}}; + + return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size); +} + +static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) { + // FIXME: not thread safe, device may not be initialized yet + static cl_uint alignment = -1; + if (alignment == (cl_uint)-1) { + ggml_cl_init(); + clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL); + } + return alignment; + + GGML_UNUSED(buffer_type); +} + +static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) { + //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend + return ggml_backend_is_cpu(backend); + + GGML_UNUSED(buffer_type); +} + +static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = { + /* .get_name = */ ggml_backend_opencl_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment, + /* .get_alloc_size = */ NULL, + /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; + + +ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() { + static ggml_backend_buffer_type buffer_type = { + /* .iface = */ ggml_backend_opencl_buffer_type_interface, + /* .context = */ nullptr, + }; + + return &buffer_type; +} + +#if 0 +// host buffer type + +static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return "CL_Host"; + + GGML_UNUSED(buft); +} + +static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) { + return "CL_Host"; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_cl_host_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_cl_host_malloc(size); + + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.get_name = ggml_backend_opencl_host_buffer_name; + buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_opencl_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_opencl_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .context = */ nullptr, + }; + + return &ggml_backend_opencl_buffer_type_host; +} + +// backend + +static const char * ggml_backend_opencl_name(ggml_backend_t backend) { + return "OpenCL"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_opencl_free(ggml_backend_t backend) { + GGML_UNUSED(backend); +} + +static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) { + return ggml_backend_opencl_buffer_type(); + + GGML_UNUSED(backend); +} + +static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { + for (int i = 0; i < graph->n_nodes; ++i) { + ggml_tensor * node = graph->nodes[i]; + switch (node->op) { + case GGML_OP_MUL_MAT: + ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0); + break; + case GGML_OP_MUL: + ggml_cl_mul(node->src[0], node->src[1], node); + break; + default: + GGML_ASSERT(false); + } + } + + return true; + + GGML_UNUSED(backend); +} + +static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_MUL_MAT: + return ggml_cl_can_mul_mat(op->src[0], op->src[1], op); + case GGML_OP_MUL: + // return ggml_can_repeat_rows(op->src[1], op->src[0]); + return true; + default: + return false; + } + + GGML_UNUSED(backend); +} + +static ggml_backend_i opencl_backend_i = { + /* .get_name = */ ggml_backend_opencl_name, + /* .free = */ ggml_backend_opencl_free, + /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_from_async = */ NULL, + /* .cpy_tensor_to_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_opencl_graph_compute, + /* .supports_op = */ ggml_backend_opencl_supports_op, +}; + +ggml_backend_t ggml_backend_opencl_init() { + ggml_backend_t backend = new ggml_backend { + /* .interface = */ opencl_backend_i, + /* .context = */ nullptr + }; + + return backend; +} + +bool ggml_backend_is_opencl(ggml_backend_t backend) { + return backend && backend->iface.get_name == ggml_backend_opencl_name; +} +#endif diff --git a/ggml-opencl.h b/ggml-opencl.h index 44d05bd64..919b00d63 100644 --- a/ggml-opencl.h +++ b/ggml-opencl.h @@ -1,6 +1,7 @@ #pragma once #include "ggml.h" +#include "ggml-backend.h" #ifdef __cplusplus extern "C" { @@ -9,17 +10,26 @@ extern "C" { GGML_API void ggml_cl_init(void); GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst); GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); -GGML_API void * ggml_cl_host_malloc(size_t size); -GGML_API void ggml_cl_host_free(void * ptr); +// GGML_API void * ggml_cl_host_malloc(size_t size); +// GGML_API void ggml_cl_host_free(void * ptr); GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor); GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); +// backend API + +// GGML_API ggml_backend_t ggml_backend_opencl_init(void); + +// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend); + +GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void); +// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void); + #ifdef __cplusplus } #endif diff --git a/ggml.c b/ggml.c index f5caeba08..6dbd7626c 100644 --- a/ggml.c +++ b/ggml.c @@ -2354,6 +2354,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { } void ggml_free(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + // make this function thread safe ggml_critical_section_start(); @@ -4362,6 +4366,23 @@ struct ggml_tensor * ggml_cpy( return ggml_cpy_impl(ctx, a, b); } +struct ggml_tensor * ggml_cast( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_type type) { + bool is_node = false; + + struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); + ggml_format_name(result, "%s (copy)", a->name); + + result->op = GGML_OP_CPY; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = a; + result->src[1] = result; + + return result; +} + // ggml_cont static struct ggml_tensor * ggml_cont_impl( @@ -14871,7 +14892,7 @@ size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tenso return i; } -static struct ggml_hash_set ggml_hash_set_new(size_t size) { +struct ggml_hash_set ggml_hash_set_new(size_t size) { size = ggml_hash_size(size); struct ggml_hash_set result; result.size = size; @@ -16620,7 +16641,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { return GGML_EXIT_SUCCESS; } -struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { +struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) { if (n_threads <= 0) { n_threads = GGML_DEFAULT_N_THREADS; } @@ -16682,14 +16703,15 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } break; case GGML_OP_MUL_MAT_ID: { + cur = 0; const struct ggml_tensor * src0 = node->src[2]; const struct ggml_tensor * src1 = node->src[1]; const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type; if (src1->type != vec_dot_type) { - cur = ggml_row_size(vec_dot_type, ggml_nelements(src1)); + cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); } const int n_as = ggml_get_op_params_i32(node, 1); - cur = GGML_PAD(cur, sizeof(int64_t)); // align + cur += GGML_PAD(cur, sizeof(int64_t)); // align cur += n_as * sizeof(int64_t); // matrix_row_counts cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows } break; diff --git a/ggml.h b/ggml.h index 4c2ff6c66..b18ba7812 100644 --- a/ggml.h +++ b/ggml.h @@ -1165,6 +1165,11 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_cast( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_type type); + // make contiguous GGML_API struct ggml_tensor * ggml_cont( struct ggml_context * ctx, @@ -1842,8 +1847,8 @@ extern "C" { // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); + GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); + GGML_API int ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data diff --git a/llama.cpp b/llama.cpp index ce413f605..fe1d8947c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1,5 +1,4 @@ #define LLAMA_API_INTERNAL -//#define LLAMA_GGML_BACKEND_CUDA_TEST // for testing only - enables ggml-cuda through ggml-backend, disables partial offloading #include "llama.h" #include "unicode.h" @@ -152,10 +151,6 @@ static bool is_float_close(float a, float b, float abs_tol) { return std::fabs(b - a) <= abs_tol; } -#ifdef GGML_USE_CPU_HBM -#include -#endif - static void zeros(std::ofstream & file, size_t n) { char zero = 0; for (size_t i = 0; i < n; ++i) { @@ -1190,12 +1185,6 @@ struct llama_mlock { #endif }; -typedef void (*offload_func_t)(struct ggml_tensor * tensor); - -static void ggml_offload_nop(struct ggml_tensor * tensor) { - (void) tensor; -} - static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { std::vector result(8, 0); const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); @@ -1211,19 +1200,14 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_ return std::string(result.data(), result.size()); } -static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) { +static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) { ggml_backend_buffer_type_t buft = nullptr; -#ifdef GGML_USE_METAL - if (n_gpu_layers > 0) { - buft = ggml_backend_metal_buffer_type(); +#if defined(GGML_USE_CUBLAS) + // host buffers should only be used when data is expected to be copied to/from the GPU + if (host_buffer) { + buft = ggml_backend_cuda_host_buffer_type(); } -#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (n_gpu_layers > 0) { - buft = ggml_backend_cuda_buffer_type(0); - } -#elif defined(GGML_USE_CUBLAS) - buft = ggml_backend_cuda_host_buffer_type(); #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); #endif @@ -1231,10 +1215,45 @@ static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) { if (buft == nullptr) { buft = ggml_backend_cpu_buffer_type(); } - return buft; - GGML_UNUSED(n_gpu_layers); + GGML_UNUSED(host_buffer); +} + +static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_METAL + buft = ggml_backend_metal_buffer_type(); +#elif defined(GGML_USE_CUBLAS) + buft = ggml_backend_cuda_buffer_type(gpu); +#elif defined(GGML_USE_CLBLAST) + buft = ggml_backend_opencl_buffer_type(); +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_cpu(true); + } + return buft; + + GGML_UNUSED(gpu); +} + +static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_CUBLAS + if (ggml_backend_cuda_get_device_count() > 1) { + buft = ggml_backend_cuda_split_buffer_type(tensor_split); + } +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_offload(fallback_gpu); + } + return buft; + + GGML_UNUSED(tensor_split); } // @@ -1445,24 +1464,24 @@ struct llama_kv_cache { std::vector k_l; // per layer std::vector v_l; - struct ggml_context * ctx = NULL; + std::vector ctxs; + std::vector bufs; - ggml_backend_buffer_t buf = NULL; + size_t total_size() const { + size_t size = 0; + for (ggml_backend_buffer_t buf : bufs) { + size += ggml_backend_buffer_get_size(buf); + } + return size; + } ~llama_kv_cache() { -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (ggml_cublas_loaded()) { - for (size_t i = 0; i < k_l.size(); ++i) { - ggml_cuda_free_data(k_l[i]); - ggml_cuda_free_data(v_l[i]); - } - } -#endif - if (ctx) { + for (struct ggml_context * ctx : ctxs) { ggml_free(ctx); } - - ggml_backend_buffer_free(buf); + for (ggml_backend_buffer_t buf : bufs) { + ggml_backend_buffer_free(buf); + } } }; @@ -1539,16 +1558,32 @@ struct llama_model { std::vector layers; + llama_split_mode split_mode; + int main_gpu; int n_gpu_layers; // gguf metadata std::unordered_map gguf_kv; - // context - struct ggml_context * ctx = NULL; + // layer -> buffer type mapping + struct layer_buft { + layer_buft() : buft_matrix(nullptr), buft(nullptr) {} + layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {} + layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {} - // the model memory buffer - ggml_backend_buffer_t buf = NULL; + ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication + ggml_backend_buffer_type_t buft; // everything else + }; + + layer_buft buft_input; + layer_buft buft_output; + std::vector buft_layer; + + // contexts where the model tensors metadata is stored + std::vector ctxs; + + // the model memory buffers for the tensor data + std::vector bufs; // model memory mapped file std::unique_ptr mapping; @@ -1564,39 +1599,32 @@ struct llama_model { int64_t t_start_us = 0; ~llama_model() { -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (ggml_cublas_loaded()) { - for (size_t i = 0; i < tensors_by_name.size(); ++i) { - ggml_cuda_free_data(tensors_by_name[i].second); - } - ggml_cuda_free_scratch(); - } -#endif - -#if defined(GGML_USE_CLBLAST) - for (size_t i = 0; i < tensors_by_name.size(); ++i) { - ggml_cl_free_data(tensors_by_name[i].second); - } -#endif - if (ctx) { + for (struct ggml_context * ctx : ctxs) { ggml_free(ctx); } - - ggml_backend_buffer_free(buf); + for (ggml_backend_buffer_t buf : bufs) { + ggml_backend_buffer_free(buf); + } } }; struct llama_context { llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {} ~llama_context() { - ggml_allocr_free(alloc); - ggml_backend_buffer_free(buf_alloc); - ggml_backend_free(backend); + ggml_backend_sched_free(sched); + + for (ggml_backend_t backend : backends) { + ggml_backend_free(backend); + } } llama_cparams cparams; - ggml_backend_t backend = nullptr; + std::vector backends; +#ifdef GGML_USE_METAL + ggml_backend_t backend_metal = nullptr; +#endif + ggml_backend_t backend_cpu = nullptr; const llama_model & model; @@ -1630,8 +1658,9 @@ struct llama_context { // memory buffers used to evaluate the model std::vector buf_compute_meta; - ggml_backend_buffer_t buf_alloc = NULL; - ggml_allocr * alloc = NULL; + ggml_backend_sched_t sched = nullptr; + // allocator for the input tensors + ggml_tallocr * alloc = nullptr; // temporary buffer for copying data to/from the backend std::vector> buf_copy; @@ -1646,16 +1675,17 @@ struct llama_context { // static bool llama_kv_cache_init( - const struct llama_hparams & hparams, struct llama_kv_cache & cache, + const llama_model & model, ggml_type ktype, ggml_type vtype, uint32_t n_ctx, - int n_gpu_layers, bool offload) { + const struct llama_hparams & hparams = model.hparams; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const uint32_t n_layer = hparams.n_layer; + const int64_t n_layer = hparams.n_layer; cache.has_shift = false; @@ -1666,62 +1696,65 @@ static bool llama_kv_cache_init( cache.cells.clear(); cache.cells.resize(n_ctx); - struct ggml_init_params params; - params.mem_size = 2u*n_layer*ggml_tensor_overhead(); - params.mem_buffer = NULL; - params.no_alloc = true; +#ifdef GGML_USE_CLBLAST + offload = false; +#endif - cache.ctx = ggml_init(params); + // count used buffer types + std::map buft_layer_count; + if (offload) { + for (int64_t i = 0; i < n_layer; ++i) { + buft_layer_count[model.buft_layer[i].buft]++; + } + } else { + buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer; + } - size_t vram_kv_cache = 0; - - if (!cache.ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__); - return false; + // create a context for each buffer type + std::map ctx_map; + for (auto & it : buft_layer_count) { + int n_layers = it.second; + struct ggml_init_params params = { + /*.mem_size =*/ 2u*n_layers*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__); + return false; + } + ctx_map[it.first] = ctx; + cache.ctxs.push_back(ctx); } cache.k_l.reserve(n_layer); cache.v_l.reserve(n_layer); - const int i_gpu_start = (int) n_layer - n_gpu_layers; - for (int i = 0; i < (int) n_layer; i++) { - ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx); - ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx); + struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); + ggml_tensor * k = ggml_new_tensor_1d(ctx, ktype, n_embd_k_gqa*n_ctx); + ggml_tensor * v = ggml_new_tensor_1d(ctx, vtype, n_embd_v_gqa*n_ctx); ggml_format_name(k, "cache_k_l%d", i); ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); cache.v_l.push_back(v); -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (i >= i_gpu_start) { - if (offload) { - ggml_cuda_assign_buffers_no_scratch(k); - ggml_cuda_assign_buffers_no_scratch(v); - vram_kv_cache += ggml_nbytes(k); - vram_kv_cache += ggml_nbytes(v); - // HACK: mark tensor as allocated - k->data = v->data = (void *)(uintptr_t)1; - } + } + + // allocate tensors and initialize the buffers to avoid NaNs in the padding + for (auto it : ctx_map) { + ggml_backend_buffer_type_t buft = it.first; + ggml_context * ctx = it.second; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (!buf) { + LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__); + return false; } -#endif // GGML_USE_CUBLAS + ggml_backend_buffer_clear(buf, 0); + LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); + cache.bufs.push_back(buf); } - // allocate tensors - cache.buf = ggml_backend_alloc_ctx_tensors_from_buft(cache.ctx, llama_default_buffer_type(n_gpu_layers)); - - // buf may be NULL with full offload - if (cache.buf) { - // initialize the buffer to avoid NaNs in the padding - ggml_backend_buffer_clear(cache.buf, 0); - } - - if (vram_kv_cache > 0) { - LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); - } - - GGML_UNUSED(i_gpu_start); - GGML_UNUSED(offload); - return true; } @@ -2354,9 +2387,8 @@ struct llama_model_loader { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) { + struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta) { struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta); - tensor->backend = backend; // TODO: ggml_set_backend ggml_set_name(tensor, ggml_get_name(meta)); n_created++; @@ -2364,7 +2396,7 @@ struct llama_model_loader { return tensor; } - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, ggml_backend_type backend, bool required = true) { + struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, bool required = true) { struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str()); if (cur == NULL) { @@ -2374,12 +2406,6 @@ struct llama_model_loader { throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } - if (backend == GGML_BACKEND_GPU_SPLIT) { - if (ne.size() == 1) { - throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str())); - } - } - { bool is_ok = true; for (size_t i = 0; i < ne.size(); ++i) { @@ -2397,7 +2423,7 @@ struct llama_model_loader { } } - return create_tensor_for(ctx, cur, backend); + return create_tensor_for(ctx, cur); } void done_getting_tensors() const { @@ -2416,26 +2442,36 @@ struct llama_model_loader { return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx); } - void init_mapping(bool prefetch = true) { - /* - // prefetch only CPU tensors - if (use_mmap) { - size_t size_pref = 0; // prefetch - - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - if (cur->backend == GGML_BACKEND_CPU) { - size_t tensor_end = gguf_get_tensor_offset(ctx_gguf, i) + ggml_nbytes(cur); - size_pref = std::max(size_pref, tensor_end); - } - } - mapping.reset(new llama_mmap(&file, gguf_get_data_offset(ctx_gguf) + size_pref, ggml_is_numa())); - } - */ + void init_mapping(bool prefetch = true, llama_mlock * lmlock = nullptr) { // prefetch the whole file - all the data is needed anyway if (use_mmap) { mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa())); } + + // compute the total size of all tensors for progress reporting + for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { + struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + size_data += ggml_nbytes(cur); + } + + if (use_mmap && mapping) { + if (lmlock) { + lmlock->init(mapping->addr); + } + mmap_used_first = mapping->size; + } + } + + void get_mapping_range(size_t * first, size_t * last, ggml_context * ctx) const { + GGML_ASSERT(mapping); + + *first = mapping->size; + *last = 0; + for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) { + const size_t offs = file_offset(ggml_get_name(tensor)); + *first = std::min(*first, offs); + *last = std::max(*last, offs + ggml_nbytes(tensor)); + } } // for backwards compatibility, does not support ggml-backend @@ -2443,8 +2479,11 @@ struct llama_model_loader { const size_t offs = file_offset(ggml_get_name(cur)); if (use_mmap && mapping) { - GGML_ASSERT(cur->data == nullptr); - cur->data = (uint8_t *)mapping->addr + offs; + if (cur->data == nullptr) { + cur->data = (uint8_t *)mapping->addr + offs; + } else { + memcpy(cur->data, (uint8_t *)mapping->addr + offs, ggml_nbytes(cur)); + } } else { GGML_ASSERT(cur->data != nullptr); file.seek(offs, SEEK_SET); @@ -2452,37 +2491,23 @@ struct llama_model_loader { } } + size_t size_done = 0; + size_t size_data = 0; + size_t mmap_used_first = -1; + size_t mmap_used_last = 0; + // Returns false if cancelled by progress_callback - bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const { - size_t size_data = 0; - - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - size_data += ggml_nbytes(cur); - } - - if (use_mmap && buf_mmap) { - if (lmlock) { - lmlock->init(mapping->addr); - } - } - -#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST) - const bool legacy_offload = true; -#else - const bool legacy_offload = false; -#endif + bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) { + GGML_ASSERT(size_data != 0 && "call init_mapping() first"); std::vector> read_buf; - size_t size_done = 0; - - size_t mmap_first = -1; - size_t mmap_last = 0; - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - GGML_ASSERT(cur); // unused tensors should have been caught by load_data already + if (!cur) { + // some tensors may be allocated in a different context + continue; + } if (progress_callback) { if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) { @@ -2492,67 +2517,48 @@ struct llama_model_loader { const size_t offs = file_offset(ggml_get_name(cur)); - if (!legacy_offload || cur->backend == GGML_BACKEND_CPU) { - if (use_mmap && mapping) { - if (buf_mmap) { - ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs); - if (lmlock) { - lmlock->grow_to(offs + ggml_nbytes(cur)); - } - mmap_first = std::min(mmap_first, offs); - mmap_last = std::max(mmap_last, offs + ggml_nbytes(cur)); - } else { - ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur)); + if (use_mmap && mapping) { + if (buf_mmap && cur->data == nullptr) { + ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs); + if (lmlock) { + lmlock->grow_to(offs + ggml_nbytes(cur)); } + mmap_used_first = std::min(mmap_used_first, offs); + mmap_used_last = std::max(mmap_used_last, offs + ggml_nbytes(cur)); } else { - if (ggml_backend_buffer_is_host(cur->buffer)) { - file.seek(offs, SEEK_SET); - file.read_raw(cur->data, ggml_nbytes(cur)); - } else { - read_buf.resize(ggml_nbytes(cur)); - file.seek(offs, SEEK_SET); - file.read_raw(read_buf.data(), ggml_nbytes(cur)); - ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur)); - } + ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur)); } } else { - // HACK: mark tensor as allocated - cur->data = (void *)(uintptr_t)1; - void * data; - if (use_mmap && mapping) { - data = (uint8_t *) mapping->addr + offs; + if (ggml_backend_buffer_is_host(cur->buffer)) { + file.seek(offs, SEEK_SET); + file.read_raw(cur->data, ggml_nbytes(cur)); } else { read_buf.resize(ggml_nbytes(cur)); file.seek(offs, SEEK_SET); file.read_raw(read_buf.data(), ggml_nbytes(cur)); - data = read_buf.data(); + ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur)); } - -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - ggml_cuda_transform_tensor(data, cur); -#elif defined(GGML_USE_CLBLAST) - GGML_ASSERT(cur->backend == GGML_BACKEND_GPU); - ggml_cl_transform_tensor(data, cur); -#else - GGML_ASSERT(!"GPU tensor without a GPU backend"); - GGML_UNUSED(data); -#endif } size_done += ggml_nbytes(cur); } - // unmap offloaded tensors and metadata - if (use_mmap && mapping) { - mapping->unmap_fragment(0, mmap_first); - mapping->unmap_fragment(mmap_last, mapping->size); + // check if this is the last call and do final cleanup + if (size_done >= size_data) { + // unmap offloaded tensors and metadata + if (use_mmap && mapping) { + mapping->unmap_fragment(0, mmap_used_first); + if (mmap_used_last != 0) { + mapping->unmap_fragment(mmap_used_last, mapping->size); + } + } + if (progress_callback) { + // Even though the model is done loading, we still honor + // cancellation since we need to free allocations. + return progress_callback(1.0f, progress_callback_user_data); + } } - if (progress_callback) { - // Even though the model is done loading, we still honor - // cancellation since we need to free allocations. - return progress_callback(1.0f, progress_callback_user_data); - } return true; } }; @@ -3181,6 +3187,7 @@ static bool llm_load_tensors( llama_model_loader & ml, llama_model & model, int n_gpu_layers, + enum llama_split_mode split_mode, int main_gpu, const float * tensor_split, bool use_mlock, @@ -3188,702 +3195,563 @@ static bool llm_load_tensors( void * progress_callback_user_data) { model.t_start_us = ggml_time_us(); - auto & ctx = model.ctx; auto & hparams = model.hparams; + model.split_mode = split_mode; + model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; - size_t ctx_size = ggml_tensor_overhead() * ml.n_tensors; + const int64_t n_layer = hparams.n_layer; + const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0); - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0); + // there is very little benefit to offloading the input layer, so always keep it on the CPU + model.buft_input = llama_default_buffer_type_cpu(true); - // create the ggml context + model.buft_layer.resize(n_layer); + + // assign cpu layers + for (int64_t i = 0; i < i_gpu_start; ++i) { + model.buft_layer[i] = llama_default_buffer_type_cpu(true); + } + +#ifdef GGML_USE_CUBLAS + if (split_mode == LLAMA_SPLIT_LAYER) { + // calculate the split points + int device_count = ggml_backend_cuda_get_device_count(); + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); + float splits[GGML_CUDA_MAX_DEVICES]; + if (all_zero) { + // default split, by free memory + for (int i = 0; i < device_count; ++i) { + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(i, &total, &free); + splits[i] = free; + } + } else { + std::copy(tensor_split, tensor_split + device_count, splits); + } + + // sum and normalize the splits to get the split points + float split_sum = 0.0f; + for (int i = 0; i < device_count; ++i) { + split_sum += splits[i]; + splits[i] = split_sum; + } + for (int i = 0; i < device_count; ++i) { + splits[i] /= split_sum; + } + + // assign the repeating layers to the devices according to the splits + int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); + for (int64_t i = i_gpu_start; i < n_layer; ++i) { + int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits; + model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); + } + // assign the output layer + if (n_gpu_layers > n_layer) { + int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits; + model.buft_output = llama_default_buffer_type_offload(layer_gpu); + } else { + model.buft_output = llama_default_buffer_type_cpu(true); + } + } else +#endif { + ggml_backend_buffer_type_t split_buft; + if (split_mode == LLAMA_SPLIT_ROW) { + split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); + } else { + // LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported + split_buft = llama_default_buffer_type_offload(main_gpu); + } + // assign the repeating layers + for (int64_t i = i_gpu_start; i < n_layer; ++i) { + model.buft_layer[i] = { + split_buft, + llama_default_buffer_type_offload(main_gpu) + }; + } + // assign the output layer + if (n_gpu_layers > n_layer) { + model.buft_output = { + split_buft, + llama_default_buffer_type_offload(main_gpu) + }; + } else { + model.buft_output = llama_default_buffer_type_cpu(true); + } + } + + // count used buffer types + std::map buft_layer_count; + buft_layer_count[model.buft_input.buft]++; + buft_layer_count[model.buft_input.buft_matrix]++; + buft_layer_count[model.buft_output.buft]++; + buft_layer_count[model.buft_output.buft_matrix]++; + for (int64_t i = 0; i < n_layer; ++i) { + buft_layer_count[model.buft_layer[i].buft]++; + buft_layer_count[model.buft_layer[i].buft_matrix]++; + } + + // create one context per buffer type + size_t ctx_size = ggml_tensor_overhead()*ml.n_tensors; + std::map ctx_map; + for (auto & it : buft_layer_count) { struct ggml_init_params params = { /*.mem_size =*/ ctx_size, /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; - - model.ctx = ggml_init(params); - if (!model.ctx) { - throw std::runtime_error(format("ggml_init() failed")); + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create context")); } + ctx_map[it.first] = ctx; + model.ctxs.push_back(ctx); } - (void) main_gpu; - - enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; - enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; - -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (ggml_cublas_loaded()) { - LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); - ggml_cuda_set_main_device(main_gpu); - - llama_backend_offload = GGML_BACKEND_GPU; - llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT; - } -#elif defined(GGML_USE_CLBLAST) - LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); - llama_backend_offload = GGML_BACKEND_GPU; - llama_backend_offload_split = GGML_BACKEND_GPU; -#endif + LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0); // create tensors for the weights { const int64_t n_embd = hparams.n_embd; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const int64_t n_layer = hparams.n_layer; + const int64_t n_embd_gqa = n_embd_v_gqa; const int64_t n_vocab = hparams.n_vocab; + const int64_t n_ff = hparams.n_ff; + + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); + + ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); + ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); + ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); + auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); }; + auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); }; + + model.layers.resize(n_layer); const auto tn = LLM_TN(model.arch); switch (model.arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors - layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend, false); - layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend, false); - layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend, false); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_inp = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, backend, false); + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, false); if (layer.ffn_gate_inp == nullptr) { GGML_ASSERT(hparams.n_expert == 0); GGML_ASSERT(hparams.n_expert_used == 0); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } else { GGML_ASSERT(hparams.n_expert > 0); GGML_ASSERT(hparams.n_expert_used > 0); // MoE branch for (uint32_t x = 0; x < hparams.n_expert; ++x) { - layer.ffn_gate_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}, backend_split); - layer.ffn_down_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}, backend_split); - layer.ffn_up_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}, backend_split); + layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}); + layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}); + layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}); } } } } break; case LLM_ARCH_BAICHUAN: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_FALCON: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) { - layer.attn_norm_2 = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend); - layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, backend); + layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}); } - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_STARCODER: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_PERSIMMON: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); - const int i_gpu_start = n_layer - n_gpu_layers; - model.layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); - layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend); - layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}, backend); - layer.attn_k_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend); - layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend); + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}); + + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}); } } break; case LLM_ARCH_BLOOM: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.tok_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU); - model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_norm = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); + model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_MPT: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false); + layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); } } break; case LLM_ARCH_STABLELM: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - /* - llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ] - */ - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_QWEN: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - } - - const uint32_t n_ff = hparams.n_ff / 2; - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd * 3}, backend); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}); } } break; case LLM_ARCH_PHI2: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_PLAMO: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_GPT2: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; default: @@ -3893,78 +3761,51 @@ static bool llm_load_tensors( ml.done_getting_tensors(); - ml.init_mapping(); + ml.init_mapping(true, use_mlock ? &model.mlock_mmap : nullptr); - // allocate tensors - size_t vram_weights = 0; - size_t buf_size = 0; + // create the backend buffers + std::vector> ctx_bufs; - ggml_backend_buffer_type_t buft = llama_default_buffer_type(n_gpu_layers); + for (auto & it : ctx_map) { + ggml_backend_buffer_type_t buft = it.first; + ggml_context * ctx = it.second; + ggml_backend_buffer_t buf = nullptr; - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { - // GGML_BACKEND_GPU tensors are for CUDA and OpenCL only, which are handled separately without ggml-backend - if (t->backend == GGML_BACKEND_CPU) { - buf_size += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), ggml_backend_buft_get_alignment(buft)); - } else { - vram_weights += ggml_nbytes(t); + // only the mmap region containing the tensors in the model is mapped to the backend buffer + // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers + // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size + if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) { + size_t first, last; + ml.get_mapping_range(&first, &last, ctx); + buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first); } - } - - // create backend buffer - ggml_backend_buffer_t buf_mmap = nullptr; - #ifdef GGML_USE_METAL - if (n_gpu_layers > 0) { - if (ml.use_mmap) { + else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) { const size_t max_size = ggml_get_max_tensor_size(ctx); - model.buf = ggml_backend_metal_buffer_from_ptr(ml.mapping->addr, ml.mapping->size, max_size); - buf_mmap = model.buf; - } else { - model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_metal_buffer_type()); + size_t first, last; + ml.get_mapping_range(&first, &last, ctx); + buf = ggml_backend_metal_buffer_from_ptr((char *) ml.mapping->addr + first, last - first, max_size); } - } -#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) - // for testing only - if (n_gpu_layers > 0) { - model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cuda_buffer_type(0)); - } #endif - - if (model.buf == nullptr) { - // CPU backend, and indirectly CUDA and OpenCL - if (ml.use_mmap) { - model.buf = ggml_backend_cpu_buffer_from_ptr(ml.mapping->addr, ml.mapping->size); - buf_mmap = model.buf; - } else { - // allocate only CPU tensors - model.buf = ggml_backend_buft_alloc_buffer(buft, buf_size); - ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(model.buf); - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { - if (t->backend == GGML_BACKEND_CPU) { - ggml_tallocr_alloc(alloc, t); - } + else { + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (buf != nullptr && use_mlock && ggml_backend_buffer_is_host(buf)) { + model.mlock_buf.init (ggml_backend_buffer_get_base(buf)); + model.mlock_buf.grow_to(ggml_backend_buffer_get_size(buf)); } - ggml_tallocr_free(alloc); } - } - - if (use_mlock && ggml_backend_buffer_is_host(model.buf)) { - model.mlock_buf.init (ggml_backend_buffer_get_base(model.buf)); - model.mlock_buf.grow_to(ggml_backend_buffer_get_size(model.buf)); + if (buf == nullptr) { + throw std::runtime_error("failed to allocate buffer"); + } + // indicate that this buffer contains weights + // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight + ggml_backend_buffer_set_usage(buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); + model.bufs.push_back(buf); + ctx_bufs.emplace_back(ctx, buf); } // print memory requirements { - size_t sys_mem_required = ctx_size + buf_size; - - if (sys_mem_required > 0) { - LLAMA_LOG_INFO("%s: system memory used = %7.2f MiB\n", __func__, sys_mem_required / 1024.0 / 1024.0); - } - if (vram_weights > 0) { - LLAMA_LOG_INFO("%s: VRAM used = %7.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0); - } - -#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); @@ -3976,23 +3817,26 @@ static bool llm_load_tensors( const int max_offloadable_layers = hparams.n_layer + 1; LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); -#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) - } -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - ggml_cuda_set_tensor_split(tensor_split); -#else - GGML_UNUSED(tensor_split); -#endif // GGML_USE_CUBLAS + for (ggml_backend_buffer_t buf : model.bufs) { + LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); + } + } // populate tensors_by_name - for (int i = 0; i < ml.n_tensors; ++i) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i)); - model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); + for (ggml_context * ctx : model.ctxs) { + for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { + model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); + } } - if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL)) { - return false; + // load tensor data + for (auto & it : ctx_bufs) { + ggml_context * ctx = it.first; + ggml_backend_buffer_t buf = it.second; + if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf, use_mlock ? &model.mlock_mmap : NULL)) { + return false; + } } model.mapping = std::move(ml.mapping); @@ -4026,13 +3870,13 @@ static int llama_model_load(const std::string & fname, llama_model & model, cons } if (!llm_load_tensors( - ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock, + ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data )) { return -2; } } catch (const std::exception & err) { - LLAMA_LOG_ERROR("error loading model: %s\n", err.what()); + LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what()); return -1; } @@ -4476,8 +4320,6 @@ struct llm_build_context { do_rope_shift (worst_case || kv_self.has_shift), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { - GGML_ASSERT(!!kv_self.ctx); - // all initializations should be done in init() } @@ -4557,6 +4399,12 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } + // these nodes are added to the graph together so that they are not reordered + // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(gf, Qcur); + ggml_build_forward_expand(gf, Kcur); + ggml_build_forward_expand(gf, Vcur); + Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, @@ -6077,199 +5925,13 @@ struct llm_build_context { } }; -// -// tensor offloading helpers -// -// TODO: will be removed with backend v2 - -enum llm_offload_func_e { - OFFLOAD_FUNC_NOP, - OFFLOAD_FUNC, - OFFLOAD_FUNC_FRC, // force offload - OFFLOAD_FUNC_KQV, - OFFLOAD_FUNC_NR, - OFFLOAD_FUNC_EMB, // embeddings - OFFLOAD_FUNC_OUT, -}; - -// TODO: will be removed with backend v2 -struct llm_offload_trie { - struct node { - ~node() { - for (int i = 0; i < 256; ++i) { - if (children[i]) { - delete children[i]; - } - } - } - - node * children[256] = { nullptr }; - llm_offload_func_e func = OFFLOAD_FUNC_NOP; - }; - - llm_offload_trie() { - root = new node; - } - - llm_offload_trie(const std::unordered_map & map) { - root = new node; - - for (const auto & kv : map) { - add(kv.first, kv.second); - } - } - - ~llm_offload_trie() { - delete root; - } - - void add(const char * name, llm_offload_func_e func) { - node * cur = root; - - for (int i = 0; ; ++i) { - const uint8_t c = name[i]; - - if (!c) { - break; - } - - if (!cur->children[c]) { - cur->children[c] = new node; - } - - cur = cur->children[c]; - } - - cur->func = func; - } - - llm_offload_func_e find(const char * name) const { - const node * cur = root; - - for (int i = 0; ; ++i) { - const uint8_t c = name[i]; - - if (!c) { - break; - } - - if (!cur->children[c]) { - return OFFLOAD_FUNC_NOP; - } - - cur = cur->children[c]; - } - - return cur->func; - } - - node * root = nullptr; -}; - -// TODO: will be removed with backend v2 -static const std::unordered_map k_offload_map = { - //{ "inp_tokens", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel - //{ "inp_embd", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel - { "pos_embd", OFFLOAD_FUNC_NR }, - - { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope) - { "KQ_mask", OFFLOAD_FUNC_FRC }, - { "K_shift", OFFLOAD_FUNC_FRC }, - - { "K_shifted", OFFLOAD_FUNC }, - - { "inp_norm", OFFLOAD_FUNC_NR }, - { "inp_norm_w", OFFLOAD_FUNC_NR }, - { "inp_norm_wb", OFFLOAD_FUNC_NR }, - - { "norm", OFFLOAD_FUNC }, - { "norm_w", OFFLOAD_FUNC }, - { "norm_wb", OFFLOAD_FUNC }, - - { "attn_norm", OFFLOAD_FUNC }, - { "attn_norm_2", OFFLOAD_FUNC }, - - { "wqkv", OFFLOAD_FUNC_KQV }, - { "bqkv", OFFLOAD_FUNC_KQV }, - { "wqkv_clamped", OFFLOAD_FUNC_KQV }, - - { "tmpk", OFFLOAD_FUNC_KQV }, - { "tmpq", OFFLOAD_FUNC_KQV }, - { "tmpv", OFFLOAD_FUNC_KQV }, - { "Kcur", OFFLOAD_FUNC_KQV }, - { "Qcur", OFFLOAD_FUNC_KQV }, - { "Vcur", OFFLOAD_FUNC_KQV }, - - { "krot", OFFLOAD_FUNC_KQV }, - { "qrot", OFFLOAD_FUNC_KQV }, - { "kpass", OFFLOAD_FUNC_KQV }, - { "qpass", OFFLOAD_FUNC_KQV }, - { "krotated", OFFLOAD_FUNC_KQV }, - { "qrotated", OFFLOAD_FUNC_KQV }, - - { "q", OFFLOAD_FUNC_KQV }, - { "k", OFFLOAD_FUNC_KQV }, - { "kq", OFFLOAD_FUNC_KQV }, - { "kq_scaled", OFFLOAD_FUNC_KQV }, - { "kq_scaled_alibi", OFFLOAD_FUNC_KQV }, - { "kq_masked", OFFLOAD_FUNC_KQV }, - { "kq_soft_max", OFFLOAD_FUNC_KQV }, - { "kq_soft_max_ext", OFFLOAD_FUNC_KQV }, - { "v", OFFLOAD_FUNC_KQV }, - { "kqv", OFFLOAD_FUNC_KQV }, - { "kqv_merged", OFFLOAD_FUNC_KQV }, - { "kqv_merged_cont", OFFLOAD_FUNC_KQV }, - { "kqv_wo", OFFLOAD_FUNC_KQV }, - { "kqv_out", OFFLOAD_FUNC_KQV }, - - { "ffn_inp", OFFLOAD_FUNC }, - { "ffn_norm", OFFLOAD_FUNC }, - - { "ffn_up", OFFLOAD_FUNC }, - { "ffn_up_b", OFFLOAD_FUNC }, - { "ffn_gate", OFFLOAD_FUNC }, - { "ffn_gate_b", OFFLOAD_FUNC }, - { "ffn_gate_par", OFFLOAD_FUNC }, - { "ffn_act", OFFLOAD_FUNC }, - { "ffn_down", OFFLOAD_FUNC }, - { "ffn_down_b", OFFLOAD_FUNC }, - { "ffn_out", OFFLOAD_FUNC }, - - { "ffn_silu", OFFLOAD_FUNC }, - { "ffn_gelu", OFFLOAD_FUNC }, - { "ffn_relu", OFFLOAD_FUNC }, - { "ffn_sqr(relu)", OFFLOAD_FUNC }, - - { "ffn_moe_logits", OFFLOAD_FUNC }, - { "ffn_moe_probs", OFFLOAD_FUNC }, - { "ffn_moe_argsort", OFFLOAD_FUNC }, - { "ffn_moe_weights", OFFLOAD_FUNC }, - { "ffn_moe_weights_sum", OFFLOAD_FUNC }, - { "ffn_moe_weights_norm", OFFLOAD_FUNC }, - { "ffn_moe_weighted", OFFLOAD_FUNC }, - { "ffn_moe_up", OFFLOAD_FUNC }, - { "ffn_moe_gate", OFFLOAD_FUNC }, - { "ffn_moe_silu", OFFLOAD_FUNC }, - { "ffn_moe_gate_par", OFFLOAD_FUNC }, - { "ffn_moe_down", OFFLOAD_FUNC }, - { "ffn_moe_out", OFFLOAD_FUNC }, - - { "l_out", OFFLOAD_FUNC }, - - { "result_norm", OFFLOAD_FUNC_EMB }, - { "result_output_no_bias", OFFLOAD_FUNC_EMB }, - { "result_output", OFFLOAD_FUNC_OUT }, -}; - -static llm_offload_trie k_offload_func_trie(k_offload_map); - static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch) { const auto & model = lctx.model; // check if we should build the worst-case graph (for memory measurement) - const bool worst_case = ggml_allocr_is_measure(lctx.alloc); + const bool worst_case = ggml_tallocr_is_measure(lctx.alloc); // keep track of the input that has already been allocated bool alloc_inp_tokens = false; @@ -6278,16 +5940,8 @@ static struct ggml_cgraph * llama_build_graph( bool alloc_inp_KQ_mask = false; bool alloc_inp_K_shift = false; -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - const bool do_offload = true; -#else - const bool do_offload = true; // TODO: set to false after finishing refactoring -#endif - - int n_non_view = 0; // number of non-view tensors that have been processed by the callback - // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) - // TODO: will be removed with backend v2 + // TODO: improve handling of input and output tensors, then replace this with ggml_set_name llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) { if (il >= 0) { ggml_format_name(cur, "%s-%d", name, il); @@ -6298,12 +5952,11 @@ static struct ggml_cgraph * llama_build_graph( // // allocate input tensors and set input data // - // TODO: will be removed with backend v2 if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) { + if (!ggml_tallocr_is_measure(lctx.alloc) && batch.token) { const int64_t n_tokens = cur->ne[0]; ggml_backend_tensor_set(cur, batch.token, 0, n_tokens*ggml_element_size(cur)); @@ -6312,10 +5965,10 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_tokens = true; } - if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0 && batch.embd) { + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) { + if (!ggml_tallocr_is_measure(lctx.alloc) && batch.embd) { const int64_t n_embd = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; @@ -6326,9 +5979,9 @@ static struct ggml_cgraph * llama_build_graph( } if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) { + if (!ggml_tallocr_is_measure(lctx.alloc) && batch.pos) { const int64_t n_tokens = cur->ne[0]; static_assert(std::is_same::value, "llama_pos must be int32_t"); @@ -6339,9 +5992,9 @@ static struct ggml_cgraph * llama_build_graph( } if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc)) { + if (!ggml_tallocr_is_measure(lctx.alloc)) { const int64_t n_kv = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; @@ -6379,9 +6032,9 @@ static struct ggml_cgraph * llama_build_graph( } if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc)) { + if (!ggml_tallocr_is_measure(lctx.alloc)) { const int64_t n_ctx = cur->ne[0]; int32_t * data; @@ -6403,136 +6056,6 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_K_shift = true; } - - // view tensors are not processed further - if (cur->view_src != nullptr) { - return; - } - - if (cur->op != GGML_OP_NONE) { - n_non_view++; - } - - // - // offload layers - // - // TODO: will be removed with backend v2 - -//#define LLAMA_OFFLOAD_DEBUG - - if (!do_offload) { - return; - } - - const int n_layer = model.hparams.n_layer; - - const int n_gpu_layers = model.n_gpu_layers; - const int i_gpu_start = n_layer - n_gpu_layers; - - // should we offload the final norm? yes if we are not computing embeddings - const bool offload_emb = lctx.embedding.empty(); - - static const std::unordered_map> k_offload_func_name = { - { OFFLOAD_FUNC_NOP, "CPU" }, - { OFFLOAD_FUNC_OUT, "CPU" }, -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - { OFFLOAD_FUNC, "GPU (CUDA)" }, - { OFFLOAD_FUNC_FRC, "GPU (CUDA) FRC" }, - { OFFLOAD_FUNC_KQV, "GPU (CUDA) KQV" }, - { OFFLOAD_FUNC_NR, "GPU (CUDA) NR" }, - { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" }, -#else - { OFFLOAD_FUNC, "CPU" }, - { OFFLOAD_FUNC_FRC, "CPU" }, - { OFFLOAD_FUNC_KQV, "CPU" }, - { OFFLOAD_FUNC_NR, "CPU" }, - { OFFLOAD_FUNC_EMB, "CPU" }, -#endif // GGML_USE_CUBLAS - }; - - // check the global map for what offload function to use for this tensor - llm_offload_func_e func_e = k_offload_func_trie.find(name); - - if (func_e == OFFLOAD_FUNC_NOP) { -#ifdef LLAMA_OFFLOAD_DEBUG - // if a tensor hasn't been offloaded, we warn the user - if (worst_case) { - LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__, - cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837"); - } -#endif - - return; - } - - // count the number of layers and respect the provided n_gpu_layers - switch (func_e) { - case OFFLOAD_FUNC_NOP: - case OFFLOAD_FUNC_OUT: - break; - case OFFLOAD_FUNC: - if (n_gpu_layers < n_layer) { - if (il < i_gpu_start) { - func_e = OFFLOAD_FUNC_NOP; - } - } - break; - case OFFLOAD_FUNC_FRC: - if (!lctx.cparams.offload_kqv) { - func_e = OFFLOAD_FUNC_NOP; - } break; - case OFFLOAD_FUNC_KQV: - if (!lctx.cparams.offload_kqv) { - func_e = OFFLOAD_FUNC_NOP; - } else { - if (n_gpu_layers < n_layer) { - if (il < i_gpu_start) { - func_e = OFFLOAD_FUNC_NOP; - } - } - } - break; - case OFFLOAD_FUNC_NR: - if (n_gpu_layers <= n_layer + 0) { - func_e = OFFLOAD_FUNC_NOP; - } - break; - case OFFLOAD_FUNC_EMB: - if (!offload_emb || n_gpu_layers < n_layer) { - func_e = OFFLOAD_FUNC_NOP; - } - break; - default: GGML_ASSERT(false); - } - - offload_func_t func = ggml_offload_nop; - - // this is needed for compatibility with Metal for example -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc; -#else - static offload_func_t ggml_offload_gpu = ggml_offload_nop; -#endif - - switch (func_e) { - case OFFLOAD_FUNC_NOP: - case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break; - case OFFLOAD_FUNC: - case OFFLOAD_FUNC_KQV: - case OFFLOAD_FUNC_FRC: - case OFFLOAD_FUNC_NR: - case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break; - default: GGML_ASSERT(false); - } - - // apply offload function to the tensor - func(cur); - -#ifdef LLAMA_OFFLOAD_DEBUG - if (worst_case) { - LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str()); - } -#endif }; struct ggml_cgraph * result = NULL; @@ -6600,27 +6123,6 @@ static struct ggml_cgraph * llama_build_graph( llm.free(); - if (worst_case) { - int n_non_view_total = 0; - - for (int i = 0; i < result->n_nodes; ++i) { - if (result->nodes[i]->view_src == nullptr) { - n_non_view_total++; - } - } - - LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total); - - if (n_non_view != n_non_view_total) { - LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__); - LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n", __func__); - LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n", __func__); - LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n", __func__); - LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n", __func__); - LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__); - } - } - return result; } @@ -6666,8 +6168,6 @@ static int llama_decode_internal( auto & kv_self = lctx.kv_self; - GGML_ASSERT(!!kv_self.ctx); - const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; @@ -6721,12 +6221,10 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - ggml_allocr_reset(lctx.alloc); + ggml_backend_sched_reset(lctx.sched); ggml_cgraph * gf = llama_build_graph(lctx, batch); - ggml_allocr_alloc_graph(lctx.alloc, gf); - // the output is always the last tensor in the graph struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; GGML_ASSERT(strcmp(res->name, "result_output") == 0); @@ -6738,30 +6236,6 @@ static int llama_decode_internal( GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); } -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - char * buf_alloc_base = (char *)ggml_backend_buffer_get_base(lctx.buf_alloc); - for (int i = 0; i < gf->n_leafs; i++) { - ggml_tensor * node = gf->leafs[i]; - if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) { - ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base); - ggml_cuda_copy_to_device(node); - } - } - - for (int i = 0; i < gf->n_nodes; i++) { - ggml_tensor * node = gf->nodes[i]; - if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) { - ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base); - } - } - - // HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed - if (!lctx.embedding.empty()) { - embeddings->backend = GGML_BACKEND_CPU; - } - res->backend = GGML_BACKEND_CPU; -#endif - // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); // for big prompts, if BLAS is enabled, it is better to use only one thread @@ -6784,15 +6258,17 @@ static int llama_decode_internal( #endif #ifdef GGML_USE_METAL - if (ggml_backend_is_metal(lctx.backend)) { - ggml_backend_metal_set_n_cb(lctx.backend, n_threads); + if (ggml_backend_is_metal(lctx.backend_metal)) { + ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); } #endif - if (ggml_backend_is_cpu(lctx.backend)) { - ggml_backend_cpu_set_n_threads(lctx.backend, n_threads); + if (lctx.backend_cpu != nullptr) { + ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); } - ggml_backend_graph_compute(lctx.backend, gf); + ggml_backend_sched_graph_compute(lctx.sched, gf); + + // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); #ifdef GGML_USE_MPI ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); @@ -6840,30 +6316,33 @@ static int llama_decode_internal( logits_out.clear(); #endif + ggml_backend_t res_backend = ggml_backend_sched_get_node_backend(lctx.sched, res); + GGML_ASSERT(res_backend != nullptr); if (batch.logits) { logits_out.resize(n_vocab * n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { if (batch.logits[i] == 0) { continue; } - ggml_backend_tensor_get(res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(res_backend, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[i] = true; #endif } } else if (lctx.logits_all) { logits_out.resize(n_vocab * n_tokens); - ggml_backend_tensor_get(res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); + ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); #endif } else { logits_out.resize(n_vocab); - ggml_backend_tensor_get(res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[0] = true; #endif } + ggml_backend_synchronize(res_backend); } // extract embeddings @@ -6871,7 +6350,9 @@ static int llama_decode_internal( auto & embedding_out = lctx.embedding; embedding_out.resize(n_embd); - ggml_backend_tensor_get(embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float)); + ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings); + ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float)); + ggml_backend_synchronize(embeddings_backend); } // measure the performance only for the single-token evals @@ -9347,48 +8828,23 @@ static int llama_apply_lora_from_file_internal( LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling); - // create a name -> tensor map of the model to accelerate lookups - // find the max tensor size to estimate the required temporary buffer size - size_t max_tensor_size = 0; - std::unordered_map model_tensors; - for (const auto & kv : model.tensors_by_name) { - model_tensors.insert(kv); - size_t f32_size = ggml_nelements(kv.second) * sizeof(float); - max_tensor_size = std::max(max_tensor_size, f32_size); - } - - // create a temporary ggml context to store the lora tensors - // TODO: use ggml-alloc - size_t lora_ctx_size = max_tensor_size * 3; - LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0); - std::vector lora_buf(lora_ctx_size); - - struct ggml_init_params params; - params.mem_size = lora_buf.size(); - params.mem_buffer = lora_buf.data(); - params.no_alloc = false; - - using unique_context = std::unique_ptr; - - unique_context lora_ctx(nullptr, ggml_free); - lora_ctx.reset(ggml_init(params)); - std::unordered_map lora_tensors; - // load base model std::unique_ptr ml; - - if (path_base_model) { + if (path_base_model) { LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr)); - ml->init_mapping(false); // no prefetching + ml->init_mapping(/*prefetch*/ false); // no prefetching } - // read tensors and apply - bool warned = false; - int n_tensors = 0; - - std::vector work_buffer; + struct tensor_meta { + std::string name; + ggml_type type; + int32_t ne[2]; + size_t offset; + }; + std::map tensor_meta_map; + // load all tensor meta while (true) { if (fin.tell() == fin.size) { // eof @@ -9401,7 +8857,7 @@ static int llama_apply_lora_from_file_internal( fin.read_raw(&n_dims, sizeof(n_dims)); fin.read_raw(&name_len, sizeof(name_len)); - fin.read_raw(&ftype, sizeof(ftype)); + fin.read_raw(&ftype, sizeof(ftype)); if (n_dims != 1 && n_dims != 2) { LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims); @@ -9415,31 +8871,23 @@ static int llama_apply_lora_from_file_internal( std::string name; { - GGML_ASSERT(name_len <= 1024); - char buf[1024]; + GGML_ASSERT(name_len < GGML_MAX_NAME); + char buf[GGML_MAX_NAME]; fin.read_raw(buf, name_len); name = std::string(buf, name_len); } - // check for lora suffix and get the type of tensor - const std::string lora_suffix = ".lora"; - size_t pos = name.rfind(lora_suffix); - if (pos == std::string::npos) { + // check for lora suffix + std::string lora_suffix; + if (name.length() > 6) { + lora_suffix = name.substr(name.length() - 6); + } + if (lora_suffix != ".loraA" && lora_suffix != ".loraB") { LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str()); return 1; } - std::string lora_type = name.substr(pos + lora_suffix.length()); - std::string base_name = name; - base_name.erase(pos); - // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str()); - - if (model_tensors.find(base_name) == model_tensors.end()) { - LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data()); - return 1; - } - - // create ggml tensor + // tensor type ggml_type wtype; switch (ftype) { case 0: wtype = GGML_TYPE_F32; break; @@ -9451,122 +8899,177 @@ static int llama_apply_lora_from_file_internal( return false; } } - ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]); - ggml_set_name(lora_tensor, name.c_str()); - // load tensor data + // data offset size_t offset = fin.tell(); - size_t tensor_data_size = ggml_nbytes(lora_tensor); offset = (offset + 31) & -32; - fin.seek(offset, SEEK_SET); - fin.read_raw(lora_tensor->data, tensor_data_size); - lora_tensors[name] = lora_tensor; + // skip tensor data + fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET); - // check if we have both A and B tensors and apply - if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() && - lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) { + tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset }); + } - ggml_tensor * dest_t = model_tensors[base_name]; + bool warned = false; + int n_tensors = 0; - offload_func_t offload_func = ggml_offload_nop; - offload_func_t offload_func_force_inplace = ggml_offload_nop; + // apply + ggml_backend_t backend_cpu = ggml_backend_cpu_init(); + if (backend_cpu == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__); + return 1; + } + ggml_backend_cpu_set_n_threads(backend_cpu, n_threads); -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) { - if (dest_t->type != GGML_TYPE_F16) { - throw std::runtime_error(format( - "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type)); - } - offload_func = ggml_cuda_assign_buffers; - offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace; - } -#endif // GGML_USE_CUBLAS + std::vector> read_buf; + for (const auto & it : model.tensors_by_name) { + const std::string & base_name = it.first; + ggml_tensor * model_t = it.second; - ggml_tensor * base_t; - if (ml) { - struct gguf_context * ctx_gguf = ml->ctx_gguf; + if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() || + tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) { + continue; + } - // load from base model - if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) { - LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); - return 1; - } + tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA"); + tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB"); - base_t = ml->get_tensor_meta(base_name.c_str()); - ml->load_data_for(base_t); - } else { - base_t = dest_t; - } + ggml_init_params lora_init_params = { + /* .mem_size */ ggml_tensor_overhead()*128 + ggml_graph_overhead(), + /* .mem_buffer */ nullptr, + /* .no_alloc */ true, + }; + ggml_context * lora_ctx = ggml_init(lora_init_params); + if (lora_ctx == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__); + ggml_backend_free(backend_cpu); + return 1; + } - if (ggml_is_quantized(base_t->type)) { - if (!warned) { - LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, " - "use a f16 or f32 base model with --lora-base\n", __func__); - warned = true; - } - } + // create tensors + ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]); + ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]); + ggml_set_name(loraA, metaA.name.c_str()); + ggml_set_name(loraB, metaB.name.c_str()); - ggml_tensor * loraA = lora_tensors[base_name + ".loraA"]; - GGML_ASSERT(loraA->type == GGML_TYPE_F32); - ggml_set_name(loraA, "loraA"); - - ggml_tensor * loraB = lora_tensors[base_name + ".loraB"]; - GGML_ASSERT(loraB->type == GGML_TYPE_F32); - ggml_set_name(loraB, "loraB"); - - if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { - LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" - " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]); + ggml_tensor * base_t; + if (ml) { + if (gguf_find_tensor(ml->ctx_gguf, base_name.c_str()) < 0) { + LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); return 1; } + base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str())); + } else { + base_t = ggml_dup_tensor(lora_ctx, model_t); + } + ggml_set_name(base_t, base_name.c_str()); + // allocate in backend buffer + ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type()); + if (lora_buf == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__); + return 1; + } + + // load tensor data + auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) { + read_buf.resize(ggml_nbytes(tensor)); + fin.seek(tensor_meta.offset, SEEK_SET); + fin.read_raw(read_buf.data(), ggml_nbytes(tensor)); + ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size()); + }; + load_tensor(metaA, loraA); + load_tensor(metaB, loraB); + + // load base model tensor data + if (ml) { + ml->load_data_for(base_t); + } else { + ggml_backend_tensor_copy(model_t, base_t); + } + + if (ggml_is_quantized(base_t->type) && !warned) { + LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, " + "use a f16 or f32 base model with --lora-base\n", __func__); + warned = true; + } + + if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { + LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" + " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]); + ggml_free(lora_ctx); + ggml_backend_buffer_free(lora_buf); + ggml_backend_free(backend_cpu); + return 1; + } + + auto build_lora_graph = [&]() { // w = w + BA*s - ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB); - offload_func(BA); + ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB); ggml_set_name(BA, "BA"); if (scaling != 1.0f) { - BA = ggml_scale_inplace(lora_ctx.get(), BA, scaling); - offload_func(BA); + BA = ggml_scale(lora_ctx, BA, scaling); ggml_set_name(BA, "BA_scaled"); } ggml_tensor * r; - if (base_t == dest_t) { - r = ggml_add_inplace(lora_ctx.get(), dest_t, BA); - offload_func_force_inplace(r); - ggml_set_name(r, "r_add_inplace"); - } - else { - r = ggml_add(lora_ctx.get(), base_t, BA); - offload_func(r); - ggml_set_name(r, "r_add"); + r = ggml_add_inplace(lora_ctx, base_t, BA); + ggml_set_name(r, "r_add"); - r = ggml_cpy(lora_ctx.get(), r, dest_t); - offload_func(r); - ggml_set_name(r, "r_cpy"); + if (base_t->type != model_t->type) { + // convert the result to the model type + r = ggml_cast(lora_ctx, r, model_t->type); + ggml_set_name(r, "r_cast"); } - struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get()); - ggml_build_forward_expand(gf, r); + return r; + }; - ggml_graph_compute_helper(work_buffer, gf, n_threads); + ggml_cgraph * gf = ggml_new_graph(lora_ctx); + ggml_tensor * r = build_lora_graph(); + ggml_build_forward_expand(gf, r); - // the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other - GGML_ASSERT(lora_tensors.size() == 2); + ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type()); + if (graph_buf == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__); + ggml_free(lora_ctx); + ggml_backend_buffer_free(lora_buf); + ggml_backend_free(backend_cpu); + return 1; + } - // we won't need these tensors again, reset the context to save memory - lora_ctx.reset(ggml_init(params)); - lora_tensors.clear(); + ggml_backend_graph_compute(backend_cpu, gf); - n_tensors++; - if (n_tensors % 4 == 0) { - LLAMA_LOG_INFO("."); - } + ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r)); + +#if 0 + // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU + //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE); + + // sched compute + ggml_build_forward_expand(gf, build_graph()); + ggml_backend_sched_init_measure(sched, gf); + + // create the graph again, since the previous one was destroyed by the measure + ggml_graph_clear(gf); + ggml_build_forward_expand(gf, build_graph()); + ggml_backend_sched_graph_compute(sched, gf); + ggml_backend_sched_free(sched); +#endif + + ggml_backend_buffer_free(lora_buf); + ggml_backend_buffer_free(graph_buf); + ggml_free(lora_ctx); + + n_tensors++; + if (n_tensors % 4 == 0) { + LLAMA_LOG_INFO("."); } } + ggml_backend_free(backend_cpu); + const int64_t t_lora_us = ggml_time_us() - t_start_lora_us; LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0); @@ -9579,6 +9082,7 @@ static int llama_apply_lora_from_file_internal( struct llama_model_params llama_model_default_params() { struct llama_model_params result = { /*.n_gpu_layers =*/ 0, + /*.split_mode =*/ LLAMA_SPLIT_LAYER, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, /*.progress_callback =*/ nullptr, @@ -9590,7 +9094,8 @@ struct llama_model_params llama_model_default_params() { }; #ifdef GGML_USE_METAL - result.n_gpu_layers = 1; + // note: we usually have plenty of VRAM, so by default offload all layers to the GPU + result.n_gpu_layers = 999; #endif return result; @@ -9780,41 +9285,53 @@ struct llama_context * llama_new_context_with_model( GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); - // reserve memory for context buffers if (!hparams.vocab_only) { - // initialize backend + // initialize backends #ifdef GGML_USE_METAL if (model->n_gpu_layers > 0) { - ctx->backend = ggml_backend_metal_init(); - if (ctx->backend == nullptr) { + ctx->backend_metal = ggml_backend_metal_init(); + if (ctx->backend_metal == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__); + llama_free(ctx); + return nullptr; } + ctx->backends.push_back(ctx->backend_metal); } -#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) - // for testing only +#elif defined(GGML_USE_CUBLAS) if (model->n_gpu_layers > 0) { - ctx->backend = ggml_backend_cuda_init(0); - if (ctx->backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CUDA backend\n", __func__); + // with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used + if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) { + ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } else { + // LLAMA_SPLIT_LAYER requires a backend for each GPU + for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) { + ggml_backend_t backend = ggml_backend_cuda_init(device); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } } } #endif - - if (ctx->backend == nullptr && ggml_backend_buffer_is_host(model->buf)) { - ctx->backend = ggml_backend_cpu_init(); - if (ctx->backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); - } - } - - if (ctx->backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize a backend\n", __func__); - delete ctx; + ctx->backend_cpu = ggml_backend_cpu_init(); + if (ctx->backend_cpu == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); + llama_free(ctx); return nullptr; } + ctx->backends.push_back(ctx->backend_cpu); - if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, type_k, type_v, - cparams.n_ctx, model->n_gpu_layers, cparams.offload_kqv)) { + if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, + cparams.n_ctx, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -9850,11 +9367,22 @@ struct llama_context * llama_new_context_with_model( } { - // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data + // buffer types used for the compute buffer of each backend + std::vector backend_buft; + for (auto * backend : ctx->backends) { + if (ggml_backend_is_cpu(backend)) { + // use host buffers for the CPU backend compute buffer + backend_buft.push_back(llama_default_buffer_type_cpu(true)); + } else { + backend_buft.push_back(ggml_backend_get_default_buffer_type(backend)); + } + } + + // buffer used to store the computation graph and the tensor meta data ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); - // create measure allocator - ctx->alloc = ggml_allocr_new_measure_from_backend(ctx->backend); + ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES); + ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); // build worst-case graph int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch); @@ -9862,50 +9390,19 @@ struct llama_context * llama_new_context_with_model( llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0)); - // measure memory requirements for the graph - size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf); + // initialize scheduler with the worst-case graph + ggml_backend_sched_init_measure(ctx->sched, gf); + // note: the number of splits during measure is higher than during inference due to the kv shift + int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); + LLAMA_LOG_INFO("%s: graph splits (measure): %d\n", __func__, n_splits); + ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); - LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute_meta.size() + alloc_size) / 1024.0 / 1024.0); - - // create allocator again with exact memory requirements - ggml_allocr_free(ctx->alloc); - - ctx->buf_alloc = ggml_backend_alloc_buffer(ctx->backend, alloc_size); - ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc); -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (model->n_gpu_layers > 0) { - // the CPU buffer adds this padding in case the malloc buffer is not aligned, so we need to do the same for the GPU buffer, since we use the same offsets - ggml_cuda_set_scratch_size(alloc_size + 64); - LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0); - - // calculate total VRAM usage - auto add_tensor = [](const ggml_tensor * t, size_t & size) { - if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) { - size += ggml_nbytes(t); - } - }; - size_t model_vram_size = 0; - for (const auto & kv : model->tensors_by_name) { - add_tensor(kv.second, model_vram_size); - } - - size_t kv_vram_size = 0; - for (auto & k : ctx->kv_self.k_l) { - add_tensor(k, kv_vram_size); - } - for (auto & v : ctx->kv_self.v_l) { - add_tensor(v, kv_vram_size); - } - - size_t ctx_vram_size = alloc_size + kv_vram_size; - size_t total_vram_size = model_vram_size + ctx_vram_size; - - LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__, - total_vram_size / 1024.0 / 1024.0, - model_vram_size / 1024.0 / 1024.0, - ctx_vram_size / 1024.0 / 1024.0); + for (ggml_backend_t backend : ctx->backends) { + ggml_backend_buffer_t buf = ggml_backend_sched_get_buffer(ctx->sched, backend); + LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, + ggml_backend_buffer_name(buf), + ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); } -#endif } } @@ -10002,9 +9499,8 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3 } int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { - return snprintf(buf, buf_size, "%s %s%s %s", + return snprintf(buf, buf_size, "%s %s %s", llama_model_arch_name(model->arch).c_str(), - model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } @@ -10026,7 +9522,14 @@ uint64_t llama_model_n_params(const struct llama_model * model) { } struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) { - return ggml_get_tensor(model->ctx, name); + auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(), + [name](const std::pair & it) { + return it.first == name; + }); + if (it == model->tensors_by_name.end()) { + return nullptr; + } + return it->second; } uint32_t llama_model_quantize( @@ -10211,7 +9714,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_embedding = ctx->embedding.size() * sizeof(float); const size_t s_kv_size = sizeof(size_t); const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = ggml_backend_buffer_get_size(ctx->kv_self.buf); + const size_t s_kv = ctx->kv_self.total_size(); const size_t s_total = ( + s_rng_size @@ -10340,7 +9843,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto n_embd_v_gqa = hparams.n_embd_v_gqa(); const auto n_ctx = cparams.n_ctx; - const size_t kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf); + const size_t kv_buf_size = kv_self.total_size(); const uint32_t kv_head = kv_self.head; const uint32_t kv_size = kv_self.size; const uint32_t kv_used = kv_self.used; @@ -10353,46 +9856,19 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat if (kv_buf_size) { const size_t elt_size = ggml_element_size(kv_self.k_l[0]); - ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); - ggml_cgraph * gf = ggml_new_graph(cpy_ctx); - - std::vector kout2d(n_layer); - std::vector vout2d(n_layer); - - for (int il = 0; il < (int) n_layer; ++il) { - kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head); - vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa); - - ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], - n_embd_k_gqa, kv_head, - elt_size*n_embd_k_gqa, 0); - - ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il], - kv_head, n_embd_v_gqa, - elt_size*n_ctx, 0); - - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il])); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v2d, vout2d[il])); - } - - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend); - - ggml_backend_graph_compute(ctx->backend, gf); - std::vector tmp_buf; for (int il = 0; il < (int) n_layer; ++il) { - tmp_buf.resize(ggml_nbytes(kout2d[il])); - ggml_backend_tensor_get(kout2d[il], tmp_buf.data(), 0, tmp_buf.size()); + tmp_buf.resize(elt_size*n_embd_k_gqa*kv_head); + ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); - tmp_buf.resize(ggml_nbytes(vout2d[il])); - ggml_backend_tensor_get(vout2d[il], tmp_buf.data(), 0, tmp_buf.size()); - data_ctx->write(tmp_buf.data(), tmp_buf.size()); + // v is not contiguous, copy row by row + tmp_buf.resize(elt_size*kv_head); + for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*elt_size*n_ctx, tmp_buf.size()); + data_ctx->write(tmp_buf.data(), tmp_buf.size()); + } } - - ggml_free(cpy_ctx); - - ggml_backend_buffer_free(buf); } for (uint32_t i = 0; i < kv_size; ++i) { @@ -10491,48 +9967,22 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used); if (kv_buf_size) { - GGML_ASSERT(ggml_backend_buffer_get_size(kv_self.buf) == kv_buf_size); + GGML_ASSERT(kv_self.total_size() == kv_buf_size); const size_t elt_size = ggml_element_size(kv_self.k_l[0]); - ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); - ggml_cgraph * gf = ggml_new_graph(cpy_ctx); + for (int il = 0; il < (int) n_layer; ++il) { + size_t k_size = elt_size*n_embd_k_gqa*kv_head; + ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); + inp += k_size; - std::vector kin2d(n_layer); - std::vector vin2d(n_layer); - - for (int il = 0; il < n_layer; ++il) { - kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head); - vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa); - - ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], - n_embd_k_gqa, kv_head, - elt_size*n_embd_k_gqa, 0); - - ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il], - kv_head, n_embd_v_gqa, - elt_size*n_ctx, 0); - - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d)); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin2d[il], v2d)); + // v is not contiguous, copy row by row + size_t v_row_size = elt_size*kv_head; + for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { + ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*elt_size*n_ctx, v_row_size); + inp += v_row_size; + } } - - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend); - - // load data into the tensors - for (int il = 0; il < n_layer; ++il) { - ggml_backend_tensor_set(kin2d[il], inp, 0, ggml_nbytes(kin2d[il])); - inp += ggml_nbytes(kin2d[il]); - - ggml_backend_tensor_set(vin2d[il], inp, 0, ggml_nbytes(vin2d[il])); - inp += ggml_nbytes(vin2d[il]); - } - - ggml_backend_graph_compute(ctx->backend, gf); - - ggml_free(cpy_ctx); - - ggml_backend_buffer_free(buf); } ctx->kv_self.head = kv_head; diff --git a/llama.h b/llama.h index 43d41b8f6..689e12d7c 100644 --- a/llama.h +++ b/llama.h @@ -118,6 +118,12 @@ extern "C" { LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, }; + enum llama_split_mode { + LLAMA_SPLIT_NONE = 0, // single GPU + LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs + LLAMA_SPLIT_ROW = 2, // split rows across GPUs + }; + typedef struct llama_token_data { llama_token id; // token id float logit; // log-odds of the token @@ -180,8 +186,16 @@ extern "C" { struct llama_model_params { int32_t n_gpu_layers; // number of layers to store in VRAM - int32_t main_gpu; // the GPU that is used for scratch and small tensors - const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES) + enum llama_split_mode split_mode; // how to split the model across multiple GPUs + + // main_gpu interpretation depends on split_mode: + // LLAMA_SPLIT_NONE: the GPU that is used for the entire model + // LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results + // LLAMA_SPLIT_LAYER: ignored + int32_t main_gpu; + + // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES + const float * tensor_split; // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. // If the provided progress_callback returns true, model loading continues. diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 7a60d7743..d9b8b106a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -376,6 +376,11 @@ struct test_case { // allocate ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1); + if (buf == NULL) { + printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1)); + ggml_free(ctx); + return false; + } // build graph ggml_build_forward_expand(gf, out); @@ -463,19 +468,23 @@ struct test_case { GGML_UNUSED(index); }; - ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud); + const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud); - if (ud.ok) { - printf("\033[1;32mOK\033[0m\n"); - } else { - printf("\033[1;31mFAIL\033[0m\n"); + if (!cmp_ok) { + printf("compare failed "); } ggml_backend_buffer_free(buf); ggml_free(ctx); - return ud.ok; + if (ud.ok && cmp_ok) { + printf("\033[1;32mOK\033[0m\n"); + return true; + } + + printf("\033[1;31mFAIL\033[0m\n"); + return false; } bool eval_perf(ggml_backend_t backend, const char * op_name) { @@ -519,6 +528,11 @@ struct test_case { // allocate ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend); + if (buf == NULL) { + printf("failed to allocate tensors\n"); + ggml_free(ctx); + return false; + } // randomize tensors initialize_tensors(ctx); From 3fe81781e3bf98b8e44946240a19f3a6ad08a11a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 12 Jan 2024 20:38:54 +0100 Subject: [PATCH 397/664] CUDA: faster q8_0 -> f16 dequantization (#4895) --- ggml-cuda.cu | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2db50437c..bd3814c72 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -523,6 +523,8 @@ static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 +#define CUDA_Q8_0_NE_ALIGN 2048 + // dmmv = dequantize_mul_mat_vec #ifndef GGML_CUDA_DMMV_X #define GGML_CUDA_DMMV_X 32 @@ -2327,6 +2329,45 @@ static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __res y[i] = x[i]; } +template +static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) { +#if __CUDA_ARCH__ >= CC_PASCAL + constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE; + + const int i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x; + const int * x0 = ((int *) vx) + blockIdx.x * nint; + half2 * y2 = (half2 *) (y + i0); + + __shared__ int vals[nint]; + +#pragma unroll + for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) { + if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) { + break; + } + + const int ix = ix0 + threadIdx.x; + vals[ix] = x0[ix]; + } + +#pragma unroll + for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) { + if (need_check && i0 + iy + 2*threadIdx.x >= k) { + return; + } + + const half * b0 = ((const half *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0); + const half d = *b0; + const char2 qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)]; + + y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d)); + } +#else + (void) vx; (void) y; (void) k; + bad_arch(); +#endif // __CUDA_ARCH__ >= CC_PASCAL +} + // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q @@ -6181,6 +6222,17 @@ static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restri dequantize_block<<>>(vx, y, k); } +static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN; + if (k % CUDA_Q8_0_NE_ALIGN == 0) { + const bool need_check = false; + dequantize_block_q8_0_f16<<>>(vx, y, k); + } else { + const bool need_check = true; + dequantize_block_q8_0_f16<<>>(vx, y, k); + } +} + template static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -6246,6 +6298,7 @@ static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict_ } static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { + int id; switch (type) { case GGML_TYPE_Q4_0: return dequantize_block_cuda; @@ -6256,6 +6309,10 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { case GGML_TYPE_Q5_1: return dequantize_block_cuda; case GGML_TYPE_Q8_0: + CUDA_CHECK(cudaGetDevice(&id)); + if (g_device_caps[id].cc >= CC_PASCAL) { + return dequantize_block_q8_0_f16_cuda; + } return dequantize_block_cuda; case GGML_TYPE_Q2_K: return dequantize_row_q2_K_cuda; From 52ee4540c0f2e11d52c839db6eb51d014ce060e1 Mon Sep 17 00:00:00 2001 From: Maximilian Winter Date: Fri, 12 Jan 2024 20:46:45 +0100 Subject: [PATCH 398/664] examples : add pydantic models to GBNF grammar generator (#4883) * Create pydantic-models-to-grammar.py * Added some comments for usage * Refactored Grammar Generator Added example and usage instruction. * Update pydantic_models_to_grammar.py * Update pydantic-models-to-grammar-examples.py * Renamed module and imported it. * Update pydantic-models-to-grammar.py * Renamed file and fixed grammar generator issue. --- .../pydantic-models-to-grammar-examples.py | 136 ++ examples/pydantic_models_to_grammar.py | 1151 +++++++++++++++++ 2 files changed, 1287 insertions(+) create mode 100644 examples/pydantic-models-to-grammar-examples.py create mode 100644 examples/pydantic_models_to_grammar.py diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic-models-to-grammar-examples.py new file mode 100644 index 000000000..a8a4919cf --- /dev/null +++ b/examples/pydantic-models-to-grammar-examples.py @@ -0,0 +1,136 @@ +# Function calling example using pydantic models. + +import json +from enum import Enum +from typing import Union, Optional + +import requests +from pydantic import BaseModel, Field + +import importlib +from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation + +# Function to get completion on the llama.cpp server with grammar. +def create_completion(prompt, grammar): + headers = {"Content-Type": "application/json"} + data = {"prompt": prompt, "grammar": grammar} + + response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data) + data = response.json() + + print(data["content"]) + return data["content"] + + +# A function for the agent to send a message to the user. +class SendMessageToUser(BaseModel): + """ + Send a message to the User. + """ + chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.") + message: str = Field(..., description="Message you want to send to the user.") + + def run(self): + print(self.message) + + +# Enum for the calculator function. +class MathOperation(Enum): + ADD = "add" + SUBTRACT = "subtract" + MULTIPLY = "multiply" + DIVIDE = "divide" + + +# Very simple calculator tool for the agent. +class Calculator(BaseModel): + """ + Perform a math operation on two numbers. + """ + number_one: Union[int, float] = Field(..., description="First number.") + operation: MathOperation = Field(..., description="Math operation to perform.") + number_two: Union[int, float] = Field(..., description="Second number.") + + def run(self): + if self.operation == MathOperation.ADD: + return self.number_one + self.number_two + elif self.operation == MathOperation.SUBTRACT: + return self.number_one - self.number_two + elif self.operation == MathOperation.MULTIPLY: + return self.number_one * self.number_two + elif self.operation == MathOperation.DIVIDE: + return self.number_one / self.number_two + else: + raise ValueError("Unknown operation.") + + +# Here the grammar gets generated by passing the available function models to generate_gbnf_grammar_and_documentation function. This also generates a documentation usable by the LLM. +# pydantic_model_list is the list of pydanitc models +# outer_object_name is an optional name for an outer object around the actual model object. Like a "function" object with "function_parameters" which contains the actual model object. If None, no outer object will be generated +# outer_object_content is the name of outer object content. +# model_prefix is the optional prefix for models in the documentation. (Default="Output Model") +# fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields") +gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( + pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function", + outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters") + +print(gbnf_grammar) +print(documentation) + +system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation + +user_message = "What is 42 * 42?" +prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" + +text = create_completion(prompt=prompt, grammar=gbnf_grammar) +# This should output something like this: +# { +# "function": "calculator", +# "function_parameters": { +# "number_one": 42, +# "operation": "multiply", +# "number_two": 42 +# } +# } +function_dictionary = json.loads(text) +if function_dictionary["function"] == "calculator": + function_parameters = {**function_dictionary["function_parameters"]} + + print(Calculator(**function_parameters).run()) + # This should output: 1764 + + +# A example structured output based on pydantic models. The LLM will create an entry for a Book database out of an unstructured text. +class Category(Enum): + """ + The category of the book. + """ + Fiction = "Fiction" + NonFiction = "Non-Fiction" + + +class Book(BaseModel): + """ + Represents an entry about a book. + """ + title: str = Field(..., description="Title of the book.") + author: str = Field(..., description="Author of the book.") + published_year: Optional[int] = Field(..., description="Publishing year of the book.") + keywords: list[str] = Field(..., description="A list of keywords.") + category: Category = Field(..., description="Category of the book.") + summary: str = Field(..., description="Summary of the book.") + + +# We need no additional parameters other than our list of pydantic models. +gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book]) + +system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation + +text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands.""" +prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant" + +text = create_completion(prompt=prompt, grammar=gbnf_grammar) + +json_data = json.loads(text) + +print(Book(**json_data)) diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py new file mode 100644 index 000000000..41b98fdc1 --- /dev/null +++ b/examples/pydantic_models_to_grammar.py @@ -0,0 +1,1151 @@ +import inspect +import json +from copy import copy +from inspect import isclass, getdoc +from types import NoneType + +from pydantic import BaseModel, create_model, Field +from typing import Any, Type, List, get_args, get_origin, Tuple, Union, Optional, _GenericAlias +from enum import Enum +from typing import get_type_hints, Callable +import re + + +class PydanticDataType(Enum): + """ + Defines the data types supported by the grammar_generator. + + Attributes: + STRING (str): Represents a string data type. + BOOLEAN (str): Represents a boolean data type. + INTEGER (str): Represents an integer data type. + FLOAT (str): Represents a float data type. + OBJECT (str): Represents an object data type. + ARRAY (str): Represents an array data type. + ENUM (str): Represents an enum data type. + CUSTOM_CLASS (str): Represents a custom class data type. + """ + STRING = "string" + TRIPLE_QUOTED_STRING = "triple_quoted_string" + MARKDOWN_STRING = "markdown_string" + BOOLEAN = "boolean" + INTEGER = "integer" + FLOAT = "float" + OBJECT = "object" + ARRAY = "array" + ENUM = "enum" + ANY = "any" + NULL = "null" + CUSTOM_CLASS = "custom-class" + CUSTOM_DICT = "custom-dict" + SET = "set" + + +def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str: + if isclass(pydantic_type) and issubclass(pydantic_type, str): + return PydanticDataType.STRING.value + elif isclass(pydantic_type) and issubclass(pydantic_type, bool): + return PydanticDataType.BOOLEAN.value + elif isclass(pydantic_type) and issubclass(pydantic_type, int): + return PydanticDataType.INTEGER.value + elif isclass(pydantic_type) and issubclass(pydantic_type, float): + return PydanticDataType.FLOAT.value + elif isclass(pydantic_type) and issubclass(pydantic_type, Enum): + return PydanticDataType.ENUM.value + + elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel): + return format_model_and_field_name(pydantic_type.__name__) + elif get_origin(pydantic_type) == list: + element_type = get_args(pydantic_type)[0] + return f"{map_pydantic_type_to_gbnf(element_type)}-list" + elif get_origin(pydantic_type) == set: + element_type = get_args(pydantic_type)[0] + return f"{map_pydantic_type_to_gbnf(element_type)}-set" + elif get_origin(pydantic_type) == Union: + union_types = get_args(pydantic_type) + union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types] + return f"union-{'-or-'.join(union_rules)}" + elif get_origin(pydantic_type) == Optional: + element_type = get_args(pydantic_type)[0] + return f"optional-{map_pydantic_type_to_gbnf(element_type)}" + elif isclass(pydantic_type): + return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}" + elif get_origin(pydantic_type) == dict: + key_type, value_type = get_args(pydantic_type) + return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}" + else: + return "unknown" + + +def format_model_and_field_name(model_name: str) -> str: + parts = re.findall('[A-Z][^A-Z]*', model_name) + if not parts: # Check if the list is empty + return model_name.lower().replace("_", "-") + return '-'.join(part.lower().replace("_", "-") for part in parts) + + +def generate_list_rule(element_type): + """ + Generate a GBNF rule for a list of a given element type. + + :param element_type: The type of the elements in the list (e.g., 'string'). + :return: A string representing the GBNF rule for a list of the given type. + """ + rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list" + element_rule = map_pydantic_type_to_gbnf(element_type) + list_rule = fr'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"' + return list_rule + + +def get_members_structure(cls, rule_name): + if issubclass(cls, Enum): + # Handle Enum types + members = [f'\"\\\"{member.value}\\\"\"' for name, member in cls.__members__.items()] + return f"{cls.__name__.lower()} ::= " + " | ".join(members) + if cls.__annotations__ and cls.__annotations__ != {}: + result = f'{rule_name} ::= "{{"' + type_list_rules = [] + # Modify this comprehension + members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param_type)}' + for name, param_type in cls.__annotations__.items() + if name != 'self'] + + result += '"," '.join(members) + result += ' "}"' + return result, type_list_rules + elif rule_name == "custom-class-any": + result = f'{rule_name} ::= ' + result += 'value' + type_list_rules = [] + return result, type_list_rules + else: + init_signature = inspect.signature(cls.__init__) + parameters = init_signature.parameters + result = f'{rule_name} ::= "{{"' + type_list_rules = [] + # Modify this comprehension too + members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param.annotation)}' + for name, param in parameters.items() + if name != 'self' and param.annotation != inspect.Parameter.empty] + + result += '", "'.join(members) + result += ' "}"' + return result, type_list_rules + + +def regex_to_gbnf(regex_pattern: str) -> str: + """ + Translate a basic regex pattern to a GBNF rule. + Note: This function handles only a subset of simple regex patterns. + """ + gbnf_rule = regex_pattern + + # Translate common regex components to GBNF + gbnf_rule = gbnf_rule.replace('\\d', '[0-9]') + gbnf_rule = gbnf_rule.replace('\\s', '[ \t\n]') + + # Handle quantifiers and other regex syntax that is similar in GBNF + # (e.g., '*', '+', '?', character classes) + + return gbnf_rule + + +def generate_gbnf_integer_rules(max_digit=None, min_digit=None): + """ + + Generate GBNF Integer Rules + + Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits. + + Parameters: + max_digit (int): The maximum number of digits for the integer. Default is None. + min_digit (int): The minimum number of digits for the integer. Default is None. + + Returns: + integer_rule (str): The identifier for the integer rule generated. + additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits. + + """ + additional_rules = [] + + # Define the rule identifier based on max_digit and min_digit + integer_rule = "integer-part" + if max_digit is not None: + integer_rule += f"-max{max_digit}" + if min_digit is not None: + integer_rule += f"-min{min_digit}" + + # Handling Integer Rules + if max_digit is not None or min_digit is not None: + # Start with an empty rule part + integer_rule_part = '' + + # Add mandatory digits as per min_digit + if min_digit is not None: + integer_rule_part += '[0-9] ' * min_digit + + # Add optional digits up to max_digit + if max_digit is not None: + optional_digits = max_digit - (min_digit if min_digit is not None else 0) + integer_rule_part += ''.join(['[0-9]? ' for _ in range(optional_digits)]) + + # Trim the rule part and append it to additional rules + integer_rule_part = integer_rule_part.strip() + if integer_rule_part: + additional_rules.append(f'{integer_rule} ::= {integer_rule_part}') + + return integer_rule, additional_rules + + +def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None, min_precision=None): + """ + Generate GBNF float rules based on the given constraints. + + :param max_digit: Maximum number of digits in the integer part (default: None) + :param min_digit: Minimum number of digits in the integer part (default: None) + :param max_precision: Maximum number of digits in the fractional part (default: None) + :param min_precision: Minimum number of digits in the fractional part (default: None) + :return: A tuple containing the float rule and additional rules as a list + + Example Usage: + max_digit = 3 + min_digit = 1 + max_precision = 2 + min_precision = 1 + generate_gbnf_float_rules(max_digit, min_digit, max_precision, min_precision) + + Output: + ('float-3-1-2-1', ['integer-part-max3-min1 ::= [0-9] [0-9] [0-9]?', 'fractional-part-max2-min1 ::= [0-9] [0-9]?', 'float-3-1-2-1 ::= integer-part-max3-min1 "." fractional-part-max2-min + *1']) + + Note: + GBNF stands for Generalized Backus-Naur Form, which is a notation technique to specify the syntax of programming languages or other formal grammars. + """ + additional_rules = [] + + # Define the integer part rule + integer_part_rule = "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( + f"-min{min_digit}" if min_digit is not None else "") + + # Define the fractional part rule based on precision constraints + fractional_part_rule = "fractional-part" + fractional_rule_part = '' + if max_precision is not None or min_precision is not None: + fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + ( + f"-min{min_precision}" if min_precision is not None else "") + # Minimum number of digits + fractional_rule_part = '[0-9]' * (min_precision if min_precision is not None else 1) + # Optional additional digits + fractional_rule_part += ''.join([' [0-9]?'] * ( + (max_precision - (min_precision if min_precision is not None else 1)) if max_precision is not None else 0)) + additional_rules.append(f'{fractional_part_rule} ::= {fractional_rule_part}') + + # Define the float rule + float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}" + additional_rules.append(f'{float_rule} ::= {integer_part_rule} "." {fractional_part_rule}') + + # Generating the integer part rule definition, if necessary + if max_digit is not None or min_digit is not None: + integer_rule_part = '[0-9]' + if min_digit is not None and min_digit > 1: + integer_rule_part += ' [0-9]' * (min_digit - 1) + if max_digit is not None: + integer_rule_part += ''.join([' [0-9]?'] * (max_digit - (min_digit if min_digit is not None else 1))) + additional_rules.append(f'{integer_part_rule} ::= {integer_rule_part.strip()}') + + return float_rule, additional_rules + + +def generate_gbnf_rule_for_type(model_name, field_name, + field_type, is_optional, processed_models, created_rules, + field_info=None) -> \ + Tuple[str, list]: + """ + Generate GBNF rule for a given field type. + + :param model_name: Name of the model. + + :param field_name: Name of the field. + :param field_type: Type of the field. + :param is_optional: Whether the field is optional. + :param processed_models: List of processed models. + :param created_rules: List of created rules. + :param field_info: Additional information about the field (optional). + + :return: Tuple containing the GBNF type and a list of additional rules. + :rtype: Tuple[str, list] + """ + rules = [] + + field_name = format_model_and_field_name(field_name) + gbnf_type = map_pydantic_type_to_gbnf(field_type) + + if isclass(field_type) and issubclass(field_type, BaseModel): + nested_model_name = format_model_and_field_name(field_type.__name__) + nested_model_rules = generate_gbnf_grammar(field_type, processed_models, created_rules) + rules.extend(nested_model_rules) + gbnf_type, rules = nested_model_name, rules + elif isclass(field_type) and issubclass(field_type, Enum): + enum_values = [f'\"\\\"{e.value}\\\"\"' for e in field_type] # Adding escaped quotes + enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}" + rules.append(enum_rule) + gbnf_type, rules = model_name + "-" + field_name, rules + elif get_origin(field_type) == list or field_type == list: # Array + element_type = get_args(field_type)[0] + element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-element", + element_type, is_optional, processed_models, + created_rules) + rules.extend(additional_rules) + array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ + rules.append(array_rule) + gbnf_type, rules = model_name + "-" + field_name, rules + + elif get_origin(field_type) == set or field_type == set: # Array + element_type = get_args(field_type)[0] + element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-element", + element_type, is_optional, processed_models, + created_rules) + rules.extend(additional_rules) + array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ + rules.append(array_rule) + gbnf_type, rules = model_name + "-" + field_name, rules + + elif gbnf_type.startswith("custom-class-"): + nested_model_rules, field_types = get_members_structure(field_type, gbnf_type) + rules.append(nested_model_rules) + elif gbnf_type.startswith("custom-dict-"): + key_type, value_type = get_args(field_type) + + additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-key-type", + key_type, is_optional, processed_models, + created_rules) + additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-value-type", + value_type, is_optional, + processed_models, created_rules) + gbnf_type = fr'{gbnf_type} ::= "{{" ( {additional_key_type} ":" {additional_value_type} ("," {additional_key_type} ":" {additional_value_type})* )? "}}" ' + + rules.extend(additional_key_rules) + rules.extend(additional_value_rules) + elif gbnf_type.startswith("union-"): + union_types = get_args(field_type) + union_rules = [] + + for union_type in union_types: + if isinstance(union_type, _GenericAlias): + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, + field_name, union_type, + False, + processed_models, created_rules) + union_rules.append(union_gbnf_type) + rules.extend(union_rules_list) + + + elif not issubclass(union_type, NoneType): + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, + field_name, union_type, + False, + processed_models, created_rules) + union_rules.append(union_gbnf_type) + rules.extend(union_rules_list) + + # Defining the union grammar rule separately + if len(union_rules) == 1: + union_grammar_rule = f"{model_name}-{field_name}-optional ::= {' | '.join(union_rules)} | null" + else: + union_grammar_rule = f"{model_name}-{field_name}-union ::= {' | '.join(union_rules)}" + rules.append(union_grammar_rule) + if len(union_rules) == 1: + gbnf_type = f"{model_name}-{field_name}-optional" + else: + gbnf_type = f"{model_name}-{field_name}-union" + elif isclass(field_type) and issubclass(field_type, str): + if field_info and hasattr(field_info, 'json_schema_extra') and field_info.json_schema_extra is not None: + + triple_quoted_string = field_info.json_schema_extra.get('triple_quoted_string', False) + markdown_string = field_info.json_schema_extra.get('markdown_string', False) + + gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value + gbnf_type = PydanticDataType.MARKDOWN_STRING.value if markdown_string else gbnf_type + + elif field_info and hasattr(field_info, 'pattern'): + # Convert regex pattern to grammar rule + regex_pattern = field_info.regex.pattern + gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}" + else: + gbnf_type = PydanticDataType.STRING.value + + elif isclass(field_type) and issubclass(field_type, float) and field_info and hasattr(field_info, + 'json_schema_extra') and field_info.json_schema_extra is not None: + # Retrieve precision attributes for floats + max_precision = field_info.json_schema_extra.get('max_precision') if field_info and hasattr(field_info, + 'json_schema_extra') else None + min_precision = field_info.json_schema_extra.get('min_precision') if field_info and hasattr(field_info, + 'json_schema_extra') else None + max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + + # Generate GBNF rule for float with given attributes + gbnf_type, rules = generate_gbnf_float_rules(max_digit=max_digits, min_digit=min_digits, + max_precision=max_precision, + min_precision=min_precision) + + elif isclass(field_type) and issubclass(field_type, int) and field_info and hasattr(field_info, + 'json_schema_extra') and field_info.json_schema_extra is not None: + # Retrieve digit attributes for integers + max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + + # Generate GBNF rule for integer with given attributes + gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits) + else: + gbnf_type, rules = gbnf_type, [] + + if gbnf_type not in created_rules: + return gbnf_type, rules + else: + if gbnf_type in created_rules: + return gbnf_type, rules + + +def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created_rules: dict) -> (list, bool, bool): + """ + + Generate GBnF Grammar + + Generates a GBnF grammar for a given model. + + :param model: A Pydantic model class to generate the grammar for. Must be a subclass of BaseModel. + :param processed_models: A set of already processed models to prevent infinite recursion. + :param created_rules: A dict containing already created rules to prevent duplicates. + :return: A list of GBnF grammar rules in string format. And two booleans indicating if an extra markdown or triple quoted string is in the grammar. + Example Usage: + ``` + model = MyModel + processed_models = set() + created_rules = dict() + + gbnf_grammar = generate_gbnf_grammar(model, processed_models, created_rules) + ``` + """ + if model in processed_models: + return [] + + processed_models.add(model) + model_name = format_model_and_field_name(model.__name__) + + if not issubclass(model, BaseModel): + # For non-Pydantic classes, generate model_fields from __annotations__ or __init__ + if hasattr(model, '__annotations__') and model.__annotations__: + model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} + else: + init_signature = inspect.signature(model.__init__) + parameters = init_signature.parameters + model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() + if name != 'self'} + else: + # For Pydantic models, use model_fields and check for ellipsis (required fields) + model_fields = model.__annotations__ + + model_rule_parts = [] + nested_rules = [] + has_markdown_code_block = False + has_triple_quoted_string = False + look_for_markdown_code_block = False + look_for_triple_quoted_string = False + for field_name, field_info in model_fields.items(): + if not issubclass(model, BaseModel): + field_type, default_value = field_info + # Check if the field is optional (not required) + is_optional = (default_value is not inspect.Parameter.empty) and (default_value is not Ellipsis) + else: + field_type = field_info + field_info = model.model_fields[field_name] + is_optional = field_info.is_required is False and get_origin(field_type) is Optional + rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, + format_model_and_field_name(field_name), + field_type, is_optional, + processed_models, created_rules, field_info) + look_for_markdown_code_block = True if rule_name == "markdown_string" else False + look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False + if not look_for_markdown_code_block and not look_for_triple_quoted_string: + if rule_name not in created_rules: + created_rules[rule_name] = additional_rules + model_rule_parts.append(f' ws \"\\\"{field_name}\\\"\" ": " {rule_name}') # Adding escaped quotes + nested_rules.extend(additional_rules) + else: + has_triple_quoted_string = look_for_markdown_code_block + has_markdown_code_block = look_for_triple_quoted_string + + fields_joined = r' "," "\n" '.join(model_rule_parts) + model_rule = fr'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"' + + if look_for_markdown_code_block or look_for_triple_quoted_string: + model_rule += ' ws "}"' + + if has_triple_quoted_string: + model_rule += '"\\n" triple-quoted-string' + if has_markdown_code_block: + model_rule += '"\\n" markdown-code-block' + all_rules = [model_rule] + nested_rules + + return all_rules, has_markdown_code_block, has_triple_quoted_string + + +def generate_gbnf_grammar_from_pydantic_models(models: List[Type[BaseModel]], outer_object_name: str = None, + outer_object_content: str = None, list_of_outputs: bool = False) -> str: + """ + Generate GBNF Grammar from Pydantic Models. + + This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated + * grammar. + + Parameters: + models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + list_of_outputs (str, optional): Allows a list of output objects + Returns: + str: The generated GBNF grammar string. + + Examples: + models = [UserModel, PostModel] + grammar = generate_gbnf_grammar_from_pydantic(models) + print(grammar) + # Output: + # root ::= UserModel | PostModel + # ... + """ + processed_models = set() + all_rules = [] + created_rules = {} + if outer_object_name is None: + + for model in models: + model_rules, _, _ = generate_gbnf_grammar(model, + processed_models, created_rules) + all_rules.extend(model_rules) + + if list_of_outputs: + root_rule = r'root ::= ws "[" grammar-models ("," grammar-models)* "]"' + "\n" + else: + root_rule = r'root ::= ws grammar-models' + "\n" + root_rule += "grammar-models ::= " + " | ".join( + [format_model_and_field_name(model.__name__) for model in models]) + all_rules.insert(0, root_rule) + return "\n".join(all_rules) + elif outer_object_name is not None: + if list_of_outputs: + root_rule = fr'root ::= ws "[" {format_model_and_field_name(outer_object_name)} ("," {format_model_and_field_name(outer_object_name)})* "]"' + "\n" + else: + root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n" + + model_rule = fr'{format_model_and_field_name(outer_object_name)} ::= ws "{{" ws "\"{outer_object_name}\"" ": " grammar-models' + + fields_joined = " | ".join( + [fr'{format_model_and_field_name(model.__name__)}-grammar-model' for model in models]) + + grammar_model_rules = f'\ngrammar-models ::= {fields_joined}' + mod_rules = [] + for model in models: + mod_rule = fr'{format_model_and_field_name(model.__name__)}-grammar-model ::= ws' + mod_rule += fr'"\"{format_model_and_field_name(model.__name__)}\"" "," ws "\"{outer_object_content}\"" ws ":" ws {format_model_and_field_name(model.__name__)}' + '\n' + mod_rules.append(mod_rule) + grammar_model_rules += "\n" + "\n".join(mod_rules) + look_for_markdown_code_block = False + look_for_triple_quoted_string = False + for model in models: + model_rules, markdown_block, triple_quoted_string = generate_gbnf_grammar(model, + processed_models, created_rules) + all_rules.extend(model_rules) + if markdown_block: + look_for_markdown_code_block = True + + if triple_quoted_string: + look_for_triple_quoted_string = True + + if not look_for_markdown_code_block and not look_for_triple_quoted_string: + model_rule += ' ws "}"' + all_rules.insert(0, root_rule + model_rule + grammar_model_rules) + return "\n".join(all_rules) + + +def get_primitive_grammar(grammar): + """ + Returns the needed GBNF primitive grammar for a given GBNF grammar string. + + Args: + grammar (str): The string containing the GBNF grammar. + + Returns: + str: GBNF primitive grammar string. + """ + type_list = [] + if "string-list" in grammar: + type_list.append(str) + if "boolean-list" in grammar: + type_list.append(bool) + if "integer-list" in grammar: + type_list.append(int) + if "float-list" in grammar: + type_list.append(float) + additional_grammar = [generate_list_rule(t) for t in type_list] + primitive_grammar = r""" +boolean ::= "true" | "false" +null ::= "null" +string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" ws +ws ::= ([ \t\n] ws)? +float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws + +integer ::= [0-9]+""" + + any_block = "" + if "custom-class-any" in grammar: + any_block = ''' +value ::= object | array | string | number | boolean | null + +object ::= + "{" ws ( + string ":" ws value + ("," ws string ":" ws value)* + )? "}" ws + +array ::= + "[" ws ( + value + ("," ws value)* + )? "]" ws + +number ::= integer | float''' + + markdown_code_block_grammar = "" + if "markdown-code-block" in grammar: + markdown_code_block_grammar = r''' +markdown-code-block ::= opening-triple-ticks markdown-code-block-content closing-triple-ticks +markdown-code-block-content ::= ( [^`] | "`" [^`] | "`" "`" [^`] )* +opening-triple-ticks ::= "```" "python" "\n" | "```" "c" "\n" | "```" "cpp" "\n" | "```" "txt" "\n" | "```" "text" "\n" | "```" "json" "\n" | "```" "javascript" "\n" | "```" "css" "\n" | "```" "html" "\n" | "```" "markdown" "\n" +closing-triple-ticks ::= "```" "\n"''' + + if "triple-quoted-string" in grammar: + markdown_code_block_grammar = r""" +triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes +triple-quoted-string-content ::= ( [^'] | "'" [^'] | "'" "'" [^'] )* +triple-quotes ::= "'''" """ + return "\n" + '\n'.join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar + + +def generate_field_markdown(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1) -> str: + indent = ' ' * depth + field_markdown = f"{indent}- **{field_name}** (`{field_type.__name__}`): " + + # Extracting field description from Pydantic Field using __model_fields__ + field_info = model.model_fields.get(field_name) + field_description = field_info.description if field_info and field_info.description else "No description available." + + field_markdown += field_description + '\n' + + # Handling nested BaseModel fields + if isclass(field_type) and issubclass(field_type, BaseModel): + field_markdown += f"{indent} - Details:\n" + for name, type_ in field_type.__annotations__.items(): + field_markdown += generate_field_markdown(name, type_, field_type, depth + 2) + + return field_markdown + + +def generate_markdown_report(pydantic_models: List[Type[BaseModel]]) -> str: + markdown = "" + for model in pydantic_models: + markdown += f"### {format_model_and_field_name(model.__name__)}\n" + + # Check if the model's docstring is different from BaseModel's docstring + class_doc = getdoc(model) + base_class_doc = getdoc(BaseModel) + class_description = class_doc if class_doc and class_doc != base_class_doc else "No specific description available." + + markdown += f"{class_description}\n\n" + markdown += "#### Fields\n" + + if isclass(model) and issubclass(model, BaseModel): + for name, field_type in model.__annotations__.items(): + markdown += generate_field_markdown(format_model_and_field_name(name), field_type, model) + markdown += "\n" + + return markdown + + +def format_json_example(example: dict, depth: int) -> str: + """ + Format a JSON example into a readable string with indentation. + + Args: + example (dict): JSON example to be formatted. + depth (int): Indentation depth. + + Returns: + str: Formatted JSON example string. + """ + indent = ' ' * depth + formatted_example = '{\n' + for key, value in example.items(): + value_text = f"'{value}'" if isinstance(value, str) else value + formatted_example += f"{indent}{key}: {value_text},\n" + formatted_example = formatted_example.rstrip(',\n') + '\n' + indent + '}' + return formatted_example + + +def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_prefix="Model", + fields_prefix="Fields", documentation_with_field_description=True) -> str: + """ + Generate text documentation for a list of Pydantic models. + + Args: + pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + model_prefix (str): Prefix for the model section. + fields_prefix (str): Prefix for the fields section. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation. + """ + documentation = "" + pyd_models = [(model, True) for model in pydantic_models] + for model, add_prefix in pyd_models: + if add_prefix: + documentation += f"{model_prefix}: {format_model_and_field_name(model.__name__)}\n" + else: + documentation += f"Model: {format_model_and_field_name(model.__name__)}\n" + + # Handling multi-line model description with proper indentation + + class_doc = getdoc(model) + base_class_doc = getdoc(BaseModel) + class_description = class_doc if class_doc and class_doc != base_class_doc else "" + if class_description != "": + documentation += " Description: " + documentation += "\n" + format_multiline_description(class_description, 2) + "\n" + + if add_prefix: + # Indenting the fields section + documentation += f" {fields_prefix}:\n" + else: + documentation += f" Fields:\n" + if isclass(model) and issubclass(model, BaseModel): + for name, field_type in model.__annotations__.items(): + # if name == "markdown_code_block": + # continue + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + if get_origin(field_type) == Union: + element_types = get_args(field_type) + for element_type in element_types: + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + documentation += generate_field_text(name, field_type, model, + documentation_with_field_description=documentation_with_field_description) + documentation += "\n" + + if hasattr(model, 'Config') and hasattr(model.Config, + 'json_schema_extra') and 'example' in model.Config.json_schema_extra: + documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n" + json_example = json.dumps(model.Config.json_schema_extra['example']) + documentation += format_multiline_description(json_example, 2) + "\n" + + return documentation + + +def generate_field_text(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + documentation_with_field_description=True) -> str: + """ + Generate text documentation for a Pydantic model field. + + Args: + field_name (str): Name of the field. + field_type (Type[Any]): Type of the field. + model (Type[BaseModel]): Pydantic model class. + depth (int): Indentation depth in the documentation. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation for the field. + """ + indent = ' ' * depth + + field_info = model.model_fields.get(field_name) + field_description = field_info.description if field_info and field_info.description else "" + + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + elif get_origin(field_type) == Union: + element_types = get_args(field_type) + types = [] + for element_type in element_types: + types.append(format_model_and_field_name(element_type.__name__)) + field_text = f"{indent}{field_name} ({' or '.join(types)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + else: + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + + if not documentation_with_field_description: + return field_text + + if field_description != "": + field_text += f"{indent} Description: " + field_description + "\n" + + # Check for and include field-specific examples if available + if hasattr(model, 'Config') and hasattr(model.Config, + 'json_schema_extra') and 'example' in model.Config.json_schema_extra: + field_example = model.Config.json_schema_extra['example'].get(field_name) + if field_example is not None: + example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example + field_text += f"{indent} Example: {example_text}\n" + + if isclass(field_type) and issubclass(field_type, BaseModel): + field_text += f"{indent} Details:\n" + for name, type_ in field_type.__annotations__.items(): + field_text += generate_field_text(name, type_, field_type, depth + 2) + + return field_text + + +def format_multiline_description(description: str, indent_level: int) -> str: + """ + Format a multiline description with proper indentation. + + Args: + description (str): Multiline description. + indent_level (int): Indentation level. + + Returns: + str: Formatted multiline description. + """ + indent = ' ' * indent_level + return indent + description.replace('\n', '\n' + indent) + + +def save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path="./grammar.gbnf", + documentation_file_path="./grammar_documentation.md"): + """ + Save GBNF grammar and documentation to specified files. + + Args: + grammar (str): GBNF grammar string. + documentation (str): Documentation string. + grammar_file_path (str): File path to save the GBNF grammar. + documentation_file_path (str): File path to save the documentation. + + Returns: + None + """ + try: + with open(grammar_file_path, 'w') as file: + file.write(grammar + get_primitive_grammar(grammar)) + print(f"Grammar successfully saved to {grammar_file_path}") + except IOError as e: + print(f"An error occurred while saving the grammar file: {e}") + + try: + with open(documentation_file_path, 'w') as file: + file.write(documentation) + print(f"Documentation successfully saved to {documentation_file_path}") + except IOError as e: + print(f"An error occurred while saving the documentation file: {e}") + + +def remove_empty_lines(string): + """ + Remove empty lines from a string. + + Args: + string (str): Input string. + + Returns: + str: String with empty lines removed. + """ + lines = string.splitlines() + non_empty_lines = [line for line in lines if line.strip() != ""] + string_no_empty_lines = "\n".join(non_empty_lines) + return string_no_empty_lines + + +def generate_and_save_gbnf_grammar_and_documentation(pydantic_model_list, + grammar_file_path="./generated_grammar.gbnf", + documentation_file_path="./generated_grammar_documentation.md", + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True): + """ + Generate GBNF grammar and documentation, and save them to specified files. + + Args: + pydantic_model_list: List of Pydantic model classes. + grammar_file_path (str): File path to save the generated GBNF grammar. + documentation_file_path (str): File path to save the generated documentation. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + None + """ + documentation = generate_text_documentation(pydantic_model_list, model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, + outer_object_content, list_of_outputs) + grammar = remove_empty_lines(grammar) + save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path) + + +def generate_gbnf_grammar_and_documentation(pydantic_model_list, outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", list_of_outputs: bool = False, + documentation_with_field_description=True): + """ + Generate GBNF grammar and documentation for a list of Pydantic models. + + Args: + pydantic_model_list: List of Pydantic model classes. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + tuple: GBNF grammar string, documentation string. + """ + documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, + outer_object_content, list_of_outputs) + grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) + return grammar, documentation + + +def generate_gbnf_grammar_and_documentation_from_dictionaries(dictionaries: List[dict], + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True): + """ + Generate GBNF grammar and documentation from a list of dictionaries. + + Args: + dictionaries (List[dict]): List of dictionaries representing Pydantic models. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + tuple: GBNF grammar string, documentation string. + """ + pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries) + documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, + outer_object_content, list_of_outputs) + grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) + return grammar, documentation + + +def create_dynamic_model_from_function(func: Callable): + """ + Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method. + + Args: + func (Callable): A function with type hints from which to create the model. + + Returns: + A dynamic Pydantic model class with the provided function as a 'run' method. + """ + # Extracting type hints from the provided function + type_hints = get_type_hints(func) + type_hints.pop('return', None) + + # Handling default values and annotations + dynamic_fields = {} + defaults = getattr(func, '__defaults__', ()) or () + defaults_index = len(type_hints) - len(defaults) + + for index, (name, typ) in enumerate(type_hints.items()): + if index >= defaults_index: + default_value = defaults[index - defaults_index] + dynamic_fields[name] = (typ, default_value) + else: + dynamic_fields[name] = (typ, ...) + + # Creating the dynamic model + dynamicModel = create_model(f'{func.__name__}', **dynamic_fields) + + dynamicModel.__doc__ = getdoc(func) + + # Wrapping the original function to handle instance 'self' + def run_method_wrapper(self): + func_args = {name: getattr(self, name) for name in type_hints} + return func(**func_args) + + # Adding the wrapped function as a 'run' method + setattr(dynamicModel, 'run', run_method_wrapper) + + return dynamicModel + + +def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): + """ + Add a 'run' method to a dynamic Pydantic model, using the provided function. + + Args: + - model (Type[BaseModel]): Dynamic Pydantic model class. + - func (Callable): Function to be added as a 'run' method to the model. + + Returns: + - Type[BaseModel]: Pydantic model class with the added 'run' method. + """ + + def run_method_wrapper(self): + func_args = {name: getattr(self, name) for name in model.model_fields} + return func(**func_args) + + # Adding the wrapped function as a 'run' method + setattr(model, 'run', run_method_wrapper) + + return model + + +def create_dynamic_models_from_dictionaries(dictionaries: List[dict]): + """ + Create a list of dynamic Pydantic model classes from a list of dictionaries. + + Args: + - dictionaries (List[dict]): List of dictionaries representing model structures. + + Returns: + - List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. + """ + dynamic_models = [] + for func in dictionaries: + model_name = format_model_and_field_name(func.get("name", "")) + dyn_model = convert_dictionary_to_to_pydantic_model(func, model_name) + dynamic_models.append(dyn_model) + return dynamic_models + + +def map_grammar_names_to_pydantic_model_class(pydantic_model_list): + output = {} + for model in pydantic_model_list: + output[format_model_and_field_name(model.__name__)] = model + + return output + + +from enum import Enum + + +def json_schema_to_python_types(schema): + type_map = { + 'any': Any, + 'string': str, + 'number': float, + 'integer': int, + 'boolean': bool, + 'array': list, + } + return type_map[schema] + + +def list_to_enum(enum_name, values): + return Enum(enum_name, {value: value for value in values}) + + +def convert_dictionary_to_to_pydantic_model(dictionary: dict, model_name: str = 'CustomModel') -> Type[BaseModel]: + """ + Convert a dictionary to a Pydantic model class. + + Args: + - dictionary (dict): Dictionary representing the model structure. + - model_name (str): Name of the generated Pydantic model. + + Returns: + - Type[BaseModel]: Generated Pydantic model class. + """ + fields = {} + + if "properties" in dictionary: + for field_name, field_data in dictionary.get("properties", {}).items(): + if field_data == 'object': + submodel = convert_dictionary_to_to_pydantic_model(dictionary, f'{model_name}_{field_name}') + fields[field_name] = (submodel, ...) + else: + field_type = field_data.get('type', 'str') + + if field_data.get("enum", []): + fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...) + if field_type == "array": + items = field_data.get("items", {}) + if items != {}: + array = {"properties": items} + array_type = convert_dictionary_to_to_pydantic_model(array, f'{model_name}_{field_name}_items') + fields[field_name] = (List[array_type], ...) + else: + fields[field_name] = (list, ...) + elif field_type == 'object': + submodel = convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}_{field_name}') + fields[field_name] = (submodel, ...) + else: + field_type = json_schema_to_python_types(field_type) + fields[field_name] = (field_type, ...) + if "function" in dictionary: + + for field_name, field_data in dictionary.get("function", {}).items(): + if field_name == "name": + model_name = field_data + elif field_name == "description": + fields["__doc__"] = field_data + elif field_name == "parameters": + return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') + if "parameters" in dictionary: + field_data = {"function": dictionary} + return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') + + custom_model = create_model(model_name, **fields) + return custom_model + + + From fa5c1fb44a2724292da545d6b7cf2a1ac0e0b989 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 12 Jan 2024 20:38:34 +0100 Subject: [PATCH 399/664] backend_sched : fix assignments ggml-ci --- ggml-backend.c | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/ggml-backend.c b/ggml-backend.c index 4c2d8b0b2..505dbba47 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1087,6 +1087,24 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g } } } + + // pass 2.4 expand rest down + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr != NULL) { + cur_allocr = node_allocr; + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.4"); + } + } + } #ifdef DEBUG_PASS2 fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); #endif @@ -1146,6 +1164,8 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g ggml_tallocr_t node_allocr = node_allocr(node); + GGML_ASSERT(node_allocr != NULL); // all nodes should be assigned by now + if (node_allocr != cur_allocr) { sched->splits[cur_split].i_end = i; cur_split++; From f238461236f4e0e18cac1a554af23c7deadc9b01 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 14:02:30 +0200 Subject: [PATCH 400/664] ggml : fix 32-bit ARM compat for IQ2_XS (whisper/1758) * ggml : fix 32-bit ARM compat * ggml : fix fix * ggml : fix fix fix --- ggml-quants.c | 39 +++++++++++++++++++++++++++++++++++---- 1 file changed, 35 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index a24b4b244..601d155d7 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -272,10 +272,13 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 // vaddvq_s16 // vpaddq_s16 +// vpaddq_s32 // vaddvq_s32 // vaddvq_f32 // vmaxvq_f32 // vcvtnq_s32_f32 +// vzip1_u8 +// vzip2_u8 inline static int32_t vaddvq_s16(int16x8_t v) { return @@ -291,6 +294,12 @@ inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { return vcombine_s16(a0, b0); } +inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) { + int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a)); + int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b)); + return vcombine_s32(a0, b0); +} + inline static int32_t vaddvq_s32(int32x4_t v) { return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); } @@ -316,6 +325,28 @@ inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { return res; } +inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) { + uint8x8_t res; + + res[0] = a[0]; res[1] = b[0]; + res[2] = a[1]; res[3] = b[1]; + res[4] = a[2]; res[5] = b[2]; + res[6] = a[3]; res[7] = b[3]; + + return res; +} + +inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { + uint8x8_t res; + + res[0] = a[4]; res[1] = b[4]; + res[2] = a[5]; res[3] = b[5]; + res[4] = a[6]; res[5] = b[6]; + res[6] = a[7]; res[7] = b[7]; + + return res; +} + // vld1q_s16_x2 // vld1q_u8_x2 // vld1q_u8_x4 @@ -7554,9 +7585,9 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - int8x16x4_t q2u; - int8x16x4_t q2s; - int8x16x4_t q8b; + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; int32x4x4_t scales32; @@ -7578,7 +7609,7 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); int32x4_t sumi = vdupq_n_s32(0); for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { - q8b = vld1q_s8_x4(q8); q8 += 64; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); From de473f5f8e19ba5e659cdf5af65fb9251dce16c5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 22:02:43 +0200 Subject: [PATCH 401/664] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 3e2c579d5..edcdb530a 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -979cc23b345006504cfc1f67c0fdf627805e3319 +400c07f00508e6f60fb25405444b5669c365b0a9 From 15ebe59210e7fd9817ff67f51fa1a5ee2d004294 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 13:44:37 +0200 Subject: [PATCH 402/664] convert : update phi-2 to latest HF repo (#4903) * convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff --- convert-hf-to-gguf.py | 39 +++++++++++++++++++++---------- gguf-py/gguf/constants.py | 3 +++ gguf-py/gguf/tensor_mapping.py | 2 ++ llama.cpp | 42 ++++++++++++++++++++++++++-------- 4 files changed, 65 insertions(+), 21 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a1c79fd47..b133f3b49 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -23,6 +23,15 @@ if 'NO_LOCAL_GGUF' not in os.environ: import gguf +# check for any of the given keys in the dictionary and return the value of the first key found +def get_key_opts(d, keys): + for k in keys: + if k in d: + return d[k] + print(f"Could not find any of {keys}") + sys.exit() + + ###### MODEL DEFINITIONS ###### class SentencePieceTokenTypes(IntEnum): @@ -257,10 +266,11 @@ class Model: toktypes.append(gguf.TokenType.USER_DEFINED) elif reverse_vocab[i] in added_vocab: tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) + if hasattr(tokenizer, "added_tokens_decoder"): + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) else: tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.NORMAL) @@ -1068,17 +1078,22 @@ class GPT2Model(Model): class Phi2Model(Model): def set_gguf_parameters(self): - block_count = self.hparams["n_layer"] + block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"]) + + rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"]) + n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"]) + n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"]) self.gguf_writer.add_name("Phi2") - self.gguf_writer.add_context_length(self.hparams["n_positions"]) - self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"])) + + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(4 * n_embd) self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(self.hparams["n_head"]) - self.gguf_writer.add_head_count_kv(self.hparams["n_head"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"])) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_add_bos_token(False) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index f0a1c51f8..972b4e9a7 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -389,6 +389,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 24a089037..e5b146106 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -191,6 +191,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.w1", # qwen "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 + "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo ), @@ -232,6 +233,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 + "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo ), diff --git a/llama.cpp b/llama.cpp index fe1d8947c..1d2eb569f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -574,6 +574,9 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, @@ -3676,8 +3679,19 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + + if (layer.wqkv == nullptr) { + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + } layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); @@ -5637,15 +5651,25 @@ struct llm_build_context { // self-attention { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); - cb(cur, "wqkv", il); + struct ggml_tensor * Qcur = nullptr; + struct ggml_tensor * Kcur = nullptr; + struct ggml_tensor * Vcur = nullptr; - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); + if (model.layers[il].wqkv) { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); + cb(cur, "wqkv", il); - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + } else { + Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq); + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk); + Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv); + } cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); From ee8243adaa9a9f51ff449213383874e49efe368f Mon Sep 17 00:00:00 2001 From: makomk Date: Sat, 13 Jan 2024 14:16:11 +0000 Subject: [PATCH 403/664] server : fix crash with multimodal models without BOS token (#4904) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c1ab8f9dc..7b33aea1f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1835,7 +1835,7 @@ struct llama_server_context slot.cache_tokens = prompt_tokens; - if (slot.n_past == slot.num_prompt_tokens) + if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); From 356327feb3f66980ab687040495d722696d98970 Mon Sep 17 00:00:00 2001 From: Ziad Ben Hadj-Alouane Date: Sat, 13 Jan 2024 09:20:46 -0500 Subject: [PATCH 404/664] server : fix deadlock that occurs in multi-prompt scenarios (#4905) * * fix deadlock * * dont ruint all whitespace --- examples/server/server.cpp | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7b33aea1f..79eacf828 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1350,14 +1350,17 @@ struct llama_server_context res.result_json["model"] = slot.oaicompat_model; } + queue_results.push_back(res); + condition_results.notify_all(); + + // done with results, unlock + lock.unlock(); + // parent multitask, if any, needs to be updated if (slot.multitask_id != -1) { update_multi_task(slot.multitask_id, slot.task_id, res); } - - queue_results.push_back(res); - condition_results.notify_all(); } void send_embedding(llama_client_slot &slot) @@ -1603,6 +1606,7 @@ struct llama_server_context } // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue + std::vector agg_results; auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { @@ -1623,8 +1627,9 @@ struct llama_server_context } aggregate_result.result_json = json{ "results", result_jsons }; - std::lock_guard lock(mutex_results); - queue_results.push_back(aggregate_result); + + agg_results.push_back(aggregate_result); + condition_results.notify_all(); queue_iterator = queue_multitasks.erase(queue_iterator); @@ -1634,6 +1639,13 @@ struct llama_server_context ++queue_iterator; } } + + // done with tasks, unlock + lock.unlock(); + + // copy aggregate results of complete multi-tasks to the results queue + std::lock_guard lock_results(mutex_results); + queue_results.insert(queue_results.end(), agg_results.begin(), agg_results.end()); } bool update_slots() { From 7dc78764e2ff86512e6e31cb0fcb8087df4b4708 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 13 Jan 2024 15:52:53 +0100 Subject: [PATCH 405/664] compare-llama-bench: tweak output format (#4910) --- scripts/compare-llama-bench.py | 34 ++++++++++++++++++++++++++-------- 1 file changed, 26 insertions(+), 8 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index bc1714487..70737f976 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -10,15 +10,15 @@ import sqlite3 try: import git from tabulate import tabulate -except ImportError: +except ImportError as e: print("ERROR: the following Python libraries are required: GitPython, tabulate.") - sys.exit(1) + raise e # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ - "cuda", "opencl", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", - "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", - "n_gpu_layers", "main_gpu", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas", + "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", + "type_k", "type_v", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: @@ -37,6 +37,7 @@ PRETTY_NAMES = { DEFAULT_SHOW = ["model_type"] # Always show these properties by default. DEFAULT_HIDE = ["model_filename"] # Always hide these properties by default. GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "] # Strip prefixes for smaller tables. +MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"} DESCRIPTION = """Creates tables from llama-bench data written to an SQLite database. Example usage (Linux): @@ -308,8 +309,13 @@ else: if gpu_blas and "gpu_info" not in properties_different: show.append("gpu_info") - show += DEFAULT_SHOW show += properties_different + + index_default = 0 + for prop in ["cpu_info", "gpu_info", "n_gpu_layers", "main_gpu"]: + if prop in show: + index_default += 1 + show = show[:index_default] + DEFAULT_SHOW + show[index_default:] for prop in DEFAULT_HIDE: try: show.remove(prop) @@ -334,6 +340,12 @@ for bool_property in BOOL_PROPERTIES: for row_table in table: row_table[ip] = "Yes" if int(row_table[ip]) == 1 else "No" +if "model_type" in show: + ip = show.index("model_type") + for (old, new) in MODEL_SUFFIX_REPLACE.items(): + for row_table in table: + row_table[ip] = row_table[ip].replace(old, new) + if "model_size" in show: ip = show.index("model_size") for row_table in table: @@ -341,10 +353,16 @@ if "model_size" in show: if "gpu_info" in show: ip = show.index("gpu_info") - for gns in GPU_NAME_STRIP: - for row_table in table: + for row_table in table: + for gns in GPU_NAME_STRIP: row_table[ip] = row_table[ip].replace(gns, "") + gpu_names = row_table[ip].split("/") + num_gpus = len(gpu_names) + all_names_the_same = len(set(gpu_names)) == 1 + if len(gpu_names) >= 2 and all_names_the_same: + row_table[ip] = f"{num_gpus}x {gpu_names[0]}" + headers = [PRETTY_NAMES[p] for p in show] headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"] From b38b5e93ae31019e87f692b69d27124eae6aac02 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 18:03:45 +0200 Subject: [PATCH 406/664] metal : refactor kernel loading code (#4794) * metal : detect more GPU families * metal : refactor kernel loading * metal : set kernel family requirements * metal : fix kernel init + fix compile options * metal : take into account simdgroup reduction support * metal : print only skipped kernels * metal : fix check for simdgroup reduction support * metal : check for Metal 3 * metal : free allocations * metal : normalize encoder:setComputePipelineStatus calls ggml-ci * metal : fix Metal3 family check ggml-ci * metal : check for simdgroup matrix mul. feature ggml-ci --- ggml-metal.m | 1048 +++++++++++++++++++++++++------------------------- 1 file changed, 530 insertions(+), 518 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index c03624073..6c28a7ee3 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -26,6 +26,8 @@ #define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) +#define GGML_METAL_MAX_KERNELS 256 + struct ggml_metal_buffer { const char * name; @@ -35,6 +37,134 @@ struct ggml_metal_buffer { id metal; }; +struct ggml_metal_kernel { + id function; + id pipeline; +}; + +enum ggml_metal_kernel_type { + GGML_METAL_KERNEL_TYPE_ADD, + GGML_METAL_KERNEL_TYPE_ADD_ROW, + GGML_METAL_KERNEL_TYPE_MUL, + GGML_METAL_KERNEL_TYPE_MUL_ROW, + GGML_METAL_KERNEL_TYPE_DIV, + GGML_METAL_KERNEL_TYPE_DIV_ROW, + GGML_METAL_KERNEL_TYPE_SCALE, + GGML_METAL_KERNEL_TYPE_SCALE_4, + GGML_METAL_KERNEL_TYPE_TANH, + GGML_METAL_KERNEL_TYPE_RELU, + GGML_METAL_KERNEL_TYPE_GELU, + GGML_METAL_KERNEL_TYPE_GELU_QUICK, + GGML_METAL_KERNEL_TYPE_SILU, + GGML_METAL_KERNEL_TYPE_SOFT_MAX, + GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, + GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, + GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, + GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, + GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, + GGML_METAL_KERNEL_TYPE_RMS_NORM, + GGML_METAL_KERNEL_TYPE_GROUP_NORM, + GGML_METAL_KERNEL_TYPE_NORM, + GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_ROPE_F32, + GGML_METAL_KERNEL_TYPE_ROPE_F16, + GGML_METAL_KERNEL_TYPE_ALIBI_F32, + GGML_METAL_KERNEL_TYPE_IM2COL_F16, + GGML_METAL_KERNEL_TYPE_UPSCALE_F32, + GGML_METAL_KERNEL_TYPE_PAD_F32, + GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, + GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, + GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, + GGML_METAL_KERNEL_TYPE_CPY_F32_F16, + GGML_METAL_KERNEL_TYPE_CPY_F32_F32, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, + //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, + //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, + GGML_METAL_KERNEL_TYPE_CPY_F16_F16, + GGML_METAL_KERNEL_TYPE_CPY_F16_F32, + GGML_METAL_KERNEL_TYPE_CONCAT, + GGML_METAL_KERNEL_TYPE_SQR, + GGML_METAL_KERNEL_TYPE_SUM_ROWS, + + GGML_METAL_KERNEL_TYPE_COUNT +}; + struct ggml_metal_context { int n_cb; @@ -50,134 +180,13 @@ struct ggml_metal_context { int n_buffers; struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; + struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; + int concur_list[GGML_MAX_CONCUR]; int concur_list_len; - // custom kernels -#define GGML_METAL_DECL_KERNEL(name) \ - id function_##name; \ - id pipeline_##name - - GGML_METAL_DECL_KERNEL(add); - GGML_METAL_DECL_KERNEL(add_row); // TODO: avoid this extra kernel, instead extend the "add" kernel to support broadcast - GGML_METAL_DECL_KERNEL(mul); - GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast - GGML_METAL_DECL_KERNEL(div); - GGML_METAL_DECL_KERNEL(div_row); - GGML_METAL_DECL_KERNEL(scale); - GGML_METAL_DECL_KERNEL(scale_4); - GGML_METAL_DECL_KERNEL(tanh); - GGML_METAL_DECL_KERNEL(relu); - GGML_METAL_DECL_KERNEL(gelu); - GGML_METAL_DECL_KERNEL(gelu_quick); - GGML_METAL_DECL_KERNEL(silu); - GGML_METAL_DECL_KERNEL(soft_max); - GGML_METAL_DECL_KERNEL(soft_max_4); - GGML_METAL_DECL_KERNEL(diag_mask_inf); - GGML_METAL_DECL_KERNEL(diag_mask_inf_8); - GGML_METAL_DECL_KERNEL(get_rows_f32); - GGML_METAL_DECL_KERNEL(get_rows_f16); - GGML_METAL_DECL_KERNEL(get_rows_q4_0); - GGML_METAL_DECL_KERNEL(get_rows_q4_1); - GGML_METAL_DECL_KERNEL(get_rows_q5_0); - GGML_METAL_DECL_KERNEL(get_rows_q5_1); - GGML_METAL_DECL_KERNEL(get_rows_q8_0); - GGML_METAL_DECL_KERNEL(get_rows_q2_K); - GGML_METAL_DECL_KERNEL(get_rows_q3_K); - GGML_METAL_DECL_KERNEL(get_rows_q4_K); - GGML_METAL_DECL_KERNEL(get_rows_q5_K); - GGML_METAL_DECL_KERNEL(get_rows_q6_K); - GGML_METAL_DECL_KERNEL(get_rows_i32); - GGML_METAL_DECL_KERNEL(get_rows_iq2_xxs); - GGML_METAL_DECL_KERNEL(get_rows_iq2_xs); - GGML_METAL_DECL_KERNEL(rms_norm); - GGML_METAL_DECL_KERNEL(group_norm); - GGML_METAL_DECL_KERNEL(norm); - GGML_METAL_DECL_KERNEL(mul_mv_f32_f32); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f16); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f32); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4); - GGML_METAL_DECL_KERNEL(mul_mv_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mv_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_f32_f32); - //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f16); - GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32); - //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_1row); - //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_l4); - GGML_METAL_DECL_KERNEL(mul_mv_id_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_f32_f32); - GGML_METAL_DECL_KERNEL(mul_mm_f16_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_f32_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_f16_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(rope_f32); - GGML_METAL_DECL_KERNEL(rope_f16); - GGML_METAL_DECL_KERNEL(alibi_f32); - GGML_METAL_DECL_KERNEL(im2col_f16); - GGML_METAL_DECL_KERNEL(upscale_f32); - GGML_METAL_DECL_KERNEL(pad_f32); - GGML_METAL_DECL_KERNEL(argsort_f32_i32_asc); - GGML_METAL_DECL_KERNEL(argsort_f32_i32_desc); - GGML_METAL_DECL_KERNEL(leaky_relu_f32); - GGML_METAL_DECL_KERNEL(cpy_f32_f16); - GGML_METAL_DECL_KERNEL(cpy_f32_f32); - GGML_METAL_DECL_KERNEL(cpy_f32_q8_0); - GGML_METAL_DECL_KERNEL(cpy_f32_q4_0); - GGML_METAL_DECL_KERNEL(cpy_f32_q4_1); - //GGML_METAL_DECL_KERNEL(cpy_f32_q5_0); - //GGML_METAL_DECL_KERNEL(cpy_f32_q5_1); - GGML_METAL_DECL_KERNEL(cpy_f16_f16); - GGML_METAL_DECL_KERNEL(cpy_f16_f32); - GGML_METAL_DECL_KERNEL(concat); - GGML_METAL_DECL_KERNEL(sqr); - GGML_METAL_DECL_KERNEL(sum_rows); - -#undef GGML_METAL_DECL_KERNEL + bool support_simdgroup_reduction; + bool support_simdgroup_mm; }; // MSL code @@ -298,19 +307,22 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { return NULL; } - MTLCompileOptions* options = nil; + // dictionary of preprocessor macros + NSMutableDictionary * prep = [NSMutableDictionary dictionary]; + #ifdef GGML_QKK_64 - options = [MTLCompileOptions new]; - options.preprocessorMacros = @{ @"QK_K" : @(64) }; + prep[@"QK_K"] = @(64); #endif - // try to disable fast-math - // NOTE: this seems to have no effect whatsoever - // instead, in order to disable fast-math, we have to build default.metallib from the command line - // using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air - // and go through the "pre-compiled library found" path above + + MTLCompileOptions* options = [MTLCompileOptions new]; + options.preprocessorMacros = prep; + //[options setFastMathEnabled:false]; ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; + + [options release]; + [prep release]; } if (error) { @@ -323,16 +335,41 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { // print MTL GPU family: GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]); + const NSInteger MTLGPUFamilyMetal3 = 5001; + // determine max supported GPU family // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf - for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { - if ([ctx->device supportsFamily:i]) { - GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); - break; + { + for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { + if ([ctx->device supportsFamily:i]) { + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); + break; + } + } + + for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) { + if ([ctx->device supportsFamily:i]) { + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i); + break; + } + } + + for (int i = MTLGPUFamilyMetal3 + 5; i >= MTLGPUFamilyMetal3; --i) { + if ([ctx->device supportsFamily:i]) { + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3 + 3, i); + break; + } } } + ctx->support_simdgroup_reduction = [ctx->device supportsFamily:MTLGPUFamilyApple7]; + ctx->support_simdgroup_reduction |= [ctx->device supportsFamily:MTLGPUFamilyMetal3]; + + ctx->support_simdgroup_mm = [ctx->device supportsFamily:MTLGPUFamilyApple7]; + + GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); + GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); if (ctx->device.maxTransferRate != 0) { @@ -346,141 +383,152 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { { NSError * error = nil; + for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { + ctx->kernels[i].function = nil; + ctx->kernels[i].pipeline = nil; + } + /* - GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \ - (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \ - (int) ctx->pipeline_##name.threadExecutionWidth); \ + GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ + (int) kernel->pipeline.threadExecutionWidth); \ */ -#define GGML_METAL_ADD_KERNEL(name) \ - ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ - ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \ - if (error) { \ - GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ - return NULL; \ +#define GGML_METAL_ADD_KERNEL(e, name, supported) \ + if (supported) { \ + struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ + kernel->function = [ctx->library newFunctionWithName:@"kernel_"#name]; \ + kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:kernel->function error:&error]; \ + GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ + (int) kernel->pipeline.threadExecutionWidth); \ + if (error) { \ + GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ + return NULL; \ + } \ + } else { \ + GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \ } - GGML_METAL_ADD_KERNEL(add); - GGML_METAL_ADD_KERNEL(add_row); - GGML_METAL_ADD_KERNEL(mul); - GGML_METAL_ADD_KERNEL(mul_row); - GGML_METAL_ADD_KERNEL(div); - GGML_METAL_ADD_KERNEL(div_row); - GGML_METAL_ADD_KERNEL(scale); - GGML_METAL_ADD_KERNEL(scale_4); - GGML_METAL_ADD_KERNEL(tanh); - GGML_METAL_ADD_KERNEL(relu); - GGML_METAL_ADD_KERNEL(gelu); - GGML_METAL_ADD_KERNEL(gelu_quick); - GGML_METAL_ADD_KERNEL(silu); - GGML_METAL_ADD_KERNEL(soft_max); - GGML_METAL_ADD_KERNEL(soft_max_4); - GGML_METAL_ADD_KERNEL(diag_mask_inf); - GGML_METAL_ADD_KERNEL(diag_mask_inf_8); - GGML_METAL_ADD_KERNEL(get_rows_f32); - GGML_METAL_ADD_KERNEL(get_rows_f16); - GGML_METAL_ADD_KERNEL(get_rows_q4_0); - GGML_METAL_ADD_KERNEL(get_rows_q4_1); - GGML_METAL_ADD_KERNEL(get_rows_q5_0); - GGML_METAL_ADD_KERNEL(get_rows_q5_1); - GGML_METAL_ADD_KERNEL(get_rows_q8_0); - GGML_METAL_ADD_KERNEL(get_rows_q2_K); - GGML_METAL_ADD_KERNEL(get_rows_q3_K); - GGML_METAL_ADD_KERNEL(get_rows_q4_K); - GGML_METAL_ADD_KERNEL(get_rows_q5_K); - GGML_METAL_ADD_KERNEL(get_rows_q6_K); - GGML_METAL_ADD_KERNEL(get_rows_i32); - GGML_METAL_ADD_KERNEL(get_rows_iq2_xxs); - GGML_METAL_ADD_KERNEL(get_rows_iq2_xs); - GGML_METAL_ADD_KERNEL(rms_norm); - GGML_METAL_ADD_KERNEL(group_norm); - GGML_METAL_ADD_KERNEL(norm); - GGML_METAL_ADD_KERNEL(mul_mv_f32_f32); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f16); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f32); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4); - GGML_METAL_ADD_KERNEL(mul_mv_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mv_iq2_xs_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_f32_f32); - //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f16); - GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32); - //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_1row); - //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_l4); - GGML_METAL_ADD_KERNEL(mul_mv_id_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xs_f32); - if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { - GGML_METAL_ADD_KERNEL(mul_mm_f32_f32); - GGML_METAL_ADD_KERNEL(mul_mm_f16_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mm_iq2_xs_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_f32_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_f16_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xs_f32); - } - GGML_METAL_ADD_KERNEL(rope_f32); - GGML_METAL_ADD_KERNEL(rope_f16); - GGML_METAL_ADD_KERNEL(alibi_f32); - GGML_METAL_ADD_KERNEL(im2col_f16); - GGML_METAL_ADD_KERNEL(upscale_f32); - GGML_METAL_ADD_KERNEL(pad_f32); - GGML_METAL_ADD_KERNEL(argsort_f32_i32_asc); - GGML_METAL_ADD_KERNEL(argsort_f32_i32_desc); - GGML_METAL_ADD_KERNEL(leaky_relu_f32); - GGML_METAL_ADD_KERNEL(cpy_f32_f16); - GGML_METAL_ADD_KERNEL(cpy_f32_f32); - GGML_METAL_ADD_KERNEL(cpy_f32_q8_0); - GGML_METAL_ADD_KERNEL(cpy_f32_q4_0); - GGML_METAL_ADD_KERNEL(cpy_f32_q4_1); - //GGML_METAL_ADD_KERNEL(cpy_f32_q5_0); - //GGML_METAL_ADD_KERNEL(cpy_f32_q5_1); - GGML_METAL_ADD_KERNEL(cpy_f16_f16); - GGML_METAL_ADD_KERNEL(cpy_f16_f32); - GGML_METAL_ADD_KERNEL(concat); - GGML_METAL_ADD_KERNEL(sqr); - GGML_METAL_ADD_KERNEL(sum_rows); + // simd_sum and simd_max requires MTLGPUFamilyApple7 -#undef GGML_METAL_ADD_KERNEL + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); } return ctx; @@ -488,137 +536,21 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); -#define GGML_METAL_DEL_KERNEL(name) \ - [ctx->function_##name release]; \ - [ctx->pipeline_##name release]; - - GGML_METAL_DEL_KERNEL(add); - GGML_METAL_DEL_KERNEL(add_row); - GGML_METAL_DEL_KERNEL(mul); - GGML_METAL_DEL_KERNEL(mul_row); - GGML_METAL_DEL_KERNEL(div); - GGML_METAL_DEL_KERNEL(div_row); - GGML_METAL_DEL_KERNEL(scale); - GGML_METAL_DEL_KERNEL(scale_4); - GGML_METAL_DEL_KERNEL(tanh); - GGML_METAL_DEL_KERNEL(relu); - GGML_METAL_DEL_KERNEL(gelu); - GGML_METAL_DEL_KERNEL(gelu_quick); - GGML_METAL_DEL_KERNEL(silu); - GGML_METAL_DEL_KERNEL(soft_max); - GGML_METAL_DEL_KERNEL(soft_max_4); - GGML_METAL_DEL_KERNEL(diag_mask_inf); - GGML_METAL_DEL_KERNEL(diag_mask_inf_8); - GGML_METAL_DEL_KERNEL(get_rows_f32); - GGML_METAL_DEL_KERNEL(get_rows_f16); - GGML_METAL_DEL_KERNEL(get_rows_q4_0); - GGML_METAL_DEL_KERNEL(get_rows_q4_1); - GGML_METAL_DEL_KERNEL(get_rows_q5_0); - GGML_METAL_DEL_KERNEL(get_rows_q5_1); - GGML_METAL_DEL_KERNEL(get_rows_q8_0); - GGML_METAL_DEL_KERNEL(get_rows_q2_K); - GGML_METAL_DEL_KERNEL(get_rows_q3_K); - GGML_METAL_DEL_KERNEL(get_rows_q4_K); - GGML_METAL_DEL_KERNEL(get_rows_q5_K); - GGML_METAL_DEL_KERNEL(get_rows_q6_K); - GGML_METAL_DEL_KERNEL(get_rows_i32); - GGML_METAL_DEL_KERNEL(get_rows_iq2_xxs); - GGML_METAL_DEL_KERNEL(get_rows_iq2_xs); - GGML_METAL_DEL_KERNEL(rms_norm); - GGML_METAL_DEL_KERNEL(group_norm); - GGML_METAL_DEL_KERNEL(norm); - GGML_METAL_DEL_KERNEL(mul_mv_f32_f32); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f16); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f32); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4); - GGML_METAL_DEL_KERNEL(mul_mv_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mv_iq2_xs_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_f32_f32); - //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f16); - GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32); - //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_1row); - //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_l4); - GGML_METAL_DEL_KERNEL(mul_mv_id_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xs_f32); - if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { - GGML_METAL_DEL_KERNEL(mul_mm_f32_f32); - GGML_METAL_DEL_KERNEL(mul_mm_f16_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mm_iq2_xs_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_f32_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_f16_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xs_f32); - } - GGML_METAL_DEL_KERNEL(rope_f32); - GGML_METAL_DEL_KERNEL(rope_f16); - GGML_METAL_DEL_KERNEL(alibi_f32); - GGML_METAL_DEL_KERNEL(im2col_f16); - GGML_METAL_DEL_KERNEL(upscale_f32); - GGML_METAL_DEL_KERNEL(pad_f32); - GGML_METAL_DEL_KERNEL(argsort_f32_i32_asc); - GGML_METAL_DEL_KERNEL(argsort_f32_i32_desc); - GGML_METAL_DEL_KERNEL(leaky_relu_f32); - GGML_METAL_DEL_KERNEL(cpy_f32_f16); - GGML_METAL_DEL_KERNEL(cpy_f32_f32); - GGML_METAL_DEL_KERNEL(cpy_f32_q8_0); - GGML_METAL_DEL_KERNEL(cpy_f32_q4_0); - GGML_METAL_DEL_KERNEL(cpy_f32_q4_1); - //GGML_METAL_DEL_KERNEL(cpy_f32_q5_0); - //GGML_METAL_DEL_KERNEL(cpy_f32_q5_1); - GGML_METAL_DEL_KERNEL(cpy_f16_f16); - GGML_METAL_DEL_KERNEL(cpy_f16_f32); - GGML_METAL_DEL_KERNEL(concat); - GGML_METAL_DEL_KERNEL(sqr); - GGML_METAL_DEL_KERNEL(sum_rows); - -#undef GGML_METAL_DEL_KERNEL for (int i = 0; i < ctx->n_buffers; ++i) { [ctx->buffers[i].metal release]; } + for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { + if (ctx->kernels[i].pipeline) { + [ctx->kernels[i].pipeline release]; + } + + if (ctx->kernels[i].function) { + [ctx->kernels[i].function release]; + } + } + [ctx->library release]; [ctx->queue release]; [ctx->device release]; @@ -930,7 +862,7 @@ void ggml_metal_graph_find_concurrency( } } -static bool ggml_metal_supports_op(const struct ggml_tensor * op) { +static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -956,9 +888,11 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SUM_ROWS: + return true; case GGML_OP_SOFT_MAX: case GGML_OP_RMS_NORM: case GGML_OP_GROUP_NORM: + return ctx->support_simdgroup_reduction; case GGML_OP_NORM: case GGML_OP_ALIBI: case GGML_OP_ROPE: @@ -967,9 +901,10 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { case GGML_OP_PAD: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: + return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - return true; + return ctx->support_simdgroup_reduction; case GGML_OP_CPY: case GGML_OP_DUP: case GGML_OP_CONT: @@ -1007,6 +942,7 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { return false; } } + bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @@ -1077,7 +1013,7 @@ bool ggml_metal_graph_compute( } break; } - if (!ggml_metal_supports_op(dst)) { + if (!ggml_metal_supports_op(ctx, dst)) { GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); GGML_ASSERT(!"unsupported op"); } @@ -1143,7 +1079,9 @@ bool ggml_metal_graph_compute( { const int64_t nb = ne00; - [encoder setComputePipelineState:ctx->pipeline_concat]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1197,18 +1135,18 @@ bool ggml_metal_graph_compute( nb = ne00 / 4; switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->pipeline_add_row; break; - case GGML_OP_MUL: pipeline = ctx->pipeline_mul_row; break; - case GGML_OP_DIV: pipeline = ctx->pipeline_div_row; break; + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; default: GGML_ASSERT(false); } bcast_row = true; } else { switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->pipeline_add; break; - case GGML_OP_MUL: pipeline = ctx->pipeline_mul; break; - case GGML_OP_DIV: pipeline = ctx->pipeline_div; break; + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; default: GGML_ASSERT(false); } } @@ -1275,9 +1213,9 @@ bool ggml_metal_graph_compute( // not sure how to avoid this // TODO: make a simpler cpy_bytes kernel - const int nth = MIN((int) ctx->pipeline_cpy_f32_f32.maxTotalThreadsPerThreadgroup, ne00); + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; - [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -1297,10 +1235,14 @@ bool ggml_metal_graph_compute( [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } - [encoder setComputePipelineState:ctx->pipeline_add]; + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1330,7 +1272,7 @@ bool ggml_metal_graph_compute( [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - const int nth = MIN((int) ctx->pipeline_add.maxTotalThreadsPerThreadgroup, ne00); + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1342,13 +1284,16 @@ bool ggml_metal_graph_compute( int64_t n = ggml_nelements(dst); + id pipeline = nil; + if (n % 4 == 0) { n /= 4; - [encoder setComputePipelineState:ctx->pipeline_scale_4]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; } else { - [encoder setComputePipelineState:ctx->pipeline_scale]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; @@ -1359,7 +1304,9 @@ bool ggml_metal_graph_compute( switch (ggml_get_unary_op(gf->nodes[i])) { case GGML_UNARY_OP_TANH: { - [encoder setComputePipelineState:ctx->pipeline_tanh]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1369,7 +1316,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_RELU: { - [encoder setComputePipelineState:ctx->pipeline_relu]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1379,7 +1328,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_GELU: { - [encoder setComputePipelineState:ctx->pipeline_gelu]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1390,7 +1341,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_GELU_QUICK: { - [encoder setComputePipelineState:ctx->pipeline_gelu_quick]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1401,7 +1354,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_SILU: { - [encoder setComputePipelineState:ctx->pipeline_silu]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1420,18 +1375,23 @@ bool ggml_metal_graph_compute( { GGML_ASSERT(ggml_is_contiguous(src0)); - [encoder setComputePipelineState:ctx->pipeline_sqr]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; const int64_t n = ggml_nelements(dst); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_SUM_ROWS: { GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - [encoder setComputePipelineState:ctx->pipeline_sum_rows]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -1465,20 +1425,23 @@ bool ggml_metal_graph_compute( { int nth = 32; // SIMD width + id pipeline = nil; + if (ne00%4 == 0) { while (nth < ne00/4 && nth < 256) { nth *= 2; } - [encoder setComputePipelineState:ctx->pipeline_soft_max_4]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; } else { while (nth < ne00 && nth < 1024) { nth *= 2; } - [encoder setComputePipelineState:ctx->pipeline_soft_max]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } const float scale = ((float *) dst->op_params)[0]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; if (id_src1) { [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1498,11 +1461,15 @@ bool ggml_metal_graph_compute( { const int n_past = ((int32_t *)(dst->op_params))[0]; + id pipeline = nil; + if (ne00%8 == 0) { - [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf_8]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; } else { - [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; } + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -1562,23 +1529,28 @@ bool ggml_metal_graph_compute( ne00 % 32 == 0 && ne00 >= 64 && (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + switch (src0->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break; - case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_0_f32]; break; - case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_1_f32]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break; - case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xxs_f32]; break; - case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xs_f32]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1602,12 +1574,14 @@ bool ggml_metal_graph_compute( int nrows = 1; //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + id pipeline = nil; + // use custom matrix x vector kernel switch (src0t) { case GGML_TYPE_F32: { GGML_ASSERT(src1t == GGML_TYPE_F32); - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; nrows = 4; } break; case GGML_TYPE_F16: @@ -1616,16 +1590,16 @@ bool ggml_metal_graph_compute( nth1 = 1; if (src1t == GGML_TYPE_F32) { if (ne11 * ne12 < 4) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; nrows = ne11; } else { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; nrows = 4; } } else { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f16]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; nrows = 4; } } break; @@ -1633,73 +1607,73 @@ bool ggml_metal_graph_compute( { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; } break; case GGML_TYPE_Q4_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; } break; case GGML_TYPE_Q5_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; } break; case GGML_TYPE_Q5_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; } break; case GGML_TYPE_Q8_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q8_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; } break; case GGML_TYPE_Q2_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q2_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; } break; case GGML_TYPE_Q3_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q3_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; } break; case GGML_TYPE_Q4_K: { nth0 = 4; //1; nth1 = 8; //32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; } break; case GGML_TYPE_Q5_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; } break; case GGML_TYPE_Q6_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q6_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; } break; case GGML_TYPE_IQ2_XXS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xxs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; } break; case GGML_TYPE_IQ2_XS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; } break; default: { @@ -1712,6 +1686,7 @@ bool ggml_metal_graph_compute( GGML_ASSERT(ne00 >= nth0*nth1); } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1818,23 +1793,28 @@ bool ggml_metal_graph_compute( if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && ne20 % 32 == 0 && ne20 >= 64 && ne11 > ne11_mm_min) { + + id pipeline = nil; + switch (src2->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_0_f32]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_1_f32]; break; - case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_0_f32]; break; - case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_1_f32]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q8_0_f32]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q2_K_f32]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q3_K_f32]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_K_f32]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_K_f32]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q6_K_f32]; break; - case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xxs_f32]; break; - case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xs_f32]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1874,91 +1854,93 @@ bool ggml_metal_graph_compute( int nrows = 1; //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + id pipeline = nil; + // use custom matrix x vector kernel switch (src2t) { case GGML_TYPE_F32: { GGML_ASSERT(src1t == GGML_TYPE_F32); - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f32_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; } break; case GGML_TYPE_F16: { GGML_ASSERT(src1t == GGML_TYPE_F32); nth0 = 32; nth1 = 1; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f16_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; } break; case GGML_TYPE_Q4_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; } break; case GGML_TYPE_Q4_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; } break; case GGML_TYPE_Q5_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; } break; case GGML_TYPE_Q5_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; } break; case GGML_TYPE_Q8_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q8_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; } break; case GGML_TYPE_Q2_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q2_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; } break; case GGML_TYPE_Q3_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q3_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; } break; case GGML_TYPE_Q4_K: { nth0 = 4; //1; nth1 = 8; //32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; } break; case GGML_TYPE_Q5_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; } break; case GGML_TYPE_Q6_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q6_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; } break; case GGML_TYPE_IQ2_XXS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xxs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; } break; case GGML_TYPE_IQ2_XS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; } break; default: { @@ -1973,6 +1955,7 @@ bool ggml_metal_graph_compute( const int64_t _ne1 = 1; // kernels needs a reference in constant memory + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -2040,25 +2023,28 @@ bool ggml_metal_graph_compute( } break; case GGML_OP_GET_ROWS: { + id pipeline = nil; + switch (src0->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_get_rows_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break; - case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_0]; break; - case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_1]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break; - case GGML_TYPE_I32: [encoder setComputePipelineState:ctx->pipeline_get_rows_i32]; break; - case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xxs]; break; - case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xs]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -2086,7 +2072,9 @@ bool ggml_metal_graph_compute( nth *= 2; } - [encoder setComputePipelineState:ctx->pipeline_rms_norm]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2115,7 +2103,9 @@ bool ggml_metal_graph_compute( // nth *= 2; //} - [encoder setComputePipelineState:ctx->pipeline_group_norm]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2137,7 +2127,9 @@ bool ggml_metal_graph_compute( const int nth = MIN(256, ne00); - [encoder setComputePipelineState:ctx->pipeline_norm]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2164,7 +2156,9 @@ bool ggml_metal_graph_compute( const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - [encoder setComputePipelineState:ctx->pipeline_alibi_f32]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2209,12 +2203,15 @@ bool ggml_metal_graph_compute( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + id pipeline = nil; + switch (src0->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_rope_f16]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; default: GGML_ASSERT(false); }; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -2277,12 +2274,15 @@ bool ggml_metal_graph_compute( const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; + id pipeline = nil; + switch (src0->type) { case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_im2col_f16]; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; default: GGML_ASSERT(false); }; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; @@ -2305,7 +2305,9 @@ bool ggml_metal_graph_compute( const int sf = dst->op_params[0]; - [encoder setComputePipelineState:ctx->pipeline_upscale_f32]; + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -2326,7 +2328,7 @@ bool ggml_metal_graph_compute( [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; - const int nth = MIN((int) ctx->pipeline_upscale_f32.maxTotalThreadsPerThreadgroup, ne0); + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2334,7 +2336,9 @@ bool ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F32); - [encoder setComputePipelineState:ctx->pipeline_pad_f32]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -2367,12 +2371,15 @@ bool ggml_metal_graph_compute( enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + id pipeline = nil; + switch (order) { - case GGML_SORT_ASC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_asc]; break; - case GGML_SORT_DESC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_desc]; break; + case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; + case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; default: GGML_ASSERT(false); }; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2386,7 +2393,9 @@ bool ggml_metal_graph_compute( float slope; memcpy(&slope, dst->op_params, sizeof(float)); - [encoder setComputePipelineState:ctx->pipeline_leaky_relu_f32]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; @@ -2403,33 +2412,36 @@ bool ggml_metal_graph_compute( int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); + id pipeline = nil; + switch (src0t) { case GGML_TYPE_F32: { GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); switch (dstt) { - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16]; break; - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q8_0]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q4_0]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q4_1]; break; - //case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q5_0]; break; - //case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q5_1]; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; + //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; + //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; default: GGML_ASSERT(false && "not implemented"); }; } break; case GGML_TYPE_F16: { switch (dstt) { - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break; - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f32]; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; default: GGML_ASSERT(false && "not implemented"); }; } break; default: GGML_ASSERT(false && "not implemented"); } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2794,9 +2806,9 @@ static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml } static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - return ggml_metal_supports_op(op); + struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; - UNUSED(backend); + return ggml_metal_supports_op(metal_ctx, op); } static struct ggml_backend_i ggml_backend_metal_i = { From c30b1ef39aeba497a943416d2897d69fee055b96 Mon Sep 17 00:00:00 2001 From: texmex76 <40733439+texmex76@users.noreply.github.com> Date: Sat, 13 Jan 2024 17:06:20 +0100 Subject: [PATCH 407/664] gguf : fix potential infinite for-loop (#4600) Co-authored-by: Bernhard Gstrein --- ggml.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml.c b/ggml.c index 6dbd7626c..de6ef34bd 100644 --- a/ggml.c +++ b/ggml.c @@ -19184,7 +19184,7 @@ void gguf_free(struct gguf_context * ctx) { if (ctx->kv) { // free string memory - not great.. - for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { + for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; if (kv->key.data) { @@ -19200,7 +19200,7 @@ void gguf_free(struct gguf_context * ctx) { if (kv->type == GGUF_TYPE_ARRAY) { if (kv->value.arr.data) { if (kv->value.arr.type == GGUF_TYPE_STRING) { - for (uint32_t j = 0; j < kv->value.arr.n; ++j) { + for (uint64_t j = 0; j < kv->value.arr.n; ++j) { struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; if (str->data) { free(str->data); @@ -19216,7 +19216,7 @@ void gguf_free(struct gguf_context * ctx) { } if (ctx->infos) { - for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { + for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; if (info->name.data) { From 722d33f34ec74c6f7046109f936d0928ffe171bc Mon Sep 17 00:00:00 2001 From: Yann Follet <131855179+YannFollet@users.noreply.github.com> Date: Sun, 14 Jan 2024 00:09:08 +0800 Subject: [PATCH 408/664] main : add parameter --no-display-prompt (#4541) * add the parameter : --no-display-prompt , combine with --log-disable it will display only the generated tokens * remove empty line --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 6 +++++- common/common.h | 1 + examples/main/main.cpp | 7 ++++++- 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 322b9f91e..c11006bcb 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -617,6 +617,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.numa = true; } else if (arg == "--verbose-prompt") { params.verbose_prompt = true; + } else if (arg == "--no-display-prompt") { + params.display_prompt = false; } else if (arg == "-r" || arg == "--reverse-prompt") { if (++i >= argc) { invalid_param = true; @@ -936,11 +938,12 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); #endif + printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); + printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); printf(" group-attention factor (default: %d)\n", params.grp_attn_n); printf(" -gaw N, --grp-attn-w N\n"); printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); - printf(" --verbose-prompt print prompt before generation\n"); printf(" -dkvc, --dump-kv-cache\n"); printf(" verbose print of the KV cache\n"); printf(" -nkvo, --no-kv-offload\n"); @@ -1582,6 +1585,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); + fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); } // diff --git a/common/common.h b/common/common.h index f29be5b5a..096468243 100644 --- a/common/common.h +++ b/common/common.h @@ -126,6 +126,7 @@ struct gpt_params { bool use_mlock = false; // use mlock to keep model in memory bool numa = false; // attempt optimizations that help on some NUMA systems bool verbose_prompt = false; // print prompt tokens before generation + bool display_prompt = true; // print prompt before generation bool infill = false; // use infill mode bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading diff --git a/examples/main/main.cpp b/examples/main/main.cpp index c53b29978..58b7f807a 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -477,6 +477,7 @@ int main(int argc, char ** argv) { bool is_antiprompt = false; bool input_echo = true; + bool display = true; bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size(); int n_past = 0; @@ -491,6 +492,7 @@ int main(int argc, char ** argv) { // the first thing we will do is to output the prompt, so set color accordingly console::set_display(console::prompt); + display = params.display_prompt; std::vector embd; std::vector embd_guidance; @@ -707,7 +709,7 @@ int main(int argc, char ** argv) { } // display text - if (input_echo) { + if (input_echo && display) { for (auto id : embd) { const std::string token_str = llama_token_to_piece(ctx, id); printf("%s", token_str.c_str()); @@ -724,6 +726,7 @@ int main(int argc, char ** argv) { // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); + display = true; } // if not currently processing queued inputs; @@ -796,6 +799,7 @@ int main(int argc, char ** argv) { // color user input only console::set_display(console::user_input); + display = params.display_prompt; std::string line; bool another_line = true; @@ -806,6 +810,7 @@ int main(int argc, char ** argv) { // done taking input, reset color console::set_display(console::reset); + display = true; // Add tokens to embd only if the input buffer is non-empty // Entering a empty line lets the user pass control back From 6b48ed089377330cdb362970a51c1c89b6d857a8 Mon Sep 17 00:00:00 2001 From: Someone Date: Sat, 13 Jan 2024 16:29:16 +0000 Subject: [PATCH 409/664] workflows: unbreak nix-build-aarch64, and split it out (#4915) The fix should be just the `sudo apt-get update` --- .github/workflows/nix-ci-aarch64.yml | 55 ++++++++++++++++++++++++++++ .github/workflows/nix-ci.yml | 41 --------------------- 2 files changed, 55 insertions(+), 41 deletions(-) create mode 100644 .github/workflows/nix-ci-aarch64.yml diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml new file mode 100644 index 000000000..be7c26d40 --- /dev/null +++ b/.github/workflows/nix-ci-aarch64.yml @@ -0,0 +1,55 @@ +name: Nix aarch64 builds + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + +jobs: + nix-build-aarch64: + if: ${{ vars.CACHIX_NAME != '' }} + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install QEMU + # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 + run: | + sudo apt-get update + sudo apt-get install -y qemu-user-static qemu-system-aarch64 + sudo usermod -a -G kvm $USER + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-platforms = aarch64-linux + extra-system-features = nixos-test kvm + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: Set-up cachix to push the results to + uses: cachix/cachix-action@v13 + with: + authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' + name: ${{ vars.CACHIX_NAME }} + - name: Show all output paths + run: > + nix run github:nix-community/nix-eval-jobs + -- --gc-roots-dir gcroot + --flake + ".#packages.aarch64-linux" + - name: Build + run: > + nix run github:Mic92/nix-fast-build + -- --skip-cached --no-nom + --systems aarch64-linux + --flake + ".#checks.aarch64-linux" diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index a38c6ead4..845b93bfb 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -69,44 +69,3 @@ jobs: -- --skip-cached --no-nom --flake ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" - nix-build-aarch64: - if: ${{ vars.CACHIX_NAME != '' }} - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install QEMU - # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 - run: | - sudo apt-get install -y qemu-user-static qemu-system-aarch64 - sudo usermod -a -G kvm $USER - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-platforms = aarch64-linux - extra-system-features = nixos-test kvm - extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: Set-up cachix to push the results to - uses: cachix/cachix-action@v13 - with: - authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: ${{ vars.CACHIX_NAME }} - - name: Show all output paths - run: > - nix run github:nix-community/nix-eval-jobs - -- --gc-roots-dir gcroot - --flake - ".#packages.aarch64-linux" - - name: Build - run: > - nix run github:Mic92/nix-fast-build - -- --skip-cached --no-nom - --systems aarch64-linux - --flake - ".#checks.aarch64-linux" From df845cc982e7e2ea7b9900e29d55b15338faa78d Mon Sep 17 00:00:00 2001 From: David Friehs Date: Sat, 13 Jan 2024 17:29:43 +0100 Subject: [PATCH 410/664] llama : minimize size used for state save/load (#4820) * examples : save-load-state: save only required state * llama : only reserve n_vocab * n_batch at most for logits llama_decode asserts that only n_batch tokens are passed each call, and n_ctx is expected to be bigger than n_batch. * llama : always reserve n_vocab * n_batch for logits llama_context de-serialization breaks if the contexts have differing capacity for logits and llama_decode will at maximum resize to n_vocab * n_batch. * llama : only save and restore used logits for batch sizes of 512 this reduces save state in the best case by around 62 MB, which can be a lot if planning to save on each message to allow regenerating messages. * llama : use ostringstream and istringstream for save and load * llama : serialize rng into minimum amount of space required * llama : break session version due to serialization changes --- examples/save-load-state/save-load-state.cpp | 21 ++++---- llama.cpp | 53 +++++++------------- llama.h | 2 +- 3 files changed, 29 insertions(+), 47 deletions(-) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 48d801110..ef952e2bd 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -45,13 +45,13 @@ int main(int argc, char ** argv) { // save state (rng, logits, embedding and kv_cache) to file { std::vector state_mem(llama_get_state_size(ctx)); + const size_t written = llama_copy_state_data(ctx, state_mem.data()); - { - FILE *fp_write = fopen("dump_state.bin", "wb"); - llama_copy_state_data(ctx, state_mem.data()); // could also copy directly to memory mapped file - fwrite(state_mem.data(), 1, state_mem.size(), fp_write); - fclose(fp_write); - } + FILE *fp_write = fopen("dump_state.bin", "wb"); + fwrite(state_mem.data(), 1, written, fp_write); + fclose(fp_write); + + fprintf(stderr, "%s : serialized state into %zd out of a maximum of %zd bytes\n", __func__, written, state_mem.size()); } // save state (last tokens) @@ -100,18 +100,17 @@ int main(int argc, char ** argv) { std::vector state_mem(llama_get_state_size(ctx2)); FILE * fp_read = fopen("dump_state.bin", "rb"); + const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read); + fclose(fp_read); - const size_t ret = fread(state_mem.data(), 1, state_mem.size(), fp_read); - if (ret != state_mem.size()) { + if (read != llama_set_state_data(ctx2, state_mem.data())) { fprintf(stderr, "\n%s : failed to read state\n", __func__); llama_free(ctx2); llama_free_model(model); return 1; } - llama_set_state_data(ctx2, state_mem.data()); - - fclose(fp_read); + fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size()); } // restore state (last tokens) diff --git a/llama.cpp b/llama.cpp index 1d2eb569f..275456088 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9379,12 +9379,8 @@ struct llama_context * llama_new_context_with_model( ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); } - // resized during inference - if (params.logits_all) { - ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab); - } else { - ctx->logits.reserve(hparams.n_vocab); - } + // resized during inference, reserve maximum + ctx->logits.reserve(hparams.n_vocab*cparams.n_batch); if (params.embedding){ ctx->embedding.resize(hparams.n_embd); @@ -9731,8 +9727,8 @@ size_t llama_get_state_size(const struct llama_context * ctx) { // for reference, std::mt19937(1337) serializes to 6701 bytes. const size_t s_rng_size = sizeof(size_t); const size_t s_rng = LLAMA_MAX_RNG_STATE; - const size_t s_logits_capacity = sizeof(size_t); const size_t s_logits_size = sizeof(size_t); + // assume worst case for logits although only currently set ones are serialized const size_t s_logits = ctx->logits.capacity() * sizeof(float); const size_t s_embedding_size = sizeof(size_t); const size_t s_embedding = ctx->embedding.size() * sizeof(float); @@ -9743,7 +9739,6 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_total = ( + s_rng_size + s_rng - + s_logits_capacity + s_logits_size + s_logits + s_embedding_size @@ -9812,37 +9807,27 @@ struct llama_data_file_context : llama_data_context { static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { // copy rng { - std::stringstream rng_ss; + std::ostringstream rng_ss; rng_ss << ctx->rng; - const size_t rng_size = rng_ss.str().size(); - char rng_buf[LLAMA_MAX_RNG_STATE]; + const std::string & rng_str = rng_ss.str(); + const size_t rng_size = rng_str.size(); - memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE); - memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size()); + GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE); - data_ctx->write(&rng_size, sizeof(rng_size)); - data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE); + data_ctx->write(&rng_size, sizeof(rng_size)); + data_ctx->write(rng_str.data(), rng_size); } // copy logits { - const size_t logits_cap = ctx->logits.capacity(); const size_t logits_size = ctx->logits.size(); - data_ctx->write(&logits_cap, sizeof(logits_cap)); data_ctx->write(&logits_size, sizeof(logits_size)); if (logits_size) { data_ctx->write(ctx->logits.data(), logits_size * sizeof(float)); } - - // If there is a gap between the size and the capacity, write padding - size_t padding_size = (logits_cap - logits_size) * sizeof(float); - if (padding_size > 0) { - std::vector padding(padding_size, 0); // Create a buffer filled with zeros - data_ctx->write(padding.data(), padding_size); - } } // copy embeddings @@ -9925,13 +9910,13 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { // set rng { size_t rng_size; - char rng_buf[LLAMA_MAX_RNG_STATE]; + memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size); - memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size); - memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE; + GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE); - std::stringstream rng_ss; - rng_ss.str(std::string(&rng_buf[0], rng_size)); + std::string rng_str((char *)inp, rng_size); inp += rng_size; + + std::istringstream rng_ss(rng_str); rng_ss >> ctx->rng; GGML_ASSERT(!rng_ss.fail()); @@ -9939,20 +9924,18 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { // set logits { - size_t logits_cap; size_t logits_size; - memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap); memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size); - GGML_ASSERT(ctx->logits.capacity() == logits_cap); + GGML_ASSERT(ctx->logits.capacity() >= logits_size); if (logits_size) { ctx->logits.resize(logits_size); - memcpy(ctx->logits.data(), inp, logits_size * sizeof(float)); - } - inp += logits_cap * sizeof(float); + memcpy(ctx->logits.data(), inp, logits_size * sizeof(float)); + inp += logits_size * sizeof(float); + } } // set embeddings diff --git a/llama.h b/llama.h index 689e12d7c..01d6fafaa 100644 --- a/llama.h +++ b/llama.h @@ -43,7 +43,7 @@ #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN -#define LLAMA_SESSION_VERSION 3 +#define LLAMA_SESSION_VERSION 4 #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. From 2d57de525541247132e354f561ff48775fba5d85 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 18:46:37 +0200 Subject: [PATCH 411/664] metal : disable log for loaded kernels (#4794) --- ggml-metal.m | 3 --- 1 file changed, 3 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 6c28a7ee3..57e444827 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -398,9 +398,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ kernel->function = [ctx->library newFunctionWithName:@"kernel_"#name]; \ kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:kernel->function error:&error]; \ - GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ - (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ - (int) kernel->pipeline.threadExecutionWidth); \ if (error) { \ GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ return NULL; \ From f172de03f11465dc6c5a0fc3a22f8ec254c6832c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 18:47:38 +0200 Subject: [PATCH 412/664] llama : fix detokenization of non-special added-tokens (#4916) Co-authored-by: goerch --- llama.cpp | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index 275456088..2190ea7aa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10305,6 +10305,8 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { case LLAMA_VOCAB_TYPE_SPM: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. if (llama_is_normal_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; llama_unescape_whitespace(result); @@ -10313,6 +10315,13 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, result.c_str(), result.length()); return result.length(); + } else if (llama_is_user_defined_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT if (length < 3) { return -3; @@ -10327,14 +10336,12 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } buf[0] = llama_token_to_byte(model->vocab, token); return 1; - } else { - // TODO: for now we accept all unsupported token types, - // suppressing them like CONTROL tokens. - // GGML_ASSERT(false); } break; } case LLAMA_VOCAB_TYPE_BPE: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. if (llama_is_normal_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; result = llama_decode_text(result); @@ -10343,12 +10350,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, result.c_str(), result.length()); return result.length(); + } else if (llama_is_user_defined_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); } else if (llama_is_control_token(model->vocab, token)) { ; - } else { - // TODO: for now we accept all unsupported token types, - // suppressing them like CONTROL tokens. - // GGML_ASSERT(false); } break; } From 0ea069b87bd296c556824e57455433b6c0357340 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 19:31:26 +0200 Subject: [PATCH 413/664] server : fix prompt caching with system prompt (#4914) --- examples/server/server.cpp | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 79eacf828..93f999298 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1180,8 +1180,9 @@ struct llama_server_context return slot.images.size() > 0; } - void send_error(task_server& task, std::string error) + void send_error(task_server& task, const std::string &error) { + LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); std::unique_lock lock(mutex_results); task_result res; res.id = task.id; @@ -1570,12 +1571,22 @@ struct llama_server_context LOG_TEE("slot unavailable\n"); // send error result send_error(task, "slot unavailable"); - return; + break; } if (task.data.contains("system_prompt")) { + if (!all_slots_are_idle) { + send_error(task, "system prompt can only be updated when all slots are idle"); + break; + } process_system_prompt_data(task.data["system_prompt"]); + + // reset cache_tokens for all slots + for (llama_client_slot &slot : slots) + { + slot.cache_tokens.clear(); + } } slot->reset(); @@ -1652,8 +1663,7 @@ struct llama_server_context // attend tasks process_tasks(); - // update the system prompt wait until all slots are idle state - if (system_need_update && all_slots_are_idle) + if (system_need_update) { LOG_TEE("updating system prompt\n"); update_system_prompt(); From 4be5ef556de830c5c4f6e45c05ef4427823fe607 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 20:45:45 +0200 Subject: [PATCH 414/664] metal : remove old API (#4919) ggml-ci --- Makefile | 9 -- examples/CMakeLists.txt | 3 - examples/metal/CMakeLists.txt | 4 - examples/metal/metal.cpp | 103 ------------- ggml-metal.h | 55 +------ ggml-metal.m | 276 +++------------------------------- llama.cpp | 4 +- 7 files changed, 27 insertions(+), 427 deletions(-) delete mode 100644 examples/metal/CMakeLists.txt delete mode 100644 examples/metal/metal.cpp diff --git a/Makefile b/Makefile index 05fe9a0f6..995b89f7a 100644 --- a/Makefile +++ b/Makefile @@ -43,10 +43,6 @@ ifeq ($(UNAME_S),Darwin) endif endif -ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' -BUILD_TARGETS += metal -endif - default: $(BUILD_TARGETS) test: $(TEST_TARGETS) @@ -671,11 +667,6 @@ lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -ifdef LLAMA_METAL -metal: examples/metal/metal.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) -endif - ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift (cd examples/batched.swift; make build) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index fa127a3aa..f67d74c55 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -37,9 +37,6 @@ else() add_subdirectory(lookup) add_subdirectory(train-text-from-scratch) add_subdirectory(imatrix) - if (LLAMA_METAL) - add_subdirectory(metal) - endif() if (LLAMA_BUILD_SERVER) add_subdirectory(server) endif() diff --git a/examples/metal/CMakeLists.txt b/examples/metal/CMakeLists.txt deleted file mode 100644 index f16d49165..000000000 --- a/examples/metal/CMakeLists.txt +++ /dev/null @@ -1,4 +0,0 @@ -set(TEST_TARGET metal) -add_executable(${TEST_TARGET} metal.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TEST_TARGET} PRIVATE ggml) diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp deleted file mode 100644 index 16c1146f9..000000000 --- a/examples/metal/metal.cpp +++ /dev/null @@ -1,103 +0,0 @@ -// Evaluate a statically exported ggml computation graph with Metal -// -// - First, export a LLaMA graph: -// -// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export -// -// - Run this tool to evaluate the exported graph: -// -// $ ./bin/metal llama.ggml -// -// The purpose of this tool is mostly for debugging and demonstration purposes. -// The main limitation of exporting computation graphs is that their sizes are static which often -// can be a problem for real-world applications. -// - -#include "ggml.h" -#include "ggml-metal.h" - -#include -#include -#include - -int main(int argc, char ** argv) { - ggml_time_init(); - - if (argc != 2) { - fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]); - return -1; - } - - const char * fname_cgraph = argv[1]; - - // load the compute graph - struct ggml_context * ctx_data = NULL; - struct ggml_context * ctx_eval = NULL; - - struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); - - // this allocates all Metal resources and memory buffers - auto * ctx_metal = ggml_metal_init(1); - - const size_t max_size_data = ggml_get_max_tensor_size(ctx_data); - const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval); - ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data); - ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval); - - // main - { - struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd"); - *(int32_t *) input->data = 1; // BOS - - ggml_metal_set_tensor(ctx_metal, input); - - // warmup - ggml_metal_graph_compute(ctx_metal, gf); - - const int n_iter = 16; - - const int64_t t0 = ggml_time_us(); - - // the actual inference happens here - for (int i = 0; i < n_iter; ++i) { - ggml_metal_graph_compute(ctx_metal, gf); - } - - const int64_t t1 = ggml_time_us(); - - printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter); - } - - // debug output - { - struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1]; - ggml_metal_get_tensor(ctx_metal, logits); - - float * ptr = (float *) ggml_get_data(logits); - - printf("logits: "); - for (int i = 0; i < 10; i++) { - printf("%8.4f ", ptr[i]); - } - printf("\n"); - int imax = 0; - double sum = 0.0; - double vmax = -1e9; - for (int i = 0; i < 32000; i++) { - sum += (double) ptr[i]; - if (ptr[i] > vmax) { - vmax = ptr[i]; - imax = i; - } - } - printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax); - } - - ggml_metal_free(ctx_metal); - - ggml_free(ctx_data); - ggml_free(ctx_eval); - - return 0; -} - diff --git a/ggml-metal.h b/ggml-metal.h index c4b7325da..cd5e2995f 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -36,64 +36,13 @@ struct ggml_cgraph; extern "C" { #endif -// -// internal API -// temporary exposed to user-code -// - -struct ggml_metal_context; - -void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data); - -// number of command buffers to use -struct ggml_metal_context * ggml_metal_init(int n_cb); -void ggml_metal_free(struct ggml_metal_context * ctx); - -void * ggml_metal_host_malloc(size_t n); -void ggml_metal_host_free (void * data); - -// set the number of command buffers to use -void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb); - -// creates a mapping between a host memory buffer and a device memory buffer -// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute -// - the mapping is used during computation to determine the arguments of the compute kernels -// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal -// - max_size specifies the maximum size of a tensor and is used to create shared views such -// that it is guaranteed that the tensor will fit in at least one of the views -// -bool ggml_metal_add_buffer( - struct ggml_metal_context * ctx, - const char * name, - void * data, - size_t size, - size_t max_size); - -// set data from host memory into the device -void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); - -// get data from the device into host memory -void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); - -// try to find operations that can be run concurrently in the graph -// you should run it again if the topology of your graph changes -void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem); - -// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized -int ggml_metal_if_optimized(struct ggml_metal_context * ctx); - -// output the concur_list for ggml_alloc -int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx); - -// same as ggml_graph_compute but uses Metal -// creates gf->n_threads command buffers in parallel -bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf); - // // backend API // user-code should use only these functions // +GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data); + GGML_API ggml_backend_t ggml_backend_metal_init(void); GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); diff --git a/ggml-metal.m b/ggml-metal.m index 57e444827..cae52c983 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -24,8 +24,6 @@ #define UNUSED(x) (void)(x) -#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) - #define GGML_METAL_MAX_KERNELS 256 struct ggml_metal_buffer { @@ -182,9 +180,6 @@ struct ggml_metal_context { struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; - int concur_list[GGML_MAX_CONCUR]; - int concur_list_len; - bool support_simdgroup_reduction; bool support_simdgroup_mm; }; @@ -200,7 +195,6 @@ struct ggml_metal_context { @implementation GGMLMetalClass @end - static void ggml_metal_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { fprintf(stderr, "%s", msg); @@ -211,11 +205,6 @@ static void ggml_metal_default_log_callback(enum ggml_log_level level, const cha ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback; void * ggml_metal_log_user_data = NULL; -void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { - ggml_metal_log_callback = log_callback; - ggml_metal_log_user_data = user_data; -} - GGML_ATTRIBUTE_FORMAT(2, 3) static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ if (ggml_metal_log_callback != NULL) { @@ -238,7 +227,18 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ } } -struct ggml_metal_context * ggml_metal_init(int n_cb) { +static void * ggml_metal_host_malloc(size_t n) { + void * data = NULL; + const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); + if (result != 0) { + GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); + return NULL; + } + + return data; +} + +static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: allocating\n", __func__); id device; @@ -264,7 +264,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); ctx->queue = [ctx->device newCommandQueue]; ctx->n_buffers = 0; - ctx->concur_list_len = 0; ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); @@ -531,7 +530,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { return ctx; } -void ggml_metal_free(struct ggml_metal_context * ctx) { +static void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); for (int i = 0; i < ctx->n_buffers; ++i) { @@ -557,33 +556,6 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { free(ctx); } -void * ggml_metal_host_malloc(size_t n) { - void * data = NULL; - const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); - if (result != 0) { - GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); - return NULL; - } - - return data; -} - -void ggml_metal_host_free(void * data) { - free(data); -} - -void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) { - ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); -} - -int ggml_metal_if_optimized(struct ggml_metal_context * ctx) { - return ctx->concur_list_len; -} - -int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) { - return ctx->concur_list; -} - // temporarily defined here for compatibility between ggml-backend and the old API struct ggml_backend_metal_buffer { @@ -656,209 +628,6 @@ static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru return nil; } -bool ggml_metal_add_buffer( - struct ggml_metal_context * ctx, - const char * name, - void * data, - size_t size, - size_t max_size) { - if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { - GGML_METAL_LOG_ERROR("%s: error: too many buffers\n", __func__); - return false; - } - - if (data) { - // verify that the buffer does not overlap with any of the existing buffers - for (int i = 0; i < ctx->n_buffers; ++i) { - const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data; - - if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { - GGML_METAL_LOG_ERROR("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); - return false; - } - } - - const size_t size_page = sysconf(_SC_PAGESIZE); - - size_t size_aligned = size; - if ((size_aligned % size_page) != 0) { - size_aligned += (size_page - (size_aligned % size_page)); - } - - // the buffer fits into the max buffer size allowed by the device - if (size_aligned <= ctx->device.maxBufferLength) { - ctx->buffers[ctx->n_buffers].name = name; - ctx->buffers[ctx->n_buffers].data = data; - ctx->buffers[ctx->n_buffers].size = size; - - ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0); - return false; - } - - GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0); - - ++ctx->n_buffers; - } else { - // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into - // one of the views - const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case - const size_t size_step = ctx->device.maxBufferLength - size_ovlp; - const size_t size_view = ctx->device.maxBufferLength; - - for (size_t i = 0; i < size; i += size_step) { - const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); - - ctx->buffers[ctx->n_buffers].name = name; - ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i); - ctx->buffers[ctx->n_buffers].size = size_step_aligned; - - ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); - return false; - } - - GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); - if (i + size_step < size) { - GGML_METAL_LOG_INFO("\n"); - } - - ++ctx->n_buffers; - } - } - -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", - ctx->device.currentAllocatedSize / 1024.0 / 1024.0, - ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } else { - GGML_METAL_LOG_INFO("\n"); - } -#else - GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0); -#endif - } - - return true; -} - -void ggml_metal_set_tensor( - struct ggml_metal_context * ctx, - struct ggml_tensor * t) { - size_t offs; - id id_dst = ggml_metal_get_buffer(ctx, t, &offs); - - memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t)); -} - -void ggml_metal_get_tensor( - struct ggml_metal_context * ctx, - struct ggml_tensor * t) { - size_t offs; - id id_src = ggml_metal_get_buffer(ctx, t, &offs); - - memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t)); -} - -void ggml_metal_graph_find_concurrency( - struct ggml_metal_context * ctx, - struct ggml_cgraph * gf, bool check_mem) { - int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time - int nodes_unused[GGML_MAX_CONCUR]; - - for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; } - for (int i = 0; i < gf->n_nodes; i++) { nodes_unused[i] = 1; } - ctx->concur_list_len = 0; - - int n_left = gf->n_nodes; - int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list - int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos - - while (n_left > 0) { - // number of nodes at a layer (that can be issued concurrently) - int concurrency = 0; - for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) { - if (nodes_unused[i]) { - // if the requirements for gf->nodes[i] are satisfied - int exe_flag = 1; - - // scan all srcs - for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) { - struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind]; - if (src_cur) { - // if is leaf nodes it's satisfied. - // TODO: ggml_is_leaf() - if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) { - continue; - } - - // otherwise this src should be the output from previous nodes. - int is_found = 0; - - // scan 2*search_depth back because we inserted barrier. - //for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) { - for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) { - if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) { - is_found = 1; - break; - } - } - if (is_found == 0) { - exe_flag = 0; - break; - } - } - } - if (exe_flag && check_mem) { - // check if nodes[i]'s data will be overwritten by a node before nodes[i]. - // if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3] - int64_t data_start = (int64_t) gf->nodes[i]->data; - int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]); - for (int j = n_start; j < i; j++) { - if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \ - && gf->nodes[j]->op != GGML_OP_VIEW \ - && gf->nodes[j]->op != GGML_OP_TRANSPOSE \ - && gf->nodes[j]->op != GGML_OP_PERMUTE) { - if (((int64_t)gf->nodes[j]->data) >= data_start + length || \ - ((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) { - continue; - } - - exe_flag = 0; - } - } - } - if (exe_flag) { - ctx->concur_list[level_pos + concurrency] = i; - nodes_unused[i] = 0; - concurrency++; - ctx->concur_list_len++; - } - } - } - n_left -= concurrency; - // adding a barrier different layer - ctx->concur_list[level_pos + concurrency] = -1; - ctx->concur_list_len++; - // jump all sorted nodes at nodes_bak - while (!nodes_unused[n_start]) { - n_start++; - } - level_pos += concurrency + 1; - } - - if (ctx->concur_list_len > GGML_MAX_CONCUR) { - GGML_METAL_LOG_WARN("%s: too many elements for metal ctx->concur_list!\n", __func__); - } -} - static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: @@ -940,19 +709,15 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const } } -bool ggml_metal_graph_compute( +static bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @autoreleasepool { - // if there is ctx->concur_list, dispatch concurrently - // else fallback to serial dispatch MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor; - const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR; - - const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes; - edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial; + const int n_nodes = gf->n_nodes; + edesc.dispatchType = MTLDispatchTypeSerial; // create multiple command buffers and enqueue them // then, we encode the graph into the command buffers in parallel @@ -983,7 +748,7 @@ bool ggml_metal_graph_compute( const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); for (int ind = node_start; ind < node_end; ++ind) { - const int i = has_concur ? ctx->concur_list[ind] : ind; + const int i = ind; if (i == -1) { [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; @@ -2823,6 +2588,11 @@ static struct ggml_backend_i ggml_backend_metal_i = { /* .supports_op = */ ggml_backend_metal_supports_op, }; +void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { + ggml_metal_log_callback = log_callback; + ggml_metal_log_user_data = user_data; +} + ggml_backend_t ggml_backend_metal_init(void) { struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); @@ -2849,7 +2619,7 @@ void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; - ggml_metal_set_n_cb(ctx, n_cb); + ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); } bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { diff --git a/llama.cpp b/llama.cpp index 2190ea7aa..66494974a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1266,7 +1266,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g struct llama_state { llama_state() { #ifdef GGML_USE_METAL - ggml_metal_log_set_callback(log_callback, log_callback_user_data); + ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data); #endif } @@ -10470,7 +10470,7 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) { g_state.log_callback = log_callback ? log_callback : llama_log_callback_default; g_state.log_callback_user_data = user_data; #ifdef GGML_USE_METAL - ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); + ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); #endif } From c71d608ce7a1584bf5072f197919dd24f3a6163f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 13 Jan 2024 21:41:37 +0100 Subject: [PATCH 415/664] ggml: cache sin/cos for RoPE (#4908) --- ggml.c | 46 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/ggml.c b/ggml.c index de6ef34bd..bcfb6652c 100644 --- a/ggml.c +++ b/ggml.c @@ -11638,6 +11638,21 @@ static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, fl return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); } +static void ggml_rope_cache_init( + float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale +) { + float theta = theta_base; + for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + rope_yarn( + theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + ); + cache[i0 + 1] *= sin_sign; + + theta *= theta_scale; + } +} + void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { @@ -11720,6 +11735,12 @@ static void ggml_compute_forward_rope_f32( for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = 0; i2 < ne2; i2++) { const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox + ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -11753,18 +11774,13 @@ static void ggml_compute_forward_rope_f32( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn( - theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta - ); - sin_theta *= sin_sign; + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; - theta_base *= theta_scale; - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11888,6 +11904,12 @@ static void ggml_compute_forward_rope_f16( for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = 0; i2 < ne2; i2++) { const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox + ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -11921,13 +11943,8 @@ static void ggml_compute_forward_rope_f16( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn( - theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta - ); - sin_theta *= sin_sign; - - theta_base *= theta_scale; + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -16722,6 +16739,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa } } break; case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } break; From 76484fbfd355df388f71d6edaa98e1692a74de7e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 00:14:46 +0200 Subject: [PATCH 416/664] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index edcdb530a..753d227a7 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -400c07f00508e6f60fb25405444b5669c365b0a9 +1890780da4ea10db88736fcde85f285abf6c64b0 From 807179ec583dcb882f97d9704577c06beb2c5ec9 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 09:44:30 +0200 Subject: [PATCH 417/664] Make Q3_K_S be the same as olf Q3_K_L for Mixtral-8x7B (#4906) Co-authored-by: Iwan Kawrakow --- llama.cpp | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 66494974a..8e20e72a2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8489,9 +8489,16 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty ++qs.i_feed_forward_w2; } else if (name.find("attn_output.weight") != std::string::npos) { if (arch != LLM_ARCH_FALCON) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + if (qs.model.hparams.n_expert == 8) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || + ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { + new_type = GGML_TYPE_Q5_K; + } + } else { + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + } } else { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; } From 147b17ac94a24d524e367cda26a9ff6245689f34 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 09:45:56 +0200 Subject: [PATCH 418/664] 2-bit quantizations (#4897) * imatrix: load * imatrix: WIP * imatrix: Add Q2_K quantization * imatrix: also guard against Q2_K_S quantization without importance matrix * imatrix: guard even more against low-bit quantization misuse --------- Co-authored-by: Iwan Kawrakow --- examples/benchmark/benchmark-matmult.cpp | 4 +- examples/quantize/quantize.cpp | 133 +++- ggml-quants.c | 950 +++++++++++++++++++++-- ggml-quants.h | 12 +- ggml.c | 36 +- ggml.h | 9 +- llama.cpp | 84 +- llama.h | 1 + tests/test-backend-ops.cpp | 2 +- 9 files changed, 1149 insertions(+), 82 deletions(-) diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index 434e1d6bd..e89f3de2f 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -194,7 +194,7 @@ int main(int argc, char ** argv) { // Set up a the benchmark matrices // printf("Creating new tensor q11 & Running quantize\n"); struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements, hist_cur.data()); + ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr); // Set up a the compute graph // printf("Creating new tensor q31\n"); @@ -207,7 +207,7 @@ int main(int argc, char ** argv) { // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements, hist_cur.data()); + ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr); // printf("Creating new tensor q32\n"); struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index f878f6911..f4e2175f1 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -5,6 +5,10 @@ #include #include #include +#include +#include +#include +#include struct quant_option { std::string name; @@ -17,6 +21,8 @@ static const std::vector QUANT_OPTIONS = { { "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", }, { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", }, { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, + { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", }, + { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, @@ -72,10 +78,14 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp // [[noreturn]] static void usage(const char * executable) { - printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); + printf(" --imatrixfile_name: use data in file_name as importance matrix for quant optimizations\n"); + printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf("Note: --include-weights and --exclude-weights cannot be used together\n"); printf("\nAllowed quantization types:\n"); for (auto & it : QUANT_OPTIONS) { if (it.name != "COPY") { @@ -83,11 +93,93 @@ static void usage(const char * executable) { } else { printf(" "); } - printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str()); + printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str()); } exit(1); } +static void load_imatrix(const std::string& imatrix_file, std::unordered_map>& imatrix_data) { + std::ifstream in(imatrix_file.c_str(), std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__,imatrix_file.c_str()); + return; + } + int n_entries; + in.read((char*)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file.c_str()); + return; + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str()); + return; + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto& e = imatrix_data[std::move(name)]; + int ncall; + in.read((char*)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n",__func__,i); + imatrix_data = {}; + return; + } + e.resize(nval); + in.read((char*)e.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n",__func__,i); + imatrix_data = {}; + return; + } + if (ncall > 0) { + for (auto& v : e) v /= ncall; + } + } + printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str()); +} + +static void prepare_imatrix(const std::string& imatrix_file, + const std::vector& included_weights, + const std::vector& excluded_weights, + std::unordered_map>& imatrix_data) { + if (!imatrix_file.empty()) { + load_imatrix(imatrix_file, imatrix_data); + } + if (imatrix_data.empty()) { + return; + } + if (!excluded_weights.empty()) { + for (auto& name : excluded_weights) { + for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) { + auto pos = it->first.find(name); + if (pos != std::string::npos) it = imatrix_data.erase(it); + else ++it; + } + } + } + if (!included_weights.empty()) { + std::unordered_map> tmp; + for (auto& name : included_weights) { + for (auto& e : imatrix_data) { + auto pos = e.first.find(name); + if (pos != std::string::npos) { + tmp.emplace(std::move(e)); + } + } + } + imatrix_data = std::move(tmp); + } + if (!imatrix_data.empty()) { + printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size())); + } +} + int main(int argc, char ** argv) { if (argc < 3) { usage(argv[0]); @@ -96,6 +188,8 @@ int main(int argc, char ** argv) { llama_model_quantize_params params = llama_model_quantize_default_params(); int arg_idx = 1; + std::string imatrix_file; + std::vector included_weights, excluded_weights; for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) { @@ -104,14 +198,42 @@ int main(int argc, char ** argv) { params.allow_requantize = true; } else if (strcmp(argv[arg_idx], "--pure") == 0) { params.pure = true; + } else if (strcmp(argv[arg_idx], "--imatrix") == 0) { + if (arg_idx < argc-1) { + imatrix_file = argv[++arg_idx]; + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--include-weights") == 0) { + if (arg_idx < argc-1) { + included_weights.push_back(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) { + if (arg_idx < argc-1) { + excluded_weights.push_back(argv[++arg_idx]); + } else { + usage(argv[0]); + } } else { usage(argv[0]); } } if (argc - arg_idx < 2) { + printf("%s: bad arguments\n", argv[0]); usage(argv[0]); } + if (!included_weights.empty() && !excluded_weights.empty()) { + usage(argv[0]); + } + + std::unordered_map> imatrix_data; + prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data); + if (!imatrix_data.empty()) { + params.imatrix = &imatrix_data; + } llama_backend_init(false); @@ -163,6 +285,13 @@ int main(int argc, char ** argv) { } } + if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) && imatrix_data.empty()) { + fprintf(stderr, "\n===============================================================================================\n"); + fprintf(stderr, "Please do not use IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); + fprintf(stderr, "===============================================================================================\n\n\n"); + return 1; + } + print_build_info(); fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str()); diff --git a/ggml-quants.c b/ggml-quants.c index 601d155d7..9290d54cf 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -5,6 +5,8 @@ #include #include #include +#include // for qsort +#include // for GGML_ASSERT #ifdef __ARM_NEON @@ -1639,6 +1641,241 @@ size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q2_K)); } +static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights, + uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, + float rmin, float rdelta, int nstep, bool use_mad) { + float min = x[0]; + float max = x[0]; + float sum_w = weights ? weights[0] : x[0]*x[0]; + float sum_x = sum_w * x[0]; + for (int i = 1; i < n; ++i) { + if (x[i] < min) min = x[i]; + if (x[i] > max) max = x[i]; + float w = weights ? weights[i] : x[i]*x[i]; + sum_w += w; + sum_x += w * x[i]; + } + if (min > 0) { + min = 0; + } + if (max <= min) { + for (int i = 0; i < n; ++i) L[i] = 0; + *the_min = -min; + return 0.f; + } + float iscale = nmax/(max - min); + float scale = 1/iscale; + float best_mad = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + L[i] = MAX(0, MIN(nmax, l)); + float diff = scale * L[i] + min - x[i]; + diff = use_mad ? fabsf(diff) : diff*diff; + float w = weights ? weights[i] : x[i]*x[i]; + best_mad += w * diff; + } + if (nstep < 1) { + *the_min = -min; + return scale; + } + for (int is = 0; is <= nstep; ++is) { + iscale = (rmin + rdelta*is + nmax)/(max - min); + float sum_l = 0, sum_l2 = 0, sum_xl = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + l = MAX(0, MIN(nmax, l)); + Laux[i] = l; + float w = weights ? weights[i] : x[i]*x[i]; + sum_l += w*l; + sum_l2 += w*l*l; + sum_xl += w*l*x[i]; + } + float D = sum_w * sum_l2 - sum_l * sum_l; + if (D > 0) { + float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D; + float this_min = (sum_l2 * sum_x - sum_l * sum_xl)/D; + if (this_min > 0) { + this_min = 0; + this_scale = sum_xl / sum_l2; + } + float mad = 0; + for (int i = 0; i < n; ++i) { + float diff = this_scale * Laux[i] + this_min - x[i]; + diff = use_mad ? fabsf(diff) : diff*diff; + float w = weights ? weights[i] : x[i]*x[i]; + mad += w * diff; + } + if (mad < best_mad) { + for (int i = 0; i < n; ++i) { + L[i] = Laux[i]; + } + best_mad = mad; + scale = this_scale; + min = this_min; + } + } + } + *the_min = -min; + return scale; +} + +static float make_qp_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, const float * quant_weights) { + float max = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + } + if (!max) { // all zero + for (int i = 0; i < n; ++i) { L[i] = 0; } + return 0.f; + } + float iscale = nmax / max; + for (int i = 0; i < n; ++i) { + L[i] = nearest_int(iscale * x[i]); + } + float scale = 1/iscale; + float best_mse = 0; + for (int i = 0; i < n; ++i) { + float diff = x[i] - scale*L[i]; + float w = quant_weights[i]; + best_mse += w*diff*diff; + } + for (int is = -4; is <= 4; ++is) { + if (is == 0) continue; + float iscale_is = (0.1f*is + nmax)/max; + float scale_is = 1/iscale_is; + float mse = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale_is*x[i]); + l = MIN(nmax, l); + float diff = x[i] - scale_is*l; + float w = quant_weights[i]; + mse += w*diff*diff; + } + if (mse < best_mse) { + best_mse = mse; + iscale = iscale_is; + } + } + float sumlx = 0; + float suml2 = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MIN(nmax, l); + L[i] = l; + float w = quant_weights[i]; + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + for (int itry = 0; itry < 5; ++itry) { + int n_changed = 0; + for (int i = 0; i < n; ++i) { + float w = quant_weights[i]; + float slx = sumlx - w*x[i]*L[i]; + float sl2 = suml2 - w*L[i]*L[i]; + if (slx > 0 && sl2 > 0) { + int new_l = nearest_int(x[i] * sl2 / slx); + new_l = MIN(nmax, new_l); + if (new_l != L[i]) { + slx += w*x[i]*new_l; + sl2 += w*new_l*new_l; + if (slx*slx*suml2 > sumlx*sumlx*sl2) { + L[i] = new_l; sumlx = slx; suml2 = sl2; + ++n_changed; + } + } + } + } + if (!n_changed) { + break; + } + } + return sumlx / suml2; +} + +static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restrict y, int k, const float * restrict quant_weights) { + GGML_ASSERT(quant_weights); + assert(k % QK_K == 0); + const int nb = k / QK_K; + const bool requantize = true; + + uint8_t L[QK_K]; + uint8_t Laux[16]; + float mins[QK_K/16]; + float scales[QK_K/16]; + float sw[QK_K/16]; + float weight[QK_K/16]; + uint8_t Ls[QK_K/16], Lm[QK_K/16]; + + for (int i = 0; i < nb; i++) { + memset(sw, 0, QK_K/16*sizeof(float)); + float sumx2 = 0; + for (int j = 0; j < QK_K; ++j) sumx2 += x[j]*x[j]; + float sigma2 = sumx2/QK_K; + for (int j = 0; j < QK_K/16; ++j) { + const float * restrict qw = quant_weights + QK_K * i + 16*j; + for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); + for (int l = 0; l < 16; ++l) sw[j] += weight[l]; + scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + } + + float dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); + float mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); + y[i].d = GGML_FP32_TO_FP16(dm); + y[i].dmin = GGML_FP32_TO_FP16(mm); + dm = GGML_FP16_TO_FP32(y[i].d); + mm = GGML_FP16_TO_FP32(y[i].dmin); + + for (int j = 0; j < QK_K/16; ++j) { + y[i].scales[j] = Ls[j] | (Lm[j] << 4); + } + + if (requantize) { + for (int j = 0; j < QK_K/16; ++j) { + const float d = dm * (y[i].scales[j] & 0xF); + if (!d) continue; + const float m = mm * (y[i].scales[j] >> 4); + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int((x[16*j + ii] + m)/d); + l = MAX(0, MIN(3, l)); + L[16*j + ii] = l; + } + } + } + +#if QK_K == 256 + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); + } + } +#else + for (int l = 0; l < 16; ++l) { + y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); + } +#endif + + x += QK_K; + + } +} + +size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row); + if (!quant_weights) { + quantize_row_q2_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q2_K_impl(src, (block_q2_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + //========================= 3-bit (de)-quantization void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) { @@ -2584,14 +2821,6 @@ static const uint8_t ksigns_iq2xs[128] = { static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; -void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k) { - (void)x; - (void)y; - (void)k; - assert(k % QK_K == 0); - //fprintf(stderr, "=========================== %s: not implemented\n", __func__); -} - void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2618,33 +2847,8 @@ void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y } } -void quantize_row_iq2_xxs(const float * restrict x, void * restrict vy, int k) { - assert(k % QK_K == 0); - block_iq2_xxs * restrict y = vy; - quantize_row_iq2_xxs_reference(x, y, k); -} - -size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_iq2_xxs * restrict y = (block_iq2_xxs *)dst + j/QK_K; - quantize_row_iq2_xxs_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_iq2_xxs)); -} - // ====================== 2.3125 bpw (de)-quantization -void quantize_row_iq2_xs_reference(const float * restrict x, block_iq2_xs * restrict y, int k) { - (void)x; - (void)y; - (void)k; - assert(k % QK_K == 0); - //fprintf(stderr, "=========================== %s: not implemented\n", __func__); -} - void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2670,23 +2874,6 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, } } -void quantize_row_iq2_xs(const float * restrict x, void * restrict vy, int k) { - assert(k % QK_K == 0); - block_iq2_xs * restrict y = vy; - quantize_row_iq2_xs_reference(x, y, k); -} - -size_t ggml_quantize_iq2_xs(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_iq2_xs * restrict y = (block_iq2_xs *)dst + j/QK_K; - quantize_row_iq2_xs_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_iq2_xs)); -} - //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -7730,3 +7917,666 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest *s = 0.125f * sumf; #endif } + +// ================================ IQ2 quantization ============================================= + +typedef struct { + uint64_t * grid; + int * map; + uint16_t * neighbours; +} iq2_entry_t; + +static iq2_entry_t iq2_data[2] = { + {NULL, NULL, NULL}, + {NULL, NULL, NULL}, +}; + +static inline int iq2_data_index(int grid_size) { + GGML_ASSERT(grid_size == 256 || grid_size == 512); + return grid_size == 256 ? 0 : 1; +} + +static int iq2_compare_func(const void * left, const void * right) { + const int * l = (const int *)left; + const int * r = (const int *)right; + return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0; +} + +static void q2xs_init_impl(int grid_size) { + const int gindex = iq2_data_index(grid_size); + if (iq2_data[gindex].grid) { + return; + } + static const uint16_t kgrid_256[256] = { + 0, 2, 5, 8, 10, 17, 20, 32, 34, 40, 42, 65, 68, 80, 88, 97, + 100, 128, 130, 138, 162, 257, 260, 272, 277, 320, 388, 408, 512, 514, 546, 642, + 1025, 1028, 1040, 1057, 1060, 1088, 1090, 1096, 1120, 1153, 1156, 1168, 1188, 1280, 1282, 1288, + 1312, 1350, 1385, 1408, 1425, 1545, 1552, 1600, 1668, 1700, 2048, 2053, 2056, 2068, 2088, 2113, + 2116, 2128, 2130, 2184, 2308, 2368, 2562, 2580, 4097, 4100, 4112, 4129, 4160, 4192, 4228, 4240, + 4245, 4352, 4360, 4384, 4432, 4442, 4480, 4644, 4677, 5120, 5128, 5152, 5157, 5193, 5248, 5400, + 5474, 5632, 5654, 6145, 6148, 6160, 6208, 6273, 6400, 6405, 6560, 6737, 8192, 8194, 8202, 8260, + 8289, 8320, 8322, 8489, 8520, 8704, 8706, 9217, 9220, 9232, 9280, 9302, 9472, 9537, 9572, 9872, + 10248, 10272, 10388, 10820, 16385, 16388, 16400, 16408, 16417, 16420, 16448, 16456, 16470, 16480, 16513, 16516, + 16528, 16640, 16672, 16737, 16768, 16773, 16897, 16912, 16968, 16982, 17000, 17408, 17416, 17440, 17536, 17561, + 17682, 17700, 17920, 18433, 18436, 18448, 18496, 18501, 18688, 18776, 18785, 18818, 19013, 19088, 20480, 20488, + 20497, 20505, 20512, 20608, 20616, 20740, 20802, 20900, 21137, 21648, 21650, 21770, 22017, 22100, 22528, 22545, + 22553, 22628, 22848, 23048, 24580, 24592, 24640, 24680, 24832, 24917, 25112, 25184, 25600, 25605, 25872, 25874, + 25988, 26690, 32768, 32770, 32778, 32833, 32898, 33028, 33048, 33088, 33297, 33793, 33796, 33808, 33813, 33856, + 33888, 34048, 34118, 34196, 34313, 34368, 34400, 34818, 35076, 35345, 36868, 36880, 36900, 36928, 37025, 37142, + 37248, 37445, 37888, 37922, 37956, 38225, 39041, 39200, 40962, 41040, 41093, 41225, 41472, 42008, 43088, 43268, + }; + static const uint16_t kgrid_512[512] = { + 0, 2, 5, 8, 10, 17, 20, 22, 25, 32, 34, 37, 40, 65, 68, 70, + 73, 80, 82, 85, 88, 97, 100, 128, 130, 133, 136, 145, 148, 153, 160, 257, + 260, 262, 265, 272, 274, 277, 280, 282, 289, 292, 320, 322, 325, 328, 337, 340, + 352, 360, 385, 388, 400, 512, 514, 517, 520, 529, 532, 544, 577, 580, 592, 597, + 640, 650, 1025, 1028, 1030, 1033, 1040, 1042, 1045, 1048, 1057, 1060, 1088, 1090, 1093, 1096, + 1105, 1108, 1110, 1120, 1153, 1156, 1168, 1280, 1282, 1285, 1288, 1297, 1300, 1312, 1345, 1348, + 1360, 1377, 1408, 1537, 1540, 1552, 1574, 1600, 1602, 1668, 2048, 2050, 2053, 2056, 2058, 2065, + 2068, 2080, 2085, 2113, 2116, 2128, 2136, 2176, 2208, 2218, 2305, 2308, 2320, 2368, 2433, 2441, + 2560, 2592, 2600, 2710, 2720, 4097, 4100, 4102, 4105, 4112, 4114, 4117, 4120, 4129, 4132, 4160, + 4162, 4165, 4168, 4177, 4180, 4192, 4202, 4225, 4228, 4240, 4352, 4354, 4357, 4360, 4369, 4372, + 4384, 4417, 4420, 4432, 4480, 4500, 4502, 4609, 4612, 4614, 4624, 4672, 4704, 5120, 5122, 5125, + 5128, 5137, 5140, 5152, 5185, 5188, 5193, 5200, 5220, 5248, 5377, 5380, 5392, 5440, 5632, 5652, + 5705, 6145, 6148, 6160, 6162, 6208, 6228, 6278, 6400, 6405, 6502, 6737, 6825, 8192, 8194, 8197, + 8200, 8202, 8209, 8212, 8224, 8257, 8260, 8272, 8320, 8352, 8449, 8452, 8464, 8512, 8520, 8549, + 8704, 8738, 8832, 8872, 9217, 9220, 9232, 9257, 9280, 9472, 9537, 9554, 9625, 9729, 9754, 9894, + 10240, 10248, 10250, 10272, 10325, 10376, 10402, 10600, 10640, 10760, 10784, 10882, 10888, 10890, 16385, 16388, + 16390, 16393, 16400, 16402, 16405, 16408, 16417, 16420, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16480, + 16485, 16513, 16516, 16528, 16640, 16642, 16645, 16648, 16657, 16660, 16672, 16705, 16708, 16720, 16768, 16773, + 16802, 16897, 16900, 16912, 16914, 16937, 16960, 17408, 17410, 17413, 17416, 17425, 17428, 17433, 17440, 17473, + 17476, 17488, 17536, 17556, 17665, 17668, 17680, 17700, 17728, 17818, 17920, 17930, 17988, 18000, 18433, 18436, + 18448, 18496, 18501, 18516, 18530, 18688, 18705, 18756, 18768, 18793, 18948, 20480, 20482, 20485, 20488, 20497, + 20500, 20512, 20520, 20545, 20548, 20560, 20608, 20737, 20740, 20752, 20757, 20800, 20802, 20992, 21060, 21162, + 21505, 21508, 21520, 21537, 21568, 21600, 21633, 21665, 21760, 21768, 21888, 21896, 22049, 22120, 22177, 22528, + 22548, 22593, 22608, 22681, 22810, 22848, 22850, 23173, 24577, 24580, 24592, 24640, 24660, 24674, 24710, 24745, + 24832, 25124, 25162, 25234, 25600, 25622, 25872, 25920, 25925, 26020, 26625, 26730, 26917, 27142, 27220, 27234, + 32768, 32770, 32773, 32776, 32785, 32788, 32800, 32810, 32833, 32836, 32848, 32896, 32898, 32936, 32938, 33025, + 33028, 33030, 33040, 33088, 33105, 33113, 33280, 33312, 33408, 33410, 33440, 33448, 33793, 33796, 33808, 33810, + 33813, 33856, 33888, 33929, 34048, 34116, 34213, 34328, 34410, 34816, 34824, 34853, 34906, 34944, 34946, 34984, + 35078, 35362, 35456, 35464, 35478, 35496, 36865, 36868, 36880, 36928, 36950, 36996, 37120, 37154, 37220, 37462, + 37513, 37888, 37893, 37956, 37968, 37976, 38185, 38288, 38290, 38465, 38993, 39078, 39241, 39445, 39520, 40960, + 40962, 40968, 40970, 40992, 41002, 41120, 41297, 41305, 41382, 41472, 41474, 41480, 41514, 41600, 41632, 42048, + 42133, 42597, 42648, 43018, 43040, 43042, 43048, 43168, 43176, 43268, 43396, 43398, 43560, 43562, 43665, 43690, + }; + const int kmap_size = 43692; + const int nwant = 2; + const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512; + uint64_t * kgrid_q2xs; + int * kmap_q2xs; + uint16_t * kneighbors_q2xs; + + printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + uint64_t * the_grid = (uint64_t *)malloc(grid_size*sizeof(uint64_t)); + for (int k = 0; k < grid_size; ++k) { + int8_t * pos = (int8_t *)(the_grid + k); + for (int i = 0; i < 8; ++i) { + int l = (kgrid[k] >> 2*i) & 0x3; + pos[i] = 2*l + 1; + } + } + kgrid_q2xs = the_grid; + iq2_data[gindex].grid = the_grid; + kmap_q2xs = (int *)malloc(kmap_size*sizeof(int)); + iq2_data[gindex].map = kmap_q2xs; + for (int i = 0; i < kmap_size; ++i) kmap_q2xs[i] = -1; + uint64_t aux64; + uint8_t * aux8 = (uint8_t *)&aux64; + for (int i = 0; i < grid_size; ++i) { + aux64 = kgrid_q2xs[i]; + uint16_t index = 0; + for (int k=0; k<8; ++k) { + uint16_t q = (aux8[k] - 1)/2; + index |= (q << 2*k); + } + kmap_q2xs[index] = i; + } + int8_t pos[8]; + int * dist2 = (int *)malloc(2*grid_size*sizeof(int)); + int num_neighbors = 0, num_not_in_map = 0; + for (int i = 0; i < kmap_size; ++i) { + if (kmap_q2xs[i] >= 0) continue; + ++num_not_in_map; + for (int k = 0; k < 8; ++k) { + int l = (i >> 2*k) & 0x3; + pos[k] = 2*l + 1; + } + for (int j = 0; j < grid_size; ++j) { + const int8_t * pg = (const int8_t *)(kgrid_q2xs + j); + int d2 = 0; + for (int k = 0; k < 8; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]); + dist2[2*j+0] = d2; + dist2[2*j+1] = j; + } + qsort(dist2, grid_size, 2*sizeof(int), iq2_compare_func); + int n = 0; int d2 = dist2[0]; + int nhave = 1; + for (int j = 0; j < grid_size; ++j) { + if (dist2[2*j] > d2) { + if (nhave == nwant) break; + d2 = dist2[2*j]; + ++nhave; + } + ++n; + } + num_neighbors += n; + } + printf("%s: %d neighbours in total\n", __func__, num_neighbors); + kneighbors_q2xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); + iq2_data[gindex].neighbours = kneighbors_q2xs; + int counter = 0; + for (int i = 0; i < kmap_size; ++i) { + if (kmap_q2xs[i] >= 0) continue; + for (int k = 0; k < 8; ++k) { + int l = (i >> 2*k) & 0x3; + pos[k] = 2*l + 1; + } + for (int j = 0; j < grid_size; ++j) { + const int8_t * pg = (const int8_t *)(kgrid_q2xs + j); + int d2 = 0; + for (int k = 0; k < 8; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]); + dist2[2*j+0] = d2; + dist2[2*j+1] = j; + } + qsort(dist2, grid_size, 2*sizeof(int), iq2_compare_func); + kmap_q2xs[i] = -(counter + 1); + int d2 = dist2[0]; + uint16_t * start = &kneighbors_q2xs[counter++]; + int n = 0, nhave = 1; + for (int j = 0; j < grid_size; ++j) { + if (dist2[2*j] > d2) { + if (nhave == nwant) break; + d2 = dist2[2*j]; + ++nhave; + } + kneighbors_q2xs[counter++] = dist2[2*j+1]; + ++n; + } + *start = n; + } + free(dist2); +} + +void ggml_init_iq2_quantization(enum ggml_type type) { + if (type == GGML_TYPE_IQ2_XXS) { + q2xs_init_impl(256); + } + else if (type == GGML_TYPE_IQ2_XS) { + q2xs_init_impl(512); + } + else { + fprintf(stderr, "======================== Why are you calling %s with type %d?\n", __func__, (int)type); + } +} + +static void q2xs_deinit_impl(int grid_size) { + GGML_ASSERT(grid_size == 256 || grid_size == 512 || grid_size == 1024); + const int gindex = iq2_data_index(grid_size); + if (iq2_data[gindex].grid) { + free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL; + free(iq2_data[gindex].map); iq2_data[gindex].map = NULL; + free(iq2_data[gindex].neighbours); iq2_data[gindex].neighbours = NULL; + } +} + +void ggml_deinit_iq2_quantization(enum ggml_type type) { + if (type == GGML_TYPE_IQ2_XXS) { + q2xs_deinit_impl(256); + } + else if (type == GGML_TYPE_IQ2_XS) { + q2xs_deinit_impl(512); + } + else { + fprintf(stderr, "======================== Why are you calling %s with type %d?\n", __func__, (int)type); + } +} + +static int iq2_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid, + const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L) { + int num_neighbors = neighbours[0]; + GGML_ASSERT(num_neighbors > 0); + float best_d2 = FLT_MAX; + int grid_index = -1; + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float d2 = 0; + for (int i = 0; i < 8; ++i) { + float q = pg[i]; + float diff = scale*q - xval[i]; + d2 += weight[i]*diff*diff; + } + if (d2 < best_d2) { + best_d2 = d2; grid_index = neighbours[j]; + } + } + GGML_ASSERT(grid_index >= 0); + const int8_t * pg = (const int8_t *)(grid + grid_index); + for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2; + return grid_index; +} + +static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(256); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(quant_weights); + GGML_ASSERT(kgrid_q2xs); + GGML_ASSERT(kmap_q2xs); + GGML_ASSERT(kneighbors_q2xs); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 3; + + const int nbl = n/256; + + block_iq2_xxs * y = vy; + + float scales[QK_K/32]; + float weight[32]; + float xval[32]; + int8_t L[32]; + int8_t Laux[32]; + float waux[32]; + bool is_on_grid[4]; + bool is_on_grid_aux[4]; + uint8_t block_signs[4]; + uint32_t q2[2*(QK_K/32)]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(q2, 0, QK_K/4); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = sumx2/QK_K; + + for (int ib = 0; ib < QK_K/32; ++ib) { + const float * xb = xbl + 32*ib; + const float * qw = quant_weights + QK_K*ibl + 32*ib; + for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + for (int i = 0; i < 32; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 4; ++k) { + int nflip = 0; + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i); + } + } + if (nflip%2) { + int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin]; + for (int i = 1; i < 8; ++i) { + float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i]; + if (ax < min) { + min = ax; imin = i; + } + } + xval[8*k+imin] = -xval[8*k+imin]; + s ^= (1 << imin); + } + block_signs[k] = s & 127; + } + float max = xval[0]; + for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + memset(L, 0, 32); + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + for (int is = -9; is <= 9; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/max; + float this_scale = 1/id; + for (int k = 0; k < 4; ++k) { + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 32; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 32; ++i) L[i] = Laux[i]; + for (int k = 0; k < 4; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 4; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 2*i); + } + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k); + } + const int8_t * pg = (const int8_t *)(kgrid_q2xs + grid_index); + for (int i = 0; i < 8; ++i) L[8*k+i] = (pg[i] - 1)/2; + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 32; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale) + // and correspondingly flip quant signs. + scale = -scale; + for (int k = 0; k < 4; ++k) block_signs[k] = (~block_signs[k]) & 127; + } + for (int k = 0; k < 4; ++k) { + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + q2[2*ib+0] |= (grid_index << 8*k); + q2[2*ib+1] |= (block_signs[k] << 7*k); + } + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + memset(y[ibl].qs, 0, QK_K/4); + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d); + float id = 1/d; + float sumqx = 0, sumq2 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + q2[2*ib+1] |= ((uint32_t)l << 28); + const float * xb = xbl + 32*ib; + const float * qw = quant_weights + QK_K*ibl + 32*ib; + for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib); + const float db = d * (1 + 2*l); + uint32_t u = 0; + for (int k = 0; k < 4; ++k) { + const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127); + const float * xk = xb + 8*k; + const float * wk = weight + 8*k; + const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); + float best_mse = 0; int best_index = aux8[k]; + for (int j = 0; j < 8; ++j) { + float diff = db * grid[j] * signs[j] - xk[j]; + best_mse += wk[j] * diff * diff; + } + for (int idx = 0; idx < 256; ++idx) { + grid = (const uint8_t *)(kgrid_q2xs + idx); + float mse = 0; + for (int j = 0; j < 8; ++j) { + float diff = db * grid[j] * signs[j] - xk[j]; + mse += wk[j] * diff * diff; + } + if (mse < best_mse) { + best_mse = mse; best_index = idx; + } + } + u |= (best_index << 8*k); + grid = (const uint8_t *)(kgrid_q2xs + best_index); + //grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); + for (int j = 0; j < 8; ++j) { + float q = db * grid[j] * signs[j]; + sumqx += wk[j] * q * xk[j]; + sumq2 += wk[j] * q * q; + } + } + q2[2*ib] = u; + if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); + } + memcpy(y[ibl].qs, q2, QK_K/4); + } +} + +static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(512); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(quant_weights); + GGML_ASSERT(kmap_q2xs); + GGML_ASSERT(kgrid_q2xs); + GGML_ASSERT(kneighbors_q2xs); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 3; + + const int nbl = n/256; + + block_iq2_xs * y = vy; + + float scales[QK_K/16]; + float weight[16]; + float xval[16]; + int8_t L[16]; + int8_t Laux[16]; + float waux[16]; + bool is_on_grid[2]; + bool is_on_grid_aux[2]; + uint8_t block_signs[2]; + uint16_t q2[2*(QK_K/16)]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(q2, 0, QK_K/4); + memset(y[ibl].scales, 0, QK_K/32); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = sumx2/QK_K; + + for (int ib = 0; ib < QK_K/16; ++ib) { + const float * xb = xbl + 16*ib; + const float * qw = quant_weights + QK_K*ibl + 16*ib; + for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 2; ++k) { + int nflip = 0; + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i); + } + } + if (nflip%2) { + int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin]; + for (int i = 1; i < 8; ++i) { + float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i]; + if (ax < min) { + min = ax; imin = i; + } + } + xval[8*k+imin] = -xval[8*k+imin]; + s ^= (1 << imin); + } + block_signs[k] = s & 127; + } + float max = xval[0]; + for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + memset(L, 0, 16); + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + is_on_grid[0] = is_on_grid[1] = true; + for (int is = -9; is <= 9; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/max; + float this_scale = 1/id; + for (int k = 0; k < 2; ++k) { + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 16; ++i) L[i] = Laux[i]; + for (int k = 0; k < 2; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 2; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 2*i); + L[8*k + i] = l; + } + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + scale = -scale; + for (int k = 0; k < 2; ++k) block_signs[k] = (~block_signs[k]) & 127; + } + for (int k = 0; k < 2; ++k) { + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + q2[2*ib+k] = grid_index | (block_signs[k] << 9); + } + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + memset(y[ibl].qs, 0, QK_K/4); + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d); + float id = 1/d; + for (int ib = 0; ib < QK_K/16; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + if (ib%2 == 0) y[ibl].scales[ib/2] = l; + else y[ibl].scales[ib/2] |= (l << 4); + } + memcpy(y[ibl].qs, q2, QK_K/4); + + } +} + +size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq2_xxs_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq2_xxs); + } + return nrow * nblock * sizeof(block_iq2_xxs); +} + +size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq2_xs_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq2_xs); + } + return nrow * nblock * sizeof(block_iq2_xs); +} + diff --git a/ggml-quants.h b/ggml-quants.h index df5e7ae80..e5d110230 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -196,8 +196,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); -void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k); -void quantize_row_iq2_xs_reference (const float * restrict x, block_iq2_xs * restrict y, int k); void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); @@ -212,8 +210,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); -void quantize_row_iq2_xxs(const float * restrict x, void * restrict y, int k); -void quantize_row_iq2_xs (const float * restrict x, void * restrict y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); @@ -246,3 +242,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); + +// +// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") +// +size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); + diff --git a/ggml.c b/ggml.c index bcfb6652c..52467475a 100644 --- a/ggml.c +++ b/ggml.c @@ -585,8 +585,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, - .from_float = quantize_row_iq2_xxs, - .from_float_reference = (ggml_from_float_t) quantize_row_iq2_xxs_reference, + .from_float = NULL, + .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, @@ -596,8 +596,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, - .from_float = quantize_row_iq2_xs, - .from_float_reference = (ggml_from_float_t) quantize_row_iq2_xs_reference, + .from_float = NULL, + .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, @@ -18665,8 +18665,11 @@ size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * return (n/QK8_0*sizeof(block_q8_0)); } -size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) { +size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, + int nrows, int n_per_row, int64_t * hist, const float * imatrix) { + (void)imatrix; size_t result = 0; + int n = nrows * n_per_row; switch (type) { case GGML_TYPE_Q4_0: { @@ -18701,8 +18704,11 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i case GGML_TYPE_Q2_K: { GGML_ASSERT(start % QK_K == 0); - block_q2_K * block = (block_q2_K*)dst + start / QK_K; - result = ggml_quantize_q2_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q2_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q3_K: { @@ -18731,14 +18737,22 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i case GGML_TYPE_IQ2_XXS: { GGML_ASSERT(start % QK_K == 0); - block_iq2_xxs * block = (block_iq2_xxs*)dst + start / QK_K; - result = ggml_quantize_iq2_xxs(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + GGML_ASSERT(imatrix); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq2_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_IQ2_XS: { GGML_ASSERT(start % QK_K == 0); - block_iq2_xs * block = (block_iq2_xs*)dst + start / QK_K; - result = ggml_quantize_iq2_xs(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + GGML_ASSERT(imatrix); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_F16: { diff --git a/ggml.h b/ggml.h index b18ba7812..1187074f7 100644 --- a/ggml.h +++ b/ggml.h @@ -2067,10 +2067,13 @@ extern "C" { GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_iq2_xs (const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist); + GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, + int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix); + + // These are needed for IQ2_XS and IQ2_XXS quantizations + GGML_API void ggml_init_iq2_quantization(enum ggml_type type); + GGML_API void ggml_deinit_iq2_quantization(enum ggml_type type); // // Importance matrix diff --git a/llama.cpp b/llama.cpp index 8e20e72a2..107b05114 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8429,9 +8429,23 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + new_type = GGML_TYPE_Q5_K; + } else if (new_type != GGML_TYPE_Q8_0) { new_type = GGML_TYPE_Q6_K; } + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + if (name.find("attn_v.weight") != std::string::npos) { + if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; + else new_type = GGML_TYPE_Q2_K; + ++qs.i_attention_wv; + } + else if (name.find("ffn_down") != std::string::npos) { + if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q2_K; + ++qs.i_feed_forward_w2; + } + else if (name == "token_embd.weight") new_type = GGML_TYPE_Q2_K; } else if (name.find("attn_v.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { @@ -8601,6 +8615,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (params->only_copy) { ftype = model.ftype; } + const std::unordered_map> * imatrix_data = nullptr; + if (params->imatrix) { + imatrix_data = static_cast>*>(params->imatrix); + if (imatrix_data) { + printf("================================ Have weights data with %d entries\n",int(imatrix_data->size())); + } + } const size_t align = GGUF_DEFAULT_ALIGNMENT; struct gguf_context * ctx_out = gguf_init_empty(); @@ -8658,6 +8679,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // placeholder for the meta data ::zeros(fout, meta_size); + std::set used_iq2; + for (int i = 0; i < ml.n_tensors; ++i) { struct ggml_tensor * tensor = ml.get_tensor_meta(i); @@ -8710,6 +8733,35 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else { const size_t nelements = ggml_nelements(tensor); + if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS) && used_iq2.find(new_type) == used_iq2.end()) { + ggml_init_iq2_quantization(new_type); + used_iq2.insert(new_type); + } + + const float * imatrix = nullptr; + if (imatrix_data) { + auto it = imatrix_data->find(tensor->name); + if (it == imatrix_data->end()) { + printf("\n====== %s: did not find weights for %s\n", __func__, tensor->name); + } else { + if (it->second.size() == (size_t)tensor->ne[0]) { + imatrix = it->second.data(); + } else { + printf("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__, + int(it->second.size()), int(tensor->ne[0]), tensor->name); + } + } + } + if ((new_type == GGML_TYPE_IQ2_XXS || + new_type == GGML_TYPE_IQ2_XS || + (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { + fprintf(stderr, "\n\n============================================================\n"); + fprintf(stderr, "Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); + fprintf(stderr, "The result will be garbage, so bailing out\n"); + fprintf(stderr, "============================================================\n\n"); + throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name)); + } + float * f32_data; if (tensor->type == GGML_TYPE_F32) { @@ -8730,21 +8782,28 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s new_data = work.data(); std::array hist_cur = {}; - static const int chunk_size = 32 * 512; + const int n_per_row = tensor->ne[0]; + const int nrows = nelements / n_per_row; + + static const int min_chunk_size = 32 * 512; + const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row); + const int nchunk = (nelements + chunk_size - 1)/chunk_size; const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; if (nthread_use < 2) { - new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data()); + new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur.data(), imatrix); } else { - size_t counter = 0; + int counter = 0; new_size = 0; - auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() { + auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, + nrows, n_per_row, imatrix]() { std::array local_hist = {}; + const int nrows_per_chunk = chunk_size / n_per_row; size_t local_size = 0; while (true) { std::unique_lock lock(mutex); - size_t first = counter; counter += chunk_size; - if (first >= nelements) { + int first_row = counter; counter += nrows_per_chunk; + if (first_row >= nrows) { if (local_size > 0) { for (int j=0; j %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); + LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); int64_t tot_count = 0; for (size_t i = 0; i < hist_cur.size(); i++) { hist_all[i] += hist_cur[i]; @@ -8774,6 +8834,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } if (tot_count > 0) { + LLAMA_LOG_INFO(" | hist: "); for (size_t i = 0; i < hist_cur.size(); i++) { LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements)); } @@ -8802,6 +8863,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s fout.close(); + for (auto type : used_iq2) { + ggml_deinit_iq2_quantization(type); + } + gguf_free(ctx_out); LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); @@ -9166,6 +9231,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { /*.quantize_output_tensor =*/ true, /*.only_copy =*/ false, /*.pure =*/ false, + /*.imatrix =*/ nullptr, }; return result; diff --git a/llama.h b/llama.h index 01d6fafaa..79c8335b6 100644 --- a/llama.h +++ b/llama.h @@ -249,6 +249,7 @@ extern "C" { bool quantize_output_tensor; // quantize output.weight bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored bool pure; // disable k-quant mixtures and quantize all tensors to the same type + void * imatrix; // pointer to importance matrix data } llama_model_quantize_params; // grammar types diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d9b8b106a..22a7856d4 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -56,7 +56,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0); std::vector dataq(ggml_row_size(tensor->type, size)); int64_t hist[16]; - ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist); + ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, nullptr); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { // This is going to create some weird integers though. From ac32902a87147f78d63c931aa8a23dee762660e7 Mon Sep 17 00:00:00 2001 From: Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com> Date: Sun, 14 Jan 2024 00:41:44 -0800 Subject: [PATCH 419/664] llama : support WinXP build with MinGW 8.1.0 (#3419) --- CMakeLists.txt | 8 ++++++-- llama.cpp | 4 ++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 668669c6d..2741568ed 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,4 @@ -cmake_minimum_required(VERSION 3.13) # for add_link_options +cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. project("llama.cpp" C CXX) set(CMAKE_EXPORT_COMPILE_COMMANDS ON) @@ -76,6 +76,10 @@ if (NOT MSVC) option(LLAMA_F16C "llama: enable F16C" ${INS_ENB}) endif() +if (WIN32) + option(LLAMA_WIN_VER "llama: Windows Version" 0x602) +endif() + # 3rd party libs option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) option(LLAMA_BLAS "llama: use BLAS" OFF) @@ -686,7 +690,7 @@ endif() if (MINGW) # Target Windows 8 for PrefetchVirtualMemory - add_compile_definitions(_WIN32_WINNT=0x602) + add_compile_definitions(_WIN32_WINNT=${LLAMA_WIN_VER}) endif() # diff --git a/llama.cpp b/llama.cpp index 107b05114..51e9bdaed 100644 --- a/llama.cpp +++ b/llama.cpp @@ -987,6 +987,7 @@ struct llama_mmap { } if (prefetch > 0) { +#if _WIN32_WINNT >= 0x602 // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG); HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll"); @@ -1004,6 +1005,9 @@ struct llama_mmap { llama_format_win_err(GetLastError()).c_str()); } } +#else + throw std::runtime_error("PrefetchVirtualMemory unavailable"); +#endif } } From 5f5fe1bd608fa2ed42af97b5f2ea31be6625fc48 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Sun, 14 Jan 2024 09:44:39 +0100 Subject: [PATCH 420/664] metal : correctly set SIMD support flags on iOS (#4923) * Correctly set support_simdgroup_reduction and support_simdgroup_mm on iPhone/iPad * log a little bit more info on iOS --- ggml-metal.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-metal.m b/ggml-metal.m index cae52c983..2ca726055 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -330,7 +330,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { } } -#if TARGET_OS_OSX // print MTL GPU family: GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]); @@ -370,6 +369,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); +#if TARGET_OS_OSX GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); if (ctx->device.maxTransferRate != 0) { GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6); From a128c38de862431f1aae9ccc40b792fbc1b8b682 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 10:53:39 +0200 Subject: [PATCH 421/664] Fix ffn_down quantization mix for MoE models (#4927) * Fix ffn_down quantization mix for MoE models In #4872 I did not consider the part where every third tensor is quantized with more bits. Fir MoE this leads to tensors of the same layer being quantized with different number of bits, which is not considered as a possibility in the inference implementation (it is assumed all experts use the same quantization). * Fix the fix * Review suggestion --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 34 ++++++++++++++++++++++++++-------- 1 file changed, 26 insertions(+), 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index 51e9bdaed..b1d6015e2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8480,13 +8480,31 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = GGML_TYPE_Q8_0; } } else if (name.find("ffn_down") != std::string::npos) { + const int n_expert = std::max(1, (int)qs.model.hparams.n_expert); + int i_layer, n_layer; + if (n_expert == 1) { + i_layer = qs.i_feed_forward_w2; + n_layer = qs.n_feed_forward_w2; + } else { + // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly + // sprinkled in the model. Hence, simply dividing i_feed_forward_w2 by n_expert does not work + // for getting the current layer as I initially thought, and we need to resort to parsing the + // tensor name. + n_layer = qs.n_feed_forward_w2 / n_expert; + if (sscanf(name.c_str(), "blk.%d.ffn_down", &i_layer) != 1) { + throw std::runtime_error(format("Failed to determine layer for tensor %s", name.c_str())); + } + if (i_layer < 0 || i_layer >= n_layer) { + throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name.c_str(), n_layer)); + } + } if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) { - if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q4_K; + if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { - new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q5_K - : arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K + new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K + : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { @@ -8494,14 +8512,14 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { if (arch == LLM_ARCH_FALCON) { - new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q6_K : - use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; + new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K : + use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } else { - if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; + if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; } } - else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) { + else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) { new_type = GGML_TYPE_Q5_K; } ++qs.i_feed_forward_w2; From 03c526749041c863b0cd842b26b8907e1ea0e0b1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 11:03:19 +0200 Subject: [PATCH 422/664] llama : use LLAMA_LOG_ macros for logging --- llama.cpp | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/llama.cpp b/llama.cpp index b1d6015e2..51821965e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1114,7 +1114,7 @@ struct llama_mlock { suggest = false; } - fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s", + LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s", size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : ""); return false; } @@ -1123,7 +1123,7 @@ struct llama_mlock { static void raw_unlock(void * addr, size_t size) { if (munlock(addr, size)) { - fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno)); + LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno)); } } #elif defined(_WIN32) @@ -1141,7 +1141,7 @@ struct llama_mlock { return true; } if (tries == 2) { - fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n", + LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n", len, size, llama_format_win_err(GetLastError()).c_str()); return false; } @@ -1150,7 +1150,7 @@ struct llama_mlock { // set size and try again. SIZE_T min_ws_size, max_ws_size; if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) { - fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n", + LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n", llama_format_win_err(GetLastError()).c_str()); return false; } @@ -1163,7 +1163,7 @@ struct llama_mlock { min_ws_size += increment; max_ws_size += increment; if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) { - fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n", + LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n", llama_format_win_err(GetLastError()).c_str()); return false; } @@ -1172,7 +1172,7 @@ struct llama_mlock { static void raw_unlock(void * ptr, size_t len) { if (!VirtualUnlock(ptr, len)) { - fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n", + LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n", llama_format_win_err(GetLastError()).c_str()); } } @@ -1184,7 +1184,7 @@ struct llama_mlock { } bool raw_lock(const void * addr, size_t len) const { - fprintf(stderr, "warning: mlock not supported on this system\n"); + LLAMA_LOG_WARN("warning: mlock not supported on this system\n"); return false; } @@ -2085,13 +2085,13 @@ namespace GGUFMeta { __func__, override_type_to_str(override->tag), override->key); switch (override->tag) { case LLAMA_KV_OVERRIDE_BOOL: { - printf("%s\n", override->bool_value ? "true" : "false"); + LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false"); } break; case LLAMA_KV_OVERRIDE_INT: { - printf("%" PRId64 "\n", override->int_value); + LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value); } break; case LLAMA_KV_OVERRIDE_FLOAT: { - printf("%.6f\n", override->float_value); + LLAMA_LOG_INFO("%.6f\n", override->float_value); } break; default: // Shouldn't be possible to end up here, but just in case... @@ -6993,7 +6993,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break; #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); + LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); #endif auto source = std::distance(buffer.begin(), it); @@ -7006,7 +7006,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length); #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str()); + LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str()); #endif it++; } @@ -7022,7 +7022,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length); #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str()); + LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str()); #endif it++; @@ -7038,7 +7038,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< raw_text_base_length = right_reminder_length; #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); + LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); #endif } else { if (source == 0) { @@ -7095,7 +7095,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } #ifdef PRETOKENIZERDEBUG - fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); @@ -7116,7 +7116,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); #ifdef PRETOKENIZERDEBUG - fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_bpe tokenizer(vocab); tokenizer.tokenize(raw_text, output); @@ -8641,7 +8641,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (params->imatrix) { imatrix_data = static_cast>*>(params->imatrix); if (imatrix_data) { - printf("================================ Have weights data with %d entries\n",int(imatrix_data->size())); + LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size())); } } @@ -8764,12 +8764,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (imatrix_data) { auto it = imatrix_data->find(tensor->name); if (it == imatrix_data->end()) { - printf("\n====== %s: did not find weights for %s\n", __func__, tensor->name); + LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name); } else { if (it->second.size() == (size_t)tensor->ne[0]) { imatrix = it->second.data(); } else { - printf("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__, + LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__, int(it->second.size()), int(tensor->ne[0]), tensor->name); } } @@ -8777,10 +8777,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS || (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { - fprintf(stderr, "\n\n============================================================\n"); - fprintf(stderr, "Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); - fprintf(stderr, "The result will be garbage, so bailing out\n"); - fprintf(stderr, "============================================================\n\n"); + LLAMA_LOG_ERROR("\n\n============================================================\n"); + LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); + LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n"); + LLAMA_LOG_ERROR("============================================================\n\n"); throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name)); } From 9408cfdad6b1c090a7e1419d4434edc260b7e47e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 11:08:09 +0200 Subject: [PATCH 423/664] scripts : sync-ggml-am.sh option to skip commits --- scripts/sync-ggml-am.sh | 14 +++++++++++++- scripts/sync-ggml.last | 2 +- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 248cf1023..6b2514a11 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -5,7 +5,7 @@ # Usage: # # $ cd /path/to/llama.cpp -# $ ./scripts/sync-ggml-am.sh +# $ ./scripts/sync-ggml-am.sh -skip hash0,hash1,hash2... # set -e @@ -24,6 +24,11 @@ fi lc=$(cat $SRC_LLAMA/scripts/sync-ggml.last) echo "Syncing ggml changes since commit $lc" +to_skip="" +if [ "$1" == "-skip" ]; then + to_skip=$2 +fi + cd $SRC_GGML git log --oneline $lc..HEAD @@ -40,6 +45,13 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then fi while read c; do + if [ -n "$to_skip" ]; then + if [[ $to_skip == *"$c"* ]]; then + echo "Skipping $c" + continue + fi + fi + git format-patch -k $c~1..$c --stdout -- \ include/ggml/ggml*.h \ src/ggml*.h \ diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 753d227a7..be9e408fb 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -1890780da4ea10db88736fcde85f285abf6c64b0 +b306d6e996ec0ace77118fa5098822cdc7f9c88f From bb0c1392479398f9aba86d9ec98db0b95ede6e6d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 13:26:53 +0200 Subject: [PATCH 424/664] llama : check LLAMA_TRACE env for extra logging (#4929) * llama : minor fix indent * llama : check LLAMA_TRACE env for extra logging ggml-ci --- llama.cpp | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/llama.cpp b/llama.cpp index 51821965e..63f37ecdb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2190,6 +2190,11 @@ struct llama_model_loader { LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") { + int trace = 0; + if (getenv("LLAMA_TRACE")) { + trace = atoi(getenv("LLAMA_TRACE")); + } + struct gguf_init_params params = { /*.no_alloc = */ true, /*.ctx = */ &ctx_meta, @@ -2242,11 +2247,10 @@ struct llama_model_loader { type_max = type; } - // TODO: make runtime configurable -#if 0 - struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); - LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str()); -#endif + if (trace > 0) { + struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str()); + } } switch (type_max) { @@ -6451,15 +6455,15 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { static const char * hex = "0123456789ABCDEF"; switch (llama_vocab_get_type(vocab)) { - case LLAMA_VOCAB_TYPE_SPM: { - const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 }; - return vocab.token_to_id.at(buf); - } - case LLAMA_VOCAB_TYPE_BPE: { - return vocab.token_to_id.at(bytes_to_unicode_bpe(ch)); - } - default: - GGML_ASSERT(false); + case LLAMA_VOCAB_TYPE_SPM: { + const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 }; + return vocab.token_to_id.at(buf); + } + case LLAMA_VOCAB_TYPE_BPE: { + return vocab.token_to_id.at(bytes_to_unicode_bpe(ch)); + } + default: + GGML_ASSERT(false); } } From 467a882fd2e5b6172897b49aa45aa29bd3f27685 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 16:21:12 +0200 Subject: [PATCH 425/664] Add ability to use importance matrix for all k-quants (#4930) Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 2 +- ggml-quants.c | 443 ++++++++++++++++++++++++++++++++- ggml-quants.h | 5 +- ggml.c | 28 ++- 4 files changed, 462 insertions(+), 16 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index f4e2175f1..2ae046933 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -82,7 +82,7 @@ static void usage(const char * executable) { printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); - printf(" --imatrixfile_name: use data in file_name as importance matrix for quant optimizations\n"); + printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n"); printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n"); printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); printf("Note: --include-weights and --exclude-weights cannot be used together\n"); diff --git a/ggml-quants.c b/ggml-quants.c index 9290d54cf..0750fe1bb 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1244,7 +1244,8 @@ static inline int nearest_int(float fval) { return (i & 0x007fffff) - 0x00400000; } -static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type) { +static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type, + const float * restrict qw) { float max = 0; float amax = 0; for (int i = 0; i < n; ++i) { @@ -1270,14 +1271,13 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * rmse_type = -rmse_type; return_early = true; } - int weight_type = rmse_type%2; float sumlx = 0; float suml2 = 0; for (int i = 0; i < n; ++i) { int l = nearest_int(iscale * x[i]); l = MAX(-nmax, MIN(nmax-1, l)); L[i] = l + nmax; - float w = weight_type == 1 ? x[i] * x[i] : 1; + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); sumlx += w*x[i]*l; suml2 += w*l*l; } @@ -1293,7 +1293,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * for (int i = 0; i < n; ++i) { int l = nearest_int(iscale * x[i]); l = MAX(-nmax, MIN(nmax-1, l)); - float w = weight_type == 1 ? x[i] * x[i] : 1; + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); sumlx += w*x[i]*l; suml2 += w*l*l; } @@ -2089,6 +2089,112 @@ size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q3_K)); } +static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q3_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + int8_t L[QK_K]; + float scales[QK_K / 16]; + float weight[16]; + float sw[QK_K / 16]; + int8_t Ls[QK_K / 16]; + + for (int i = 0; i < nb; i++) { + + float sumx2 = 0; + for (int j = 0; j < QK_K; ++j) sumx2 += x[j]*x[j]; + float sigma2 = 2*sumx2/QK_K; + + for (int j = 0; j < QK_K/16; ++j) { + if (quant_weights) { + const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL; + for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]); + } else { + for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l]; + } + float sumw = 0; + for (int l = 0; l < 16; ++l) sumw += weight[l]; + sw[j] = sumw; + + scales[j] = make_qx_quants(16, 4, x + 16*j, L + 16*j, 1, weight); + + } + + memset(y[i].scales, 0, 12); + + float d_block = make_qx_quants(QK_K/16, 32, scales, Ls, 1, sw); + for (int j = 0; j < QK_K/16; ++j) { + int l = Ls[j]; + if (j < 8) { + y[i].scales[j] = l & 0xF; + } else { + y[i].scales[j-8] |= ((l & 0xF) << 4); + } + l >>= 4; + y[i].scales[j%4 + 8] |= (l << (2*(j/4))); + } + y[i].d = GGML_FP32_TO_FP16(d_block); + + int8_t sc; + for (int j = 0; j < QK_K/16; ++j) { + sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4; + sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32; + float d = GGML_FP16_TO_FP32(y[i].d) * sc; + if (!d) { + continue; + } + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int(x[16*j + ii]/d); + l = MAX(-4, MIN(3, l)); + L[16*j + ii] = l + 4; + } + } + + memset(y[i].hmask, 0, QK_K/8); + // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc. + int m = 0; + uint8_t hm = 1; + for (int j = 0; j < QK_K; ++j) { + if (L[j] > 3) { + y[i].hmask[m] |= hm; + L[j] -= 4; + } + if (++m == QK_K/8) { + m = 0; hm <<= 1; + } + } + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); + } + } + + x += QK_K; + } +#endif +} + +size_t quantize_q3_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row); + if (!quant_weights) { + quantize_row_q3_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q3_K_impl(src, (block_q3_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== 4-bit (de)-quantization void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) { @@ -2254,6 +2360,108 @@ size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q4_K)); } +static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q4_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + uint8_t L[QK_K]; + uint8_t Laux[32]; + float weights[32]; + float mins[QK_K/32]; + float scales[QK_K/32]; + + for (int i = 0; i < nb; i++) { + + float sum_x2 = 0; + for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; + float sigma2 = sum_x2/QK_K; + float av_x = sqrtf(sigma2); + + float max_scale = 0; // as we are deducting the min, scales are always positive + float max_min = 0; + for (int j = 0; j < QK_K/32; ++j) { + if (quant_weights) { + const float * qw = quant_weights + QK_K*i + 32*j; + for (int l = 0; l < 32; ++l) weights[l] = qw[l] * sqrtf(sigma2 + x[32*j + l]*x[32*j + l]); + } else { + for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); + } + scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + //scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false); + float scale = scales[j]; + if (scale > max_scale) { + max_scale = scale; + } + float min = mins[j]; + if (min > max_min) { + max_min = min; + } + } + + float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; + float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + for (int j = 0; j < QK_K/32; ++j) { + uint8_t ls = nearest_int(inv_scale*scales[j]); + uint8_t lm = nearest_int(inv_min*mins[j]); + ls = MIN(63, ls); + lm = MIN(63, lm); + if (j < 4) { + y[i].scales[j] = ls; + y[i].scales[j+4] = lm; + } else { + y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4); + y[i].scales[j-4] |= ((ls >> 4) << 6); + y[i].scales[j-0] |= ((lm >> 4) << 6); + } + } + y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); + y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + + uint8_t sc, m; + for (int j = 0; j < QK_K/32; ++j) { + get_scale_min_k4(j, y[i].scales, &sc, &m); + const float d = GGML_FP16_TO_FP32(y[i].d) * sc; + if (!d) continue; + const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m; + for (int ii = 0; ii < 32; ++ii) { + int l = nearest_int((x[32*j + ii] + dm)/d); + l = MAX(0, MIN(15, l)); + L[32*j + ii] = l; + } + } + uint8_t * q = y[i].qs; + for (int j = 0; j < QK_K; j += 64) { + for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4); + q += 32; + } + + x += QK_K; + + } +#endif +} + +size_t quantize_q4_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row); + if (!quant_weights) { + quantize_row_q4_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q4_K_impl(src, (block_q4_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== 5-bit (de)-quantization void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) { @@ -2349,7 +2557,7 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict #else float max_scale = 0, amax = 0; for (int j = 0; j < QK_K/16; ++j) { - scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1); + scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL); float abs_scale = fabsf(scales[j]); if (abs_scale > amax) { amax = abs_scale; @@ -2460,6 +2668,123 @@ size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q5_K)); } +static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q5_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + uint8_t L[QK_K]; + float mins[QK_K/32]; + float scales[QK_K/32]; + float weights[32]; + uint8_t Laux[32]; + + for (int i = 0; i < nb; i++) { + + float sum_x2 = 0; + for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; + float sigma2 = sum_x2/QK_K; + float av_x = sqrtf(sigma2); + + float max_scale = 0; // as we are deducting the min, scales are always positive + float max_min = 0; + for (int j = 0; j < QK_K/32; ++j) { + if (quant_weights) { + const float * qw = quant_weights + QK_K*i + 32*j; + for (int l = 0; l < 32; ++l) weights[l] = qw[l] * sqrtf(sigma2 + x[32*j + l]*x[32*j + l]); + } else { + for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); + } + scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + float scale = scales[j]; + if (scale > max_scale) { + max_scale = scale; + } + float min = mins[j]; + if (min > max_min) { + max_min = min; + } + } + + float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; + float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + for (int j = 0; j < QK_K/32; ++j) { + uint8_t ls = nearest_int(inv_scale*scales[j]); + uint8_t lm = nearest_int(inv_min*mins[j]); + ls = MIN(63, ls); + lm = MIN(63, lm); + if (j < 4) { + y[i].scales[j] = ls; + y[i].scales[j+4] = lm; + } else { + y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4); + y[i].scales[j-4] |= ((ls >> 4) << 6); + y[i].scales[j-0] |= ((lm >> 4) << 6); + } + } + y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); + y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + + uint8_t sc, m; + for (int j = 0; j < QK_K/32; ++j) { + get_scale_min_k4(j, y[i].scales, &sc, &m); + const float d = GGML_FP16_TO_FP32(y[i].d) * sc; + if (!d) continue; + const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m; + for (int ii = 0; ii < 32; ++ii) { + int l = nearest_int((x[32*j + ii] + dm)/d); + l = MAX(0, MIN(31, l)); + L[32*j + ii] = l; + } + } + + uint8_t * restrict qh = y[i].qh; + uint8_t * restrict ql = y[i].qs; + memset(qh, 0, QK_K/8); + + uint8_t m1 = 1, m2 = 2; + for (int n = 0; n < QK_K; n += 64) { + for (int j = 0; j < 32; ++j) { + int l1 = L[n + j]; + if (l1 > 15) { + l1 -= 16; qh[j] |= m1; + } + int l2 = L[n + j + 32]; + if (l2 > 15) { + l2 -= 16; qh[j] |= m2; + } + ql[j] = l1 | (l2 << 4); + } + m1 <<= 2; m2 <<= 2; + ql += 32; + } + + x += QK_K; + + } +#endif +} + +size_t quantize_q5_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row); + if (!quant_weights) { + quantize_row_q5_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q5_K_impl(src, (block_q5_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== 6-bit (de)-quantization void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) { @@ -2476,7 +2801,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict for (int ib = 0; ib < QK_K/16; ++ib) { - const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1); + const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, NULL); scales[ib] = scale; const float abs_scale = fabsf(scale); @@ -2608,6 +2933,112 @@ size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * return (n/QK_K*sizeof(block_q6_K)); } +static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q6_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + int8_t L[QK_K]; + float scales[QK_K/16]; + //float weights[16]; + + for (int i = 0; i < nb; i++) { + + //float sum_x2 = 0; + //for (int j = 0; j < QK_K; ++j) sum_x2 += x[j]*x[j]; + //float sigma2 = sum_x2/QK_K; + + float max_scale = 0; + float max_abs_scale = 0; + + for (int ib = 0; ib < QK_K/16; ++ib) { + + float scale; + if (quant_weights) { + const float * qw = quant_weights + QK_K*i + 16*ib; + //for (int j = 0; j < 16; ++j) weights[j] = qw[j] * sqrtf(sigma2 + x[16*ib + j]*x[16*ib + j]); + //scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, weights); + scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, qw); + } else { + scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, NULL); + } + scales[ib] = scale; + + const float abs_scale = fabsf(scale); + if (abs_scale > max_abs_scale) { + max_abs_scale = abs_scale; + max_scale = scale; + } + + } + + if (!max_abs_scale) { + memset(&y[i], 0, sizeof(block_q6_K)); + y[i].d = GGML_FP32_TO_FP16(0.f); + x += QK_K; + continue; + } + + float iscale = -128.f/max_scale; + y[i].d = GGML_FP32_TO_FP16(1/iscale); + for (int ib = 0; ib < QK_K/16; ++ib) { + y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib])); + } + + for (int j = 0; j < QK_K/16; ++j) { + float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j]; + if (!d) { + continue; + } + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int(x[16*j + ii]/d); + l = MAX(-32, MIN(31, l)); + L[16*j + ii] = l + 32; + } + } + + uint8_t * restrict ql = y[i].ql; + uint8_t * restrict qh = y[i].qh; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + const uint8_t q1 = L[j + l + 0] & 0xF; + const uint8_t q2 = L[j + l + 32] & 0xF; + const uint8_t q3 = L[j + l + 64] & 0xF; + const uint8_t q4 = L[j + l + 96] & 0xF; + ql[l+ 0] = q1 | (q3 << 4); + ql[l+32] = q2 | (q4 << 4); + qh[l] = (L[j + l] >> 4) | ((L[j + l + 32] >> 4) << 2) | ((L[j + l + 64] >> 4) << 4) | ((L[j + l + 96] >> 4) << 6); + } + ql += 64; + qh += 32; + } + + x += QK_K; + + } +#endif +} + +size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row); + if (!quant_weights) { + quantize_row_q6_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q6_K_impl(src, (block_q6_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== "True" 2-bit (de)-quantization static const uint64_t iq2xxs_grid[256] = { diff --git a/ggml-quants.h b/ggml-quants.h index e5d110230..99467936a 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -249,4 +249,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); - +size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q5_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q6_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml.c b/ggml.c index 52467475a..ef5888ab2 100644 --- a/ggml.c +++ b/ggml.c @@ -18713,26 +18713,38 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i case GGML_TYPE_Q3_K: { GGML_ASSERT(start % QK_K == 0); - block_q3_K * block = (block_q3_K*)dst + start / QK_K; - result = ggml_quantize_q3_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q3_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q4_K: { GGML_ASSERT(start % QK_K == 0); - block_q4_K * block = (block_q4_K*)dst + start / QK_K; - result = ggml_quantize_q4_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q4_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q5_K: { GGML_ASSERT(start % QK_K == 0); - block_q5_K * block = (block_q5_K*)dst + start / QK_K; - result = ggml_quantize_q5_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q5_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q6_K: { GGML_ASSERT(start % QK_K == 0); - block_q6_K * block = (block_q6_K*)dst + start / QK_K; - result = ggml_quantize_q6_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q6_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_IQ2_XXS: { From a836c8f534ab789b02da149fbdaf7735500bff74 Mon Sep 17 00:00:00 2001 From: David Pflug Date: Sun, 14 Jan 2024 10:46:00 -0500 Subject: [PATCH 426/664] llama : fix missing quotes (#4937) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 63f37ecdb..7af38718c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7099,7 +7099,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } #ifdef PRETOKENIZERDEBUG - LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); @@ -7120,7 +7120,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); #ifdef PRETOKENIZERDEBUG - LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_bpe tokenizer(vocab); tokenizer.tokenize(raw_text, output); From 4a3156de2fac9a8ee4279de7804d4e352dcfe121 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 15 Jan 2024 07:48:06 +0200 Subject: [PATCH 427/664] CUDA: faster dequantize kernels for Q4_0 and Q4_1 (#4938) Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 77 +++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 73 insertions(+), 4 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index bd3814c72..a870718a7 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1105,6 +1105,61 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in #endif // GGML_CUDA_F16 } +template +static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { + + const int i = blockIdx.x; + + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_0 * x = (const block_q4_0 *)vx + ib; + const float d = __half2float(x->d); + const float dm = -8*d; + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d * (q[l] & 0xF) + dm; + y[l+16] = d * (q[l] >> 4) + dm; + } +} + +template +static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { + + const int i = blockIdx.x; + + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_1 * x = (const block_q4_1 *)vx + ib; + const float2 d = __half22float2(x->dm); + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d.x * (q[l] & 0xF) + d.y; + y[l+16] = d.x * (q[l] >> 4) + d.y; + } +} + //================================== k-quants template @@ -6253,6 +6308,20 @@ static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cu #endif } +template +static void dequantize_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + dequantize_block_q4_0<<>>(vx, y, nb32); +} + +template +static void dequantize_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + dequantize_block_q4_1<<>>(vx, y, nb32); +} + template static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -6301,9 +6370,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { int id; switch (type) { case GGML_TYPE_Q4_0: - return dequantize_block_cuda; + return dequantize_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_block_cuda; + return dequantize_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: @@ -6338,9 +6407,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: - return dequantize_block_cuda; + return dequantize_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_block_cuda; + return dequantize_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: From 2faaef39799c97a53bec3898141478700da25757 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 15 Jan 2024 10:09:38 +0200 Subject: [PATCH 428/664] llama : check for 256 divisibility for IQ2_XS, IQ2_XXS (#4950) Co-authored-by: Iwan Kawrakow --- llama.cpp | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 7af38718c..f9718060d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8559,7 +8559,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty //} bool convert_incompatible_tensor = false; if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || - new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) { + new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || + new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -8571,6 +8572,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } if (convert_incompatible_tensor) { switch (new_type) { + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break; case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break; case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; From ddb008d845cd50bb090bf051f570130524042936 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 15 Jan 2024 13:27:00 +0200 Subject: [PATCH 429/664] cuda : fix dequantize kernel names (#4938) --- ggml-cuda.cu | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index a870718a7..c3e14bc96 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6309,14 +6309,14 @@ static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cu } template -static void dequantize_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; dequantize_block_q4_0<<>>(vx, y, nb32); } template -static void dequantize_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; dequantize_block_q4_1<<>>(vx, y, nb32); @@ -6370,9 +6370,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { int id; switch (type) { case GGML_TYPE_Q4_0: - return dequantize_q4_0_cuda; + return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_q4_1_cuda; + return dequantize_row_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: @@ -6407,9 +6407,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: - return dequantize_q4_0_cuda; + return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_q4_1_cuda; + return dequantize_row_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: From d9aa4ffa6e0296d42f1f676dd85de97c8491eb73 Mon Sep 17 00:00:00 2001 From: "Victor Z. Peng" Date: Mon, 15 Jan 2024 04:41:46 -0800 Subject: [PATCH 430/664] awq-py : fix typo in awq-py/README.md (#4947) --- awq-py/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/awq-py/README.md b/awq-py/README.md index 59354f4e3..16e68d027 100644 --- a/awq-py/README.md +++ b/awq-py/README.md @@ -43,7 +43,7 @@ Example for llama model # For llama7b and llama2 models python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf # For mistral and mpt models -python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf +python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf ``` ## Quantize From 4483396751c79dea540808b9cb9238245d06da2b Mon Sep 17 00:00:00 2001 From: David Friehs Date: Mon, 15 Jan 2024 14:06:52 +0100 Subject: [PATCH 431/664] llama : apply classifier-free guidance to logits directly (#4951) --- common/sampling.cpp | 9 ++++--- llama.cpp | 60 ++++++++++++++++++++++++++++++--------------- llama.h | 17 +++++++++---- 3 files changed, 57 insertions(+), 29 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 8e45909f1..dd1ffeb1b 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -190,6 +190,11 @@ static llama_token llama_sampling_sample_impl( logits[it->first] += it->second; } + if (ctx_cfg) { + float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); + llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); + } + cur.clear(); for (llama_token token_id = 0; token_id < n_vocab; token_id++) { @@ -198,10 +203,6 @@ static llama_token llama_sampling_sample_impl( llama_token_data_array cur_p = { cur.data(), cur.size(), false }; - if (ctx_cfg) { - llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale); - } - // apply penalties const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); diff --git a/llama.cpp b/llama.cpp index f9718060d..46c4d11c8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7898,39 +7898,59 @@ static void llama_log_softmax(float * array, size_t size) { } } +void llama_sample_apply_guidance( + struct llama_context * ctx, + float * logits, + float * logits_guidance, + float scale) { + GGML_ASSERT(ctx); + + const auto t_start_sample_us = ggml_time_us(); + const auto n_vocab = llama_n_vocab(llama_get_model(ctx)); + + llama_log_softmax(logits, n_vocab); + llama_log_softmax(logits_guidance, n_vocab); + + for (int i = 0; i < n_vocab; ++i) { + auto & l = logits[i]; + const auto & g = logits_guidance[i]; + + l = scale * (l - g) + g; + } + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; +} + void llama_sample_classifier_free_guidance( struct llama_context * ctx, llama_token_data_array * candidates, struct llama_context * guidance_ctx, float scale) { - int64_t t_start_sample_us = ggml_time_us(); - GGML_ASSERT(ctx); + int64_t t_start_sample_us; - auto n_vocab = llama_n_vocab(llama_get_model(ctx)); + t_start_sample_us = ggml_time_us(); + const size_t n_vocab = llama_n_vocab(llama_get_model(ctx)); - GGML_ASSERT(n_vocab == (int)candidates->size); + GGML_ASSERT(n_vocab == candidates->size); GGML_ASSERT(!candidates->sorted); - std::vector logits_base; - logits_base.reserve(candidates->size); - for (size_t i = 0; i < candidates->size; ++i) { - logits_base.push_back(candidates->data[i].logit); - } - llama_log_softmax(logits_base.data(), candidates->size); - - float* logits_guidance = llama_get_logits(guidance_ctx); - llama_log_softmax(logits_guidance, n_vocab); - - for (int i = 0; i < n_vocab; ++i) { - float logit_guidance = logits_guidance[i]; - float logit_base = logits_base[i]; - candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance; + std::vector logits_base(n_vocab); + for (size_t i = 0; i < n_vocab; ++i) { + logits_base[i] = candidates->data[i].logit; } - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + float * logits_guidance = llama_get_logits(guidance_ctx); + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale); + t_start_sample_us = ggml_time_us(); + + for (size_t i = 0; i < n_vocab; ++i) { + candidates->data[i].logit = logits_base[i]; } + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; } llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) { diff --git a/llama.h b/llama.h index 79c8335b6..a570b0d69 100644 --- a/llama.h +++ b/llama.h @@ -714,14 +714,21 @@ extern "C" { float penalty_present); /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806 - /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted. - /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. - /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. - LLAMA_API void llama_sample_classifier_free_guidance( + /// @param logits Logits extracted from the original generation context. + /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. + /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. + LLAMA_API void llama_sample_apply_guidance( + struct llama_context * ctx, + float * logits, + float * logits_guidance, + float scale); + + LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance( struct llama_context * ctx, llama_token_data_array * candidates, struct llama_context * guidance_ctx, - float scale); + float scale), + "use llama_sample_apply_guidance() instead"); /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. LLAMA_API void llama_sample_softmax( From 3e5ca7931c68152e4ec18d126e9c832dd84914c8 Mon Sep 17 00:00:00 2001 From: ngc92 <7938269+ngc92@users.noreply.github.com> Date: Mon, 15 Jan 2024 20:40:48 +0200 Subject: [PATCH 432/664] pass cpu-architecture arguments only to host code (C;C++) (#4943) --- CMakeLists.txt | 34 +++++++++++++++++++--------------- 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 2741568ed..7bd640966 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -594,6 +594,13 @@ if (NOT MSVC) endif() endif() +function(add_compile_option_cpp ARG) + # Adds a compile option to C/C++ only, but not for Cuda. + # Use, e.g., for CPU-architecture flags. + add_compile_options($<$:${ARG}>) + add_compile_options($<$:${ARG}>) +endfunction() + if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) message(STATUS "ARM detected") if (MSVC) @@ -628,8 +635,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE include(cmake/FindSIMD.cmake) endif () if (LLAMA_AVX512) - add_compile_options($<$:/arch:AVX512>) - add_compile_options($<$:/arch:AVX512>) + add_compile_option_cpp(/arch:AVX512) # MSVC has no compile-time flags enabling specific # AVX512 extensions, neither it defines the # macros corresponding to the extensions. @@ -643,37 +649,35 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE add_compile_definitions($<$:__AVX512VNNI__>) endif() elseif (LLAMA_AVX2) - add_compile_options($<$:/arch:AVX2>) - add_compile_options($<$:/arch:AVX2>) + add_compile_option_cpp(/arch:AVX2) elseif (LLAMA_AVX) - add_compile_options($<$:/arch:AVX>) - add_compile_options($<$:/arch:AVX>) + add_compile_option_cpp(/arch:AVX) endif() else() if (LLAMA_NATIVE) - add_compile_options(-march=native) + add_compile_option_cpp(-march=native) endif() if (LLAMA_F16C) - add_compile_options(-mf16c) + add_compile_option_cpp(-mf16c) endif() if (LLAMA_FMA) - add_compile_options(-mfma) + add_compile_option_cpp(-mfma) endif() if (LLAMA_AVX) - add_compile_options(-mavx) + add_compile_option_cpp(-mavx) endif() if (LLAMA_AVX2) - add_compile_options(-mavx2) + add_compile_option_cpp(-mavx2) endif() if (LLAMA_AVX512) - add_compile_options(-mavx512f) - add_compile_options(-mavx512bw) + add_compile_option_cpp(-mavx512f) + add_compile_option_cpp(-mavx512bw) endif() if (LLAMA_AVX512_VBMI) - add_compile_options(-mavx512vbmi) + add_compile_option_cpp(-mavx512vbmi) endif() if (LLAMA_AVX512_VNNI) - add_compile_options(-mavx512vnni) + add_compile_option_cpp(-mavx512vnni) endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") From e0324285a569d0583cf2f4a07a2402221ee25f58 Mon Sep 17 00:00:00 2001 From: stduhpf Date: Tue, 16 Jan 2024 12:04:32 +0100 Subject: [PATCH 433/664] speculative : threading options (#4959) * speculative: expose draft threading * fix usage format * accept -td and -tbd args * speculative: revert default behavior when -td is unspecified * fix trailing whitespace --- common/common.cpp | 22 ++++++++++++++++++++++ common/common.h | 2 ++ examples/speculative/speculative.cpp | 4 ++++ 3 files changed, 28 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index c11006bcb..2b0865fff 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -167,6 +167,24 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.n_threads_batch <= 0) { params.n_threads_batch = std::thread::hardware_concurrency(); } + } else if (arg == "-td" || arg == "--threads-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_threads_draft = std::stoi(argv[i]); + if (params.n_threads_draft <= 0) { + params.n_threads_draft = std::thread::hardware_concurrency(); + } + } else if (arg == "-tbd" || arg == "--threads-batch-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_threads_batch_draft = std::stoi(argv[i]); + if (params.n_threads_batch_draft <= 0) { + params.n_threads_batch_draft = std::thread::hardware_concurrency(); + } } else if (arg == "-p" || arg == "--prompt") { if (++i >= argc) { invalid_param = true; @@ -845,6 +863,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N\n"); printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n"); + printf(" -td N, --threads-draft N"); + printf(" number of threads to use during generation (default: same as --threads)"); + printf(" -tbd N, --threads-batch-draft N\n"); + printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n"); printf(" -p PROMPT, --prompt PROMPT\n"); printf(" prompt to start generation with (default: empty)\n"); printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); diff --git a/common/common.h b/common/common.h index 096468243..1f43e6282 100644 --- a/common/common.h +++ b/common/common.h @@ -46,7 +46,9 @@ struct gpt_params { uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); + int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_threads_batch_draft = -1; int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 20f1fb5bf..7b3af01f3 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -65,6 +65,10 @@ int main(int argc, char ** argv) { // load the draft model params.model = params.model_draft; params.n_gpu_layers = params.n_gpu_layers_draft; + if (params.n_threads_draft > 0) { + params.n_threads = params.n_threads_draft; + } + params.n_threads_batch = params.n_threads_batch_draft; std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); { From d75c232e1da56f19ac4d2530dadbe0ab3a11fde5 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 16 Jan 2024 12:14:19 +0100 Subject: [PATCH 434/664] finetune : use LLAMA_FILE_MAGIC_GGLA (#4961) This commit replaces the magic number LLAMA_FILE_MAGIC_LORA used in finetune.cpp with LLAMA_FILE_MAGIC_GGLA defined in llama.h. Signed-off-by: Daniel Bevenius --- examples/finetune/finetune.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index eaca42fc1..a6620fd73 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1138,9 +1138,8 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor return tn_buf.data(); }; - uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla' // write_magic - file.write_u32(LLAMA_FILE_MAGIC_LORA); // magic + file.write_u32(LLAMA_FILE_MAGIC_GGLA); // magic file.write_u32(1); // version // write_hparams file.write_u32(lora->hparams.lora_r); From a0b3ac8c48b66206b9c5921ce57bd5c0ea6557c3 Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Tue, 16 Jan 2024 03:16:33 -0800 Subject: [PATCH 435/664] ggml : introduce GGML_CALL function annotation (#4850) This change makes it possible to build ggml-cuda.cu and ggml-metal.m as independent dynamic shared objects, that may be conditionally linked at runtime in a multiplatform binary. It introduces a GGML_CALL annotation that documents which functions have a cyclic call relationship, between the application code and GPU modules. This change does nothing, unless the build defines -DGGML_MULTIPLATFORM which causes back-references and function pointers to conform to MS ABI which is supported by NVCC, ROCm, XCode, GCC and Clang across platforms --- ggml-backend-impl.h | 60 +++++++++++----------- ggml-backend.c | 80 ++++++++++++++--------------- ggml-backend.h | 50 +++++++++--------- ggml-cuda.cu | 121 ++++++++++++++++++++++---------------------- ggml-cuda.h | 32 ++++++------ ggml-metal.h | 4 +- ggml-metal.m | 42 +++++++-------- ggml.c | 32 ++++++------ ggml.h | 58 ++++++++++++--------- 9 files changed, 244 insertions(+), 235 deletions(-) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 1db32901f..1397828d9 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -16,14 +16,14 @@ extern "C" { typedef void * ggml_backend_buffer_type_context_t; struct ggml_backend_buffer_type_i { - const char * (*get_name) (ggml_backend_buffer_type_t buft); - ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); - size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment - size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding - bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend + const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft); + ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); + size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment + size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding + bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend // check if tensor data is in host memory // should be equivalent to supports_backend(buft, ggml_backend_cpu_init()) - bool (*is_host) (ggml_backend_buffer_type_t buft); + bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft); }; struct ggml_backend_buffer_type { @@ -35,15 +35,15 @@ extern "C" { typedef void * ggml_backend_buffer_context_t; struct ggml_backend_buffer_i { - const char * (*get_name) (ggml_backend_buffer_t buffer); - void (*free_buffer)(ggml_backend_buffer_t buffer); - void * (*get_base) (ggml_backend_buffer_t buffer); - void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer - void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); - void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras + const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer); + void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer); + void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer); + void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer + void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value); + void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras }; struct ggml_backend_buffer { @@ -54,7 +54,7 @@ extern "C" { enum ggml_backend_buffer_usage usage; }; - ggml_backend_buffer_t ggml_backend_buffer_init( + GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( ggml_backend_buffer_type_t buft, struct ggml_backend_buffer_i iface, ggml_backend_buffer_context_t context, @@ -70,31 +70,31 @@ extern "C" { typedef void * ggml_backend_context_t; struct ggml_backend_i { - const char * (*get_name)(ggml_backend_t backend); + const char * (*GGML_CALL get_name)(ggml_backend_t backend); - void (*free)(ggml_backend_t backend); + void (*GGML_CALL free)(ggml_backend_t backend); // buffer allocation - ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend); + ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend); // (optional) asynchronous tensor data access - void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); + void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); // (optional) complete all pending operations - void (*synchronize)(ggml_backend_t backend); + void (*GGML_CALL synchronize)(ggml_backend_t backend); // compute graph with a plan - ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); - void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); + void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async) - bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation - bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); }; struct ggml_backend { @@ -107,9 +107,9 @@ extern "C" { // Backend registry // - typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data); + typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data); - void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data); + GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data); #ifdef __cplusplus } diff --git a/ggml-backend.c b/ggml-backend.c index 505dbba47..f5424fb90 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -19,7 +19,7 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { return buft->iface.get_name(buft); } -ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { return buft->iface.alloc_buffer(buft, size); } @@ -27,7 +27,7 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) { return buft->iface.get_alignment(buft); } -size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { +GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { // get_alloc_size is optional, defaults to ggml_nbytes if (buft->iface.get_alloc_size) { return buft->iface.get_alloc_size(buft, tensor); @@ -48,7 +48,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) { // backend buffer -ggml_backend_buffer_t ggml_backend_buffer_init( +GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( ggml_backend_buffer_type_t buft, struct ggml_backend_buffer_i iface, ggml_backend_buffer_context_t context, @@ -95,7 +95,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { return base; } -void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { +GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { // init_tensor is optional if (buffer->iface.init_tensor) { buffer->iface.init_tensor(buffer, tensor); @@ -191,7 +191,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten } } -void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); @@ -201,7 +201,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size); } -void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); @@ -318,9 +318,9 @@ struct ggml_backend_reg { static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG]; static size_t ggml_backend_registry_count = 0; -static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data); +GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data); -static void ggml_backend_registry_init(void) { +GGML_CALL static void ggml_backend_registry_init(void) { static bool initialized = false; if (initialized) { @@ -333,18 +333,18 @@ static void ggml_backend_registry_init(void) { // add forward decls here to avoid including the backend headers #ifdef GGML_USE_CUBLAS - extern void ggml_backend_cuda_reg_devices(void); + extern GGML_CALL void ggml_backend_cuda_reg_devices(void); ggml_backend_cuda_reg_devices(); #endif #ifdef GGML_USE_METAL - extern ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); - extern ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); + extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); + extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL); #endif } -void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { +GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG); size_t id = ggml_backend_registry_count; @@ -439,33 +439,33 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) { // backend CPU -static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { return "CPU"; GGML_UNUSED(buffer); } -static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { return (void *)buffer->context; } -static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { free(buffer->context); } -static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); GGML_UNUSED(buffer); } -static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { memcpy(data, (const char *)tensor->data + offset, size); GGML_UNUSED(buffer); } -static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { if (ggml_backend_buffer_is_host(src->buffer)) { memcpy(dst->data, src->data, ggml_nbytes(src)); return true; @@ -475,7 +475,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con GGML_UNUSED(buffer); } -static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { memset(buffer->context, value, buffer->size); } @@ -506,13 +506,13 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 -static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU"; GGML_UNUSED(buft); } -static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? @@ -521,25 +521,25 @@ static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_back return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size); } -static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return TENSOR_ALIGNMENT; GGML_UNUSED(buft); } -static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_cpu(backend); GGML_UNUSED(buft); } -static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { +GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return true; GGML_UNUSED(buft); } -ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, @@ -561,23 +561,23 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { #include -static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU_HBM"; GGML_UNUSED(buft); } -static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { +GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { return "CPU_HBM"; GGML_UNUSED(buf); } -static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { hbw_free(buffer->context); } -static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { //void * ptr = hbw_malloc(size); void * ptr; int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); @@ -617,20 +617,20 @@ struct ggml_backend_cpu_context { size_t work_size; }; -static const char * ggml_backend_cpu_name(ggml_backend_t backend) { +GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) { return "CPU"; GGML_UNUSED(backend); } -static void ggml_backend_cpu_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; free(cpu_ctx->work_data); free(cpu_ctx); free(backend); } -static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { return ggml_backend_cpu_buffer_type(); GGML_UNUSED(backend); @@ -641,7 +641,7 @@ struct ggml_backend_plan_cpu { struct ggml_cgraph cgraph; }; -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { +GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); @@ -656,7 +656,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend return cpu_plan; } -static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; free(cpu_plan->cplan.work_data); @@ -665,7 +665,7 @@ static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backen GGML_UNUSED(backend); } -static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); @@ -673,7 +673,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac GGML_UNUSED(backend); } -static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); @@ -690,7 +690,7 @@ static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c return true; } -static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { +GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_MUL_MAT: return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; @@ -732,7 +732,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { return cpu_backend; } -bool ggml_backend_is_cpu(ggml_backend_t backend) { +GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_cpu_name; } @@ -743,11 +743,11 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { ctx->n_threads = n_threads; } -ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { +GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size); } -static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) { +GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) { return ggml_backend_cpu_init(); GGML_UNUSED(params); diff --git a/ggml-backend.h b/ggml-backend.h index 4eb244af1..12b4b4ab7 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -17,12 +17,12 @@ extern "C" { // // buffer type - GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); - GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); - GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); - GGML_API size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); - GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); - GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); + GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); + GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); + GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); + GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); + GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); + GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); // buffer enum ggml_backend_buffer_usage { @@ -30,18 +30,18 @@ extern "C" { GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1, }; - GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); - GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); - GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); - GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer); + GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); + GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); + GGML_API GGML_CALL void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); + GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer); // // Backend @@ -58,8 +58,8 @@ extern "C" { GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); GGML_API void ggml_backend_synchronize(ggml_backend_t backend); @@ -80,13 +80,13 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_cpu_init(void); - GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); + GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend); + GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); // Create a backend buffer from an existing pointer - GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); + GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); - GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); + GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); #ifdef GGML_USE_CPU_HBM GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); @@ -183,7 +183,7 @@ extern "C" { GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph); GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy); - typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data); + typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data); // Compare the output of two backends GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c3e14bc96..568c411af 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7615,11 +7615,11 @@ struct cuda_pool_alloc { static bool g_cublas_loaded = false; -bool ggml_cublas_loaded(void) { +GGML_CALL bool ggml_cublas_loaded(void) { return g_cublas_loaded; } -void ggml_init_cublas() { +GGML_CALL void ggml_init_cublas() { static bool initialized = false; if (!initialized) { @@ -7707,7 +7707,7 @@ void ggml_init_cublas() { } } -void * ggml_cuda_host_malloc(size_t size) { +GGML_CALL void * ggml_cuda_host_malloc(size_t size) { if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { return nullptr; } @@ -7725,7 +7725,7 @@ void * ggml_cuda_host_malloc(size_t size) { return ptr; } -void ggml_cuda_host_free(void * ptr) { +GGML_CALL void ggml_cuda_host_free(void * ptr) { CUDA_CHECK(cudaFreeHost(ptr)); } @@ -9242,7 +9242,7 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); } -bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { if (!g_cublas_loaded) return false; const int64_t ne10 = src1->ne[0]; @@ -10013,7 +10013,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -static void ggml_cuda_set_main_device(const int main_device) { +GGML_CALL static void ggml_cuda_set_main_device(const int main_device) { if (main_device >= g_device_count) { fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", main_device, g_device_count, g_main_device); @@ -10028,7 +10028,7 @@ static void ggml_cuda_set_main_device(const int main_device) { } } -bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { if (!g_cublas_loaded) return false; ggml_cuda_func_t func; @@ -10186,7 +10186,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ return true; } -int ggml_cuda_get_device_count() { +GGML_CALL int ggml_cuda_get_device_count() { int device_count; if (cudaGetDeviceCount(&device_count) != cudaSuccess) { return 0; @@ -10194,7 +10194,7 @@ int ggml_cuda_get_device_count() { return device_count; } -void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { +GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); snprintf(description, description_size, "%s", prop.name); @@ -10244,27 +10244,27 @@ struct ggml_backend_cuda_buffer_context { } }; -static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->name.c_str(); } -static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { +GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; } -static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; CUDA_CHECK(cudaFree(ctx->dev_ptr)); delete ctx; } -static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->dev_ptr; } -static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { +GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { @@ -10296,7 +10296,7 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g } } -static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -10307,7 +10307,7 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg CUDA_CHECK(cudaDeviceSynchronize()); } -static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -10318,7 +10318,7 @@ static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, co CUDA_CHECK(cudaDeviceSynchronize()); } -static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_cuda(src->buffer)) { ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -10335,7 +10335,7 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co return false; } -static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); @@ -10357,19 +10357,18 @@ static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { }; // cuda buffer type - struct ggml_backend_cuda_buffer_type_context { int device; std::string name; }; -static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; return ctx->name.c_str(); } -static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; ggml_cuda_set_device(buft_ctx->device); @@ -10388,13 +10387,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); } -static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; UNUSED(buft); } -static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { +GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); int64_t nrows_split = row_high - row_low; @@ -10414,7 +10413,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t UNUSED(buft); } -static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { if (!ggml_backend_is_cuda(backend)) { return false; } @@ -10434,7 +10433,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { /* .is_host = */ NULL, }; -ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { // FIXME: this is not thread safe if (device >= ggml_backend_cuda_get_device_count()) { return nullptr; @@ -10479,7 +10478,7 @@ struct ggml_backend_cuda_split_buffer_context { std::vector tensor_extras; }; -static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { return GGML_CUDA_NAME "_Split"; UNUSED(buffer); @@ -10490,19 +10489,19 @@ static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_ // return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; //} -static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; delete ctx; } -static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced return (void *)0x1000; UNUSED(buffer); } -static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { +GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; @@ -10552,7 +10551,7 @@ static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buf tensor->extra = extra; } -static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { // split tensors must always be set in their entirety at once GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); @@ -10586,7 +10585,7 @@ static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buff } } -static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { // split tensors must always be set in their entirety at once GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); @@ -10620,7 +10619,7 @@ static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buff } } -static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { UNUSED(buffer); UNUSED(value); } @@ -10639,13 +10638,13 @@ static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { // cuda split buffer type -static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { return GGML_CUDA_NAME "_Split"; UNUSED(buft); } -static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point // instead, we allocate them for each tensor separately in init_tensor // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, @@ -10655,13 +10654,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(gg return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); } -static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; UNUSED(buft); } -static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { +GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; size_t total_size = 0; @@ -10688,13 +10687,13 @@ static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_bu return total_size; } -static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_cuda(backend); UNUSED(buft); } -static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { +GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return false; UNUSED(buft); @@ -10709,7 +10708,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, }; -ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { // FIXME: this is not thread safe static std::map, struct ggml_backend_buffer_type> buft_map; @@ -10745,23 +10744,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten // host buffer type -static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { return GGML_CUDA_NAME "_Host"; UNUSED(buft); } -static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { return GGML_CUDA_NAME "_Host"; UNUSED(buffer); } -static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_cuda_host_free(buffer->context); } -static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { void * ptr = ggml_cuda_host_malloc(size); if (ptr == nullptr) { @@ -10777,7 +10776,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm return buffer; } -ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { /* .iface = */ { /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, @@ -10795,26 +10794,26 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { // backend -static const char * ggml_backend_cuda_name(ggml_backend_t backend) { +GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; return cuda_ctx->name.c_str(); } -static void ggml_backend_cuda_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_cuda_free(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; delete cuda_ctx; delete backend; } -static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; return ggml_backend_cuda_buffer_type(cuda_ctx->device); } -static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); @@ -10823,7 +10822,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0])); } -static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); @@ -10832,7 +10831,7 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); } -static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) { @@ -10843,7 +10842,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggm return false; } -static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0])); @@ -10851,7 +10850,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { UNUSED(backend); } -static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -10890,7 +10889,7 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph return true; } -static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -11016,7 +11015,7 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .supports_op = */ ggml_backend_cuda_supports_op, }; -ggml_backend_t ggml_backend_cuda_init(int device) { +GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { ggml_init_cublas(); // TODO: remove from ggml.c if (device < 0 || device >= ggml_cuda_get_device_count()) { @@ -11040,35 +11039,35 @@ ggml_backend_t ggml_backend_cuda_init(int device) { return cuda_backend; } -bool ggml_backend_is_cuda(ggml_backend_t backend) { +GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_cuda_name; } -int ggml_backend_cuda_get_device_count() { +GGML_CALL int ggml_backend_cuda_get_device_count() { return ggml_cuda_get_device_count(); } -void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { +GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { ggml_cuda_get_device_description(device, description, description_size); } -void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { +GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { ggml_cuda_set_device(device); CUDA_CHECK(cudaMemGetInfo(free, total)); } // backend registry -static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { +GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data); return cuda_backend; UNUSED(params); } -extern "C" int ggml_backend_cuda_reg_devices(); +extern "C" GGML_CALL int ggml_backend_cuda_reg_devices(); -int ggml_backend_cuda_reg_devices() { +GGML_CALL int ggml_backend_cuda_reg_devices() { int device_count = ggml_cuda_get_device_count(); //int device_count = 1; // DEBUG: some tools require delaying CUDA initialization for (int i = 0; i < device_count; i++) { diff --git a/ggml-cuda.h b/ggml-cuda.h index d19cbf3fd..b1ebd61d7 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -18,34 +18,34 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 // Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`. -GGML_API void ggml_init_cublas(void); +GGML_API GGML_CALL void ggml_init_cublas(void); // Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`. -GGML_API bool ggml_cublas_loaded(void); +GGML_API GGML_CALL bool ggml_cublas_loaded(void); -GGML_API void * ggml_cuda_host_malloc(size_t size); -GGML_API void ggml_cuda_host_free(void * ptr); +GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size); +GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr); -GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); -GGML_API int ggml_cuda_get_device_count(void); -GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL int ggml_cuda_get_device_count(void); +GGML_API GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size); // backend API -GGML_API ggml_backend_t ggml_backend_cuda_init(int device); +GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device); -GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); +GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend); -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); -GGML_API int ggml_backend_cuda_get_device_count(void); -GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); -GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); +GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void); +GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); #ifdef __cplusplus } diff --git a/ggml-metal.h b/ggml-metal.h index cd5e2995f..8b0bfc5f1 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -47,11 +47,11 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void); GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); -GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size); +GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size); GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb); -GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); // helper to check if the device supports a specific family // ideally, the user code should be doing these checks diff --git a/ggml-metal.m b/ggml-metal.m index 2ca726055..867f2fd48 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2294,13 +2294,13 @@ static void ggml_backend_metal_free_device(void) { } } -static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { return "Metal"; UNUSED(buffer); } -static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; for (int i = 0; i < ctx->n_buffers; i++) { @@ -2315,25 +2315,25 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) free(ctx); } -static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; return ctx->all_data; } -static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); UNUSED(buffer); } -static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { memcpy(data, (const char *)tensor->data + offset, size); UNUSED(buffer); } -static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { if (ggml_backend_buffer_is_host(src->buffer)) { memcpy(dst->data, src->data, ggml_nbytes(src)); return true; @@ -2343,7 +2343,7 @@ static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, c UNUSED(buffer); } -static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; memset(ctx->all_data, value, ctx->all_size); @@ -2363,13 +2363,13 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { // default buffer type -static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "Metal"; UNUSED(buft); } -static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); const size_t size_page = sysconf(_SC_PAGESIZE); @@ -2421,24 +2421,24 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); } -static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 32; UNUSED(buft); } -static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend); UNUSED(buft); } -static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { +GGML_CALL static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return true; UNUSED(buft); } -ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = { /* .iface = */ { /* .get_name = */ ggml_backend_metal_buffer_type_get_name, @@ -2456,7 +2456,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { // buffer from ptr -ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { +GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); ctx->all_data = data; @@ -2543,31 +2543,31 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz // backend -static const char * ggml_backend_metal_name(ggml_backend_t backend) { +GGML_CALL static const char * ggml_backend_metal_name(ggml_backend_t backend) { return "Metal"; UNUSED(backend); } -static void ggml_backend_metal_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_metal_free(ggml_backend_t backend) { struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; ggml_metal_free(ctx); free(backend); } -static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { return ggml_backend_metal_buffer_type(); UNUSED(backend); } -static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_graph_compute(metal_ctx, cgraph); } -static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { +GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_supports_op(metal_ctx, op); @@ -2630,9 +2630,9 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } -ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning +GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning -ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) { +GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) { return ggml_backend_metal_init(); GGML_UNUSED(params); diff --git a/ggml.c b/ggml.c index ef5888ab2..5779f32d2 100644 --- a/ggml.c +++ b/ggml.c @@ -1990,19 +1990,19 @@ void ggml_print_objects(const struct ggml_context * ctx) { GGML_PRINT("%s: --- end ---\n", __func__); } -int64_t ggml_nelements(const struct ggml_tensor * tensor) { +GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } -int64_t ggml_nrows(const struct ggml_tensor * tensor) { +GGML_CALL int64_t ggml_nrows(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } -size_t ggml_nbytes(const struct ggml_tensor * tensor) { +GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) { size_t nbytes; size_t blck_size = ggml_blck_size(tensor->type); if (blck_size == 1) { @@ -2025,15 +2025,15 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN); } -int ggml_blck_size(enum ggml_type type) { +GGML_CALL int ggml_blck_size(enum ggml_type type) { return type_traits[type].blck_size; } -size_t ggml_type_size(enum ggml_type type) { +GGML_CALL size_t ggml_type_size(enum ggml_type type) { return type_traits[type].type_size; } -size_t ggml_row_size(enum ggml_type type, int64_t ne) { +GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) { assert(ne % ggml_blck_size(type) == 0); return ggml_type_size(type)*ne/ggml_blck_size(type); } @@ -2042,15 +2042,15 @@ double ggml_type_sizef(enum ggml_type type) { return ((double)(type_traits[type].type_size))/type_traits[type].blck_size; } -const char * ggml_type_name(enum ggml_type type) { +GGML_CALL const char * ggml_type_name(enum ggml_type type) { return type_traits[type].type_name; } -bool ggml_is_quantized(enum ggml_type type) { +GGML_CALL bool ggml_is_quantized(enum ggml_type type) { return type_traits[type].is_quantized; } -const char * ggml_op_name(enum ggml_op op) { +GGML_CALL const char * ggml_op_name(enum ggml_op op) { return GGML_OP_NAME[op]; } @@ -2062,7 +2062,7 @@ const char * ggml_unary_op_name(enum ggml_unary_op op) { return GGML_UNARY_OP_NAME[op]; } -const char * ggml_op_desc(const struct ggml_tensor * t) { +GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) { if (t->op == GGML_OP_UNARY) { enum ggml_unary_op uop = ggml_get_unary_op(t); return ggml_unary_op_name(uop); @@ -2072,7 +2072,7 @@ const char * ggml_op_desc(const struct ggml_tensor * t) { } } -size_t ggml_element_size(const struct ggml_tensor * tensor) { +GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) { return ggml_type_size(tensor->type); } @@ -2154,11 +2154,11 @@ size_t ggml_tensor_overhead(void) { return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE; } -bool ggml_is_transposed(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } -bool ggml_is_contiguous(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return @@ -2177,7 +2177,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -bool ggml_is_permuted(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3]; @@ -3079,7 +3079,7 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) { return (float *)(tensor->data); } -enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { +GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { GGML_ASSERT(tensor->op == GGML_OP_UNARY); return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0); } @@ -11653,7 +11653,7 @@ static void ggml_rope_cache_init( } } -void ggml_rope_yarn_corr_dims( +GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims diff --git a/ggml.h b/ggml.h index 1187074f7..837c52e68 100644 --- a/ggml.h +++ b/ggml.h @@ -187,6 +187,16 @@ # define GGML_API #endif +#ifdef GGML_MULTIPLATFORM +# if defined(_WIN32) +# define GGML_CALL +# else +# define GGML_CALL __attribute__((__ms_abi__)) +# endif +#else +# define GGML_CALL +#endif + // TODO: support for clang #ifdef __GNUC__ # define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint))) @@ -649,41 +659,41 @@ extern "C" { GGML_API void ggml_print_object (const struct ggml_object * obj); GGML_API void ggml_print_objects(const struct ggml_context * ctx); - GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor); - GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor); - GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor); - GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN + GGML_API GGML_CALL int64_t ggml_nelements (const struct ggml_tensor * tensor); + GGML_API GGML_CALL int64_t ggml_nrows (const struct ggml_tensor * tensor); + GGML_API GGML_CALL size_t ggml_nbytes (const struct ggml_tensor * tensor); + GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN - GGML_API int ggml_blck_size(enum ggml_type type); - GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block - GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row + GGML_API GGML_CALL int ggml_blck_size(enum ggml_type type); + GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block + GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row GGML_DEPRECATED( GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float "use ggml_row_size() instead"); - GGML_API const char * ggml_type_name(enum ggml_type type); - GGML_API const char * ggml_op_name (enum ggml_op op); - GGML_API const char * ggml_op_symbol(enum ggml_op op); + GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type); + GGML_API GGML_CALL const char * ggml_op_name (enum ggml_op op); + GGML_API const char * ggml_op_symbol(enum ggml_op op); - GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op); - GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name + GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op); + GGML_API GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name - GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor); + GGML_API GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor); - GGML_API bool ggml_is_quantized(enum ggml_type type); + GGML_API GGML_CALL bool ggml_is_quantized(enum ggml_type type); // TODO: temporary until model loading of ggml examples is refactored GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype); - GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor); - GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor); - GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); - GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars + GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); + GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1); @@ -770,7 +780,7 @@ extern "C" { GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); - GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); + GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name); @@ -1413,7 +1423,7 @@ extern "C" { float beta_slow); // compute correction dims for YaRN RoPE scaling - void ggml_rope_yarn_corr_dims( + GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); // xPos RoPE, in-place, returns view(a) From 122ed4840cc6d209df6043e027f9f8a03aee01da Mon Sep 17 00:00:00 2001 From: Maximilian Winter Date: Tue, 16 Jan 2024 13:10:48 +0100 Subject: [PATCH 436/664] examples : fix and improv docs for the grammar generator (#4909) * Create pydantic-models-to-grammar.py * Added some comments for usage * Refactored Grammar Generator Added example and usage instruction. * Update pydantic_models_to_grammar.py * Update pydantic-models-to-grammar-examples.py * Renamed module and imported it. * Update pydantic-models-to-grammar.py * Renamed file and fixed grammar generator issue. * Fixed some issues and bugs of the grammar generator. Imporved Documentation * Update pydantic_models_to_grammar.py --- examples/pydantic_models_to_grammar.py | 877 +++++++++++++++---------- 1 file changed, 519 insertions(+), 358 deletions(-) diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py index 41b98fdc1..848c1c367 100644 --- a/examples/pydantic_models_to_grammar.py +++ b/examples/pydantic_models_to_grammar.py @@ -4,6 +4,7 @@ from copy import copy from inspect import isclass, getdoc from types import NoneType +from docstring_parser import parse from pydantic import BaseModel, create_model, Field from typing import Any, Type, List, get_args, get_origin, Tuple, Union, Optional, _GenericAlias from enum import Enum @@ -25,9 +26,10 @@ class PydanticDataType(Enum): ENUM (str): Represents an enum data type. CUSTOM_CLASS (str): Represents a custom class data type. """ + STRING = "string" TRIPLE_QUOTED_STRING = "triple_quoted_string" - MARKDOWN_STRING = "markdown_string" + MARKDOWN_CODE_BLOCK = "markdown_code_block" BOOLEAN = "boolean" INTEGER = "integer" FLOAT = "float" @@ -78,10 +80,10 @@ def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str: def format_model_and_field_name(model_name: str) -> str: - parts = re.findall('[A-Z][^A-Z]*', model_name) + parts = re.findall("[A-Z][^A-Z]*", model_name) if not parts: # Check if the list is empty return model_name.lower().replace("_", "-") - return '-'.join(part.lower().replace("_", "-") for part in parts) + return "-".join(part.lower().replace("_", "-") for part in parts) def generate_list_rule(element_type): @@ -93,29 +95,31 @@ def generate_list_rule(element_type): """ rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list" element_rule = map_pydantic_type_to_gbnf(element_type) - list_rule = fr'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"' + list_rule = rf'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"' return list_rule def get_members_structure(cls, rule_name): if issubclass(cls, Enum): # Handle Enum types - members = [f'\"\\\"{member.value}\\\"\"' for name, member in cls.__members__.items()] + members = [f'"\\"{member.value}\\""' for name, member in cls.__members__.items()] return f"{cls.__name__.lower()} ::= " + " | ".join(members) if cls.__annotations__ and cls.__annotations__ != {}: result = f'{rule_name} ::= "{{"' type_list_rules = [] # Modify this comprehension - members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param_type)}' - for name, param_type in cls.__annotations__.items() - if name != 'self'] + members = [ + f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}' + for name, param_type in cls.__annotations__.items() + if name != "self" + ] result += '"," '.join(members) result += ' "}"' return result, type_list_rules elif rule_name == "custom-class-any": - result = f'{rule_name} ::= ' - result += 'value' + result = f"{rule_name} ::= " + result += "value" type_list_rules = [] return result, type_list_rules else: @@ -124,9 +128,11 @@ def get_members_structure(cls, rule_name): result = f'{rule_name} ::= "{{"' type_list_rules = [] # Modify this comprehension too - members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param.annotation)}' - for name, param in parameters.items() - if name != 'self' and param.annotation != inspect.Parameter.empty] + members = [ + f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}' + for name, param in parameters.items() + if name != "self" and param.annotation != inspect.Parameter.empty + ] result += '", "'.join(members) result += ' "}"' @@ -141,8 +147,8 @@ def regex_to_gbnf(regex_pattern: str) -> str: gbnf_rule = regex_pattern # Translate common regex components to GBNF - gbnf_rule = gbnf_rule.replace('\\d', '[0-9]') - gbnf_rule = gbnf_rule.replace('\\s', '[ \t\n]') + gbnf_rule = gbnf_rule.replace("\\d", "[0-9]") + gbnf_rule = gbnf_rule.replace("\\s", "[ \t\n]") # Handle quantifiers and other regex syntax that is similar in GBNF # (e.g., '*', '+', '?', character classes) @@ -158,12 +164,12 @@ def generate_gbnf_integer_rules(max_digit=None, min_digit=None): Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits. Parameters: - max_digit (int): The maximum number of digits for the integer. Default is None. - min_digit (int): The minimum number of digits for the integer. Default is None. + max_digit (int): The maximum number of digits for the integer. Default is None. + min_digit (int): The minimum number of digits for the integer. Default is None. Returns: - integer_rule (str): The identifier for the integer rule generated. - additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits. + integer_rule (str): The identifier for the integer rule generated. + additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits. """ additional_rules = [] @@ -178,21 +184,21 @@ def generate_gbnf_integer_rules(max_digit=None, min_digit=None): # Handling Integer Rules if max_digit is not None or min_digit is not None: # Start with an empty rule part - integer_rule_part = '' + integer_rule_part = "" # Add mandatory digits as per min_digit if min_digit is not None: - integer_rule_part += '[0-9] ' * min_digit + integer_rule_part += "[0-9] " * min_digit # Add optional digits up to max_digit if max_digit is not None: optional_digits = max_digit - (min_digit if min_digit is not None else 0) - integer_rule_part += ''.join(['[0-9]? ' for _ in range(optional_digits)]) + integer_rule_part += "".join(["[0-9]? " for _ in range(optional_digits)]) # Trim the rule part and append it to additional rules integer_rule_part = integer_rule_part.strip() if integer_rule_part: - additional_rules.append(f'{integer_rule} ::= {integer_rule_part}') + additional_rules.append(f"{integer_rule} ::= {integer_rule_part}") return integer_rule, additional_rules @@ -224,21 +230,26 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None additional_rules = [] # Define the integer part rule - integer_part_rule = "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( + integer_part_rule = ( + "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( f"-min{min_digit}" if min_digit is not None else "") + ) # Define the fractional part rule based on precision constraints fractional_part_rule = "fractional-part" - fractional_rule_part = '' + fractional_rule_part = "" if max_precision is not None or min_precision is not None: fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + ( - f"-min{min_precision}" if min_precision is not None else "") + f"-min{min_precision}" if min_precision is not None else "" + ) # Minimum number of digits - fractional_rule_part = '[0-9]' * (min_precision if min_precision is not None else 1) + fractional_rule_part = "[0-9]" * (min_precision if min_precision is not None else 1) # Optional additional digits - fractional_rule_part += ''.join([' [0-9]?'] * ( - (max_precision - (min_precision if min_precision is not None else 1)) if max_precision is not None else 0)) - additional_rules.append(f'{fractional_part_rule} ::= {fractional_rule_part}') + fractional_rule_part += "".join( + [" [0-9]?"] * ((max_precision - ( + min_precision if min_precision is not None else 1)) if max_precision is not None else 0) + ) + additional_rules.append(f"{fractional_part_rule} ::= {fractional_rule_part}") # Define the float rule float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}" @@ -246,20 +257,19 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None # Generating the integer part rule definition, if necessary if max_digit is not None or min_digit is not None: - integer_rule_part = '[0-9]' + integer_rule_part = "[0-9]" if min_digit is not None and min_digit > 1: - integer_rule_part += ' [0-9]' * (min_digit - 1) + integer_rule_part += " [0-9]" * (min_digit - 1) if max_digit is not None: - integer_rule_part += ''.join([' [0-9]?'] * (max_digit - (min_digit if min_digit is not None else 1))) - additional_rules.append(f'{integer_part_rule} ::= {integer_rule_part.strip()}') + integer_rule_part += "".join([" [0-9]?"] * (max_digit - (min_digit if min_digit is not None else 1))) + additional_rules.append(f"{integer_part_rule} ::= {integer_rule_part.strip()}") return float_rule, additional_rules -def generate_gbnf_rule_for_type(model_name, field_name, - field_type, is_optional, processed_models, created_rules, - field_info=None) -> \ - Tuple[str, list]: +def generate_gbnf_rule_for_type( + model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None +) -> Tuple[str, list]: """ Generate GBNF rule for a given field type. @@ -282,20 +292,19 @@ def generate_gbnf_rule_for_type(model_name, field_name, if isclass(field_type) and issubclass(field_type, BaseModel): nested_model_name = format_model_and_field_name(field_type.__name__) - nested_model_rules = generate_gbnf_grammar(field_type, processed_models, created_rules) + nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules) rules.extend(nested_model_rules) gbnf_type, rules = nested_model_name, rules elif isclass(field_type) and issubclass(field_type, Enum): - enum_values = [f'\"\\\"{e.value}\\\"\"' for e in field_type] # Adding escaped quotes + enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}" rules.append(enum_rule) gbnf_type, rules = model_name + "-" + field_name, rules - elif get_origin(field_type) == list or field_type == list: # Array + elif get_origin(field_type) == list: # Array element_type = get_args(field_type)[0] - element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-element", - element_type, is_optional, processed_models, - created_rules) + element_rule_name, additional_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules + ) rules.extend(additional_rules) array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ rules.append(array_rule) @@ -303,10 +312,9 @@ def generate_gbnf_rule_for_type(model_name, field_name, elif get_origin(field_type) == set or field_type == set: # Array element_type = get_args(field_type)[0] - element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-element", - element_type, is_optional, processed_models, - created_rules) + element_rule_name, additional_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules + ) rules.extend(additional_rules) array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ rules.append(array_rule) @@ -318,15 +326,13 @@ def generate_gbnf_rule_for_type(model_name, field_name, elif gbnf_type.startswith("custom-dict-"): key_type, value_type = get_args(field_type) - additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-key-type", - key_type, is_optional, processed_models, - created_rules) - additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-value-type", - value_type, is_optional, - processed_models, created_rules) - gbnf_type = fr'{gbnf_type} ::= "{{" ( {additional_key_type} ":" {additional_value_type} ("," {additional_key_type} ":" {additional_value_type})* )? "}}" ' + additional_key_type, additional_key_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-key-type", key_type, is_optional, processed_models, created_rules + ) + additional_value_type, additional_value_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-value-type", value_type, is_optional, processed_models, created_rules + ) + gbnf_type = rf'{gbnf_type} ::= "{{" ( {additional_key_type} ": " {additional_value_type} ("," "\n" ws {additional_key_type} ":" {additional_value_type})* )? "}}" ' rules.extend(additional_key_rules) rules.extend(additional_value_rules) @@ -336,19 +342,16 @@ def generate_gbnf_rule_for_type(model_name, field_name, for union_type in union_types: if isinstance(union_type, _GenericAlias): - union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, - field_name, union_type, - False, - processed_models, created_rules) + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type( + model_name, field_name, union_type, False, processed_models, created_rules + ) union_rules.append(union_gbnf_type) rules.extend(union_rules_list) - elif not issubclass(union_type, NoneType): - union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, - field_name, union_type, - False, - processed_models, created_rules) + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type( + model_name, field_name, union_type, False, processed_models, created_rules + ) union_rules.append(union_gbnf_type) rules.extend(union_rules_list) @@ -363,45 +366,58 @@ def generate_gbnf_rule_for_type(model_name, field_name, else: gbnf_type = f"{model_name}-{field_name}-union" elif isclass(field_type) and issubclass(field_type, str): - if field_info and hasattr(field_info, 'json_schema_extra') and field_info.json_schema_extra is not None: - - triple_quoted_string = field_info.json_schema_extra.get('triple_quoted_string', False) - markdown_string = field_info.json_schema_extra.get('markdown_string', False) + if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None: + triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False) + markdown_string = field_info.json_schema_extra.get("markdown_code_block", False) gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value - gbnf_type = PydanticDataType.MARKDOWN_STRING.value if markdown_string else gbnf_type + gbnf_type = PydanticDataType.MARKDOWN_CODE_BLOCK.value if markdown_string else gbnf_type - elif field_info and hasattr(field_info, 'pattern'): + elif field_info and hasattr(field_info, "pattern"): # Convert regex pattern to grammar rule regex_pattern = field_info.regex.pattern gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}" else: gbnf_type = PydanticDataType.STRING.value - elif isclass(field_type) and issubclass(field_type, float) and field_info and hasattr(field_info, - 'json_schema_extra') and field_info.json_schema_extra is not None: + elif ( + isclass(field_type) + and issubclass(field_type, float) + and field_info + and hasattr(field_info, "json_schema_extra") + and field_info.json_schema_extra is not None + ): # Retrieve precision attributes for floats - max_precision = field_info.json_schema_extra.get('max_precision') if field_info and hasattr(field_info, - 'json_schema_extra') else None - min_precision = field_info.json_schema_extra.get('min_precision') if field_info and hasattr(field_info, - 'json_schema_extra') else None - max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None - min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None + max_precision = ( + field_info.json_schema_extra.get("max_precision") if field_info and hasattr(field_info, + "json_schema_extra") else None + ) + min_precision = ( + field_info.json_schema_extra.get("min_precision") if field_info and hasattr(field_info, + "json_schema_extra") else None + ) + max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None + min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None # Generate GBNF rule for float with given attributes - gbnf_type, rules = generate_gbnf_float_rules(max_digit=max_digits, min_digit=min_digits, - max_precision=max_precision, - min_precision=min_precision) + gbnf_type, rules = generate_gbnf_float_rules( + max_digit=max_digits, min_digit=min_digits, max_precision=max_precision, min_precision=min_precision + ) - elif isclass(field_type) and issubclass(field_type, int) and field_info and hasattr(field_info, - 'json_schema_extra') and field_info.json_schema_extra is not None: + elif ( + isclass(field_type) + and issubclass(field_type, int) + and field_info + and hasattr(field_info, "json_schema_extra") + and field_info.json_schema_extra is not None + ): # Retrieve digit attributes for integers - max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None - min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None + max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None + min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None # Generate GBNF rule for integer with given attributes gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits) @@ -443,13 +459,13 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created if not issubclass(model, BaseModel): # For non-Pydantic classes, generate model_fields from __annotations__ or __init__ - if hasattr(model, '__annotations__') and model.__annotations__: + if hasattr(model, "__annotations__") and model.__annotations__: model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} else: init_signature = inspect.signature(model.__init__) parameters = init_signature.parameters - model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() - if name != 'self'} + model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() if + name != "self"} else: # For Pydantic models, use model_fields and check for ellipsis (required fields) model_fields = model.__annotations__ @@ -469,51 +485,55 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created field_type = field_info field_info = model.model_fields[field_name] is_optional = field_info.is_required is False and get_origin(field_type) is Optional - rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, - format_model_and_field_name(field_name), - field_type, is_optional, - processed_models, created_rules, field_info) - look_for_markdown_code_block = True if rule_name == "markdown_string" else False + rule_name, additional_rules = generate_gbnf_rule_for_type( + model_name, format_model_and_field_name(field_name), field_type, is_optional, processed_models, + created_rules, field_info + ) + look_for_markdown_code_block = True if rule_name == "markdown_code_block" else False look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False if not look_for_markdown_code_block and not look_for_triple_quoted_string: if rule_name not in created_rules: created_rules[rule_name] = additional_rules - model_rule_parts.append(f' ws \"\\\"{field_name}\\\"\" ": " {rule_name}') # Adding escaped quotes + model_rule_parts.append(f' ws "\\"{field_name}\\"" ":" ws {rule_name}') # Adding escaped quotes nested_rules.extend(additional_rules) else: - has_triple_quoted_string = look_for_markdown_code_block - has_markdown_code_block = look_for_triple_quoted_string + has_triple_quoted_string = look_for_triple_quoted_string + has_markdown_code_block = look_for_markdown_code_block fields_joined = r' "," "\n" '.join(model_rule_parts) - model_rule = fr'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"' - - if look_for_markdown_code_block or look_for_triple_quoted_string: - model_rule += ' ws "}"' + model_rule = rf'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"' + has_special_string = False if has_triple_quoted_string: + model_rule += '"\\n" ws "}"' model_rule += '"\\n" triple-quoted-string' + has_special_string = True if has_markdown_code_block: + model_rule += '"\\n" ws "}"' model_rule += '"\\n" markdown-code-block' + has_special_string = True all_rules = [model_rule] + nested_rules - return all_rules, has_markdown_code_block, has_triple_quoted_string + return all_rules, has_special_string -def generate_gbnf_grammar_from_pydantic_models(models: List[Type[BaseModel]], outer_object_name: str = None, - outer_object_content: str = None, list_of_outputs: bool = False) -> str: +def generate_gbnf_grammar_from_pydantic_models( + models: List[Type[BaseModel]], outer_object_name: str = None, outer_object_content: str = None, + list_of_outputs: bool = False +) -> str: """ Generate GBNF Grammar from Pydantic Models. This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated * grammar. - Parameters: - models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - list_of_outputs (str, optional): Allows a list of output objects + Args: + models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + list_of_outputs (str, optional): Allows a list of output objects Returns: - str: The generated GBNF grammar string. + str: The generated GBNF grammar string. Examples: models = [UserModel, PostModel] @@ -527,52 +547,53 @@ def generate_gbnf_grammar_from_pydantic_models(models: List[Type[BaseModel]], ou all_rules = [] created_rules = {} if outer_object_name is None: - for model in models: - model_rules, _, _ = generate_gbnf_grammar(model, - processed_models, created_rules) + model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules) all_rules.extend(model_rules) if list_of_outputs: - root_rule = r'root ::= ws "[" grammar-models ("," grammar-models)* "]"' + "\n" + root_rule = r'root ::= (" "| "\n") "[" ws grammar-models ("," ws grammar-models)* ws "]"' + "\n" else: - root_rule = r'root ::= ws grammar-models' + "\n" + root_rule = r'root ::= (" "| "\n") grammar-models' + "\n" root_rule += "grammar-models ::= " + " | ".join( [format_model_and_field_name(model.__name__) for model in models]) all_rules.insert(0, root_rule) return "\n".join(all_rules) elif outer_object_name is not None: if list_of_outputs: - root_rule = fr'root ::= ws "[" {format_model_and_field_name(outer_object_name)} ("," {format_model_and_field_name(outer_object_name)})* "]"' + "\n" + root_rule = ( + rf'root ::= (" "| "\n") "[" ws {format_model_and_field_name(outer_object_name)} ("," ws {format_model_and_field_name(outer_object_name)})* ws "]"' + + "\n" + ) else: root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n" - model_rule = fr'{format_model_and_field_name(outer_object_name)} ::= ws "{{" ws "\"{outer_object_name}\"" ": " grammar-models' + model_rule = ( + rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"{outer_object_name}\"" ":" ws grammar-models' + ) fields_joined = " | ".join( - [fr'{format_model_and_field_name(model.__name__)}-grammar-model' for model in models]) + [rf"{format_model_and_field_name(model.__name__)}-grammar-model" for model in models]) - grammar_model_rules = f'\ngrammar-models ::= {fields_joined}' + grammar_model_rules = f"\ngrammar-models ::= {fields_joined}" mod_rules = [] for model in models: - mod_rule = fr'{format_model_and_field_name(model.__name__)}-grammar-model ::= ws' - mod_rule += fr'"\"{format_model_and_field_name(model.__name__)}\"" "," ws "\"{outer_object_content}\"" ws ":" ws {format_model_and_field_name(model.__name__)}' + '\n' + mod_rule = rf"{format_model_and_field_name(model.__name__)}-grammar-model ::= " + mod_rule += ( + rf'"\"{model.__name__}\"" "," ws "\"{outer_object_content}\"" ":" ws {format_model_and_field_name(model.__name__)}' + "\n" + ) mod_rules.append(mod_rule) grammar_model_rules += "\n" + "\n".join(mod_rules) - look_for_markdown_code_block = False - look_for_triple_quoted_string = False + for model in models: - model_rules, markdown_block, triple_quoted_string = generate_gbnf_grammar(model, - processed_models, created_rules) + model_rules, has_special_string = generate_gbnf_grammar(model, processed_models, + created_rules) + + if not has_special_string: + model_rules[0] += r'"\n" ws "}"' + all_rules.extend(model_rules) - if markdown_block: - look_for_markdown_code_block = True - if triple_quoted_string: - look_for_triple_quoted_string = True - - if not look_for_markdown_code_block and not look_for_triple_quoted_string: - model_rule += ' ws "}"' all_rules.insert(0, root_rule + model_rule + grammar_model_rules) return "\n".join(all_rules) @@ -582,10 +603,10 @@ def get_primitive_grammar(grammar): Returns the needed GBNF primitive grammar for a given GBNF grammar string. Args: - grammar (str): The string containing the GBNF grammar. + grammar (str): The string containing the GBNF grammar. Returns: - str: GBNF primitive grammar string. + str: GBNF primitive grammar string. """ type_list = [] if "string-list" in grammar: @@ -611,7 +632,7 @@ integer ::= [0-9]+""" any_block = "" if "custom-class-any" in grammar: - any_block = ''' + any_block = """ value ::= object | array | string | number | boolean | null object ::= @@ -626,7 +647,7 @@ array ::= ("," ws value)* )? "]" ws -number ::= integer | float''' +number ::= integer | float""" markdown_code_block_grammar = "" if "markdown-code-block" in grammar: @@ -641,90 +662,32 @@ closing-triple-ticks ::= "```" "\n"''' triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes triple-quoted-string-content ::= ( [^'] | "'" [^'] | "'" "'" [^'] )* triple-quotes ::= "'''" """ - return "\n" + '\n'.join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar + return "\n" + "\n".join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar -def generate_field_markdown(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1) -> str: - indent = ' ' * depth - field_markdown = f"{indent}- **{field_name}** (`{field_type.__name__}`): " - - # Extracting field description from Pydantic Field using __model_fields__ - field_info = model.model_fields.get(field_name) - field_description = field_info.description if field_info and field_info.description else "No description available." - - field_markdown += field_description + '\n' - - # Handling nested BaseModel fields - if isclass(field_type) and issubclass(field_type, BaseModel): - field_markdown += f"{indent} - Details:\n" - for name, type_ in field_type.__annotations__.items(): - field_markdown += generate_field_markdown(name, type_, field_type, depth + 2) - - return field_markdown - - -def generate_markdown_report(pydantic_models: List[Type[BaseModel]]) -> str: - markdown = "" - for model in pydantic_models: - markdown += f"### {format_model_and_field_name(model.__name__)}\n" - - # Check if the model's docstring is different from BaseModel's docstring - class_doc = getdoc(model) - base_class_doc = getdoc(BaseModel) - class_description = class_doc if class_doc and class_doc != base_class_doc else "No specific description available." - - markdown += f"{class_description}\n\n" - markdown += "#### Fields\n" - - if isclass(model) and issubclass(model, BaseModel): - for name, field_type in model.__annotations__.items(): - markdown += generate_field_markdown(format_model_and_field_name(name), field_type, model) - markdown += "\n" - - return markdown - - -def format_json_example(example: dict, depth: int) -> str: +def generate_markdown_documentation( + pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", + documentation_with_field_description=True +) -> str: """ - Format a JSON example into a readable string with indentation. + Generate markdown documentation for a list of Pydantic models. Args: - example (dict): JSON example to be formatted. - depth (int): Indentation depth. + pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + model_prefix (str): Prefix for the model section. + fields_prefix (str): Prefix for the fields section. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - str: Formatted JSON example string. - """ - indent = ' ' * depth - formatted_example = '{\n' - for key, value in example.items(): - value_text = f"'{value}'" if isinstance(value, str) else value - formatted_example += f"{indent}{key}: {value_text},\n" - formatted_example = formatted_example.rstrip(',\n') + '\n' + indent + '}' - return formatted_example - - -def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_prefix="Model", - fields_prefix="Fields", documentation_with_field_description=True) -> str: - """ - Generate text documentation for a list of Pydantic models. - - Args: - pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. - model_prefix (str): Prefix for the model section. - fields_prefix (str): Prefix for the fields section. - documentation_with_field_description (bool): Include field descriptions in the documentation. - - Returns: - str: Generated text documentation. + str: Generated text documentation. """ documentation = "" pyd_models = [(model, True) for model in pydantic_models] for model, add_prefix in pyd_models: if add_prefix: - documentation += f"{model_prefix}: {format_model_and_field_name(model.__name__)}\n" + documentation += f"{model_prefix}: {model.__name__}\n" else: - documentation += f"Model: {format_model_and_field_name(model.__name__)}\n" + documentation += f"Model: {model.__name__}\n" # Handling multi-line model description with proper indentation @@ -733,7 +696,7 @@ def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_pr class_description = class_doc if class_doc and class_doc != base_class_doc else "" if class_description != "": documentation += " Description: " - documentation += "\n" + format_multiline_description(class_description, 2) + "\n" + documentation += format_multiline_description(class_description, 0) + "\n" if add_prefix: # Indenting the fields section @@ -753,35 +716,192 @@ def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_pr for element_type in element_types: if isclass(element_type) and issubclass(element_type, BaseModel): pyd_models.append((element_type, False)) - documentation += generate_field_text(name, field_type, model, - documentation_with_field_description=documentation_with_field_description) + documentation += generate_field_markdown( + name, field_type, model, documentation_with_field_description=documentation_with_field_description + ) documentation += "\n" - if hasattr(model, 'Config') and hasattr(model.Config, - 'json_schema_extra') and 'example' in model.Config.json_schema_extra: + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n" - json_example = json.dumps(model.Config.json_schema_extra['example']) + json_example = json.dumps(model.Config.json_schema_extra["example"]) documentation += format_multiline_description(json_example, 2) + "\n" return documentation -def generate_field_text(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, - documentation_with_field_description=True) -> str: +def generate_field_markdown( + field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + documentation_with_field_description=True +) -> str: + """ + Generate markdown documentation for a Pydantic model field. + + Args: + field_name (str): Name of the field. + field_type (Type[Any]): Type of the field. + model (Type[BaseModel]): Pydantic model class. + depth (int): Indentation depth in the documentation. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation for the field. + """ + indent = " " * depth + + field_info = model.model_fields.get(field_name) + field_description = field_info.description if field_info and field_info.description else "" + + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + elif get_origin(field_type) == Union: + element_types = get_args(field_type) + types = [] + for element_type in element_types: + types.append(format_model_and_field_name(element_type.__name__)) + field_text = f"{indent}{field_name} ({' or '.join(types)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + else: + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + + if not documentation_with_field_description: + return field_text + + if field_description != "": + field_text += f" Description: " + field_description + "\n" + + # Check for and include field-specific examples if available + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: + field_example = model.Config.json_schema_extra["example"].get(field_name) + if field_example is not None: + example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example + field_text += f"{indent} Example: {example_text}\n" + + if isclass(field_type) and issubclass(field_type, BaseModel): + field_text += f"{indent} Details:\n" + for name, type_ in field_type.__annotations__.items(): + field_text += generate_field_markdown(name, type_, field_type, depth + 2) + + return field_text + + +def format_json_example(example: dict, depth: int) -> str: + """ + Format a JSON example into a readable string with indentation. + + Args: + example (dict): JSON example to be formatted. + depth (int): Indentation depth. + + Returns: + str: Formatted JSON example string. + """ + indent = " " * depth + formatted_example = "{\n" + for key, value in example.items(): + value_text = f"'{value}'" if isinstance(value, str) else value + formatted_example += f"{indent}{key}: {value_text},\n" + formatted_example = formatted_example.rstrip(",\n") + "\n" + indent + "}" + return formatted_example + + +def generate_text_documentation( + pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", + documentation_with_field_description=True +) -> str: + """ + Generate text documentation for a list of Pydantic models. + + Args: + pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + model_prefix (str): Prefix for the model section. + fields_prefix (str): Prefix for the fields section. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation. + """ + documentation = "" + pyd_models = [(model, True) for model in pydantic_models] + for model, add_prefix in pyd_models: + if add_prefix: + documentation += f"{model_prefix}: {model.__name__}\n" + else: + documentation += f"Model: {model.__name__}\n" + + # Handling multi-line model description with proper indentation + + class_doc = getdoc(model) + base_class_doc = getdoc(BaseModel) + class_description = class_doc if class_doc and class_doc != base_class_doc else "" + if class_description != "": + documentation += " Description: " + documentation += "\n" + format_multiline_description(class_description, 2) + "\n" + + if isclass(model) and issubclass(model, BaseModel): + documentation_fields = "" + for name, field_type in model.__annotations__.items(): + # if name == "markdown_code_block": + # continue + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + if get_origin(field_type) == Union: + element_types = get_args(field_type) + for element_type in element_types: + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + documentation_fields += generate_field_text( + name, field_type, model, documentation_with_field_description=documentation_with_field_description + ) + if documentation_fields != "": + if add_prefix: + documentation += f" {fields_prefix}:\n{documentation_fields}" + else: + documentation += f" Fields:\n{documentation_fields}" + documentation += "\n" + + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: + documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n" + json_example = json.dumps(model.Config.json_schema_extra["example"]) + documentation += format_multiline_description(json_example, 2) + "\n" + + return documentation + + +def generate_field_text( + field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + documentation_with_field_description=True +) -> str: """ Generate text documentation for a Pydantic model field. Args: - field_name (str): Name of the field. - field_type (Type[Any]): Type of the field. - model (Type[BaseModel]): Pydantic model class. - depth (int): Indentation depth in the documentation. - documentation_with_field_description (bool): Include field descriptions in the documentation. + field_name (str): Name of the field. + field_type (Type[Any]): Type of the field. + model (Type[BaseModel]): Pydantic model class. + depth (int): Indentation depth in the documentation. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - str: Generated text documentation for the field. + str: Generated text documentation for the field. """ - indent = ' ' * depth + indent = " " * depth field_info = model.model_fields.get(field_name) field_description = field_info.description if field_info and field_info.description else "" @@ -817,9 +937,9 @@ def generate_field_text(field_name: str, field_type: Type[Any], model: Type[Base field_text += f"{indent} Description: " + field_description + "\n" # Check for and include field-specific examples if available - if hasattr(model, 'Config') and hasattr(model.Config, - 'json_schema_extra') and 'example' in model.Config.json_schema_extra: - field_example = model.Config.json_schema_extra['example'].get(field_name) + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: + field_example = model.Config.json_schema_extra["example"].get(field_name) if field_example is not None: example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example field_text += f"{indent} Example: {example_text}\n" @@ -837,39 +957,40 @@ def format_multiline_description(description: str, indent_level: int) -> str: Format a multiline description with proper indentation. Args: - description (str): Multiline description. - indent_level (int): Indentation level. + description (str): Multiline description. + indent_level (int): Indentation level. Returns: - str: Formatted multiline description. + str: Formatted multiline description. """ - indent = ' ' * indent_level - return indent + description.replace('\n', '\n' + indent) + indent = " " * indent_level + return indent + description.replace("\n", "\n" + indent) -def save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path="./grammar.gbnf", - documentation_file_path="./grammar_documentation.md"): +def save_gbnf_grammar_and_documentation( + grammar, documentation, grammar_file_path="./grammar.gbnf", documentation_file_path="./grammar_documentation.md" +): """ Save GBNF grammar and documentation to specified files. Args: - grammar (str): GBNF grammar string. - documentation (str): Documentation string. - grammar_file_path (str): File path to save the GBNF grammar. - documentation_file_path (str): File path to save the documentation. + grammar (str): GBNF grammar string. + documentation (str): Documentation string. + grammar_file_path (str): File path to save the GBNF grammar. + documentation_file_path (str): File path to save the documentation. Returns: - None + None """ try: - with open(grammar_file_path, 'w') as file: + with open(grammar_file_path, "w") as file: file.write(grammar + get_primitive_grammar(grammar)) print(f"Grammar successfully saved to {grammar_file_path}") except IOError as e: print(f"An error occurred while saving the grammar file: {e}") try: - with open(documentation_file_path, 'w') as file: + with open(documentation_file_path, "w") as file: file.write(documentation) print(f"Documentation successfully saved to {documentation_file_path}") except IOError as e: @@ -881,10 +1002,10 @@ def remove_empty_lines(string): Remove empty lines from a string. Args: - string (str): Input string. + string (str): Input string. Returns: - str: String with empty lines removed. + str: String with empty lines removed. """ lines = string.splitlines() non_empty_lines = [line for line in lines if line.strip() != ""] @@ -892,95 +1013,109 @@ def remove_empty_lines(string): return string_no_empty_lines -def generate_and_save_gbnf_grammar_and_documentation(pydantic_model_list, - grammar_file_path="./generated_grammar.gbnf", - documentation_file_path="./generated_grammar_documentation.md", - outer_object_name: str = None, - outer_object_content: str = None, - model_prefix: str = "Output Model", - fields_prefix: str = "Output Fields", - list_of_outputs: bool = False, - documentation_with_field_description=True): +def generate_and_save_gbnf_grammar_and_documentation( + pydantic_model_list, + grammar_file_path="./generated_grammar.gbnf", + documentation_file_path="./generated_grammar_documentation.md", + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True, +): """ Generate GBNF grammar and documentation, and save them to specified files. Args: - pydantic_model_list: List of Pydantic model classes. - grammar_file_path (str): File path to save the generated GBNF grammar. - documentation_file_path (str): File path to save the generated documentation. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - model_prefix (str): Prefix for the model section in the documentation. - fields_prefix (str): Prefix for the fields section in the documentation. - list_of_outputs (bool): Whether the output is a list of items. - documentation_with_field_description (bool): Include field descriptions in the documentation. + pydantic_model_list: List of Pydantic model classes. + grammar_file_path (str): File path to save the generated GBNF grammar. + documentation_file_path (str): File path to save the generated documentation. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - None + None """ - documentation = generate_text_documentation(pydantic_model_list, model_prefix, fields_prefix, - documentation_with_field_description=documentation_with_field_description) - grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, - outer_object_content, list_of_outputs) + documentation = generate_markdown_documentation( + pydantic_model_list, model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description + ) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, + list_of_outputs) grammar = remove_empty_lines(grammar) save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path) -def generate_gbnf_grammar_and_documentation(pydantic_model_list, outer_object_name: str = None, - outer_object_content: str = None, - model_prefix: str = "Output Model", - fields_prefix: str = "Output Fields", list_of_outputs: bool = False, - documentation_with_field_description=True): +def generate_gbnf_grammar_and_documentation( + pydantic_model_list, + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True, +): """ Generate GBNF grammar and documentation for a list of Pydantic models. Args: - pydantic_model_list: List of Pydantic model classes. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - model_prefix (str): Prefix for the model section in the documentation. - fields_prefix (str): Prefix for the fields section in the documentation. - list_of_outputs (bool): Whether the output is a list of items. - documentation_with_field_description (bool): Include field descriptions in the documentation. + pydantic_model_list: List of Pydantic model classes. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - tuple: GBNF grammar string, documentation string. + tuple: GBNF grammar string, documentation string. """ - documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, - documentation_with_field_description=documentation_with_field_description) - grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, - outer_object_content, list_of_outputs) + documentation = generate_markdown_documentation( + copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description + ) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, + list_of_outputs) grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) return grammar, documentation -def generate_gbnf_grammar_and_documentation_from_dictionaries(dictionaries: List[dict], - outer_object_name: str = None, - outer_object_content: str = None, - model_prefix: str = "Output Model", - fields_prefix: str = "Output Fields", - list_of_outputs: bool = False, - documentation_with_field_description=True): +def generate_gbnf_grammar_and_documentation_from_dictionaries( + dictionaries: List[dict], + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True, +): """ Generate GBNF grammar and documentation from a list of dictionaries. Args: - dictionaries (List[dict]): List of dictionaries representing Pydantic models. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - model_prefix (str): Prefix for the model section in the documentation. - fields_prefix (str): Prefix for the fields section in the documentation. - list_of_outputs (bool): Whether the output is a list of items. - documentation_with_field_description (bool): Include field descriptions in the documentation. + dictionaries (List[dict]): List of dictionaries representing Pydantic models. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - tuple: GBNF grammar string, documentation string. + tuple: GBNF grammar string, documentation string. """ pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries) - documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, - documentation_with_field_description=documentation_with_field_description) - grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, - outer_object_content, list_of_outputs) + documentation = generate_markdown_documentation( + copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description + ) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, + list_of_outputs) grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) return grammar, documentation @@ -990,41 +1125,61 @@ def create_dynamic_model_from_function(func: Callable): Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method. Args: - func (Callable): A function with type hints from which to create the model. + func (Callable): A function with type hints from which to create the model. Returns: - A dynamic Pydantic model class with the provided function as a 'run' method. + A dynamic Pydantic model class with the provided function as a 'run' method. """ - # Extracting type hints from the provided function - type_hints = get_type_hints(func) - type_hints.pop('return', None) - # Handling default values and annotations + # Get the signature of the function + sig = inspect.signature(func) + + # Parse the docstring + docstring = parse(func.__doc__) + dynamic_fields = {} - defaults = getattr(func, '__defaults__', ()) or () - defaults_index = len(type_hints) - len(defaults) + param_docs = [] + for param in sig.parameters.values(): + # Exclude 'self' parameter + if param.name == "self": + continue - for index, (name, typ) in enumerate(type_hints.items()): - if index >= defaults_index: - default_value = defaults[index - defaults_index] - dynamic_fields[name] = (typ, default_value) + # Assert that the parameter has a type annotation + if param.annotation == inspect.Parameter.empty: + raise TypeError(f"Parameter '{param.name}' in function '{func.__name__}' lacks a type annotation") + + # Find the parameter's description in the docstring + param_doc = next((d for d in docstring.params if d.arg_name == param.name), None) + + # Assert that the parameter has a description + if not param_doc or not param_doc.description: + raise ValueError( + f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring") + + # Add parameter details to the schema + param_doc = next((d for d in docstring.params if d.arg_name == param.name), None) + param_docs.append((param.name, param_doc)) + if param.default == inspect.Parameter.empty: + default_value = ... else: - dynamic_fields[name] = (typ, ...) - + default_value = param.default + dynamic_fields[param.name] = ( + param.annotation if param.annotation != inspect.Parameter.empty else str, default_value) # Creating the dynamic model - dynamicModel = create_model(f'{func.__name__}', **dynamic_fields) + dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) - dynamicModel.__doc__ = getdoc(func) + for param_doc in param_docs: + dynamic_model.model_fields[param_doc[0]].description = param_doc[1].description + + dynamic_model.__doc__ = docstring.short_description - # Wrapping the original function to handle instance 'self' def run_method_wrapper(self): - func_args = {name: getattr(self, name) for name in type_hints} + func_args = {name: getattr(self, name) for name, _ in dynamic_fields.items()} return func(**func_args) # Adding the wrapped function as a 'run' method - setattr(dynamicModel, 'run', run_method_wrapper) - - return dynamicModel + setattr(dynamic_model, "run", run_method_wrapper) + return dynamic_model def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): @@ -1032,11 +1187,11 @@ def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): Add a 'run' method to a dynamic Pydantic model, using the provided function. Args: - - model (Type[BaseModel]): Dynamic Pydantic model class. - - func (Callable): Function to be added as a 'run' method to the model. + model (Type[BaseModel]): Dynamic Pydantic model class. + func (Callable): Function to be added as a 'run' method to the model. Returns: - - Type[BaseModel]: Pydantic model class with the added 'run' method. + Type[BaseModel]: Pydantic model class with the added 'run' method. """ def run_method_wrapper(self): @@ -1044,7 +1199,7 @@ def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): return func(**func_args) # Adding the wrapped function as a 'run' method - setattr(model, 'run', run_method_wrapper) + setattr(model, "run", run_method_wrapper) return model @@ -1054,15 +1209,15 @@ def create_dynamic_models_from_dictionaries(dictionaries: List[dict]): Create a list of dynamic Pydantic model classes from a list of dictionaries. Args: - - dictionaries (List[dict]): List of dictionaries representing model structures. + dictionaries (List[dict]): List of dictionaries representing model structures. Returns: - - List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. + List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. """ dynamic_models = [] for func in dictionaries: model_name = format_model_and_field_name(func.get("name", "")) - dyn_model = convert_dictionary_to_to_pydantic_model(func, model_name) + dyn_model = convert_dictionary_to_pydantic_model(func, model_name) dynamic_models.append(dyn_model) return dynamic_models @@ -1080,12 +1235,12 @@ from enum import Enum def json_schema_to_python_types(schema): type_map = { - 'any': Any, - 'string': str, - 'number': float, - 'integer': int, - 'boolean': bool, - 'array': list, + "any": Any, + "string": str, + "number": float, + "integer": int, + "boolean": bool, + "array": list, } return type_map[schema] @@ -1094,58 +1249,64 @@ def list_to_enum(enum_name, values): return Enum(enum_name, {value: value for value in values}) -def convert_dictionary_to_to_pydantic_model(dictionary: dict, model_name: str = 'CustomModel') -> Type[BaseModel]: +def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "CustomModel") -> Type[BaseModel]: """ Convert a dictionary to a Pydantic model class. Args: - - dictionary (dict): Dictionary representing the model structure. - - model_name (str): Name of the generated Pydantic model. + dictionary (dict): Dictionary representing the model structure. + model_name (str): Name of the generated Pydantic model. Returns: - - Type[BaseModel]: Generated Pydantic model class. + Type[BaseModel]: Generated Pydantic model class. """ fields = {} if "properties" in dictionary: for field_name, field_data in dictionary.get("properties", {}).items(): - if field_data == 'object': - submodel = convert_dictionary_to_to_pydantic_model(dictionary, f'{model_name}_{field_name}') + if field_data == "object": + submodel = convert_dictionary_to_pydantic_model(dictionary, f"{model_name}_{field_name}") fields[field_name] = (submodel, ...) else: - field_type = field_data.get('type', 'str') + field_type = field_data.get("type", "str") if field_data.get("enum", []): fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...) - if field_type == "array": + elif field_type == "array": items = field_data.get("items", {}) if items != {}: array = {"properties": items} - array_type = convert_dictionary_to_to_pydantic_model(array, f'{model_name}_{field_name}_items') + array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items") fields[field_name] = (List[array_type], ...) else: fields[field_name] = (list, ...) - elif field_type == 'object': - submodel = convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}_{field_name}') + elif field_type == "object": + submodel = convert_dictionary_to_pydantic_model(field_data, f"{model_name}_{field_name}") fields[field_name] = (submodel, ...) + elif field_type == "required": + required = field_data.get("enum", []) + for key, field in fields.items(): + if key not in required: + fields[key] = (Optional[fields[key][0]], ...) else: field_type = json_schema_to_python_types(field_type) fields[field_name] = (field_type, ...) if "function" in dictionary: - for field_name, field_data in dictionary.get("function", {}).items(): if field_name == "name": model_name = field_data elif field_name == "description": fields["__doc__"] = field_data elif field_name == "parameters": - return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') + return convert_dictionary_to_pydantic_model(field_data, f"{model_name}") + if "parameters" in dictionary: field_data = {"function": dictionary} - return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') - + return convert_dictionary_to_pydantic_model(field_data, f"{model_name}") + if "required" in dictionary: + required = dictionary.get("required", []) + for key, field in fields.items(): + if key not in required: + fields[key] = (Optional[fields[key][0]], ...) custom_model = create_model(model_name, **fields) return custom_model - - - From 7c8d3abd1a17c28fc56b1a4814bc4b29f91d7454 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Tue, 16 Jan 2024 14:33:02 +0100 Subject: [PATCH 437/664] metal : log `recommendedMaxWorkingSetSize` on iOS 16+ (#4936) * metal: Log `recommendedMaxWorkingSetSize` on iOS 16+ * Only log on iOS and macOS, ignoring tvOS and other platforms * Check for Xcode version before using recommendedMaxWorkingSetSize --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 58 ++++++++++++++++++++++++---------------------------- 1 file changed, 27 insertions(+), 31 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 867f2fd48..44134d1d9 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -369,8 +369,12 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); + +#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) + if (@available(macOS 10.12, iOS 16.0, *)) { + GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); + } +#elif TARGET_OS_OSX if (ctx->device.maxTransferRate != 0) { GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6); } else { @@ -2369,6 +2373,25 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe UNUSED(buft); } +static void ggml_backend_metal_log_allocated_size(id device) { +#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) + if (@available(macOS 10.12, iOS 16.0, *)) { + GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", + device.currentAllocatedSize / 1024.0 / 1024.0, + device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + + if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { + GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); + } else { + GGML_METAL_LOG_INFO("\n"); + } + } else { + GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); + } +#endif + UNUSED(device); +} + GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); @@ -2401,22 +2424,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff } GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); - - -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", - device.currentAllocatedSize / 1024.0 / 1024.0, - device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } else { - GGML_METAL_LOG_INFO("\n"); - } -#else - GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); -#endif - + ggml_backend_metal_log_allocated_size(device); return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); } @@ -2524,19 +2532,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, } } -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", - device.currentAllocatedSize / 1024.0 / 1024.0, - device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } else { - GGML_METAL_LOG_INFO("\n"); - } -#else - GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); -#endif + ggml_backend_metal_log_allocated_size(device); return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); } From 3a48d558a69c88ac17efcaa5900cd9eb19596ac4 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Tue, 16 Jan 2024 14:41:27 +0100 Subject: [PATCH 438/664] metal : replace loop of dispatch_async with dispatch_apply (#4934) * Replace loop of dispatch_async with dispatch_apply * Update ggml-metal.m --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 2882 +++++++++++++++++++++++++------------------------- 1 file changed, 1439 insertions(+), 1443 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 44134d1d9..c21dc465a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -737,1475 +737,249 @@ static bool ggml_metal_graph_compute( ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc]; } - for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { - const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; + const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; + dispatch_apply(n_cb, ctx->d_queue, ^(size_t iter) { + const int cb_idx = iter; - dispatch_async(ctx->d_queue, ^{ - size_t offs_src0 = 0; - size_t offs_src1 = 0; - size_t offs_dst = 0; + size_t offs_src0 = 0; + size_t offs_src1 = 0; + size_t offs_dst = 0; - id command_buffer = ctx->command_buffers[cb_idx]; - id encoder = ctx->command_encoders[cb_idx]; + id command_buffer = ctx->command_buffers[cb_idx]; + id encoder = ctx->command_encoders[cb_idx]; - const int node_start = (cb_idx + 0) * n_nodes_per_cb; - const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); + const int node_start = (cb_idx + 0) * n_nodes_per_cb; + const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); - for (int ind = node_start; ind < node_end; ++ind) { - const int i = ind; + for (int ind = node_start; ind < node_end; ++ind) { + const int i = ind; - if (i == -1) { - [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; - continue; - } + if (i == -1) { + [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; + continue; + } - //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); + //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); - struct ggml_tensor * src0 = gf->nodes[i]->src[0]; - struct ggml_tensor * src1 = gf->nodes[i]->src[1]; - struct ggml_tensor * dst = gf->nodes[i]; + struct ggml_tensor * src0 = gf->nodes[i]->src[0]; + struct ggml_tensor * src1 = gf->nodes[i]->src[1]; + struct ggml_tensor * dst = gf->nodes[i]; - switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop -> next node - } continue; - default: - { - } break; - } + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + { + // noop -> next node + } continue; + default: + { + } break; + } - if (!ggml_metal_supports_op(ctx, dst)) { - GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); - GGML_ASSERT(!"unsupported op"); - } + if (!ggml_metal_supports_op(ctx, dst)) { + GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); + GGML_ASSERT(!"unsupported op"); + } #ifndef GGML_METAL_NDEBUG - [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; + [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; #endif - const int64_t ne00 = src0 ? src0->ne[0] : 0; - const int64_t ne01 = src0 ? src0->ne[1] : 0; - const int64_t ne02 = src0 ? src0->ne[2] : 0; - const int64_t ne03 = src0 ? src0->ne[3] : 0; + const int64_t ne00 = src0 ? src0->ne[0] : 0; + const int64_t ne01 = src0 ? src0->ne[1] : 0; + const int64_t ne02 = src0 ? src0->ne[2] : 0; + const int64_t ne03 = src0 ? src0->ne[3] : 0; - const uint64_t nb00 = src0 ? src0->nb[0] : 0; - const uint64_t nb01 = src0 ? src0->nb[1] : 0; - const uint64_t nb02 = src0 ? src0->nb[2] : 0; - const uint64_t nb03 = src0 ? src0->nb[3] : 0; + const uint64_t nb00 = src0 ? src0->nb[0] : 0; + const uint64_t nb01 = src0 ? src0->nb[1] : 0; + const uint64_t nb02 = src0 ? src0->nb[2] : 0; + const uint64_t nb03 = src0 ? src0->nb[3] : 0; - const int64_t ne10 = src1 ? src1->ne[0] : 0; - const int64_t ne11 = src1 ? src1->ne[1] : 0; - const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); + const int64_t ne10 = src1 ? src1->ne[0] : 0; + const int64_t ne11 = src1 ? src1->ne[1] : 0; + const int64_t ne12 = src1 ? src1->ne[2] : 0; + const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); - const uint64_t nb10 = src1 ? src1->nb[0] : 0; - const uint64_t nb11 = src1 ? src1->nb[1] : 0; - const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); + const uint64_t nb10 = src1 ? src1->nb[0] : 0; + const uint64_t nb11 = src1 ? src1->nb[1] : 0; + const uint64_t nb12 = src1 ? src1->nb[2] : 0; + const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; + const int64_t ne0 = dst ? dst->ne[0] : 0; + const int64_t ne1 = dst ? dst->ne[1] : 0; + const int64_t ne2 = dst ? dst->ne[2] : 0; + const int64_t ne3 = dst ? dst->ne[3] : 0; - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; + const uint64_t nb0 = dst ? dst->nb[0] : 0; + const uint64_t nb1 = dst ? dst->nb[1] : 0; + const uint64_t nb2 = dst ? dst->nb[2] : 0; + const uint64_t nb3 = dst ? dst->nb[3] : 0; - const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; - const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; - const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; + const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; + const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; + const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; - id id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; - id id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; - id id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; + id id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; + id id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; + id id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; - //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); - //if (src0) { - // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, - // ggml_is_contiguous(src0), src0->name); - //} - //if (src1) { - // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, - // ggml_is_contiguous(src1), src1->name); - //} - //if (dst) { - // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, - // dst->name); - //} + //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); + //if (src0) { + // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, + // ggml_is_contiguous(src0), src0->name); + //} + //if (src1) { + // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, + // ggml_is_contiguous(src1), src1->name); + //} + //if (dst) { + // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, + // dst->name); + //} - switch (dst->op) { - case GGML_OP_CONCAT: - { - const int64_t nb = ne00; + switch (dst->op) { + case GGML_OP_CONCAT: + { + const int64_t nb = ne00; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; - const int nth = MIN(1024, ne0); + const int nth = MIN(1024, ne0); - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ADD: - case GGML_OP_MUL: - case GGML_OP_DIV: - { - const size_t offs = 0; + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_DIV: + { + const size_t offs = 0; - bool bcast_row = false; + bool bcast_row = false; - int64_t nb = ne00; + int64_t nb = ne00; - id pipeline = nil; + id pipeline = nil; - if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { - GGML_ASSERT(ggml_is_contiguous(src0)); - - // src1 is a row - GGML_ASSERT(ne11 == 1); - - nb = ne00 / 4; - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; - default: GGML_ASSERT(false); - } - - bcast_row = true; - } else { - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; - default: GGML_ASSERT(false); - } - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; - - if (bcast_row) { - const int64_t n = ggml_nelements(dst)/4; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } else { - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - } break; - case GGML_OP_ACC: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - GGML_ASSERT(dstt == GGML_TYPE_F32); - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - - const size_t pnb1 = ((int32_t *) dst->op_params)[0]; - const size_t pnb2 = ((int32_t *) dst->op_params)[1]; - const size_t pnb3 = ((int32_t *) dst->op_params)[2]; - const size_t offs = ((int32_t *) dst->op_params)[3]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - // run a separete kernel to cpy src->dst - // not sure how to avoid this - // TODO: make a simpler cpy_bytes kernel - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_SCALE: - { + if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { GGML_ASSERT(ggml_is_contiguous(src0)); - const float scale = *(const float *) dst->op_params; + // src1 is a row + GGML_ASSERT(ne11 == 1); - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - n /= 4; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; + nb = ne00 / 4; + switch (dst->op) { + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; + default: GGML_ASSERT(false); } - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; + bcast_row = true; + } else { + switch (dst->op) { + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; + default: GGML_ASSERT(false); + } + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; + + if (bcast_row) { + const int64_t n = ggml_nelements(dst)/4; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(gf->nodes[i])) { - case GGML_UNARY_OP_TANH: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_RELU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_SILU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - default: - { - GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ASSERT(false); - } - } break; - case GGML_OP_SQR: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SUM_ROWS: - { - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SOFT_MAX: - { - int nth = 32; // SIMD width - - id pipeline = nil; - - if (ne00%4 == 0) { - while (nth < ne00/4 && nth < 256) { - nth *= 2; - } - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; - } else { - while (nth < ne00 && nth < 1024) { - nth *= 2; - } - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; - } - - const float scale = ((float *) dst->op_params)[0]; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - if (id_src1) { - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_DIAG_MASK_INF: - { - const int n_past = ((int32_t *)(dst->op_params))[0]; - - id pipeline = nil; - - if (ne00%8 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; - - if (ne00%8 == 0) { - [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - else { - [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - } break; - case GGML_OP_MUL_MAT: - { - GGML_ASSERT(ne00 == ne10); - - // TODO: assert that dim2 and dim3 are contiguous - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - const uint r2 = ne12/ne02; - const uint r3 = ne13/ne03; - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - int ne11_mm_min = 1; - -#if 0 - // the numbers below are measured on M2 Ultra for 7B and 13B models - // these numbers do not translate to other devices or model sizes - // TODO: need to find a better approach - if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) { - switch (src0t) { - case GGML_TYPE_F16: ne11_mm_min = 2; break; - case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; - case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; - case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; - case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; - case GGML_TYPE_Q5_0: // not tested yet - case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet - case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; - default: ne11_mm_min = 1; break; - } - } -#endif - - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && - !ggml_is_transposed(src0) && - !ggml_is_transposed(src1) && - src1t == GGML_TYPE_F32 && - ne00 % 32 == 0 && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { - //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; - default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - // use custom matrix x vector kernel - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; - nrows = 4; - } break; - case GGML_TYPE_F16: - { - nth0 = 32; - nth1 = 1; - if (src1t == GGML_TYPE_F32) { - if (ne11 * ne12 < 4) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; - nrows = ne11; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; - nrows = 4; - } - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; - nrows = 4; - } - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; - } break; - default: - { - GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); - GGML_ASSERT(false && "not implemented"); - } - }; - - if (ggml_is_quantized(src0t)) { - GGML_ASSERT(ne00 >= nth0*nth1); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; - - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || - src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { - const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif - } - else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (ne11 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_MUL_MAT_ID: - { - //GGML_ASSERT(ne00 == ne10); - //GGML_ASSERT(ne03 == ne13); - - GGML_ASSERT(src0t == GGML_TYPE_I32); - - const int n_as = ((int32_t *) dst->op_params)[1]; - - // TODO: make this more general - GGML_ASSERT(n_as <= 8); - - // max size of the src1ids array in the kernel stack - GGML_ASSERT(ne11 <= 512); - - struct ggml_tensor * src2 = gf->nodes[i]->src[2]; - - const int64_t ne20 = src2 ? src2->ne[0] : 0; - const int64_t ne21 = src2 ? src2->ne[1] : 0; - const int64_t ne22 = src2 ? src2->ne[2] : 0; - const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); - const uint64_t nb21 = src2 ? src2->nb[1] : 0; - const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); - - const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); - - GGML_ASSERT(!ggml_is_transposed(src2)); - GGML_ASSERT(!ggml_is_transposed(src1)); - - GGML_ASSERT(src1t == GGML_TYPE_F32); - - const uint r2 = ne12/ne22; - const uint r3 = ne13/ne23; - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - int ne11_mm_min = n_as; - - const int idx = ((int32_t *) dst->op_params)[0]; - - // batch size - GGML_ASSERT(ne01 == ne11); - - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - // !!! - // TODO: for now, always use mat-vec kernels until we figure out how to improve the - // indirect matrix multiplication - // !!! - if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && - ne20 % 32 == 0 && ne20 >= 64 && - ne11 > ne11_mm_min) { - - id pipeline = nil; - - switch (src2->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; - default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:7]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:9]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:16]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; - [encoder setBytes:&idx length:sizeof(idx) atIndex:18]; - // TODO: how to make this an array? read Metal docs - for (int j = 0; j < 8; ++j) { - // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 - struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; - - size_t offs_src_cur = 0; - id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); - - [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; - } - - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - // use custom matrix x vector kernel - switch (src2t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - nth0 = 32; - nth1 = 1; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; - } break; - default: - { - GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); - GGML_ASSERT(false && "not implemented"); - } - }; - - if (ggml_is_quantized(src2t)) { - GGML_ASSERT(ne20 >= nth0*nth1); - } - - const int64_t _ne1 = 1; // kernels needs a reference in constant memory - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6]; - [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8]; - [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:20]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:21]; - [encoder setBytes:&idx length:sizeof(idx) atIndex:22]; - // TODO: how to make this an array? read Metal docs - for (int j = 0; j < 8; ++j) { - // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 - struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; - - size_t offs_src_cur = 0; - id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); - - [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; - } - - if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || - src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || - src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { - const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif - } - else if (src2t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (_ne1 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_GET_ROWS: - { - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; - case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; - } break; - case GGML_OP_RMS_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - int nth = 32; // SIMD width - - while (nth < ne00/4 && nth < 1024) { - nth *= 2; - } - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_GROUP_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - - //float eps; - //memcpy(&eps, dst->op_params, sizeof(float)); - - const float eps = 1e-6f; // TODO: temporarily hardcoded - - const int32_t n_groups = ((int32_t *) dst->op_params)[0]; - - int nth = 32; // SIMD width - - //while (nth < ne00/4 && nth < 1024) { - // nth *= 2; - //} - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&eps length:sizeof( float) atIndex:9]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_NORM: - { - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - const int nth = MIN(256, ne00); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT((src0t == GGML_TYPE_F32)); - - const int nth = MIN(1024, ne00); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; - [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; - [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ROPE: - { - GGML_ASSERT(ne10 == ne02); - - const int nth = MIN(1024, ne00); - - const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; - default: GGML_ASSERT(false); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_IM2COL: - { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int32_t N = src1->ne[is_2D ? 3 : 2]; - const int32_t IC = src1->ne[is_2D ? 2 : 1]; - const int32_t IH = is_2D ? src1->ne[1] : 1; - const int32_t IW = src1->ne[0]; - - const int32_t KH = is_2D ? src0->ne[1] : 1; - const int32_t KW = src0->ne[0]; - - const int32_t OH = is_2D ? dst->ne[2] : 1; - const int32_t OW = dst->ne[1]; - - const int32_t CHW = IC * KH * KW; - - const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; - const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; - default: GGML_ASSERT(false); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; - [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; - [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; - [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; - [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; - [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; - [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; - [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; - [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; - [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; - - [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; - } break; - case GGML_OP_UPSCALE: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int sf = dst->op_params[0]; - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; - + } else { const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_PAD: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } + } break; + case GGML_OP_ACC: + { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + GGML_ASSERT(dstt == GGML_TYPE_F32); - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + const size_t pnb1 = ((int32_t *) dst->op_params)[0]; + const size_t pnb2 = ((int32_t *) dst->op_params)[1]; + const size_t pnb3 = ((int32_t *) dst->op_params)[2]; + const size_t offs = ((int32_t *) dst->op_params)[3]; - const int nth = MIN(1024, ne0); + const bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ARGSORT: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); + if (!inplace) { + // run a separete kernel to cpy src->dst + // not sure how to avoid this + // TODO: make a simpler cpy_bytes kernel - const int nrows = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - - id pipeline = nil; - - switch (order) { - case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; - case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; - default: GGML_ASSERT(false); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - - [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)]; - } break; - case GGML_OP_LEAKY_RELU: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - float slope; - memcpy(&slope, dst->op_params, sizeof(float)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_DUP: - case GGML_OP_CPY: - case GGML_OP_CONT: - { - GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); - - int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); - - id pipeline = nil; - - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); - - switch (dstt) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; - //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; - //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - }; - } break; - case GGML_TYPE_F16: - { - switch (dstt) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - }; - } break; - default: GGML_ASSERT(false && "not implemented"); - } + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -2227,31 +1001,1253 @@ static bool ggml_metal_graph_compute( [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - default: - { - GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ASSERT(false); } - } + + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; + [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; + [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; + [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; + [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; + [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + + [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_SCALE: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + + const float scale = *(const float *) dst->op_params; + + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + n /= 4; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(gf->nodes[i])) { + case GGML_UNARY_OP_TANH: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_RELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_GELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + + [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_GELU_QUICK: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + + [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_SILU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + + [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + default: + { + GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + GGML_ASSERT(false); + } + } break; + case GGML_OP_SQR: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SUM_ROWS: + { + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SOFT_MAX: + { + int nth = 32; // SIMD width + + id pipeline = nil; + + if (ne00%4 == 0) { + while (nth < ne00/4 && nth < 256) { + nth *= 2; + } + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; + } else { + while (nth < ne00 && nth < 1024) { + nth *= 2; + } + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; + } + + const float scale = ((float *) dst->op_params)[0]; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + if (id_src1) { + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_DIAG_MASK_INF: + { + const int n_past = ((int32_t *)(dst->op_params))[0]; + + id pipeline = nil; + + if (ne00%8 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; + + if (ne00%8 == 0) { + [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } + else { + [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } + } break; + case GGML_OP_MUL_MAT: + { + GGML_ASSERT(ne00 == ne10); + + // TODO: assert that dim2 and dim3 are contiguous + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + const uint r2 = ne12/ne02; + const uint r3 = ne13/ne03; + + // find the break-even point where the matrix-matrix kernel becomes more efficient compared + // to the matrix-vector kernel + int ne11_mm_min = 1; + +#if 0 + // the numbers below are measured on M2 Ultra for 7B and 13B models + // these numbers do not translate to other devices or model sizes + // TODO: need to find a better approach + if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) { + switch (src0t) { + case GGML_TYPE_F16: ne11_mm_min = 2; break; + case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; + case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; + case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; + case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; + case GGML_TYPE_Q5_0: // not tested yet + case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet + case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; + case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; + default: ne11_mm_min = 1; break; + } + } +#endif + + // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs + // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel + if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && + !ggml_is_transposed(src0) && + !ggml_is_transposed(src1) && + src1t == GGML_TYPE_F32 && + ne00 % 32 == 0 && ne00 >= 64 && + (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { + //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; + default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; + [encoder setThreadgroupMemoryLength:8192 atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + } else { + int nth0 = 32; + int nth1 = 1; + int nrows = 1; + //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + // use custom matrix x vector kernel + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; + nrows = 4; + } break; + case GGML_TYPE_F16: + { + nth0 = 32; + nth1 = 1; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; + nrows = ne11; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; + nrows = 4; + } + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; + nrows = 4; + } + } break; + case GGML_TYPE_Q4_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; + } break; + case GGML_TYPE_Q4_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; + } break; + case GGML_TYPE_Q5_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; + } break; + case GGML_TYPE_Q5_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; + } break; + case GGML_TYPE_Q8_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; + } break; + case GGML_TYPE_Q2_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; + } break; + case GGML_TYPE_Q3_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; + } break; + case GGML_TYPE_Q4_K: + { + nth0 = 4; //1; + nth1 = 8; //32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; + } break; + case GGML_TYPE_Q5_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; + } break; + case GGML_TYPE_Q6_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; + } break; + default: + { + GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); + GGML_ASSERT(false && "not implemented"); + } + }; + + if (ggml_is_quantized(src0t)) { + GGML_ASSERT(ne00 >= nth0*nth1); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; + + if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || + src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || + src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { + const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q3_K) { +#ifdef GGML_QKK_64 + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#else + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#endif + } + else if (src0t == GGML_TYPE_Q5_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q6_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else { + const int64_t ny = (ne11 + nrows - 1)/nrows; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + } + } break; + case GGML_OP_MUL_MAT_ID: + { + //GGML_ASSERT(ne00 == ne10); + //GGML_ASSERT(ne03 == ne13); + + GGML_ASSERT(src0t == GGML_TYPE_I32); + + const int n_as = ((int32_t *) dst->op_params)[1]; + + // TODO: make this more general + GGML_ASSERT(n_as <= 8); + + // max size of the src1ids array in the kernel stack + GGML_ASSERT(ne11 <= 512); + + struct ggml_tensor * src2 = gf->nodes[i]->src[2]; + + const int64_t ne20 = src2 ? src2->ne[0] : 0; + const int64_t ne21 = src2 ? src2->ne[1] : 0; + const int64_t ne22 = src2 ? src2->ne[2] : 0; + const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); + + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); + + const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); + + GGML_ASSERT(!ggml_is_transposed(src2)); + GGML_ASSERT(!ggml_is_transposed(src1)); + + GGML_ASSERT(src1t == GGML_TYPE_F32); + + const uint r2 = ne12/ne22; + const uint r3 = ne13/ne23; + + // find the break-even point where the matrix-matrix kernel becomes more efficient compared + // to the matrix-vector kernel + int ne11_mm_min = n_as; + + const int idx = ((int32_t *) dst->op_params)[0]; + + // batch size + GGML_ASSERT(ne01 == ne11); + + // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs + // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel + // !!! + // TODO: for now, always use mat-vec kernels until we figure out how to improve the + // indirect matrix multiplication + // !!! + if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && + ne20 % 32 == 0 && ne20 >= 64 && + ne11 > ne11_mm_min) { + + id pipeline = nil; + + switch (src2->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; + default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:5]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; + [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:7]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:9]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:16]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; + [encoder setBytes:&idx length:sizeof(idx) atIndex:18]; + // TODO: how to make this an array? read Metal docs + for (int j = 0; j < 8; ++j) { + // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 + struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; + + size_t offs_src_cur = 0; + id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); + + [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; + } + + [encoder setThreadgroupMemoryLength:8192 atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + } else { + int nth0 = 32; + int nth1 = 1; + int nrows = 1; + //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + // use custom matrix x vector kernel + switch (src2t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; + } break; + case GGML_TYPE_F16: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + nth0 = 32; + nth1 = 1; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; + } break; + case GGML_TYPE_Q4_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; + } break; + case GGML_TYPE_Q4_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; + } break; + case GGML_TYPE_Q5_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; + } break; + case GGML_TYPE_Q5_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; + } break; + case GGML_TYPE_Q8_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; + } break; + case GGML_TYPE_Q2_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; + } break; + case GGML_TYPE_Q3_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; + } break; + case GGML_TYPE_Q4_K: + { + nth0 = 4; //1; + nth1 = 8; //32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; + } break; + case GGML_TYPE_Q5_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; + } break; + case GGML_TYPE_Q6_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; + } break; + default: + { + GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); + GGML_ASSERT(false && "not implemented"); + } + }; + + if (ggml_is_quantized(src2t)) { + GGML_ASSERT(ne20 >= nth0*nth1); + } + + const int64_t _ne1 = 1; // kernels needs a reference in constant memory + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; + [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6]; + [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8]; + [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; + [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; + [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:20]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:21]; + [encoder setBytes:&idx length:sizeof(idx) atIndex:22]; + // TODO: how to make this an array? read Metal docs + for (int j = 0; j < 8; ++j) { + // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 + struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; + + size_t offs_src_cur = 0; + id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); + + [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; + } + + if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || + src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || + src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { + const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_Q3_K) { +#ifdef GGML_QKK_64 + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#else + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#endif + } + else if (src2t == GGML_TYPE_Q5_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_Q6_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else { + const int64_t ny = (_ne1 + nrows - 1)/nrows; + [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + } + } break; + case GGML_OP_GET_ROWS: + { + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; + [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; + } break; + case GGML_OP_RMS_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + int nth = 32; // SIMD width + + while (nth < ne00/4 && nth < 1024) { + nth *= 2; + } + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + const int64_t nrows = ggml_nrows(src0); + + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_GROUP_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + + //float eps; + //memcpy(&eps, dst->op_params, sizeof(float)); + + const float eps = 1e-6f; // TODO: temporarily hardcoded + + const int32_t n_groups = ((int32_t *) dst->op_params)[0]; + + int nth = 32; // SIMD width + + //while (nth < ne00/4 && nth < 1024) { + // nth *= 2; + //} + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&eps length:sizeof( float) atIndex:9]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_NORM: + { + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + const int nth = MIN(256, ne00); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; + + const int64_t nrows = ggml_nrows(src0); + + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ALIBI: + { + GGML_ASSERT((src0t == GGML_TYPE_F32)); + + const int nth = MIN(1024, ne00); + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); + + const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; + [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; + [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ROPE: + { + GGML_ASSERT(ne10 == ne02); + + const int nth = MIN(1024, ne00); + + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; + default: GGML_ASSERT(false); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; + [encoder setBytes:&mode length:sizeof( int) atIndex:21]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_IM2COL: + { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int32_t N = src1->ne[is_2D ? 3 : 2]; + const int32_t IC = src1->ne[is_2D ? 2 : 1]; + const int32_t IH = is_2D ? src1->ne[1] : 1; + const int32_t IW = src1->ne[0]; + + const int32_t KH = is_2D ? src0->ne[1] : 1; + const int32_t KW = src0->ne[0]; + + const int32_t OH = is_2D ? dst->ne[2] : 1; + const int32_t OW = dst->ne[1]; + + const int32_t CHW = IC * KH * KW; + + const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; + const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; + default: GGML_ASSERT(false); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } break; + case GGML_OP_UPSCALE: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int sf = dst->op_params[0]; + + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_PAD: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ARGSORT: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); + + const int nrows = ggml_nrows(src0); + + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + + id pipeline = nil; + + switch (order) { + case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; + case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; + default: GGML_ASSERT(false); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + + [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)]; + } break; + case GGML_OP_LEAKY_RELU: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + float slope; + memcpy(&slope, dst->op_params, sizeof(float)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_DUP: + case GGML_OP_CPY: + case GGML_OP_CONT: + { + GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); + + int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); + + id pipeline = nil; + + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); + + switch (dstt) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; + //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; + //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + }; + } break; + case GGML_TYPE_F16: + { + switch (dstt) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + }; + } break; + default: GGML_ASSERT(false && "not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + default: + { + GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + GGML_ASSERT(false); + } + } #ifndef GGML_METAL_NDEBUG - [encoder popDebugGroup]; + [encoder popDebugGroup]; #endif - } + } - if (encoder != nil) { - [encoder endEncoding]; - encoder = nil; - } + if (encoder != nil) { + [encoder endEncoding]; + encoder = nil; + } - [command_buffer commit]; - }); - } - - // wait for all threads to finish - dispatch_barrier_sync(ctx->d_queue, ^{}); + [command_buffer commit]; + }); // check status of command buffers // needed to detect if the device ran out-of-memory for example (#1881) From 862f5e41ab1fdf12d6f59455aad3f5dd8258f805 Mon Sep 17 00:00:00 2001 From: Neuman Vong Date: Wed, 17 Jan 2024 00:47:34 +1100 Subject: [PATCH 439/664] android : introduce starter project example (#4926) * Introduce starter project for Android Based on examples/llama.swiftui. * Add github workflow * Set NDK version * Only build arm64-v8a in CI * Sync bench code * Rename CI prop to skip-armeabi-v7a * Remove unused tests --- .github/workflows/build.yml | 25 ++ examples/llama.android/.gitignore | 33 ++ examples/llama.android/README.md | 0 examples/llama.android/app/.gitignore | 1 + examples/llama.android/app/build.gradle.kts | 91 ++++ examples/llama.android/app/proguard-rules.pro | 21 + .../app/src/main/AndroidManifest.xml | 30 ++ .../app/src/main/cpp/CMakeLists.txt | 50 +++ .../app/src/main/cpp/llama-android.cpp | 394 ++++++++++++++++++ .../java/com/example/llama/Downloadable.kt | 119 ++++++ .../src/main/java/com/example/llama/Llm.kt | 172 ++++++++ .../java/com/example/llama/MainActivity.kt | 154 +++++++ .../java/com/example/llama/MainViewModel.kt | 104 +++++ .../java/com/example/llama/ui/theme/Color.kt | 11 + .../java/com/example/llama/ui/theme/Theme.kt | 70 ++++ .../java/com/example/llama/ui/theme/Type.kt | 34 ++ .../res/drawable/ic_launcher_background.xml | 170 ++++++++ .../res/drawable/ic_launcher_foreground.xml | 30 ++ .../main/res/mipmap-anydpi/ic_launcher.xml | 6 + .../res/mipmap-anydpi/ic_launcher_round.xml | 6 + .../src/main/res/mipmap-hdpi/ic_launcher.webp | Bin 0 -> 1404 bytes .../res/mipmap-hdpi/ic_launcher_round.webp | Bin 0 -> 2898 bytes .../src/main/res/mipmap-mdpi/ic_launcher.webp | Bin 0 -> 982 bytes .../res/mipmap-mdpi/ic_launcher_round.webp | Bin 0 -> 1772 bytes .../main/res/mipmap-xhdpi/ic_launcher.webp | Bin 0 -> 1900 bytes .../res/mipmap-xhdpi/ic_launcher_round.webp | Bin 0 -> 3918 bytes .../main/res/mipmap-xxhdpi/ic_launcher.webp | Bin 0 -> 2884 bytes .../res/mipmap-xxhdpi/ic_launcher_round.webp | Bin 0 -> 5914 bytes .../main/res/mipmap-xxxhdpi/ic_launcher.webp | Bin 0 -> 3844 bytes .../res/mipmap-xxxhdpi/ic_launcher_round.webp | Bin 0 -> 7778 bytes .../app/src/main/res/values/colors.xml | 10 + .../app/src/main/res/values/strings.xml | 3 + .../app/src/main/res/values/themes.xml | 5 + .../app/src/main/res/xml/backup_rules.xml | 13 + .../main/res/xml/data_extraction_rules.xml | 19 + examples/llama.android/build.gradle.kts | 5 + examples/llama.android/gradle.properties | 23 + .../gradle/wrapper/gradle-wrapper.jar | Bin 0 -> 59203 bytes .../gradle/wrapper/gradle-wrapper.properties | 6 + examples/llama.android/gradlew | 185 ++++++++ examples/llama.android/settings.gradle.kts | 17 + 41 files changed, 1807 insertions(+) create mode 100644 examples/llama.android/.gitignore create mode 100644 examples/llama.android/README.md create mode 100644 examples/llama.android/app/.gitignore create mode 100644 examples/llama.android/app/build.gradle.kts create mode 100644 examples/llama.android/app/proguard-rules.pro create mode 100644 examples/llama.android/app/src/main/AndroidManifest.xml create mode 100644 examples/llama.android/app/src/main/cpp/CMakeLists.txt create mode 100644 examples/llama.android/app/src/main/cpp/llama-android.cpp create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/Llm.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt create mode 100644 examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml create mode 100644 examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml create mode 100644 examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml create mode 100644 examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml create mode 100644 examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/values/colors.xml create mode 100644 examples/llama.android/app/src/main/res/values/strings.xml create mode 100644 examples/llama.android/app/src/main/res/values/themes.xml create mode 100644 examples/llama.android/app/src/main/res/xml/backup_rules.xml create mode 100644 examples/llama.android/app/src/main/res/xml/data_extraction_rules.xml create mode 100644 examples/llama.android/build.gradle.kts create mode 100644 examples/llama.android/gradle.properties create mode 100644 examples/llama.android/gradle/wrapper/gradle-wrapper.jar create mode 100644 examples/llama.android/gradle/wrapper/gradle-wrapper.properties create mode 100755 examples/llama.android/gradlew create mode 100644 examples/llama.android/settings.gradle.kts diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0a28a1111..367df07a7 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -515,6 +515,31 @@ jobs: - name: Build Xcode project run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build + android-build: + runs-on: ubuntu-latest + + steps: + - name: Clone + uses: actions/checkout@v3 + + - name: Set up JDK + uses: actions/setup-java@v3 + with: + java-version: 17 + distribution: zulu + + - name: Setup Android SDK + uses: android-actions/setup-android@v3 + with: + log-accepted-android-sdk-licenses: false + + - name: Build + run: | + cd examples/llama.android + + # Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820). + ./gradlew build --no-daemon -Pskip-armeabi-v7a + # freeBSD-latest: # runs-on: macos-12 # steps: diff --git a/examples/llama.android/.gitignore b/examples/llama.android/.gitignore new file mode 100644 index 000000000..347e252ef --- /dev/null +++ b/examples/llama.android/.gitignore @@ -0,0 +1,33 @@ +# Gradle files +.gradle/ +build/ + +# Local configuration file (sdk path, etc) +local.properties + +# Log/OS Files +*.log + +# Android Studio generated files and folders +captures/ +.externalNativeBuild/ +.cxx/ +*.apk +output.json + +# IntelliJ +*.iml +.idea/ +misc.xml +deploymentTargetDropDown.xml +render.experimental.xml + +# Keystore files +*.jks +*.keystore + +# Google Services (e.g. APIs or Firebase) +google-services.json + +# Android Profiling +*.hprof diff --git a/examples/llama.android/README.md b/examples/llama.android/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/examples/llama.android/app/.gitignore b/examples/llama.android/app/.gitignore new file mode 100644 index 000000000..796b96d1c --- /dev/null +++ b/examples/llama.android/app/.gitignore @@ -0,0 +1 @@ +/build diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts new file mode 100644 index 000000000..7815a8025 --- /dev/null +++ b/examples/llama.android/app/build.gradle.kts @@ -0,0 +1,91 @@ +plugins { + id("com.android.application") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "com.example.llama" + compileSdk = 34 + + ndkVersion = "26.1.10909125" + + defaultConfig { + applicationId = "com.example.llama" + minSdk = 33 + targetSdk = 34 + versionCode = 1 + versionName = "1.0" + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + vectorDrawables { + useSupportLibrary = true + } + ndk { + // Workaround for https://github.com/llvm/llvm-project/issues/65820 + // affecting armeabi-v7a. Skip armeabi-v7a when invoked with + // -Pskip-armeabi-v7a (e.g., ./gradlew build -Pskip-armeabi-v7a). + if (project.hasProperty("skip-armeabi-v7a")) { + abiFilters += listOf("arm64-v8a", "x86_64", "x86") + } + } + externalNativeBuild { + cmake { + cppFlags += listOf() + arguments += listOf() + } + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + buildFeatures { + compose = true + } + composeOptions { + kotlinCompilerExtensionVersion = "1.5.1" + } + packaging { + resources { + excludes += "/META-INF/{AL2.0,LGPL2.1}" + } + } + externalNativeBuild { + cmake { + path = file("src/main/cpp/CMakeLists.txt") + version = "3.22.1" + } + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2") + implementation("androidx.activity:activity-compose:1.8.2") + implementation(platform("androidx.compose:compose-bom:2023.08.00")) + implementation("androidx.compose.ui:ui") + implementation("androidx.compose.ui:ui-graphics") + implementation("androidx.compose.ui:ui-tooling-preview") + implementation("androidx.compose.material3:material3") + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") + androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00")) + androidTestImplementation("androidx.compose.ui:ui-test-junit4") + debugImplementation("androidx.compose.ui:ui-tooling") + debugImplementation("androidx.compose.ui:ui-test-manifest") +} diff --git a/examples/llama.android/app/proguard-rules.pro b/examples/llama.android/app/proguard-rules.pro new file mode 100644 index 000000000..f1b424510 --- /dev/null +++ b/examples/llama.android/app/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/examples/llama.android/app/src/main/AndroidManifest.xml b/examples/llama.android/app/src/main/AndroidManifest.xml new file mode 100644 index 000000000..41a358a29 --- /dev/null +++ b/examples/llama.android/app/src/main/AndroidManifest.xml @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt new file mode 100644 index 000000000..85139329a --- /dev/null +++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt @@ -0,0 +1,50 @@ + +# For more information about using CMake with Android Studio, read the +# documentation: https://d.android.com/studio/projects/add-native-code.html. +# For more examples on how to use CMake, see https://github.com/android/ndk-samples. + +# Sets the minimum CMake version required for this project. +cmake_minimum_required(VERSION 3.22.1) + +# Declares the project name. The project name can be accessed via ${ PROJECT_NAME}, +# Since this is the top level CMakeLists.txt, the project name is also accessible +# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level +# build script scope). +project("llama-android") + +include(FetchContent) +FetchContent_Declare( + llama + GIT_REPOSITORY https://github.com/ggerganov/llama.cpp + GIT_TAG master +) + +# Also provides "common" +FetchContent_MakeAvailable(llama) + +# Creates and names a library, sets it as either STATIC +# or SHARED, and provides the relative paths to its source code. +# You can define multiple libraries, and CMake builds them for you. +# Gradle automatically packages shared libraries with your APK. +# +# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define +# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME} +# is preferred for the same purpose. +# +# In order to load a library into your app from Java/Kotlin, you must call +# System.loadLibrary() and pass the name of the library defined here; +# for GameActivity/NativeActivity derived applications, the same library name must be +# used in the AndroidManifest.xml file. +add_library(${CMAKE_PROJECT_NAME} SHARED + # List C/C++ source files with relative paths to this CMakeLists.txt. + llama-android.cpp) + +# Specifies libraries CMake should link to your target library. You +# can link libraries from various origins, such as libraries defined in this +# build script, prebuilt third-party libraries, or Android system libraries. +target_link_libraries(${CMAKE_PROJECT_NAME} + # List libraries link to the target library + llama + common + android + log) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp new file mode 100644 index 000000000..d5e705dce --- /dev/null +++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp @@ -0,0 +1,394 @@ +#include +#include +#include +#include +#include +#include +#include "llama.h" +#include "common/common.h" + +// Write C++ code here. +// +// Do not forget to dynamically load the C++ library into your application. +// +// For instance, +// +// In MainActivity.java: +// static { +// System.loadLibrary("llama-android"); +// } +// +// Or, in MainActivity.kt: +// companion object { +// init { +// System.loadLibrary("llama-android") +// } +// } + +#define TAG "llama-android.cpp" +#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__) +#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__) + +jclass la_int_var; +jmethodID la_int_var_value; +jmethodID la_int_var_inc; + +static void log_callback(ggml_log_level level, const char * fmt, void * data) { + if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data); + else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data); + else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data); + else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data); +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { + llama_model_params model_params = llama_model_default_params(); + + auto path_to_model = env->GetStringUTFChars(filename, 0); + LOGi("Loading model from %s", path_to_model); + + auto model = llama_load_model_from_file(path_to_model, model_params); + env->ReleaseStringUTFChars(filename, path_to_model); + + if (!model) { + LOGe("load_model() failed"); + env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed"); + return 0; + } + + return reinterpret_cast(model); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) { + llama_free_model(reinterpret_cast(model)); +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { + auto model = reinterpret_cast(jmodel); + + if (!model) { + LOGe("new_context(): model cannot be null"); + env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null"); + return 0; + } + + int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2)); + LOGi("Using %d threads", n_threads); + + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.seed = 1234; + ctx_params.n_ctx = 2048; + ctx_params.n_threads = n_threads; + ctx_params.n_threads_batch = n_threads; + + llama_context * context = llama_new_context_with_model(model, ctx_params); + + if (!context) { + LOGe("llama_new_context_with_model() returned null)"); + env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), + "llama_new_context_with_model() returned null)"); + return 0; + } + + return reinterpret_cast(context); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) { + llama_free(reinterpret_cast(context)); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) { + llama_backend_free(); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) { + llama_log_set(log_callback, NULL); +} + +extern "C" +JNIEXPORT jstring JNICALL +Java_com_example_llama_Llm_bench_1model( + JNIEnv *env, + jobject, + jlong context_pointer, + jlong model_pointer, + jlong batch_pointer, + jint pp, + jint tg, + jint pl, + jint nr + ) { + auto pp_avg = 0.0; + auto tg_avg = 0.0; + auto pp_std = 0.0; + auto tg_std = 0.0; + + const auto context = reinterpret_cast(context_pointer); + const auto model = reinterpret_cast(model_pointer); + const auto batch = reinterpret_cast(batch_pointer); + + const int n_ctx = llama_n_ctx(context); + + LOGi("n_ctx = %d", n_ctx); + + int i, j; + int nri; + for (nri = 0; nri < nr; nri++) { + LOGi("Benchmark prompt processing (pp)"); + + llama_batch_clear(*batch); + + const int n_tokens = pp; + for (i = 0; i < n_tokens; i++) { + llama_batch_add(*batch, 0, i, { 0 }, false); + } + + batch->logits[batch->n_tokens - 1] = true; + llama_kv_cache_clear(context); + + const auto t_pp_start = ggml_time_us(); + if (llama_decode(context, *batch) != 0) { + LOGi("llama_decode() failed during prompt processing"); + } + const auto t_pp_end = ggml_time_us(); + + // bench text generation + + LOGi("Benchmark text generation (tg)"); + + llama_kv_cache_clear(context); + const auto t_tg_start = ggml_time_us(); + for (i = 0; i < tg; i++) { + + llama_batch_clear(*batch); + for (j = 0; j < pl; j++) { + llama_batch_add(*batch, 0, i, { j }, true); + } + + LOGi("llama_decode() text generation: %d", i); + if (llama_decode(context, *batch) != 0) { + LOGi("llama_decode() failed during text generation"); + } + } + + const auto t_tg_end = ggml_time_us(); + + llama_kv_cache_clear(context); + + const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0; + const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0; + + const auto speed_pp = double(pp) / t_pp; + const auto speed_tg = double(pl * tg) / t_tg; + + pp_avg += speed_pp; + tg_avg += speed_tg; + + pp_std += speed_pp * speed_pp; + tg_std += speed_tg * speed_tg; + + LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg); + } + + pp_avg /= double(nr); + tg_avg /= double(nr); + + if (nr > 1) { + pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1)); + tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1)); + } else { + pp_std = 0; + tg_std = 0; + } + + char model_desc[128]; + llama_model_desc(model, model_desc, sizeof(model_desc)); + + const auto model_size = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0; + const auto model_n_params = double(llama_model_n_params(model)) / 1e9; + + const auto backend = "(Android)"; // TODO: What should this be? + + std::stringstream result; + result << std::setprecision(2); + result << "| model | size | params | backend | test | t/s |\n"; + result << "| --- | --- | --- | --- | --- | --- |\n"; + result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n"; + result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n"; + + return env->NewStringUTF(result.str().c_str()); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { + llama_batch_free(*reinterpret_cast(batch_pointer)); +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { + + // Source: Copy of llama.cpp:llama_batch_init but heap-allocated. + + llama_batch *batch = new llama_batch { + 0, + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + 0, + 0, + 0, + }; + + if (embd) { + batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd); + } else { + batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens); + } + + batch->pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens); + batch->n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens); + batch->seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens); + for (int i = 0; i < n_tokens; ++i) { + batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max); + } + batch->logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens); + + return reinterpret_cast(batch); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) { + llama_backend_init(numa); +} + +extern "C" +JNIEXPORT jstring JNICALL +Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) { + return env->NewStringUTF(llama_print_system_info()); +} + +extern "C" +JNIEXPORT jint JNICALL +Java_com_example_llama_Llm_completion_1init( + JNIEnv *env, + jobject, + jlong context_pointer, + jlong batch_pointer, + jstring jtext, + jint n_len + ) { + + const auto text = env->GetStringUTFChars(jtext, 0); + const auto context = reinterpret_cast(context_pointer); + const auto batch = reinterpret_cast(batch_pointer); + + const auto tokens_list = llama_tokenize(context, text, 1); + + auto n_ctx = llama_n_ctx(context); + auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size()); + + LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req); + + if (n_kv_req > n_ctx) { + LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough"); + } + + for (auto id : tokens_list) { + LOGi("%s", llama_token_to_piece(context, id).c_str()); + } + + llama_batch_clear(*batch); + + // evaluate the initial prompt + for (auto i = 0; i < tokens_list.size(); i++) { + llama_batch_add(*batch, tokens_list[i], i, { 0 }, false); + } + + // llama_decode will output logits only for the last token of the prompt + batch->logits[batch->n_tokens - 1] = true; + + if (llama_decode(context, *batch) != 0) { + LOGe("llama_decode() failed"); + } + + env->ReleaseStringUTFChars(jtext, text); + + return batch->n_tokens; +} + +extern "C" +JNIEXPORT jstring JNICALL +Java_com_example_llama_Llm_completion_1loop( + JNIEnv * env, + jobject, + jlong context_pointer, + jlong batch_pointer, + jint n_len, + jobject intvar_ncur +) { + const auto context = reinterpret_cast(context_pointer); + const auto batch = reinterpret_cast(batch_pointer); + const auto model = llama_get_model(context); + + if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur); + if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I"); + if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V"); + + auto n_vocab = llama_n_vocab(model); + auto logits = llama_get_logits_ith(context, batch->n_tokens - 1); + + std::vector candidates; + candidates.reserve(n_vocab); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + // sample the most likely token + const auto new_token_id = llama_sample_token_greedy(context, &candidates_p); + + const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value); + if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + return env->NewStringUTF(""); + } + + auto new_token_chars = llama_token_to_piece(context, new_token_id); + LOGi("new_token_chars: `%s`", new_token_chars.c_str()); + auto new_token = env->NewStringUTF(new_token_chars.c_str()); + + llama_batch_clear(*batch); + llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true); + + env->CallVoidMethod(intvar_ncur, la_int_var_inc); + + if (llama_decode(context, *batch) != 0) { + LOGe("llama_decode() returned null"); + } + + return new_token; +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { + llama_kv_cache_clear(reinterpret_cast(context)); +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt b/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt new file mode 100644 index 000000000..78c231ae5 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt @@ -0,0 +1,119 @@ +package com.example.llama + +import android.app.DownloadManager +import android.net.Uri +import android.util.Log +import androidx.compose.material3.Button +import androidx.compose.material3.Text +import androidx.compose.runtime.Composable +import androidx.compose.runtime.getValue +import androidx.compose.runtime.mutableDoubleStateOf +import androidx.compose.runtime.mutableStateOf +import androidx.compose.runtime.remember +import androidx.compose.runtime.rememberCoroutineScope +import androidx.compose.runtime.setValue +import androidx.core.database.getLongOrNull +import androidx.core.net.toUri +import kotlinx.coroutines.delay +import kotlinx.coroutines.launch +import java.io.File + +data class Downloadable(val name: String, val source: Uri, val destination: File) { + companion object { + @JvmStatic + private val tag: String? = this::class.qualifiedName + + sealed interface State + data object Ready: State + data class Downloading(val id: Long): State + data class Downloaded(val downloadable: Downloadable): State + data class Error(val message: String): State + + @JvmStatic + @Composable + fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) { + var status: State by remember { + mutableStateOf( + if (item.destination.exists()) Downloaded(item) + else Ready + ) + } + var progress by remember { mutableDoubleStateOf(0.0) } + + val coroutineScope = rememberCoroutineScope() + + suspend fun waitForDownload(result: Downloading, item: Downloadable): State { + while (true) { + val cursor = dm.query(DownloadManager.Query().setFilterById(result.id)) + + if (cursor == null) { + Log.e(tag, "dm.query() returned null") + return Error("dm.query() returned null") + } + + if (!cursor.moveToFirst() || cursor.count < 1) { + cursor.close() + Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?") + return Ready + } + + val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR) + val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES) + val sofar = cursor.getLongOrNull(pix) ?: 0 + val total = cursor.getLongOrNull(tix) ?: 1 + cursor.close() + + if (sofar == total) { + return Downloaded(item) + } + + progress = (sofar * 1.0) / total + + delay(1000L) + } + } + + fun onClick() { + when (val s = status) { + is Downloaded -> { + viewModel.load(item.destination.path) + } + + is Downloading -> { + coroutineScope.launch { + status = waitForDownload(s, item) + } + } + + else -> { + item.destination.delete() + + val request = DownloadManager.Request(item.source).apply { + setTitle("Downloading model") + setDescription("Downloading model: ${item.name}") + setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI) + setDestinationUri(item.destination.toUri()) + } + + viewModel.log("Saving ${item.name} to ${item.destination.path}") + Log.i(tag, "Saving ${item.name} to ${item.destination.path}") + + val id = dm.enqueue(request) + status = Downloading(id) + onClick() + } + } + } + + Button(onClick = { onClick() }, enabled = status !is Downloading) { + when (status) { + is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%") + is Downloaded -> Text("Load ${item.name}") + is Ready -> Text("Download ${item.name}") + is Error -> Text("Download ${item.name}") + } + } + } + + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt new file mode 100644 index 000000000..5f3270372 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt @@ -0,0 +1,172 @@ +package com.example.llama + +import android.util.Log +import kotlinx.coroutines.CoroutineDispatcher +import kotlinx.coroutines.asCoroutineDispatcher +import kotlinx.coroutines.flow.Flow +import kotlinx.coroutines.flow.flow +import kotlinx.coroutines.flow.flowOn +import kotlinx.coroutines.withContext +import java.util.concurrent.Executors +import kotlin.concurrent.thread + +class Llm { + private val tag: String? = this::class.simpleName + + private val threadLocalState: ThreadLocal = ThreadLocal.withInitial { State.Idle } + + private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor { + thread(start = false, name = "Llm-RunLoop") { + Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}") + + // No-op if called more than once. + System.loadLibrary("llama-android") + + // Set llama log handler to Android + log_to_android() + backend_init(false) + + Log.d(tag, system_info()) + + it.run() + }.apply { + uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable -> + Log.e(tag, "Unhandled exception", exception) + } + } + }.asCoroutineDispatcher() + + private val nlen: Int = 64 + + private external fun log_to_android() + private external fun load_model(filename: String): Long + private external fun free_model(model: Long) + private external fun new_context(model: Long): Long + private external fun free_context(context: Long) + private external fun backend_init(numa: Boolean) + private external fun backend_free() + private external fun free_batch(batch: Long) + private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long + private external fun bench_model( + context: Long, + model: Long, + batch: Long, + pp: Int, + tg: Int, + pl: Int, + nr: Int + ): String + + private external fun system_info(): String + + private external fun completion_init( + context: Long, + batch: Long, + text: String, + nLen: Int + ): Int + + private external fun completion_loop( + context: Long, + batch: Long, + nLen: Int, + ncur: IntVar + ): String + + private external fun kv_cache_clear(context: Long) + + suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String { + return withContext(runLoop) { + when (val state = threadLocalState.get()) { + is State.Loaded -> { + Log.d(tag, "bench(): $state") + bench_model(state.context, state.model, state.batch, pp, tg, pl, nr) + } + + else -> throw IllegalStateException("No model loaded") + } + } + } + + suspend fun load(pathToModel: String) { + withContext(runLoop) { + when (threadLocalState.get()) { + is State.Idle -> { + val model = load_model(pathToModel) + if (model == 0L) throw IllegalStateException("load_model() failed") + + val context = new_context(model) + if (context == 0L) throw IllegalStateException("new_context() failed") + + val batch = new_batch(512, 0, 1) + if (batch == 0L) throw IllegalStateException("new_batch() failed") + + Log.i(tag, "Loaded model $pathToModel") + threadLocalState.set(State.Loaded(model, context, batch)) + } + else -> throw IllegalStateException("Model already loaded") + } + } + } + + fun send(message: String): Flow = flow { + when (val state = threadLocalState.get()) { + is State.Loaded -> { + val ncur = IntVar(completion_init(state.context, state.batch, message, nlen)) + while (ncur.value <= nlen) { + val str = completion_loop(state.context, state.batch, nlen, ncur) + if (str.isEmpty()) { + break + } + emit(str) + } + kv_cache_clear(state.context) + } + else -> {} + } + }.flowOn(runLoop) + + /** + * Unloads the model and frees resources. + * + * This is a no-op if there's no model loaded. + */ + suspend fun unload() { + withContext(runLoop) { + when (val state = threadLocalState.get()) { + is State.Loaded -> { + free_context(state.context) + free_model(state.model) + free_batch(state.batch) + + threadLocalState.set(State.Idle) + } + else -> {} + } + } + } + + companion object { + private class IntVar(value: Int) { + @Volatile + var value: Int = value + private set + + fun inc() { + synchronized(this) { + value += 1 + } + } + } + + private sealed interface State { + data object Idle: State + data class Loaded(val model: Long, val context: Long, val batch: Long): State + } + + // Enforce only one instance of Llm. + private val _instance: Llm = Llm() + + fun instance(): Llm = _instance + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt new file mode 100644 index 000000000..9da04f7d3 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt @@ -0,0 +1,154 @@ +package com.example.llama + +import android.app.ActivityManager +import android.app.DownloadManager +import android.content.ClipData +import android.content.ClipboardManager +import android.net.Uri +import android.os.Bundle +import android.os.StrictMode +import android.os.StrictMode.VmPolicy +import android.text.format.Formatter +import androidx.activity.ComponentActivity +import androidx.activity.compose.setContent +import androidx.activity.viewModels +import androidx.compose.foundation.layout.Box +import androidx.compose.foundation.layout.Column +import androidx.compose.foundation.layout.Row +import androidx.compose.foundation.layout.fillMaxSize +import androidx.compose.foundation.layout.padding +import androidx.compose.foundation.lazy.LazyColumn +import androidx.compose.foundation.lazy.items +import androidx.compose.foundation.lazy.rememberLazyListState +import androidx.compose.material3.Button +import androidx.compose.material3.LocalContentColor +import androidx.compose.material3.MaterialTheme +import androidx.compose.material3.OutlinedTextField +import androidx.compose.material3.Surface +import androidx.compose.material3.Text +import androidx.compose.runtime.Composable +import androidx.compose.ui.Modifier +import androidx.compose.ui.unit.dp +import androidx.core.content.getSystemService +import com.example.llama.ui.theme.LlamaAndroidTheme +import java.io.File + +class MainActivity( + activityManager: ActivityManager? = null, + downloadManager: DownloadManager? = null, + clipboardManager: ClipboardManager? = null, +): ComponentActivity() { + private val tag: String? = this::class.simpleName + + private val activityManager by lazy { activityManager ?: getSystemService()!! } + private val downloadManager by lazy { downloadManager ?: getSystemService()!! } + private val clipboardManager by lazy { clipboardManager ?: getSystemService()!! } + + private val viewModel: MainViewModel by viewModels() + + // Get a MemoryInfo object for the device's current memory status. + private fun availableMemory(): ActivityManager.MemoryInfo { + return ActivityManager.MemoryInfo().also { memoryInfo -> + activityManager.getMemoryInfo(memoryInfo) + } + } + + override fun onCreate(savedInstanceState: Bundle?) { + super.onCreate(savedInstanceState) + + StrictMode.setVmPolicy( + VmPolicy.Builder(StrictMode.getVmPolicy()) + .detectLeakedClosableObjects() + .build() + ) + + val free = Formatter.formatFileSize(this, availableMemory().availMem) + val total = Formatter.formatFileSize(this, availableMemory().totalMem) + + viewModel.log("Current memory: $free / $total") + viewModel.log("Downloads directory: ${getExternalFilesDir(null)}") + + val extFilesDir = getExternalFilesDir(null) + + val models = listOf( + Downloadable( + "Phi-2 7B (Q4_0, 1.6 GiB)", + Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"), + File(extFilesDir, "phi-2-q4_0.gguf"), + ), + Downloadable( + "TinyLlama 1.1B (f16, 2.2 GiB)", + Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"), + File(extFilesDir, "tinyllama-1.1-f16.gguf"), + ), + Downloadable( + "Phi 2 DPO (Q3_K_M, 1.48 GiB)", + Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"), + File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf") + ), + ) + + setContent { + LlamaAndroidTheme { + // A surface container using the 'background' color from the theme + Surface( + modifier = Modifier.fillMaxSize(), + color = MaterialTheme.colorScheme.background + ) { + MainCompose( + viewModel, + clipboardManager, + downloadManager, + models, + ) + } + + } + } + } +} + +@Composable +fun MainCompose( + viewModel: MainViewModel, + clipboard: ClipboardManager, + dm: DownloadManager, + models: List +) { + Column { + val scrollState = rememberLazyListState() + + Box(modifier = Modifier.weight(1f)) { + LazyColumn(state = scrollState) { + items(viewModel.messages) { + Text( + it, + style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current), + modifier = Modifier.padding(16.dp) + ) + } + } + } + OutlinedTextField( + value = viewModel.message, + onValueChange = { viewModel.updateMessage(it) }, + label = { Text("Message") }, + ) + Row { + Button({ viewModel.send() }) { Text("Send") } + Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") } + Button({ viewModel.clear() }) { Text("Clear") } + Button({ + viewModel.messages.joinToString("\n").let { + clipboard.setPrimaryClip(ClipData.newPlainText("", it)) + } + }) { Text("Copy") } + } + + Column { + for (model in models) { + Downloadable.Button(viewModel, dm, model) + } + } + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt new file mode 100644 index 000000000..be95e2221 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -0,0 +1,104 @@ +package com.example.llama + +import android.util.Log +import androidx.compose.runtime.getValue +import androidx.compose.runtime.mutableStateOf +import androidx.compose.runtime.setValue +import androidx.lifecycle.ViewModel +import androidx.lifecycle.viewModelScope +import kotlinx.coroutines.flow.catch +import kotlinx.coroutines.launch + +class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { + companion object { + @JvmStatic + private val NanosPerSecond = 1_000_000_000.0 + } + + private val tag: String? = this::class.simpleName + + var messages by mutableStateOf(listOf("Initializing...")) + private set + + var message by mutableStateOf("") + private set + + override fun onCleared() { + super.onCleared() + + viewModelScope.launch { + try { + llm.unload() + } catch (exc: IllegalStateException) { + messages += exc.message!! + } + } + } + + fun send() { + val text = message + message = "" + + // Add to messages console. + messages += text + messages += "" + + viewModelScope.launch { + llm.send(text) + .catch { + Log.e(tag, "send() failed", it) + messages += it.message!! + } + .collect { messages = messages.dropLast(1) + (messages.last() + it) } + } + } + + fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) { + viewModelScope.launch { + try { + val start = System.nanoTime() + val warmupResult = llm.bench(pp, tg, pl, nr) + val end = System.nanoTime() + + messages += warmupResult + + val warmup = (end - start).toDouble() / NanosPerSecond + messages += "Warm up time: $warmup seconds, please wait..." + + if (warmup > 5.0) { + messages += "Warm up took too long, aborting benchmark" + return@launch + } + + messages += llm.bench(512, 128, 1, 3) + } catch (exc: IllegalStateException) { + Log.e(tag, "bench() failed", exc) + messages += exc.message!! + } + } + } + + fun load(pathToModel: String) { + viewModelScope.launch { + try { + llm.load(pathToModel) + messages += "Loaded $pathToModel" + } catch (exc: IllegalStateException) { + Log.e(tag, "load() failed", exc) + messages += exc.message!! + } + } + } + + fun updateMessage(newMessage: String) { + message = newMessage + } + + fun clear() { + messages = listOf() + } + + fun log(message: String) { + messages += message + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt new file mode 100644 index 000000000..40c30e8d9 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt @@ -0,0 +1,11 @@ +package com.example.llama.ui.theme + +import androidx.compose.ui.graphics.Color + +val Purple80 = Color(0xFFD0BCFF) +val PurpleGrey80 = Color(0xFFCCC2DC) +val Pink80 = Color(0xFFEFB8C8) + +val Purple40 = Color(0xFF6650a4) +val PurpleGrey40 = Color(0xFF625b71) +val Pink40 = Color(0xFF7D5260) diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt new file mode 100644 index 000000000..e742220a8 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt @@ -0,0 +1,70 @@ +package com.example.llama.ui.theme + +import android.app.Activity +import android.os.Build +import androidx.compose.foundation.isSystemInDarkTheme +import androidx.compose.material3.MaterialTheme +import androidx.compose.material3.darkColorScheme +import androidx.compose.material3.dynamicDarkColorScheme +import androidx.compose.material3.dynamicLightColorScheme +import androidx.compose.material3.lightColorScheme +import androidx.compose.runtime.Composable +import androidx.compose.runtime.SideEffect +import androidx.compose.ui.graphics.toArgb +import androidx.compose.ui.platform.LocalContext +import androidx.compose.ui.platform.LocalView +import androidx.core.view.WindowCompat + +private val DarkColorScheme = darkColorScheme( + primary = Purple80, + secondary = PurpleGrey80, + tertiary = Pink80 +) + +private val LightColorScheme = lightColorScheme( + primary = Purple40, + secondary = PurpleGrey40, + tertiary = Pink40 + + /* Other default colors to override + background = Color(0xFFFFFBFE), + surface = Color(0xFFFFFBFE), + onPrimary = Color.White, + onSecondary = Color.White, + onTertiary = Color.White, + onBackground = Color(0xFF1C1B1F), + onSurface = Color(0xFF1C1B1F), + */ +) + +@Composable +fun LlamaAndroidTheme( + darkTheme: Boolean = isSystemInDarkTheme(), + // Dynamic color is available on Android 12+ + dynamicColor: Boolean = true, + content: @Composable () -> Unit +) { + val colorScheme = when { + dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> { + val context = LocalContext.current + if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context) + } + + darkTheme -> DarkColorScheme + else -> LightColorScheme + } + val view = LocalView.current + if (!view.isInEditMode) { + SideEffect { + val window = (view.context as Activity).window + window.statusBarColor = colorScheme.primary.toArgb() + WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme + } + } + + MaterialTheme( + colorScheme = colorScheme, + typography = Typography, + content = content + ) +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt new file mode 100644 index 000000000..0b87946ca --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt @@ -0,0 +1,34 @@ +package com.example.llama.ui.theme + +import androidx.compose.material3.Typography +import androidx.compose.ui.text.TextStyle +import androidx.compose.ui.text.font.FontFamily +import androidx.compose.ui.text.font.FontWeight +import androidx.compose.ui.unit.sp + +// Set of Material typography styles to start with +val Typography = Typography( + bodyLarge = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Normal, + fontSize = 16.sp, + lineHeight = 24.sp, + letterSpacing = 0.5.sp + ) + /* Other default text styles to override + titleLarge = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Normal, + fontSize = 22.sp, + lineHeight = 28.sp, + letterSpacing = 0.sp + ), + labelSmall = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Medium, + fontSize = 11.sp, + lineHeight = 16.sp, + letterSpacing = 0.5.sp + ) + */ +) diff --git a/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml b/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml new file mode 100644 index 000000000..07d5da9cb --- /dev/null +++ b/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml @@ -0,0 +1,170 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml b/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml new file mode 100644 index 000000000..7706ab9e6 --- /dev/null +++ b/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml @@ -0,0 +1,30 @@ + + + + + + + + + + + diff --git a/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml 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UmY2RIYtNPVDpE$%vda+HD#3m z&VuXJ{BK&Qe+rBa7eq}Q(bq|tn(RrJAk|ztj2(i{d>nmQnM?;HF2k&9sA6up5tmjl z7lySlzMbifH17-m-Lwa_F&e7nOH?ESi3#ckR3tsM+jsck3`oG!uMS}|eAwVXv>}qxwq?QY%QJ0}r@^;fhuUA9W z*BVl>TGo&N004@xSiwDUXUvp51sVmqO3m)=B55aPwf@0=e}cN+$-BdKxY`YrT_4)0 z_d10#i44Q*rFr8MC>*)v$EJvz``(pb{e&*6k+b zsMz%($|1+8hn8c2?P(l@;Rb&CsZeYoCI3?2!LqjbwPXW3z4G$Qfj=cT5Yb%vY0(AX oeb?AaKtwrnc|$|zzw9vfvn^aJJ!zd)XFXqqy0000001=f@-~a#s literal 0 HcmV?d00001 diff --git a/examples/llama.android/app/src/main/res/values/colors.xml b/examples/llama.android/app/src/main/res/values/colors.xml new file mode 100644 index 000000000..ca1931bca --- /dev/null +++ b/examples/llama.android/app/src/main/res/values/colors.xml @@ -0,0 +1,10 @@ + + + #FFBB86FC + #FF6200EE + #FF3700B3 + #FF03DAC5 + #FF018786 + #FF000000 + #FFFFFFFF + diff --git a/examples/llama.android/app/src/main/res/values/strings.xml b/examples/llama.android/app/src/main/res/values/strings.xml new file mode 100644 index 000000000..7a9d314e2 --- /dev/null +++ b/examples/llama.android/app/src/main/res/values/strings.xml @@ -0,0 +1,3 @@ + + LlamaAndroid + diff --git a/examples/llama.android/app/src/main/res/values/themes.xml b/examples/llama.android/app/src/main/res/values/themes.xml new file mode 100644 index 000000000..8a24fda56 --- /dev/null +++ b/examples/llama.android/app/src/main/res/values/themes.xml @@ -0,0 +1,5 @@ + + + + + + + + + +

+
+ + + + +)LITERAL"; +unsigned int index_html_len = sizeof(index_html); diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp index e09b3c8c5..647abe116 100644 --- a/examples/server/index.js.hpp +++ b/examples/server/index.js.hpp @@ -1,1903 +1,4 @@ -unsigned char index_js[] = { - 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x74, 0x28, 0x29, - 0x7b, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x45, - 0x72, 0x72, 0x6f, 0x72, 0x28, 0x22, 0x43, 0x79, 0x63, 0x6c, 0x65, 0x20, - 0x64, 0x65, 0x74, 0x65, 0x63, 0x74, 0x65, 0x64, 0x22, 0x29, 0x7d, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x53, 0x79, 0x6d, 0x62, 0x6f, - 0x6c, 0x2e, 0x66, 0x6f, 0x72, 0x28, 0x22, 0x70, 0x72, 0x65, 0x61, 0x63, - 0x74, 0x2d, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x73, 0x22, 0x29, 0x3b, - 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x65, 0x28, 0x29, - 0x7b, 0x69, 0x66, 0x28, 0x66, 0x3e, 0x31, 0x29, 0x7b, 0x66, 0x2d, 0x2d, - 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', + integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', + string: ` "\\"" ( + [^"\\\\] | + "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\\"" space`, + null: '"null" space', }; -unsigned int json_schema_to_grammar_mjs_len = 3695; + +const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; +const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; +const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; + +export class SchemaConverter { + constructor(propOrder) { + this._propOrder = propOrder || {}; + this._rules = new Map(); + this._rules.set('space', SPACE_RULE); + } + + _formatLiteral(literal) { + const escaped = JSON.stringify(literal).replace( + GRAMMAR_LITERAL_ESCAPE_RE, + m => GRAMMAR_LITERAL_ESCAPES[m] + ); + return `"${escaped}"`; + } + + _addRule(name, rule) { + let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); + let key = escName; + + if (this._rules.has(escName)) { + if (this._rules.get(escName) === rule) { + return key; + } + + let i = 0; + while (this._rules.has(`${escName}${i}`)) { + i += 1; + } + key = `${escName}${i}`; + } + + this._rules.set(key, rule); + return key; + } + + visit(schema, name) { + const schemaType = schema.type; + const ruleName = name || 'root'; + + if (schema.oneOf || schema.anyOf) { + const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => + this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) + ).join(' | '); + + return this._addRule(ruleName, rule); + } else if ('const' in schema) { + return this._addRule(ruleName, this._formatLiteral(schema.const)); + } else if ('enum' in schema) { + const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); + return this._addRule(ruleName, rule); + } else if (schemaType === 'object' && 'properties' in schema) { + // TODO: `required` keyword (from python implementation) + const propOrder = this._propOrder; + const propPairs = Object.entries(schema.properties).sort((a, b) => { + // sort by position in prop_order (if specified) then by key + const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; + const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; + return orderA - orderB || a[0].localeCompare(b[0]); + }); + + let rule = '"{" space'; + propPairs.forEach(([propName, propSchema], i) => { + const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); + if (i > 0) { + rule += ' "," space'; + } + rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; + }); + rule += ' "}" space'; + + return this._addRule(ruleName, rule); + } else if (schemaType === 'array' && 'items' in schema) { + // TODO `prefixItems` keyword (from python implementation) + const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); + const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; + return this._addRule(ruleName, rule); + } else { + if (!PRIMITIVE_RULES[schemaType]) { + throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); + } + return this._addRule( + ruleName === 'root' ? 'root' : schemaType, + PRIMITIVE_RULES[schemaType] + ); + } + } + + formatGrammar() { + let grammar = ''; + this._rules.forEach((rule, name) => { + grammar += `${name} ::= ${rule}\n`; + }); + return grammar; + } +} +)LITERAL"; +unsigned int json_schema_to_grammar_mjs_len = sizeof(json_schema_to_grammar_mjs); From e6f291d15844398f8326940fe5ad7f2e02b5aa56 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 20:17:30 +0200 Subject: [PATCH 578/664] server : fix context shift (#5195) * server : fix context shift + simplify self-extend * server : take system_tokens into account * server : more n_past fixes * server : rever n_past_se changes --- examples/server/chat.sh | 1 + examples/server/server.cpp | 109 ++++++++++++++++++++----------------- 2 files changed, 60 insertions(+), 50 deletions(-) diff --git a/examples/server/chat.sh b/examples/server/chat.sh index 014360121..da0a6ca68 100755 --- a/examples/server/chat.sh +++ b/examples/server/chat.sh @@ -48,6 +48,7 @@ chat_completion() { top_p: 0.9, n_keep: $n_keep, n_predict: 256, + cache_prompt: true, stop: ["\n### Human:"], stream: true }')" diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 11dd82c33..21bdce8ed 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -185,7 +185,7 @@ struct llama_client_slot llama_sampling_context *ctx_sampling = nullptr; int32_t ga_i = 0; // group-attention state - int32_t ga_n = 1;// group-attention factor + int32_t ga_n = 1; // group-attention factor int32_t ga_w = 512; // group-attention width int32_t n_past_se = 0; // self-extend @@ -219,7 +219,8 @@ struct llama_client_slot sent_token_probs_index = 0; infill = false; ga_i = 0; - n_past_se = 0; + n_past_se = 0; + generated_token_probs.clear(); for (slot_image & img : images) @@ -1227,7 +1228,7 @@ struct llama_server_context std::vector append_tokens = tokenize(json_prompt, false); // has next image for (int i = 0; i < (int) append_tokens.size(); ++i) { - llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); + llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true); slot.n_past += 1; } } @@ -1295,6 +1296,8 @@ struct llama_server_context for (llama_client_slot &slot : slots) { slot.cache_tokens.clear(); + slot.n_past = 0; + slot.n_past_se = 0; } } @@ -1364,26 +1367,26 @@ struct llama_server_context kv_cache_clear(); } return true; - } else { - task_server task; - task.type = TASK_TYPE_NEXT_RESPONSE; - task.target_id = -1; - queue_tasks.post(task); } + task_server task; + task.type = TASK_TYPE_NEXT_RESPONSE; + task.target_id = -1; + queue_tasks.post(task); + for (llama_client_slot &slot : slots) { if (slot.ga_n == 1) { - if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) + if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) { // Shift context - const int n_left = slot.n_past - slot.params.n_keep - 1; + const int n_left = system_tokens.size() + slot.n_past - slot.params.n_keep - 1; const int n_discard = n_left / 2; LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard); for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1429,8 +1432,10 @@ struct llama_server_context slot.i_batch = batch.n_tokens; const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + // TODO: we always have to take into account the "system_tokens" + // this is not great and needs to be improved somehow + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); slot.n_past += 1; } @@ -1481,8 +1486,8 @@ struct llama_server_context prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } @@ -1582,8 +1587,8 @@ struct llama_server_context } LOG_VERBOSE("prompt ingested", { - {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"n_past", slot.n_past}, + {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); @@ -1591,10 +1596,13 @@ struct llama_server_context // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; + int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; - int ga_i = slot.ga_i; + + int32_t ga_i = slot.ga_i; int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { if (slot.ga_n != 1) @@ -1606,7 +1614,7 @@ struct llama_server_context } } llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); - slot_npast += 1; + slot_npast++; } if (has_images && !ingest_images(slot, n_batch)) @@ -1666,6 +1674,7 @@ struct llama_server_context slot.n_past_se += n_tokens; } } + llama_batch batch_view = { n_tokens, @@ -1782,51 +1791,51 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); + printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" --numa attempt optimizations that help on some NUMA systems\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); + printf(" number of layers to store in VRAM\n"); printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); #endif printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); + printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); - printf(" set an alias for the model, will be added as `model` field in completion response\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); - printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); - printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); - printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); - printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); - printf(" -spf FNAME, --system-prompt-file FNAME\n"); - printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); - printf(" --log-disable disables logging to a file.\n"); + printf(" set an alias for the model, will be added as `model` field in completion response\n"); + printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); + printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); + printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); + printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); + printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); + printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); + printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); + printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); + printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); + printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -spf FNAME, --system-prompt-file FNAME\n"); + printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); + printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); + printf(" --log-disable disables logging to a file.\n"); printf("\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); + printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); printf("\n"); } From e0085fdf7c758f0bc2746fc106fb29dd9df959de Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 21:19:26 +0200 Subject: [PATCH 579/664] Revert "server : change deps.sh xxd files to string literals (#5221)" This reverts commit 4003be0e5feef320f3707786f22722b73cff9356. --- examples/server/completion.js.hpp | 651 ++- examples/server/deps.sh | 11 +- examples/server/index.html.hpp | 3829 ++++++++++++----- examples/server/index.js.hpp | 1907 +++++++- .../server/json-schema-to-grammar.mjs.hpp | 424 +- 5 files changed, 5454 insertions(+), 1368 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 5609ee3bf..fe5f81228 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -1,204 +1,449 @@ -const char completion_js[] = R"LITERAL( -const paramDefaults = { - stream: true, - n_predict: 500, - temperature: 0.2, - stop: [""] +unsigned char completion_js[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72, + 0x75, 0x65, 0x2c, 0x0a, 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0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x67, 0x65, 0x6e, 0x65, + 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, + 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f, 0x6d, 0x6f, 0x64, 0x65, + 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22, 0x29, 0x2e, 0x74, 0x68, 0x65, + 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72, 0x2e, 0x6a, 0x73, 0x6f, + 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, + 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, + 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; - -let generation_settings = null; - - -// Completes the prompt as a generator. Recommended for most use cases. -// -// Example: -// -// import { llama } from '/completion.js' -// -// const request = llama("Tell me a joke", {n_predict: 800}) -// for await (const chunk of request) { -// document.write(chunk.data.content) -// } -// -export async function* llama(prompt, params = {}, config = {}) { - let controller = config.controller; - - if (!controller) { - controller = new AbortController(); - } - - const completionParams = { ...paramDefaults, ...params, prompt }; - - const response = await fetch("/completion", { - method: 'POST', - body: JSON.stringify(completionParams), - headers: { - 'Connection': 'keep-alive', - 'Content-Type': 'application/json', - 'Accept': 'text/event-stream', - ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) - }, - signal: controller.signal, - }); - - const reader = response.body.getReader(); - const decoder = new TextDecoder(); - - let content = ""; - let leftover = ""; // Buffer for partially read lines - - try { - let cont = true; - - while (cont) { - const result = await reader.read(); - if (result.done) { - break; - } - - // Add any leftover data to the current chunk of data - const text = leftover + decoder.decode(result.value); - - // Check if the last character is a line break - const endsWithLineBreak = text.endsWith('\n'); - - // Split the text into lines - let lines = text.split('\n'); - - // If the text doesn't end with a line break, then the last line is incomplete - // Store it in leftover to be added to the next chunk of data - if (!endsWithLineBreak) { - leftover = lines.pop(); - } else { - leftover = ""; // Reset leftover if we have a line break at the end - } - - // Parse all sse events and add them to result - const regex = /^(\S+):\s(.*)$/gm; - for (const line of lines) { - const match = regex.exec(line); - if (match) { - result[match[1]] = match[2] - // since we know this is llama.cpp, let's just decode the json in data - if (result.data) { - result.data = JSON.parse(result.data); - content += result.data.content; - - // yield - yield result; - - // if we got a stop token from server, we will break here - if (result.data.stop) { - if (result.data.generation_settings) { - generation_settings = result.data.generation_settings; - } - cont = false; - break; - } - } - if (result.error) { - result.error = JSON.parse(result.error); - if (result.error.content.includes('slot unavailable')) { - // Throw an error to be caught by upstream callers - throw new Error('slot unavailable'); - } else { - console.error(`llama.cpp error: ${result.error.content}`); - } - } - if (result.error) { - result.error = JSON.parse(result.error); - console.error(`llama.cpp error: ${result.error.content}`); - } - } - } - } - } catch (e) { - if (e.name !== 'AbortError') { - console.error("llama error: ", e); - } - throw e; - } - finally { - controller.abort(); - } - - return content; -} - -// Call llama, return an event target that you can subscribe to -// -// Example: -// -// import { llamaEventTarget } from '/completion.js' -// -// const conn = llamaEventTarget(prompt) -// conn.addEventListener("message", (chunk) => { -// document.write(chunk.detail.content) -// }) -// -export const llamaEventTarget = (prompt, params = {}, config = {}) => { - const eventTarget = new EventTarget(); - (async () => { - let content = ""; - for await (const chunk of llama(prompt, params, config)) { - if (chunk.data) { - content += chunk.data.content; - eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); - } - if (chunk.data.generation_settings) { - eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); - } - if (chunk.data.timings) { - eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); - } - } - eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); - })(); - return eventTarget; -} - -// Call llama, return a promise that resolves to the completed text. This does not support streaming -// -// Example: -// -// llamaPromise(prompt).then((content) => { -// document.write(content) -// }) -// -// or -// -// const content = await llamaPromise(prompt) -// document.write(content) -// -export const llamaPromise = (prompt, params = {}, config = {}) => { - return new Promise(async (resolve, reject) => { - let content = ""; - try { - for await (const chunk of llama(prompt, params, config)) { - content += chunk.data.content; - } - resolve(content); - } catch (error) { - reject(error); - } - }); -}; - -/** - * (deprecated) - */ -export const llamaComplete = async (params, controller, callback) => { - for await (const chunk of llama(params.prompt, params, { controller })) { - callback(chunk); - } -} - -// Get the model info from the server. This is useful for getting the context window and so on. -export const llamaModelInfo = async () => { - if (!generation_settings) { - generation_settings = await fetch("/model.json").then(r => r.json()); - } - return generation_settings; -} -)LITERAL"; -unsigned int completion_js_len = sizeof(completion_js); +unsigned int completion_js_len = 5346; diff --git a/examples/server/deps.sh b/examples/server/deps.sh index c0a9de9f9..ea23e6450 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -15,13 +15,6 @@ cd $PUBLIC for FILE in $FILES; do echo "generate $FILE.hpp" - # Use C++11 string literals instead of ugly xxd. - f=$(echo $FILE | sed 's/\./_/g' -e 's/-/_/g') - echo "const char $f[] = R\"LITERAL(" > $DIR/$FILE.hpp - cat $FILE >> $DIR/$FILE.hpp - echo ")LITERAL\";" >> $DIR/$FILE.hpp - echo "unsigned int ${f}_len = sizeof($f);" >> $DIR/$FILE.hpp - - #Deprecated old xxd - #xxd -i $FILE > $DIR/$FILE.hpp + # use simple flag for old version of xxd + xxd -i $FILE > $DIR/$FILE.hpp done diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 603d12068..20551520e 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -1,1038 +1,2791 @@ -const char index_html[] = R"LITERAL( - - - - - - - llama.cpp - chat - - - - - - - -
- -
-
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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', - string: ` "\\"" ( - [^"\\\\] | - "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\\"" space`, - null: '"null" space', +unsigned char json_schema_to_grammar_mjs[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x53, 0x50, 0x41, 0x43, 0x45, 0x5f, + 0x52, 0x55, 0x4c, 0x45, 0x20, 0x3d, 0x20, 0x27, 0x22, 0x20, 0x22, 0x3f, + 0x27, 0x3b, 0x0a, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x50, 0x52, + 0x49, 0x4d, 0x49, 0x54, 0x49, 0x56, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, + 0x53, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x62, 0x6f, 0x6f, 0x6c, + 0x65, 0x61, 0x6e, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x74, 0x72, 0x75, 0x65, + 0x22, 0x20, 0x7c, 0x20, 0x22, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x22, 0x29, + 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x6e, + 0x75, 0x6d, 0x62, 0x65, 0x72, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x2d, 0x22, + 0x3f, 0x20, 0x28, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x20, 0x7c, 0x20, 0x5b, + 0x31, 0x2d, 0x39, 0x5d, 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0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a }; - -const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; -const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; -const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; - -export class SchemaConverter { - constructor(propOrder) { - this._propOrder = propOrder || {}; - this._rules = new Map(); - this._rules.set('space', SPACE_RULE); - } - - _formatLiteral(literal) { - const escaped = JSON.stringify(literal).replace( - GRAMMAR_LITERAL_ESCAPE_RE, - m => GRAMMAR_LITERAL_ESCAPES[m] - ); - return `"${escaped}"`; - } - - _addRule(name, rule) { - let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); - let key = escName; - - if (this._rules.has(escName)) { - if (this._rules.get(escName) === rule) { - return key; - } - - let i = 0; - while (this._rules.has(`${escName}${i}`)) { - i += 1; - } - key = `${escName}${i}`; - } - - this._rules.set(key, rule); - return key; - } - - visit(schema, name) { - const schemaType = schema.type; - const ruleName = name || 'root'; - - if (schema.oneOf || schema.anyOf) { - const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => - this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) - ).join(' | '); - - return this._addRule(ruleName, rule); - } else if ('const' in schema) { - return this._addRule(ruleName, this._formatLiteral(schema.const)); - } else if ('enum' in schema) { - const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); - return this._addRule(ruleName, rule); - } else if (schemaType === 'object' && 'properties' in schema) { - // TODO: `required` keyword (from python implementation) - const propOrder = this._propOrder; - const propPairs = Object.entries(schema.properties).sort((a, b) => { - // sort by position in prop_order (if specified) then by key - const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; - const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; - return orderA - orderB || a[0].localeCompare(b[0]); - }); - - let rule = '"{" space'; - propPairs.forEach(([propName, propSchema], i) => { - const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); - if (i > 0) { - rule += ' "," space'; - } - rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; - }); - rule += ' "}" space'; - - return this._addRule(ruleName, rule); - } else if (schemaType === 'array' && 'items' in schema) { - // TODO `prefixItems` keyword (from python implementation) - const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); - const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; - return this._addRule(ruleName, rule); - } else { - if (!PRIMITIVE_RULES[schemaType]) { - throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); - } - return this._addRule( - ruleName === 'root' ? 'root' : schemaType, - PRIMITIVE_RULES[schemaType] - ); - } - } - - formatGrammar() { - let grammar = ''; - this._rules.forEach((rule, name) => { - grammar += `${name} ::= ${rule}\n`; - }); - return grammar; - } -} -)LITERAL"; -unsigned int json_schema_to_grammar_mjs_len = sizeof(json_schema_to_grammar_mjs); +unsigned int json_schema_to_grammar_mjs_len = 3695; From e8dc55d0065d076d4c20f3c4bfca562701b4edfe Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 30 Jan 2024 19:04:37 -0500 Subject: [PATCH 580/664] kompute : llama-bench support and ggml_cpu_has_kompute() (#5226) --- common/common.cpp | 1 + examples/llama-bench/llama-bench.cpp | 15 +++++++++++---- ggml.c | 11 ++++++++++- ggml.h | 1 + llama.cpp | 5 ----- 5 files changed, 23 insertions(+), 10 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 288013676..0dd1c50cf 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1521,6 +1521,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); + fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index f239415d3..542cc7bb8 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -563,6 +563,7 @@ struct test { static const bool cuda; static const bool opencl; static const bool vulkan; + static const bool kompute; static const bool metal; static const bool gpu_blas; static const bool blas; @@ -647,6 +648,9 @@ struct test { if (vulkan) { return "Vulkan"; } + if (kompute) { + return "Kompute"; + } if (metal) { return "Metal"; } @@ -662,7 +666,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", @@ -686,8 +690,9 @@ struct test { field == "avg_ns" || field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "opencl" || field == "vulkan"|| field == "metal" || field == "gpu_blas" || field == "blas" || - field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") { + if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || + field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || + field == "mul_mat_q") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -714,7 +719,8 @@ struct test { } std::vector values = { build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), + std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), @@ -743,6 +749,7 @@ const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cublas(); const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::vulkan = !!ggml_cpu_has_vulkan(); +const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); diff --git a/ggml.c b/ggml.c index a7a9ea319..b2c8baaa8 100644 --- a/ggml.c +++ b/ggml.c @@ -20473,6 +20473,14 @@ int ggml_cpu_has_vulkan(void) { #endif } +int ggml_cpu_has_kompute(void) { +#if defined(GGML_USE_KOMPUTE) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_sycl(void) { #if defined(GGML_USE_SYCL) return 1; @@ -20482,7 +20490,8 @@ int ggml_cpu_has_sycl(void) { } int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_sycl(); + return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || + ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index bf782e6ad..afc87b843 100644 --- a/ggml.h +++ b/ggml.h @@ -2266,6 +2266,7 @@ extern "C" { GGML_API int ggml_cpu_has_cublas (void); GGML_API int ggml_cpu_has_clblast (void); GGML_API int ggml_cpu_has_vulkan (void); + GGML_API int ggml_cpu_has_kompute (void); GGML_API int ggml_cpu_has_gpublas (void); GGML_API int ggml_cpu_has_sse3 (void); GGML_API int ggml_cpu_has_ssse3 (void); diff --git a/llama.cpp b/llama.cpp index 7b9a5c079..a490eeab2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6878,11 +6878,6 @@ static int llama_decode_internal( n_threads = std::min(4, n_threads); } - const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1; - if ((ggml_cpu_has_cublas() || ggml_cpu_has_vulkan()) && fully_offloaded) { - n_threads = 1; - } - #ifdef GGML_USE_MPI const int64_t n_layer = hparams.n_layer; ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); From 01684139c352561840ae55ec627ab58abc3e06ab Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 31 Jan 2024 10:38:07 +0800 Subject: [PATCH 581/664] support SYCL backend windows build (#5208) * support SYCL backend windows build * add windows build in CI * add for win build CI * correct install oneMKL * fix install issue * fix ci * fix install cmd * fix install cmd * fix install cmd * fix install cmd * fix install cmd * fix win build * fix win build * fix win build * restore other CI part * restore as base * rm no new line * fix no new line issue, add -j * fix grammer issue * allow to trigger manually, fix format issue * fix format * add newline * fix format * fix format * fix format issuse --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- .github/workflows/build.yml | 25 ++++ .github/workflows/editorconfig.yml | 6 + .gitignore | 1 + CMakeLists.txt | 6 +- README_sycl.md => README-sycl.md | 198 +++++++++++++++++++++++++++-- README.md | 4 +- examples/sycl/win-build-sycl.bat | 23 ++++ examples/sycl/win-run-llama2.bat | 13 ++ scripts/install-oneapi.bat | 19 +++ 9 files changed, 281 insertions(+), 14 deletions(-) rename README_sycl.md => README-sycl.md (58%) create mode 100644 examples/sycl/win-build-sycl.bat create mode 100644 examples/sycl/win-run-llama2.bat create mode 100644 scripts/install-oneapi.bat diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index fb719a550..c6db1666e 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -565,6 +565,31 @@ jobs: path: | cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip + windows-latest-cmake-sycl: + runs-on: windows-latest + defaults: + run: + shell: bash + + env: + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_offline.exe + WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel + + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + with: + fetch-depth: 0 + + - name: Install + run: scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + + - name: Build + id: cmake_build + run: examples/sycl/win-build-sycl.bat + ios-xcode-build: runs-on: macos-latest diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index b4e535acf..0e0993cd4 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -1,6 +1,12 @@ name: EditorConfig Checker on: + workflow_dispatch: # allows manual triggering + inputs: + create_release: + description: 'Create new release' + required: true + type: boolean push: branches: - master diff --git a/.gitignore b/.gitignore index cb0069bfb..b84459b92 100644 --- a/.gitignore +++ b/.gitignore @@ -89,3 +89,4 @@ examples/jeopardy/results.txt poetry.lock poetry.toml +nppBackup diff --git a/CMakeLists.txt b/CMakeLists.txt index 65a6f3971..15a1101aa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -507,7 +507,11 @@ if (LLAMA_SYCL) set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h) set(GGML_SOURCES_SYCL ggml-sycl.cpp) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + if (WIN32) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib) + else() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + endif() endif() if (LLAMA_KOMPUTE) diff --git a/README_sycl.md b/README-sycl.md similarity index 58% rename from README_sycl.md rename to README-sycl.md index d5a1818f5..2b2cfe03a 100644 --- a/README_sycl.md +++ b/README-sycl.md @@ -8,10 +8,14 @@ [Linux](#linux) +[Windows](#windows) + [Environment Variable](#environment-variable) [Known Issue](#known-issue) +[Q&A](#q&a) + [Todo](#todo) ## Background @@ -33,7 +37,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |OS|Status|Verified| |-|-|-| |Linux|Support|Ubuntu 22.04| -|Windows|Ongoing| | +|Windows|Support|Windows 11| ## Intel GPU @@ -42,7 +46,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |-|-|-| |Intel Data Center Max Series| Support| Max 1550| |Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770| +|Intel Arc Series| Support| Arc 770, 730M| |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| @@ -131,6 +135,7 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx #build all binary cmake --build . --config Release -v +cd .. ``` or @@ -195,7 +200,7 @@ GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building or run by script: ``` -./examples/sycl/run_llama2.sh +./examples/sycl/run-llama2.sh ``` Note: @@ -205,11 +210,175 @@ Note: 5. Check the device ID in output -Like： +Like: ``` Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ``` +## Windows + +### Setup Environment + +1. Install Intel GPU driver. + +Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). + +2. Install Intel® oneAPI Base toolkit. + +a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). + +Recommend to install to default folder: **/opt/intel/oneapi**. + +Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder. + +b. Enable oneAPI running environment: + +- In Search, input 'oneAPI'. + +Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" + +- In Run: + +In CMD: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +c. Check GPU + +In oneAPI command line: + +``` +sycl-ls +``` + +There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. + +Output (example): +``` +[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] +[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] + +``` + +3. Install cmake & make + +a. Download & install cmake for windows: https://cmake.org/download/ + +b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/ + + +### Build locally: + +In oneAPI command line window: + +``` +mkdir -p build +cd build +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +:: for FP16 +:: faster for long-prompt inference +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON + +:: for FP32 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + + +:: build example/main only +:: make main + +:: build all binary +make -j +cd .. +``` + +or + +``` +.\examples\sycl\win-build-sycl.bat +``` + +Note: + +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +### Run + +1. Put model file to folder **models** + +2. Enable oneAPI running environment + +- In Search, input 'oneAPI'. + +Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" + +- In Run: + +In CMD: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +3. List device ID + +Run without parameter: + +``` +build\bin\ls-sycl-device.exe + +or + +build\bin\main.exe +``` + +Check the ID in startup log, like: + +``` +found 4 SYCL devices: + Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, + max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 + Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, + max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 + Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + +``` + +|Attribute|Note| +|-|-| +|compute capability 1.3|Level-zero running time, recommended | +|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| + +4. Set device ID and execute llama.cpp + +Set device ID = 0 by **set GGML_SYCL_DEVICE=0** + +``` +set GGML_SYCL_DEVICE=0 +build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 +``` +or run by script: + +``` +.\examples\sycl\win-run-llama2.bat +``` + +Note: + +- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. + + +5. Check the device ID in output + +Like: +``` +Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device +``` ## Environment Variable @@ -220,7 +389,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device |LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.
For FP32/FP16, LLAMA_SYCL=ON is mandatory.| |LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference.
For FP32, not set it.| |CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path| -|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path| +|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path| #### Running @@ -232,19 +401,24 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ## Known Issue -- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. - - Miss to enable oneAPI running environment. - - Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. - - - Hang during startup llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. Solution: add **--no-mmap**. +## Q&A + +- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. + + Miss to enable oneAPI running environment. + + Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. + +- In Windows, no result, not error. + + Miss to enable oneAPI running environment. + ## Todo - Support to build in Windows. diff --git a/README.md b/README.md index b37348a74..7746cb510 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 + - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 @@ -604,7 +606,7 @@ Building the program with BLAS support may lead to some performance improvements llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). - For detailed info, please refer to [llama.cpp for SYCL](README_sycl.md). + For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). ### Prepare Data & Run diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat new file mode 100644 index 000000000..f9d43f8ed --- /dev/null +++ b/examples/sycl/win-build-sycl.bat @@ -0,0 +1,23 @@ + +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + +mkdir -p build +cd build +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +:: for FP16 +:: faster for long-prompt inference +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON + +:: for FP32 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + + +:: build example/main only +:: make main + +:: build all binary +make -j +cd .. diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat new file mode 100644 index 000000000..28d935541 --- /dev/null +++ b/examples/sycl/win-run-llama2.bat @@ -0,0 +1,13 @@ +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + +INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + + +set GGML_SYCL_DEVICE=0 +rem set GGML_SYCL_DEBUG=1 +.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0 + + diff --git a/scripts/install-oneapi.bat b/scripts/install-oneapi.bat new file mode 100644 index 000000000..e99bef14a --- /dev/null +++ b/scripts/install-oneapi.bat @@ -0,0 +1,19 @@ +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + + +set URL=%1 +set COMPONENTS=%2 + +curl.exe --output %TEMP%\webimage.exe --url %URL% --retry 5 --retry-delay 5 +start /b /wait %TEMP%\webimage.exe -s -x -f webimage_extracted --log extract.log +del %TEMP%\webimage.exe +if "%COMPONENTS%"=="" ( + webimage_extracted\bootstrapper.exe -s --action install --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=. +) else ( + webimage_extracted\bootstrapper.exe -s --action install --components=%COMPONENTS% --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=. +) +set installer_exit_code=%ERRORLEVEL% +rd /s/q "webimage_extracted" +exit /b %installer_exit_code% From d62520eb2cc1d7168a30edec6110e1daefbd959f Mon Sep 17 00:00:00 2001 From: Yiming Cui Date: Wed, 31 Jan 2024 11:04:21 +0800 Subject: [PATCH 582/664] Fix typos of IQ2_XXS and IQ3_XXS in llama.cpp (#5231) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index a490eeab2..bb23689fa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2713,10 +2713,10 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small"; case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; - case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; - case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XSS - 3.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; default: return "unknown, may not work"; } From f8e9140cb46eebaa867e1184a9946e4840eec772 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 31 Jan 2024 11:44:19 +0100 Subject: [PATCH 583/664] Vulkan Fixes (#5223) * Fix Vulkan F16 models * Fix Vulkan context shift crash * Add Vulkan to common.cpp dump_non_result_info_yaml function * Fix bug in Vulkan CPY op * Fix small matrix multiplication errors in AMD GPUs on Windows or with amdvlk Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> --------- Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> --- common/common.cpp | 1 + ggml-vulkan-shaders.hpp | 1952 +++++++++++++---------------------- ggml-vulkan.cpp | 14 +- ggml_vk_generate_shaders.py | 4 +- 4 files changed, 704 insertions(+), 1267 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 0dd1c50cf..9d976c7c8 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1520,6 +1520,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); + fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 321e36383..e2e9be22c 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -890,7 +890,7 @@ const uint64_t cpy_f32_f32_len = 2472; unsigned char dequant_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x87,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x81,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -898,7 +898,7 @@ unsigned char dequant_f16_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x4f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -910,23 +910,23 @@ unsigned char dequant_f16_data[] = { 0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00, 0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x4e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x5c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5d,0x00,0x00,0x00, +0x5a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x5b,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5b,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x5d,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x5b,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x80,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x7e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -945,330 +945,109 @@ unsigned char dequant_f16_data[] = { 0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x1b,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00, -0x24,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 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-0xc8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x60,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -1280,23 +1059,23 @@ unsigned char dequant_f16_fp32_data[] = { 0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 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0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -15341,22 +14820,184 @@ unsigned char get_rows_f16_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x55,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x53,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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+0x61,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x61,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x63,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, @@ -15388,198 +15029,28 @@ unsigned char get_rows_f16_data[] = { 0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x16,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 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-0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x00,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_f16_len = 1940; - -unsigned char get_rows_f16_f32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x65,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -15613,51 +15084,51 @@ unsigned char get_rows_f16_f32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x67,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15676,23 +15147,23 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, 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+15194,28 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x53,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x56,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x64,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x65,0x00,0x00,0x00, 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+0x79,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, @@ -15770,7 +15237,7 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x7b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00, 0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00, @@ -15783,51 +15250,51 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x67,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15846,23 +15313,23 @@ unsigned char get_rows_f16_fp32_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x55,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x57,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x64,0x00,0x00,0x00, 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+0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -15893,31 +15360,27 @@ unsigned char get_rows_f16_fp32_data[] = { 0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x53,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00, 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-0xf8,0x00,0x02,0x00,0x7d,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0xf7,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -15938,7 +15401,7 @@ unsigned char get_rows_f16_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -15951,42 +15414,42 @@ unsigned char get_rows_f16_fp32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, -0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5a,0x00,0x00,0x00, -0x5b,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00, -0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x73,0x00,0x04,0x00, 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+0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x75,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x58,0x00,0x00,0x00, +0x76,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x72,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x76,0x00,0x00,0x00, +0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_fp32_len = 1996; +const uint64_t get_rows_f16_fp32_len = 1948; unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -52701,7 +52164,7 @@ const uint64_t mul_f32_len = 1456; unsigned char mul_mat_vec_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xba,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xb6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -52709,9 +52172,9 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x13,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0xad,0x00,0x00,0x00, +0x51,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xaa,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -52729,23 +52192,23 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x63,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x64,0x00,0x00,0x00, +0x62,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xaa,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00, +0xa7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa8,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0xab,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0xa8,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xb5,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0xb2,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -52760,7 +52223,7 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x17,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00, @@ -52775,7 +52238,7 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00, +0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0x30,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, @@ -52784,26 +52247,22 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x50,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x54,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x64,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x65,0x00,0x00,0x00, -0x66,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00, 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+0x3b,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x09,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x89,0x00,0x00,0x00, +0x08,0x01,0x00,0x00,0x1d,0x00,0x03,0x00,0xa7,0x00,0x00,0x00, +0x17,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xa8,0x00,0x00,0x00, +0xa7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xa9,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xa9,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x2c,0x00,0x06,0x00,0x0a,0x00,0x00,0x00,0xb2,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00, 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0e,0x00,0x00,0x00, @@ -52819,122 +52278,91 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 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+0x2e,0x00,0x00,0x00,0x8b,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x87,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x82,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x38,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x54,0x00,0x00,0x00, +0x55,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00, +0x56,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x73,0x00,0x04,0x00, +0x17,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x54,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x51,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x4d,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, +0x73,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x5c,0x00,0x00,0x00, +0x5b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00, +0x66,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00, +0x66,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x69,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6b,0x00,0x00,0x00, +0x69,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x17,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x72,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00, +0x73,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, +0x57,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x72,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0x73,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, +0x6b,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x17,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x83,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, +0x5c,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x83,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0x84,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x00,0x00,0x00, +0xb5,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x22,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00, +0xe0,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x88,0x00,0x00,0x00, +0x89,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x30,0x00,0x00,0x00, +0xa4,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0xf7,0x00,0x03,0x00,0xa6,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfa,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0xa6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x00,0x00,0x00, +0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x2a,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00, +0xac,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x1e,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x1b,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xaa,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xa6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t mul_mat_vec_f16_f32_len = 2788; +const uint64_t mul_mat_vec_f16_f32_len = 2372; unsigned char mul_mat_vec_nc_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 1d93ec6bb..bccc40bf5 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -817,7 +817,7 @@ static void ggml_vk_load_shaders() { // mulmat std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; - std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 8, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, vk_device.subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -2873,7 +2873,8 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (op == GGML_OP_CPY) { GGML_ASSERT(!transfer_src0); GGML_ASSERT(!transfer_src1); - d_sz = dst->ne[1] * dst->nb[1]; + x_sz = ggml_nbytes(src0); + d_sz = ggml_nbytes(dst); if (extra->offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; @@ -4556,8 +4557,15 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } ggml_vk_preallocate_buffers(); + int last_node = cgraph->n_nodes - 1; + + // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly + while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) { + last_node -= 1; + } + for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + ggml_vk_build_graph(cgraph->nodes[i], i == last_node); } ggml_compute_params params = {}; diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index d0861fde4..6b1b82bf3 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -19,8 +19,8 @@ shader_int8_ext = """ # Type-specific defines shader_f16_defines = """ -#define QUANT_K 32 -#define QUANT_R 2 +#define QUANT_K 1 +#define QUANT_R 1 #define A_TYPE float16_t """ From dabcc5b471348e4ae03ddacc41e19ad75fb2f041 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 31 Jan 2024 13:43:03 +0100 Subject: [PATCH 584/664] ggml : limit n_threads to the max n_tasks (#5238) --- ggml.c | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index b2c8baaa8..afd9c6c61 100644 --- a/ggml.c +++ b/ggml.c @@ -16985,12 +16985,16 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa struct ggml_cplan cplan; memset(&cplan, 0, sizeof(struct ggml_cplan)); + int max_tasks = 1; + // thread scheduling for the different operations + work buffer size estimation for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; const int n_tasks = ggml_get_n_tasks(node, n_threads); + max_tasks = MAX(max_tasks, n_tasks); + size_t cur = 0; switch (node->op) { @@ -17157,7 +17161,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa work_size += CACHE_LINE_SIZE*(n_threads - 1); } - cplan.n_threads = n_threads; + cplan.n_threads = MIN(max_tasks, n_threads); cplan.work_size = work_size; cplan.work_data = NULL; From b2b9f025e7821e78bd501d75d01838c26de07a57 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 31 Jan 2024 21:04:46 +0800 Subject: [PATCH 585/664] format license text, restore apache license by legal suggestion (#5233) --- examples/sycl/ls-sycl-device.cpp | 10 ++++++---- ggml-sycl.cpp | 15 +++++++++++---- ggml-sycl.h | 9 +++++---- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 42847154a..52442e4ca 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -1,7 +1,9 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + #include "ggml-sycl.h" diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 3fc346975..1cc55ef52 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -1,7 +1,14 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// #include #include diff --git a/ggml-sycl.h b/ggml-sycl.h index 0eabb53cc..ba0c61473 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -1,7 +1,8 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// #pragma once From 15606309a05ccf7fadbaad5538cb7c32acb1e06b Mon Sep 17 00:00:00 2001 From: JidongZhang-THU <1119708529@qq.com> Date: Wed, 31 Jan 2024 21:10:15 +0800 Subject: [PATCH 586/664] llava : add MobileVLM support (#5132) * New Feature: 1. Sum_Rows: fix cuda kernel overflow fix block shape error when nrows too big 2. Im2Col: Support Batch in cuda Support f32 to f32 both in cpu && cuda 3. DepthWiseConv: Support by Im2Col && MulMat 4. Pool_2d: Supoort avg pooling in cuda 5. HardSigmoid: Imp in cuda 6. HardSwish: Imp in cuda * fix tabs instead of spaces * code clean * CUDA POOL2D * ADD POOL2D test case in test-backend-ops.cpp * code clean * fix pool2d_kernel nits * fix bug in pool2d kernel * fix avg pooling, count_include_pad nits * test-backend-ops : add more pool_2d tests * cuda : fix warnings and formatting * ggml : check types in release builds too in pool_2d * test-backend-ops : remove f16 pool_2d tests * cuda : more style fixes * Add assert in ggml_cuda_op_pool2d * pool2d float padding fallback * test-backend-ops : add dst_type to im2col --------- Co-authored-by: slaren --- examples/llava/MobileVLM-README.md | 58 +++++++- ggml-cuda.cu | 209 ++++++++++++++++++++++++++--- ggml.c | 118 +++++++++++++--- ggml.h | 3 +- tests/test-backend-ops.cpp | 74 +++++++++- 5 files changed, 421 insertions(+), 41 deletions(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index c6258eba6..9eba791da 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -111,17 +111,71 @@ llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 m llama_print_timings: total time = 34570.79 ms ``` +## Orin compile and run +### compile +```sh +make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 +``` + +### run on Orin +### case 1 +**input** +```sh +./llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + --image /data/local/tmp/demo.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" \ + --n-gpu-layers 999 +``` +**output** +```sh + +encode_image_with_clip: image encoded in 296.62 ms by CLIP ( 2.06 ms per image patch) + + Susan Wise Bauer + +llama_print_timings: load time = 1067.64 ms +llama_print_timings: sample time = 1.53 ms / 6 runs ( 0.25 ms per token, 3934.43 tokens per second) +llama_print_timings: prompt eval time = 306.84 ms / 246 tokens ( 1.25 ms per token, 801.72 tokens per second) +llama_print_timings: eval time = 91.50 ms / 6 runs ( 15.25 ms per token, 65.58 tokens per second) +llama_print_timings: total time = 1352.63 ms / 252 tokens +``` + +### case 2 +**input** +```sh +./llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" \ + --n-gpu-layers 999 + +``` +**output** +```sh +encode_image_with_clip: image encoded in 302.15 ms by CLIP ( 2.10 ms per image patch) + + The image features a cat lying in the grass. + +llama_print_timings: load time = 1057.07 ms +llama_print_timings: sample time = 3.27 ms / 11 runs ( 0.30 ms per token, 3360.83 tokens per second) +llama_print_timings: prompt eval time = 213.60 ms / 232 tokens ( 0.92 ms per token, 1086.14 tokens per second) +llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 ms per token, 66.01 tokens per second) +llama_print_timings: total time = 1365.47 ms / 243 tokens +``` + ## Minor shortcomings The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost. ## TODO -- [ ] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` +- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` - [ ] Optimize LDP projector performance - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`; - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc. -- [ ] run MobileVLM on `Jetson Orin` +- [x] run MobileVLM on `Jetson Orin` - [ ] Support more model variants, such as `MobileVLM-3B`. diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 949bc8a1c..e56595742 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -524,6 +524,8 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define CUDA_SILU_BLOCK_SIZE 256 #define CUDA_TANH_BLOCK_SIZE 256 #define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_HARDSIGMOID_BLOCK_SIZE 256 +#define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_CPY_BLOCK_SIZE 32 #define CUDA_SCALE_BLOCK_SIZE 256 @@ -540,6 +542,7 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define CUDA_PAD_BLOCK_SIZE 256 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 +#define CUDA_POOL2D_BLOCK_SIZE 256 #define CUDA_Q8_0_NE_ALIGN 2048 @@ -823,6 +826,24 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) { dst[i] = fmaxf(x[i], 0); } +static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +static __global__ void hardswish_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} + static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { @@ -5823,7 +5844,7 @@ static __global__ void alibi_f32(const float * x, float * dst, const int ncols, } static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) { - const int row = blockIdx.y; + const int row = blockIdx.x; const int col = threadIdx.x; float sum = 0.0f; @@ -6145,9 +6166,10 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } -static __global__ void im2col_f32_f16( - const float * x, half * dst, - int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW, +template +static __global__ void im2col_kernel( + const float * x, T * dst, int batch_offset, + int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW, int s0, int s1, int p0, int p1, int d0, int d1) { const int i = threadIdx.x + blockIdx.x * blockDim.x; if (i >= pelements) { @@ -6160,21 +6182,73 @@ static __global__ void im2col_f32_f16( const int ky = (i - kd) / OW; const int ix = i % OW; + const int oh = blockIdx.y; + const int batch = blockIdx.z / IC; + const int ic = blockIdx.z % IC; + const int64_t iiw = ix * s0 + kx * d0 - p0; - const int64_t iih = blockIdx.y * s1 + ky * d1 - p1; + const int64_t iih = oh * s1 + ky * d1 - p1; const int64_t offset_dst = - (blockIdx.y * OW + ix) * CHW + - (blockIdx.z * (KW * KH) + ky * KW + kx); + ((batch * OH + oh) * OW + ix) * CHW + + (ic * (KW * KH) + ky * KW + kx); if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = __float2half(0.0f); + dst[offset_dst] = 0.0f; } else { - const int64_t offset_src = blockIdx.z * offset_delta; - dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]); + const int64_t offset_src = ic * offset_delta + batch * batch_offset; + dst[offset_dst] = x[offset_src + iih * IW + iiw]; } } +template +static __global__ void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx >= parallel_elements) { + return; + } + + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = max(0, start_h); + const int eh = min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = max(0, start_w); + const int ew = min(iw, start_w + kw); + const To scale = 1. / (kh * kw); + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + #if __CUDA_ARCH__ >= 350 + Ti cur = __ldg(i_ptr + i * iw + j); + #else + Ti cur = i_ptr[i * iw + j]; + #endif + switch (op) { + case GGML_OP_POOL_AVG: res += cur * scale; break; + case GGML_OP_POOL_MAX: res = max(res, (To)cur); break; + } + } + } + o_ptr[cur_oh * ow + cur_ow] = res; +} + template static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { @@ -6388,6 +6462,16 @@ static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ relu_f32<<>>(x, dst, k); } +static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; + hardsigmoid_f32<<>>(x, dst, k); +} + +static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE; + hardswish_f32<<>>(x, dst, k); +} + static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) { const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; leaky_relu_f32<<>>(x, dst, k, negative_slope); @@ -7475,7 +7559,7 @@ static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { const dim3 block_dims(WARP_SIZE, 1, 1); - const dim3 block_nums(1, nrows, 1); + const dim3 block_nums(nrows, 1, 1); k_sum_rows_f32<<>>(x, dst, ncols); } @@ -7587,14 +7671,15 @@ static void soft_max_f32_cuda(const float * x, const float * y, float * dst, con } } -static void im2col_f32_f16_cuda(const float* x, half* dst, +template +static void im2col_cuda(const float* x, T* dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, - int offset_delta, + int batch, int batch_offset, int offset_delta, int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { const int parallel_elements = OW * KW * KH; const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; - dim3 block_nums(num_blocks, OH, IC); - im2col_f32_f16<<>>(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); + dim3 block_nums(num_blocks, OH, batch * IC); + im2col_kernel<<>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); } // buffer pool for cuda @@ -8179,6 +8264,34 @@ static void ggml_cuda_op_relu( (void) src1_dd; } +static void ggml_cuda_op_hardsigmoid( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_cuda_op_hardswish( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + static void ggml_cuda_op_leaky_relu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { @@ -8810,13 +8923,46 @@ static void ggml_cuda_op_alibi( (void) src1_dd; } +static void ggml_cuda_op_pool2d( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE; + dim3 block_nums(num_blocks); + pool2d_nchw_kernel<<>>(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_dd, dst_dd, op); + + (void) src1; + (void) src1_dd; +} + static void ggml_cuda_op_im2col( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; @@ -8838,8 +8984,14 @@ static void ggml_cuda_op_im2col( const int64_t OW = dst->ne[1]; const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const int64_t batch = src1->ne[3]; + const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 - im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + if(dst->type == GGML_TYPE_F16) { + im2col_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } else { + im2col_cuda(src1_dd, (float*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } (void) src0; (void) src0_dd; @@ -9435,6 +9587,13 @@ static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); } +static void ggml_cuda_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardsigmoid); +} + +static void ggml_cuda_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardswish); +} static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu); } @@ -10220,6 +10379,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); } +static void ggml_cuda_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pool2d); +} + static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); } @@ -10321,6 +10484,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_UNARY_OP_RELU: func = ggml_cuda_relu; break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_cuda_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_cuda_hardswish; + break; default: return false; } @@ -10395,6 +10564,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_OP_IM2COL: func = ggml_cuda_im2col; break; + case GGML_OP_POOL_2D: + func = ggml_cuda_pool2d; + break; case GGML_OP_SUM_ROWS: func = ggml_cuda_sum_rows; break; @@ -11123,6 +11295,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: return true; @@ -11221,6 +11395,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ROPE: case GGML_OP_ALIBI: case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: diff --git a/ggml.c b/ggml.c index afd9c6c61..ee994c875 100644 --- a/ggml.c +++ b/ggml.c @@ -5349,7 +5349,7 @@ GGML_API struct ggml_tensor * ggml_conv_1d( int s0, int p0, int d0) { - struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K] + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K] struct ggml_tensor * result = ggml_mul_mat(ctx, @@ -5427,16 +5427,15 @@ struct ggml_tensor * ggml_conv_depthwise_2d( int p1, int d0, int d1) { + struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), - s0, s1, p0, p1, d0, d1, true); // [N * IC, OH, OW, KH * KW] - - struct ggml_tensor * result = - ggml_mul_mat(ctx, - ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1), // [OC，1, KH, KW] => [1, OC, 1, KH * KW] - ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3])); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW] + struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] return result; @@ -5457,7 +5456,8 @@ struct ggml_tensor * ggml_im2col( int p1, int d0, int d1, - bool is_2D) { + bool is_2D, + enum ggml_type dst_type) { if(is_2D) { GGML_ASSERT(a->ne[2] == b->ne[2]); @@ -5481,7 +5481,7 @@ struct ggml_tensor * ggml_im2col( is_2D ? b->ne[3] : 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); @@ -5506,7 +5506,7 @@ struct ggml_tensor * ggml_conv_2d( int p1, int d0, int d1) { - struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW] + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW] struct ggml_tensor * result = ggml_mul_mat(ctx, @@ -5632,12 +5632,13 @@ struct ggml_tensor * ggml_pool_2d( is_node = true; } + struct ggml_tensor * result; const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), a->ne[2], }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); + result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); @@ -5645,7 +5646,6 @@ struct ggml_tensor * ggml_pool_2d( result->op = GGML_OP_POOL_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - return result; } @@ -12493,6 +12493,92 @@ static void ggml_compute_forward_conv_transpose_1d( } } +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + int64_t t0 = ggml_perf_time_us(); + UNUSED(t0); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (params->type == GGML_TASK_INIT) { + return; + } + + if (params->type == GGML_TASK_FINALIZE) { + return; + } + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + + // src0: kernel [OC, IC, KH, KW] // src1: image [N, IC, IH, IW] // dst: result [N, OH, OW, IC*KH*KW] @@ -12583,14 +12669,14 @@ static void ggml_compute_forward_im2col( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - switch (src0->type) { + switch (dst->type) { case GGML_TYPE_F16: { ggml_compute_forward_im2col_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - GGML_ASSERT(false); + ggml_compute_forward_im2col_f32(params, src0, src1, dst); } break; default: { @@ -12781,8 +12867,8 @@ static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, const struct ggml_tensor * src, struct ggml_tensor * dst) { - assert(src->type == GGML_TYPE_F32); - assert(params->ith == 0); + GGML_ASSERT(src->type == GGML_TYPE_F32); + GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; diff --git a/ggml.h b/ggml.h index afc87b843..e0a4799f3 100644 --- a/ggml.h +++ b/ggml.h @@ -1495,7 +1495,8 @@ extern "C" { int p1, int d0, int d1, - bool is_2D); + bool is_2D, + enum ggml_type dst_type); GGML_API struct ggml_tensor * ggml_conv_depthwise_2d( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 1d29070b6..eb06123d2 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -227,6 +227,14 @@ static std::string var_to_str(ggml_type type) { return ggml_type_name(type); } +static std::string var_to_str(ggml_op_pool pool) { + switch (pool) { + case GGML_OP_POOL_AVG: return "avg"; + case GGML_OP_POOL_MAX: return "max"; + default: return std::to_string(pool); + } +} + #define VARS_TO_STR1(a) VAR_TO_STR(a) #define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b) #define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c) @@ -238,6 +246,7 @@ static std::string var_to_str(ggml_type type) { #define VARS_TO_STR9(a, b, c, d, e, f, g, h, i) VAR_TO_STR(a) + "," + VARS_TO_STR8(b, c, d, e, f, g, h, i) #define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j) #define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k) +#define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l) #ifdef GGML_USE_SYCL static bool inline _isinf(float f) { @@ -1162,10 +1171,45 @@ struct test_alibi : public test_case { } }; +// GGML_OP_POOL2D +struct test_pool2d : public test_case { + enum ggml_op_pool pool_type; + const ggml_type type_input; + const std::array ne_input; + // kernel size + const int k0; + const int k1; + // stride + const int s0; + const int s1; + // padding + const int p0; + const int p1; + + std::string vars() override { + return VARS_TO_STR9(pool_type, type_input, ne_input, k0, k1, s0, s1, p0, p1); + } + + test_pool2d(ggml_op_pool pool_type = GGML_OP_POOL_AVG, + ggml_type type_input = GGML_TYPE_F32, + std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] + int k0 = 3, int k1 = 3, + int s0 = 1, int s1 = 1, + int p0 = 1, int p1 = 1) + : pool_type(pool_type), type_input(type_input), ne_input(ne_input), k0(k0), k1(k1), s0(s0), s1(s1), p0(p0), p1(p1) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); + ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1); + return out; + } +}; + // GGML_OP_IM2COL struct test_im2col : public test_case { const ggml_type type_input; const ggml_type type_kernel; + const ggml_type dst_type; const std::array ne_input; const std::array ne_kernel; // stride @@ -1181,22 +1225,22 @@ struct test_im2col : public test_case { const bool is_2D; std::string vars() override { - return VARS_TO_STR11(type_input, type_kernel, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D); + return VARS_TO_STR12(type_input, type_kernel, dst_type, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D); } - test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, + test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, ggml_type dst_type = GGML_TYPE_F32, std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] std::array ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1] int s0 = 1, int s1 = 1, int p0 = 1, int p1 = 1, int d0 = 1, int d1 = 1, bool is_2D = true) - : type_input(type_input), type_kernel(type_kernel), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {} + : type_input(type_input), type_kernel(type_kernel), dst_type(dst_type), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data()); - ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D); + ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D, dst_type); return out; } }; @@ -1912,6 +1956,27 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } + for (ggml_type type_input : {GGML_TYPE_F32}) { + for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) { + for (int k0 : {1, 3}) { + for (int k1 : {1, 3}) { + for (int s0 : {1, 2}) { + for (int s1 : {1, 2}) { + for (int p0 : {0, 1}) { + for (int p1 : {0, 1}) { + test_cases.emplace_back(new test_pool2d(pool_type, type_input, {10, 10, 3, 1}, k0, k1, s0, s1, p0, p1)); + } + } + } + } + } + } + } + } + + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1})); @@ -2049,7 +2114,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } test_cases.emplace_back(new test_alibi()); - test_cases.emplace_back(new test_im2col()); test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); From efb7bdbbd061d087c788598b97992c653f992ddd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 15:35:41 +0200 Subject: [PATCH 587/664] metal : add im2col F32 dst support (#5132) --- ggml-metal.m | 13 ++++++++++--- ggml-metal.metal | 33 +++++++++++++++++++++++++++++---- 2 files changed, 39 insertions(+), 7 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index f87859552..5260ed827 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -135,6 +135,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, GGML_METAL_KERNEL_TYPE_IM2COL_F16, + GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, @@ -506,6 +507,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); @@ -630,6 +632,10 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_ALIBI: case GGML_OP_ROPE: case GGML_OP_IM2COL: + return true; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + return false; case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARGSORT: @@ -2015,7 +2021,7 @@ static bool ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; @@ -2023,6 +2029,7 @@ static bool ggml_metal_graph_compute( const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int32_t N = src1->ne[is_2D ? 3 : 2]; @@ -2043,8 +2050,8 @@ static bool ggml_metal_graph_compute( id pipeline = nil; - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + switch (dst->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; default: GGML_ASSERT(false); }; diff --git a/ggml-metal.metal b/ggml-metal.metal index 2614d82e8..efed6ad46 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1775,9 +1775,29 @@ kernel void kernel_rope( template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope; template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope; -kernel void kernel_im2col_f16( +typedef void (im2col_t)( device const float * x, - device half * dst, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template +kernel void kernel_im2col( + device const float * x, + device char * dst, constant int32_t & ofs0, constant int32_t & ofs1, constant int32_t & IW, @@ -1800,14 +1820,19 @@ kernel void kernel_im2col_f16( (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); + device T * pdst = (device T *) (dst); + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = 0.0f; + pdst[offset_dst] = 0.0f; } else { const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; - dst[offset_dst] = x[offset_src + iih * IW + iiw]; + pdst[offset_dst] = x[offset_src + iih * IW + iiw]; } } +template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; +template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; + kernel void kernel_upscale_f32( device const char * src0, device char * dst, From 5cb04dbc16d1da38c8fdcc0111b40e67d00dd1c3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 17:30:17 +0200 Subject: [PATCH 588/664] llama : remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD (#5240) * llama : remove LLAMA_MAX_DEVICES from llama.h ggml-ci * Update llama.cpp Co-authored-by: slaren * server : remove LLAMA_MAX_DEVICES ggml-ci * llama : remove LLAMA_SUPPORTS_GPU_OFFLOAD ggml-ci * train : remove LLAMA_SUPPORTS_GPU_OFFLOAD * readme : add deprecation notice * readme : change deprecation notice to "remove" and fix url * llama : remove gpu includes from llama.h ggml-ci --------- Co-authored-by: slaren --- README.md | 3 +- common/common.cpp | 56 ++++++++++---------- common/common.h | 66 ++++++++++++------------ common/train.cpp | 12 ++--- examples/batched-bench/batched-bench.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 16 +++--- examples/server/server.cpp | 44 ++++++++-------- llama.cpp | 39 +++++++++++--- llama.h | 29 ++++------- 9 files changed, 143 insertions(+), 124 deletions(-) diff --git a/README.md b/README.md index 7746cb510..e6ed1d429 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 +- Remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD: https://github.com/ggerganov/llama.cpp/pull/5240 +- Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: diff --git a/common/common.cpp b/common/common.cpp index 9d976c7c8..ce739b15c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -583,20 +583,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_gpu_layers = std::stoi(argv[i]); -#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { if (++i >= argc) { invalid_param = true; break; } params.n_gpu_layers_draft = std::stoi(argv[i]); -#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; @@ -637,11 +637,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - if (split_arg.size() >= LLAMA_MAX_DEVICES) { + if (split_arg.size() >= llama_max_devices()) { invalid_param = true; break; } - for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { + for (size_t i = 0; i < llama_max_devices(); ++i) { if (i < split_arg.size()) { params.tensor_split[i] = std::stof(split_arg[i]); } else { @@ -989,30 +989,30 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); - if (llama_mlock_supported()) { + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) { + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -ngld N, --n-gpu-layers-draft N\n"); - printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); -#endif // LLAMA_SUPPORTS_GPU_OFFLOAD + if (llama_supports_gpu_offload()) { + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -ngld N, --n-gpu-layers-draft N\n"); + printf(" number of layers to store in VRAM for the draft model\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); + printf(" -ts SPLIT, --tensor-split SPLIT\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); + } printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); @@ -1651,7 +1651,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES); + const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); diff --git a/common/common.h b/common/common.h index 214a379b5..24a99d728 100644 --- a/common/common.h +++ b/common/common.h @@ -43,40 +43,40 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed - int32_t n_threads = get_num_physical_cores(); - int32_t n_threads_draft = -1; - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_threads_batch_draft = -1; - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 8; // number of tokens to draft during speculative decoding - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_parallel = 1; // number of parallel sequences to decode - int32_t n_sequences = 1; // number of sequences to decode - float p_accept = 0.5f; // speculative decoding accept probability - float p_split = 0.1f; // speculative decoding split probability - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_beams = 0; // if non-zero then use beam search of given width. - int32_t grp_attn_n = 1; // group-attention factor - int32_t grp_attn_w = 512; // group-attention width - int32_t n_print = -1; // print token count every n tokens (-1 = disabled) - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor - float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor - float yarn_beta_fast = 32.0f; // YaRN low correction dim - float yarn_beta_slow = 1.0f; // YaRN high correction dim - int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment - // pinging @cebtenzzre + int32_t n_threads = get_num_physical_cores(); + int32_t n_threads_draft = -1; + int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_threads_batch_draft = -1; + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 8; // number of tokens to draft during speculative decoding + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + float p_accept = 0.5f; // speculative decoding accept probability + float p_split = 0.1f; // speculative decoding split probability + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + int32_t n_beams = 0; // if non-zero then use beam search of given width. + int32_t grp_attn_n = 1; // group-attention factor + int32_t grp_attn_w = 512; // group-attention width + int32_t n_print = -1; // print token count every n tokens (-1 = disabled) + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f; // YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment + // pinging @cebtenzzre // // sampling parameters struct llama_sampling_params sparams; diff --git a/common/train.cpp b/common/train.cpp index e6f2f7a2f..e4c3d5df6 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1363,12 +1363,12 @@ bool consume_common_train_arg( *invalid_param = true; return true; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params->n_gpu_layers = std::stoi(argv[i]); -#else - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (llama_supports_gpu_offload()) { + params->n_gpu_layers = std::stoi(argv[i]); + } else { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "-h" || arg == "--help") { params->print_usage = true; return true; diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 7924db267..b52d68457 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -88,7 +88,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); - const std::vector t_split (LLAMA_MAX_DEVICES, 0.0f); + const std::vector t_split(llama_max_devices(), 0.0f); model_params.n_gpu_layers = n_gpu_layers; model_params.tensor_split = t_split.data(); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 542cc7bb8..c5a6f744e 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -160,7 +160,7 @@ struct cmd_params { std::vector main_gpu; std::vector no_kv_offload; std::vector mul_mat_q; - std::vector> tensor_split; + std::vector> tensor_split; int reps; bool verbose; output_formats output_format; @@ -179,7 +179,7 @@ static const cmd_params cmd_params_defaults = { /* main_gpu */ {0}, /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, - /* tensor_split */ {{}}, + /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -380,10 +380,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { const std::regex regex{R"([;/]+)"}; std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - std::array tensor_split; - for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { + std::vector tensor_split(llama_max_devices()); + for (size_t i = 0; i < llama_max_devices(); ++i) { if (i < split_arg.size()) { tensor_split[i] = std::stof(split_arg[i]); } else { @@ -459,7 +459,7 @@ struct cmd_params_instance { int main_gpu; bool no_kv_offload; bool mul_mat_q; - std::array tensor_split; + std::vector tensor_split; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -582,7 +582,7 @@ struct test { int main_gpu; bool no_kv_offload; bool mul_mat_q; - std::array tensor_split; + std::vector tensor_split; int n_prompt; int n_gen; std::string test_time; @@ -704,7 +704,7 @@ struct test { std::vector get_values() const { std::string tensor_split_str; int max_nonzero = 0; - for (int i = 0; i < LLAMA_MAX_DEVICES; i++) { + for (size_t i = 0; i < llama_max_devices(); i++) { if (tensor_split[i] > 0) { max_nonzero = i; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 21bdce8ed..ea77125ea 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1789,28 +1789,28 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); - if (llama_mlock_supported()) + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); -#endif + if (llama_supports_gpu_offload()) { + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); + printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); + } printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); @@ -2066,13 +2066,13 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params.n_gpu_layers = std::stoi(argv[i]); -#else - LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " + if (llama_supports_gpu_offload()) { + params.n_gpu_layers = std::stoi(argv[i]); + } else { + LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); -#endif + } } else if (arg == "--split-mode" || arg == "-sm") { @@ -2115,9 +2115,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) + for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { if (i_device < split_arg.size()) { diff --git a/llama.cpp b/llama.cpp index bb23689fa..9b249ba9c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10090,18 +10090,45 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { return result; } -int32_t llama_max_devices(void) { - return LLAMA_MAX_DEVICES; +size_t llama_max_devices(void) { +#if defined(GGML_USE_METAL) + return 1; +#elif defined(GGML_USE_CUBLAS) + return GGML_CUDA_MAX_DEVICES; +#elif defined(GGML_USE_SYCL) + return GGML_SYCL_MAX_DEVICES; +#else + return 1; +#endif } -bool llama_mmap_supported(void) { +bool llama_supports_mmap(void) { return llama_mmap::SUPPORTED; } -bool llama_mlock_supported(void) { +bool llama_supports_mlock(void) { return llama_mlock::SUPPORTED; } +bool llama_supports_gpu_offload(void) { +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) + // Defined when llama.cpp is compiled with support for offloading model layers to GPU. + return true; +#else + return false; +#endif +} + +// deprecated: +bool llama_mmap_supported(void) { + return llama_supports_mmap(); +} + +bool llama_mlock_supported(void) { + return llama_supports_mlock(); +} + void llama_backend_init(bool numa) { ggml_time_init(); @@ -10133,8 +10160,8 @@ int64_t llama_time_us(void) { } struct llama_model * llama_load_model_from_file( - const char * path_model, - struct llama_model_params params) { + const char * path_model, + struct llama_model_params params) { ggml_time_init(); llama_model * model = new llama_model; diff --git a/llama.h b/llama.h index 17d43d039..9a60e9bfb 100644 --- a/llama.h +++ b/llama.h @@ -3,15 +3,7 @@ #include "ggml.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUBLAS -#include "ggml-cuda.h" -#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES -#elif defined(GGML_USE_SYCL) -#include "ggml-sycl.h" -#define LLAMA_MAX_DEVICES GGML_SYCL_MAX_DEVICES -#else -#define LLAMA_MAX_DEVICES 1 -#endif // GGML_USE_CUBLAS + #include #include #include @@ -49,12 +41,6 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 4 -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) -// Defined when llama.cpp is compiled with support for offloading model layers to GPU. -#define LLAMA_SUPPORTS_GPU_OFFLOAD -#endif - #ifdef __cplusplus extern "C" { #endif @@ -201,7 +187,7 @@ extern "C" { // LLAMA_SPLIT_LAYER: ignored int32_t main_gpu; - // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES + // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() const float * tensor_split; // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. @@ -338,9 +324,14 @@ extern "C" { LLAMA_API int64_t llama_time_us(void); - LLAMA_API int32_t llama_max_devices(void); - LLAMA_API bool llama_mmap_supported (void); - LLAMA_API bool llama_mlock_supported(void); + LLAMA_API size_t llama_max_devices(void); + + LLAMA_API bool llama_supports_mmap (void); + LLAMA_API bool llama_supports_mlock (void); + LLAMA_API bool llama_supports_gpu_offload(void); + + LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead"); + LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead"); LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); From d3bac7d58408c602ec1f1e423695f1df8410bb03 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 18:47:10 +0200 Subject: [PATCH 589/664] llama : reorder build_orion() at correct place (#5118) --- llama.cpp | 239 +++++++++++++++++++++++++++--------------------------- 1 file changed, 119 insertions(+), 120 deletions(-) diff --git a/llama.cpp b/llama.cpp index 9b249ba9c..02b0a485a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4666,126 +4666,6 @@ struct llm_build_context { ctx0 = nullptr; } } - struct ggml_cgraph * build_orion() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); - - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - // norm - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, model.layers[il].attn_norm_b, - LLM_NORM, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - // compute Q and K and RoPE them - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); - // if (model.layers[il].bq) { - // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); - // cb(Qcur, "Qcur", il); - // } - - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); - // if (model.layers[il].bk) { - // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); - // cb(Kcur, "Kcur", il); - // } - - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - // if (model.layers[il].bv) { - // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); - // cb(Vcur, "Vcur", il); - // } - - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Qcur, "Qcur", il); - - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Kcur, "Kcur", il); - - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, - NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, model.output_norm_b, - LLM_NORM, cb, -1); - cb(cur, "result_norm", -1); - - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - - struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -6589,6 +6469,125 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_orion() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + // if (model.layers[il].bq) { + // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + // cb(Qcur, "Qcur", il); + // } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + // if (model.layers[il].bk) { + // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + // cb(Kcur, "Kcur", il); + // } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + // if (model.layers[il].bv) { + // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + // cb(Vcur, "Vcur", il); + // } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( From 1cfb5372cf5707c8ec6dde7c874f4a44a6c4c915 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 31 Jan 2024 19:21:55 +0000 Subject: [PATCH 590/664] Fix broken Vulkan Cmake (properly) (#5230) * build vulkan as object * vulkan ci --- .github/workflows/build.yml | 6 ++++-- CMakeLists.txt | 8 ++------ 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c6db1666e..f4c374ce5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -356,6 +356,8 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' + - build: 'vulkan' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -406,7 +408,7 @@ jobs: - name: Install Vulkan SDK id: get_vulkan - if: ${{ matrix.build == 'kompute' }} + if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} run: | curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install @@ -451,7 +453,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 diff --git a/CMakeLists.txt b/CMakeLists.txt index 15a1101aa..1ee455b3a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -423,10 +423,7 @@ if (LLAMA_VULKAN) if (Vulkan_FOUND) message(STATUS "Vulkan found") - set(GGML_HEADERS_VULKAN ggml-vulkan.h) - set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) - - add_library(ggml-vulkan STATIC ggml-vulkan.cpp ggml-vulkan.h) + add_library(ggml-vulkan OBJECT ggml-vulkan.cpp ggml-vulkan.h) if (BUILD_SHARED_LIBS) set_target_properties(ggml-vulkan PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() @@ -1012,7 +1009,6 @@ add_library(ggml OBJECT ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} @@ -1094,7 +1090,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_VULKAN}" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") From ce32060198b7e2d6a13a9b8e1e1369e3c295ae2a Mon Sep 17 00:00:00 2001 From: Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Date: Thu, 1 Feb 2024 17:19:51 +0800 Subject: [PATCH 591/664] llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair --- convert-hf-to-gguf.py | 152 ++++++++++++++++++++++++ gguf-py/gguf/constants.py | 18 +++ gguf-py/gguf/gguf_writer.py | 3 + gguf-py/gguf/tensor_mapping.py | 14 ++- llama.cpp | 205 ++++++++++++++++++++++++++++++++- 5 files changed, 387 insertions(+), 5 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 6ab7f486e..4ebab07b3 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -203,6 +203,8 @@ class Model: return CodeShellModel if model_architecture == "OrionForCausalLM": return OrionModel + if model_architecture == "InternLM2ForCausalLM": + return InternLM2Model return Model def _is_model_safetensors(self) -> bool: @@ -254,6 +256,8 @@ class Model: return gguf.MODEL_ARCH.CODESHELL if arch == "OrionForCausalLM": return gguf.MODEL_ARCH.ORION + if arch == "InternLM2ForCausalLM": + return gguf.MODEL_ARCH.INTERNLM2 raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1344,6 +1348,154 @@ class CodeShellModel(Model): self.gguf_writer.add_tensor("output.weight", data) print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + +class InternLM2Model(Model): + def set_vocab(self): + # (TODO): Is there a better way? + # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character + # \x00 specially and convert it into an emoji character to prevent it from being mistakenly + # recognized as an empty string in C++. + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + sys.exit(1) + + sentencepiece_model = model.ModelProto() + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + + tokenizer = SentencePieceProcessor(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.id_to_piece(token_id) + text = piece.encode("utf-8") + score = tokenizer.get_score(token_id) + if text == b"\x00": + # (TODO): fixme + # Hack here and replace the \x00 characters. + print(f"InternLM2 convert token '{text}' to '🐉'!") + text = "🐉" + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + self.gguf_writer.add_name("InternLM2") + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + + def post_write_tensors(self, tensor_map, name, data_torch): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + def write_tensors(self): + from einops import rearrange + + num_heads = self.hparams.get("num_attention_heads") + num_kv_heads = self.hparams.get("num_key_value_heads") + hidden_size = self.hparams.get("hidden_size") + q_per_kv = num_heads // num_kv_heads + head_dim = hidden_size // num_heads + num_groups = num_heads // q_per_kv + + block_count = self.hparams["num_hidden_layers"] + model_kv = dict(self.get_tensors()) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv" + for name, data_torch in model_kv.items(): + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + if re.match(qkv_pattern, name): + bid = re.findall(qkv_pattern, name)[0] + qkv = data_torch + qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) + q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] + q = rearrange(q, " o g n i -> o (g n i)").T + k = rearrange(k, " o g n i -> o (g n i)").T + v = rearrange(v, " o g n i -> o (g n i)").T + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v) + else: + self.post_write_tensors(tensor_map, name, data_torch) + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index f5c933a41..ed8e26f83 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -72,6 +72,7 @@ class Keys: PAD_ID = "tokenizer.ggml.padding_token_id" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_PREFIX = "tokenizer.ggml.add_space_prefix" HF_JSON = "tokenizer.huggingface.json" RWKV = "tokenizer.rwkv.world" CHAT_TEMPLATE = "tokenizer.chat_template" @@ -102,6 +103,7 @@ class MODEL_ARCH(IntEnum): PLAMO = auto() CODESHELL = auto() ORION = auto() + INTERNLM2 = auto() class MODEL_TENSOR(IntEnum): @@ -153,6 +155,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", + MODEL_ARCH.INTERNLM2: "internlm2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -446,6 +449,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.INTERNLM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d93aaa877..16808196e 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -411,6 +411,9 @@ class GGUFWriter: def add_add_eos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_EOS, value) + def add_add_space_prefix(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) + def add_chat_template(self, value: str) -> None: self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index de177af13..4f16d8504 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -19,6 +19,7 @@ class TensorNameMap: "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 + "model.tok_embeddings", # internlm2 ), # Token type embeddings @@ -42,7 +43,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen - "output", # llama-pth bloom + "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 ), @@ -51,7 +52,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon - "model.norm", # llama-hf baichuan + "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth "embeddings.LayerNorm", # bert "transformer.norm_f", # mpt @@ -84,6 +85,7 @@ class TensorNameMap: "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo + "model.layers.{bid}.attention_norm", # internlm2 ), # Attention norm 2 @@ -111,6 +113,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.query", # bert "transformer.h.{bid}.attn.q_proj", # gpt-j "model.layers.layers.{bid}.self_attn.q_proj", # plamo + "model.layers.{bid}.attention.wq" # internlm2 ), # Attention key @@ -120,6 +123,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j "model.layers.layers.{bid}.self_attn.k_proj", # plamo + "model.layers.{bid}.attention.wk" # internlm2 ), # Attention value @@ -129,6 +133,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j "model.layers.layers.{bid}.self_attn.v_proj", # plamo + "model.layers.{bid}.attention.wv" # internlm2 ), # Attention output @@ -147,6 +152,7 @@ class TensorNameMap: "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo + "model.layers.{bid}.attention.wo", # internlm2 ), # Rotary embeddings @@ -169,6 +175,7 @@ class TensorNameMap: "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 + "model.layers.{bid}.ffn_norm", # internlm2 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -194,6 +201,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo + "model.layers.{bid}.feed_forward.w3", # internlm2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -212,6 +220,7 @@ class TensorNameMap: "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen "model.layers.layers.{bid}.mlp.gate_proj", # plamo + "model.layers.{bid}.feed_forward.w1", # internlm2 ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -236,6 +245,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo + "model.layers.{bid}.feed_forward.w2", # internlm2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 02b0a485a..e8f44c2cb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -204,6 +204,7 @@ enum llm_arch { LLM_ARCH_PLAMO, LLM_ARCH_CODESHELL, LLM_ARCH_ORION, + LLM_ARCH_INTERNLM2, LLM_ARCH_UNKNOWN, }; @@ -226,6 +227,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_PLAMO, "plamo" }, { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, }; enum llm_kv { @@ -278,6 +280,7 @@ enum llm_kv { LLM_KV_TOKENIZER_PAD_ID, LLM_KV_TOKENIZER_ADD_BOS, LLM_KV_TOKENIZER_ADD_EOS, + LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, }; @@ -332,6 +335,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" }, { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, + { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, }; @@ -669,7 +673,23 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, - + { + LLM_ARCH_INTERNLM2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1377,6 +1397,7 @@ enum e_model { MODEL_13B, MODEL_14B, MODEL_15B, + MODEL_20B, MODEL_30B, MODEL_34B, MODEL_40B, @@ -1618,6 +1639,8 @@ struct llama_vocab { id special_suffix_id = 32008; id special_eot_id = 32010; + bool add_space_prefix = true; + int find_bpe_rank(const std::string & token_left, const std::string & token_right) const { GGML_ASSERT(token_left.find(' ') == std::string::npos); GGML_ASSERT(token_left.find('\n') == std::string::npos); @@ -2731,6 +2754,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; + case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; case MODEL_40B: return "40B"; @@ -2743,6 +2767,14 @@ static const char * llama_model_type_name(e_model type) { default: return "?B"; } } +static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ + switch (type) { + case LLAMA_VOCAB_TYPE_SPM: return "SPM"; + case LLAMA_VOCAB_TYPE_BPE: return "BPE"; + default: return "unknown"; + } +} + static void llm_load_arch(llama_model_loader & ml, llama_model & model) { model.arch = ml.get_arch(); @@ -3006,6 +3038,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_INTERNLM2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_7B; break; + case 48: model.type = e_model::MODEL_20B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3057,6 +3098,11 @@ static void llm_load_vocab( vocab.special_unk_id = 0; vocab.special_sep_id = -1; vocab.special_pad_id = -1; + + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); + if (add_space_prefix_keyidx != -1) { + vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); + } // The default value of add_space_prefix is true. } else if (tokenizer_name == "gpt2") { vocab.type = LLAMA_VOCAB_TYPE_BPE; @@ -3269,7 +3315,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); - LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix + LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); @@ -4018,8 +4064,35 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_INTERNLM2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6588,6 +6661,126 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_internlm2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + }; static struct ggml_cgraph * llama_build_graph( @@ -6746,6 +6939,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_orion(); } break; + case LLM_ARCH_INTERNLM2: + { + result = llm.build_internlm2(); + } break; default: GGML_ASSERT(false); } @@ -7688,7 +7885,9 @@ static std::vector llama_tokenize_internal(const llama_vocab & // auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); if (&fragment == &fragment_buffer.front()) { - raw_text = " " + raw_text; // prefix with space if the first token is not special + if (vocab.add_space_prefix) { + raw_text = " " + raw_text; // prefix with space if the first token is not special + } } #ifdef PRETOKENIZERDEBUG From d71ac90985854b0905e1abba778e407e17f9f887 Mon Sep 17 00:00:00 2001 From: Ali Nehzat Date: Fri, 2 Feb 2024 02:18:53 +1100 Subject: [PATCH 592/664] make : generate .a library for static linking (#5205) --- Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 781f0bf8c..bf9e085de 100644 --- a/Makefile +++ b/Makefile @@ -586,8 +586,11 @@ train.o: common/train.cpp common/train.h libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) +libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) + ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS) + clean: - rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples From 8ca511cadee2c67f0bd8c7034a2513778ee9a1b7 Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 1 Feb 2024 18:30:17 +0100 Subject: [PATCH 593/664] cuda : fix LLAMA_CUDA_F16 (#5262) --- ggml-cuda.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e56595742..3242a0b4a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8657,9 +8657,9 @@ static void ggml_cuda_op_dequantize_mul_mat_vec( if (src1_convert_f16) { src1_dfloat = src1_dfloat_a.alloc(ne00); - ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00, - ne00, 1, sizeof(float), 0, 0, - ne00, 1, sizeof(half), 0, 0, stream); + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); } #else const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion From 4d0924a8902010d31bd737b6f1f594943d120d0f Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Thu, 1 Feb 2024 19:25:24 +0100 Subject: [PATCH 594/664] Vulkan Phi Fix for AMD Proprietary Drivers (#5260) * Replace tanh to avoid NaN in gelu shader on AMD proprietary driver * Fix another Vulkan CPY buffer size bug --- ggml-vulkan-shaders.hpp | 132 +++++++++++++++++++----------------- ggml-vulkan.cpp | 17 +++-- ggml_vk_generate_shaders.py | 3 +- 3 files changed, 83 insertions(+), 69 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index e2e9be22c..195410c02 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -14670,14 +14670,14 @@ const uint64_t f32_to_f16_fp32_len = 1596; unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x45,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x38,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -14696,15 +14696,15 @@ unsigned char gelu_f32_data[] = { 0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x42,0x00,0x00,0x00, +0x36,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -14731,64 +14731,70 @@ unsigned char gelu_f32_data[] = { 0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, 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+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, +0xf7,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00, +0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x11,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, +0x2a,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00, 0x11,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x11,0x00,0x00,0x00, +0x33,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, +0x30,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x85,0x00,0x05,0x00, +0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, -0x34,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x37,0x00,0x00,0x00, -0x35,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00, -0x11,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x32,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x31,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, -0x3b,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x3b,0x00,0x00,0x00, -0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x31,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x85,0x00,0x05,0x00, -0x11,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x30,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00, -0x3f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x43,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, +0x3f,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, +0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x88,0x00,0x05,0x00, +0x11,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x41,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x11,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x42,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x44,0x00,0x00,0x00, +0x3c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x26,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x38,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x49,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t gelu_f32_len = 1408; +const uint64_t gelu_f32_len = 1484; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index bccc40bf5..b1e0006bb 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -2876,6 +2876,9 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm x_sz = ggml_nbytes(src0); d_sz = ggml_nbytes(dst); + if (extra_src0->offset + x_sz >= d_X->size) { + x_sz = VK_WHOLE_SIZE; + } if (extra->offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; } @@ -2911,12 +2914,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm break; } - x_sz *= ne02 * ne03; - if (y_sz != VK_WHOLE_SIZE) { - y_sz *= ne12 * ne13; - } if (op != GGML_OP_CPY) { - d_sz *= ne02 * ne03; + if (x_sz != VK_WHOLE_SIZE) { + x_sz *= ne02 * ne03; + } + if (y_sz != VK_WHOLE_SIZE) { + y_sz *= ne12 * ne13; + } + if (d_sz != VK_WHOLE_SIZE) { + d_sz *= ne02 * ne03; + } } if (!use_src1 && op == GGML_OP_SOFT_MAX) { diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 6b1b82bf3..67981a751 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -1689,7 +1689,8 @@ void main() { } const float xi = float(data_a[i]); - data_d[i] = D_TYPE(0.5f*xi*(1.0f + tanh(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)))); + const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi); + data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1))); } """ From 128dcbd3c9c4b12f42b560a4430427d7b2828628 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 2 Feb 2024 03:48:53 +0800 Subject: [PATCH 595/664] add --no-mmap in llama-bench (#5257) * add --no-mmap, show sycl backend * fix conflict * fix code format, change print for --no-mmap * ren no_mmap to mmap, show mmap when not default value in printer * update guide for mmap * mv position to reduce model reload --- README-sycl.md | 2 +- examples/llama-bench/llama-bench.cpp | 60 +++++++++++++++++++++++++--- ggml-sycl.cpp | 34 +++++++++++++++- ggml-sycl.h | 3 +- 4 files changed, 89 insertions(+), 10 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 2b2cfe03a..b8ee212b8 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -405,7 +405,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. - Solution: add **--no-mmap**. + Solution: add **--no-mmap** or **--mmap 0**. ## Q&A diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c5a6f744e..e36c061a2 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -20,6 +20,7 @@ #include "llama.h" #include "common.h" #include "ggml-cuda.h" +#include "ggml-sycl.h" // utils static uint64_t get_time_ns() { @@ -120,6 +121,22 @@ static std::string get_gpu_info() { id += "/"; } } +#endif +#ifdef GGML_USE_SYCL + int device_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); + + for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + if (device_list[i] >0 ){ + char buf[128]; + ggml_sycl_get_device_description(i, buf, sizeof(buf)); + id += buf; + id += "/"; + } + } + if (id.length() >2 ) { + id.pop_back(); + } #endif // TODO: other backends return id; @@ -161,6 +178,7 @@ struct cmd_params { std::vector no_kv_offload; std::vector mul_mat_q; std::vector> tensor_split; + std::vector use_mmap; int reps; bool verbose; output_formats output_format; @@ -180,6 +198,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, + /* use_mmap */ {true}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -201,6 +220,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); @@ -370,6 +390,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); + } else if (arg == "-mmp" || arg == "--mmap") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; @@ -441,6 +468,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } + if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } return params; @@ -460,6 +488,7 @@ struct cmd_params_instance { bool no_kv_offload; bool mul_mat_q; std::vector tensor_split; + bool use_mmap; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -468,6 +497,7 @@ struct cmd_params_instance { mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); + mparams.use_mmap = use_mmap; return mparams; } @@ -477,6 +507,7 @@ struct cmd_params_instance { n_gpu_layers == other.n_gpu_layers && split_mode == other.split_mode && main_gpu == other.main_gpu && + use_mmap == other.use_mmap && tensor_split == other.tensor_split; } @@ -503,6 +534,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) + for (const auto & mmp : params.use_mmap) for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) @@ -527,6 +559,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, }; instances.push_back(instance); } @@ -549,6 +582,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, }; instances.push_back(instance); } @@ -565,6 +599,7 @@ struct test { static const bool vulkan; static const bool kompute; static const bool metal; + static const bool sycl; static const bool gpu_blas; static const bool blas; static const std::string cpu_info; @@ -583,6 +618,7 @@ struct test { bool no_kv_offload; bool mul_mat_q; std::vector tensor_split; + bool use_mmap; int n_prompt; int n_gen; std::string test_time; @@ -605,6 +641,7 @@ struct test { no_kv_offload = inst.no_kv_offload; mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; + use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; n_gen = inst.n_gen; // RFC 3339 date-time format @@ -654,25 +691,29 @@ struct test { if (metal) { return "Metal"; } + if (sycl) { + return GGML_SYCL_NAME; + } if (gpu_blas) { return "GPU BLAS"; } if (blas) { return "BLAS"; } + return "CPU"; } static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", + "mul_mat_q", "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -691,8 +732,8 @@ struct test { return INT; } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || - field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q") { + field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || + field == "mul_mat_q" || field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -720,13 +761,13 @@ struct test { std::vector values = { build_commit, std::to_string(build_number), std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, + std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -753,6 +794,7 @@ const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); +const bool test::sycl = !!ggml_cpu_has_sycl(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); @@ -895,6 +937,9 @@ struct markdown_printer : public printer { if (field == "no_kv_offload") { return "nkvo"; } + if (field == "use_mmap") { + return "mmap"; + } if (field == "tensor_split") { return "ts"; } @@ -938,6 +983,9 @@ struct markdown_printer : public printer { if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.push_back("tensor_split"); } + if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { + fields.push_back("use_mmap"); + } fields.push_back("test"); fields.push_back("t/s"); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 1cc55ef52..e8ba48353 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2928,7 +2928,6 @@ void ggml_sycl_set_main_device(int main_device); void ggml_sycl_set_mul_mat_q(bool mul_mat_q); void ggml_sycl_set_scratch_size(size_t scratch_size); void ggml_sycl_free_scratch(void); -int ggml_sycl_get_device_count(void); void ggml_sycl_get_device_description(int device, char * description, size_t description_size); bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); @@ -14493,6 +14492,37 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ return true; } +GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { + int max_compute_units = -1; + for(int i=0;i Date: Thu, 1 Feb 2024 23:20:13 -0800 Subject: [PATCH 596/664] llama : fix memory leak in llama_batch_free (#5252) The llama_batch_init allocates memory for a fixed number of tokens. However, the llama_batch_free only frees memory for the number of tokens that were added to the batch. This change-set uses a null terminated array for the batch seq_id, and frees all the elements until the nullptr is reached. This change-set also changes the name of the first parameter from `n_tokens` to `n_tokens_alloc` to more clearly indicate that this value is the number of tokens allocated to the batch, not the number of tokens in the batch. --- llama.cpp | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/llama.cpp b/llama.cpp index e8f44c2cb..6bf7f9efb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11377,22 +11377,24 @@ struct llama_batch llama_batch_get_one( }; } -struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) { +struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) { llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; if (embd) { - batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd); + batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd); } else { - batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens); + batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc); } - batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens); - batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens); - batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens); - for (int i = 0; i < n_tokens; ++i) { + batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc); + batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc); + batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1)); + for (int i = 0; i < n_tokens_alloc; ++i) { batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max); } - batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens); + batch.seq_id[n_tokens_alloc] = nullptr; + + batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc); return batch; } @@ -11403,7 +11405,7 @@ void llama_batch_free(struct llama_batch batch) { if (batch.pos) free(batch.pos); if (batch.n_seq_id) free(batch.n_seq_id); if (batch.seq_id) { - for (int i = 0; i < batch.n_tokens; ++i) { + for (int i = 0; batch.seq_id[i] != nullptr; ++i) { free(batch.seq_id[i]); } free(batch.seq_id); From af3ba5d94627d337e32a95129e31a3064c459f6b Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 2 Feb 2024 15:53:27 +0800 Subject: [PATCH 597/664] [SYCL] update guide of SYCL backend (#5254) * update guide for make installation, memory, gguf model link, rm todo for windows build * add vs install requirement * update for gpu device check * update help of llama-bench * fix grammer issues --- README-sycl.md | 64 +++++++++++++++++++++++++++----- examples/llama-bench/README.md | 34 ++++++++++------- examples/sycl/win-run-llama2.bat | 2 +- 3 files changed, 77 insertions(+), 23 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index b8ee212b8..f7edc1c3e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -42,6 +42,8 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). ## Intel GPU +### Verified + |Intel GPU| Status | Verified Model| |-|-|-| |Intel Data Center Max Series| Support| Max 1550| @@ -50,6 +52,17 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| +Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. + +### Memory + +The memory is a limitation to run LLM on GPUs. + +When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors: buffer size = 3577.56 MiB`. + +For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+. + +For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. ## Linux @@ -105,7 +118,7 @@ source /opt/intel/oneapi/setvars.sh sycl-ls ``` -There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. +There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. Output (example): ``` @@ -152,6 +165,8 @@ Note: 1. Put model file to folder **models** +You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. + 2. Enable oneAPI running environment ``` @@ -223,7 +238,13 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). -2. Install Intel® oneAPI Base toolkit. +Note: **The driver is mandatory for compute function**. + +2. Install Visual Studio. + +Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows. + +3. Install Intel® oneAPI Base toolkit. a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). @@ -252,7 +273,7 @@ In oneAPI command line: sycl-ls ``` -There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. +There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. Output (example): ``` @@ -260,15 +281,21 @@ Output (example): [opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] [opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] - ``` -3. Install cmake & make +4. Install cmake & make -a. Download & install cmake for windows: https://cmake.org/download/ +a. Download & install cmake for Windows: https://cmake.org/download/ -b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/ +b. Download & install make for Windows provided by mingw-w64 +- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases. + + Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z). + +- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**. + +- Add the **bin** folder path in the Windows system PATH environment. ### Build locally: @@ -309,6 +336,8 @@ Note: 1. Put model file to folder **models** +You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. + 2. Enable oneAPI running environment - In Search, input 'oneAPI'. @@ -419,8 +448,25 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Miss to enable oneAPI running environment. +- Meet compile error. + + Remove folder **build** and try again. + +- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux. + + Please run **sudo sycl-ls**. + + If you see it in result, please add video/render group to your ID: + + ``` + sudo usermod -aG render username + sudo usermod -aG video username + ``` + + Then **relogin**. + + If you do not see it, please check the installation GPU steps again. + ## Todo -- Support to build in Windows. - - Support multiple cards. diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index d02824bfa..374e40a7d 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -23,19 +23,23 @@ usage: ./llama-bench [options] options: -h, --help - -m, --model (default: models/7B/ggml-model-q4_0.gguf) - -p, --n-prompt (default: 512) - -n, --n-gen (default: 128) - -b, --batch-size (default: 512) - --memory-f32 <0|1> (default: 0) - -t, --threads (default: 16) - -ngl N, --n-gpu-layers (default: 99) - -mg i, --main-gpu (default: 0) - -mmq, --mul-mat-q <0|1> (default: 1) - -ts, --tensor_split - -r, --repetitions (default: 5) - -o, --output (default: md) - -v, --verbose (default: 0) + -m, --model (default: models/7B/ggml-model-q4_0.gguf) + -p, --n-prompt (default: 512) + -n, --n-gen (default: 128) + -b, --batch-size (default: 512) + -ctk , --cache-type-k (default: f16) + -ctv , --cache-type-v (default: f16) + -t, --threads (default: 112) + -ngl, --n-gpu-layers (default: 99) + -sm, --split-mode (default: layer) + -mg, --main-gpu (default: 0) + -nkvo, --no-kv-offload <0|1> (default: 0) + -mmp, --mmap <0|1> (default: 1) + -mmq, --mul-mat-q <0|1> (default: 1) + -ts, --tensor_split (default: 0) + -r, --repetitions (default: 5) + -o, --output (default: md) + -v, --verbose (default: 0) Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. ``` @@ -51,6 +55,10 @@ Each test is repeated the number of times given by `-r`, and the results are ave For a description of the other options, see the [main example](../main/README.md). +Note: + +- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`. + ## Examples ### Text generation with different models diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index 28d935541..cf621c675 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -2,7 +2,7 @@ :: Copyright (C) 2024 Intel Corporation :: SPDX-License-Identifier: MIT -INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force From e805f0fa9951081ce0a86378a7aa52b6f636b82d Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Fri, 2 Feb 2024 15:54:14 +0800 Subject: [PATCH 598/664] [SYCL] get MAX_MEM_ALLOC from device property (#5270) * get max alloc size from device prop * fix macro typo --- ggml-sycl.cpp | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index e8ba48353..4ee2eed38 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -337,6 +337,7 @@ namespace dpct } size_t get_global_mem_size() const { return _global_mem_size; } size_t get_local_mem_size() const { return _local_mem_size; } + size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; } /// Returns the maximum clock rate of device's global memory in kHz. If /// compiler does not support this API then returns default value 3200000 kHz. unsigned int get_memory_clock_rate() const { return _memory_clock_rate; } @@ -398,6 +399,10 @@ namespace dpct { _local_mem_size = local_mem_size; } + void set_max_mem_alloc_size(size_t max_mem_alloc_size) + { + _max_mem_alloc_size = max_mem_alloc_size; + } void set_max_work_group_size(int max_work_group_size) { _max_work_group_size = max_work_group_size; @@ -465,6 +470,7 @@ namespace dpct int _max_register_size_per_work_group; size_t _global_mem_size; size_t _local_mem_size; + size_t _max_mem_alloc_size; size_t _max_nd_range_size[3]; int _max_nd_range_size_i[3]; uint32_t _device_id; @@ -516,6 +522,7 @@ namespace dpct dev.get_info()); prop.set_global_mem_size(dev.get_info()); prop.set_local_mem_size(dev.get_info()); + prop.set_max_mem_alloc_size(dev.get_info()); #if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6) if (dev.has(sycl::aspect::ext_intel_memory_clock_rate)) @@ -644,6 +651,11 @@ namespace dpct return get_device_info().get_global_mem_size(); } + size_t get_max_mem_alloc_size() const + { + return get_device_info().get_max_mem_alloc_size(); + } + /// Get the number of bytes of free and total memory on the SYCL device. /// \param [out] free_memory The number of bytes of free memory on the SYCL device. /// \param [out] total_memory The number of bytes of total memory on the SYCL device. @@ -11311,10 +11323,10 @@ void ggml_init_sycl() try { GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); int64_t total_vram = 0; -#if defined(GGML_SYCL_FP16) - fprintf(stderr, "%s: GGML_SYCL_FP16: yes\n", __func__); +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_SYCL_FP16: no\n", __func__); + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif @@ -14788,6 +14800,12 @@ static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_ty UNUSED(buft); } +static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return dpct::get_current_device().get_max_mem_alloc_size(); + + UNUSED(buft); +} + static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); @@ -14818,7 +14836,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .get_name = */ ggml_backend_sycl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength + /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend, /* .is_host = */ nullptr, From 6b91b1e0a92ac2e4e269eec6361ca53a61ced6c6 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 2 Feb 2024 08:56:31 +0100 Subject: [PATCH 599/664] docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL --- .devops/main-intel.Dockerfile | 16 ++--- .devops/main-vulkan.Dockerfile | 29 +++++++++ .devops/server-intel.Dockerfile | 15 +++-- .devops/server-vulkan.Dockerfile | 29 +++++++++ README-sycl.md | 102 +++++++++++++++++++------------ README.md | 64 ++++++++++++++----- 6 files changed, 188 insertions(+), 67 deletions(-) create mode 100644 .devops/main-vulkan.Dockerfile create mode 100644 .devops/server-vulkan.Dockerfile diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile index e1e6acc24..572e5d8ea 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/main-intel.Dockerfile @@ -1,8 +1,8 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 -ARG UBUNTU_VERSION=22.04 -FROM intel/hpckit:$ONEAPI_VERSION as build +FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git @@ -10,16 +10,18 @@ WORKDIR /app COPY . . -# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance RUN mkdir build && \ cd build && \ - cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ - cmake --build . --config Release --target main server + if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ + echo "LLAMA_SYCL_F16 is set" && \ + export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ + fi && \ + cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build . --config Release --target main -FROM ubuntu:$UBUNTU_VERSION as runtime +FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime COPY --from=build /app/build/bin/main /main -COPY --from=build /app/build/bin/server /server ENV LC_ALL=C.utf8 diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile new file mode 100644 index 000000000..bca460365 --- /dev/null +++ b/.devops/main-vulkan.Dockerfile @@ -0,0 +1,29 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION as build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk + +# Build it +WORKDIR /app +COPY . . +RUN mkdir build && \ + cd build && \ + cmake .. -DLLAMA_VULKAN=1 && \ + cmake --build . --config Release --target main + +# Clean up +WORKDIR / +RUN cp /app/build/bin/main /main && \ + rm -rf /app + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/main" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile index e343d278c..312f2df80 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/server-intel.Dockerfile @@ -1,8 +1,8 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 -ARG UBUNTU_VERSION=22.04 -FROM intel/hpckit:$ONEAPI_VERSION as build +FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git @@ -10,13 +10,16 @@ WORKDIR /app COPY . . -# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance RUN mkdir build && \ cd build && \ - cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ - cmake --build . --config Release --target main server + if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ + echo "LLAMA_SYCL_F16 is set" && \ + export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ + fi && \ + cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build . --config Release --target server -FROM ubuntu:$UBUNTU_VERSION as runtime +FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime COPY --from=build /app/build/bin/server /server diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile new file mode 100644 index 000000000..e0add6fc3 --- /dev/null +++ b/.devops/server-vulkan.Dockerfile @@ -0,0 +1,29 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION as build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk + +# Build it +WORKDIR /app +COPY . . +RUN mkdir build && \ + cd build && \ + cmake .. -DLLAMA_VULKAN=1 && \ + cmake --build . --config Release --target server + +# Clean up +WORKDIR / +RUN cp /app/build/bin/server /server && \ + rm -rf /app + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/README-sycl.md b/README-sycl.md index f7edc1c3e..7aa4274a9 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -1,22 +1,15 @@ # llama.cpp for SYCL -[Background](#background) - -[OS](#os) - -[Intel GPU](#intel-gpu) - -[Linux](#linux) - -[Windows](#windows) - -[Environment Variable](#environment-variable) - -[Known Issue](#known-issue) - -[Q&A](#q&a) - -[Todo](#todo) +- [Background](#background) +- [OS](#os) +- [Intel GPU](#intel-gpu) +- [Docker](#docker) +- [Linux](#linux) +- [Windows](#windows) +- [Environment Variable](#environment-variable) +- [Known Issue](#known-issue) +- [Q&A](#q&a) +- [Todo](#todo) ## Background @@ -36,7 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |OS|Status|Verified| |-|-|-| -|Linux|Support|Ubuntu 22.04| +|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| |Windows|Support|Windows 11| @@ -50,7 +43,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |Intel Data Center Flex Series| Support| Flex 170| |Intel Arc Series| Support| Arc 770, 730M| |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| +|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. @@ -64,6 +57,38 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. +## Docker + +Note: +- Only docker on Linux is tested. Docker on WSL may not work. +- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that) + +### Build the image + +You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. + + +```sh +# For F16: +#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile . + +# Or, for F32: +docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile . + +# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example +``` + +### Run + +```sh +# Firstly, find all the DRI cards: +ls -la /dev/dri +# Then, pick the card that you want to use. + +# For example with "/dev/dri/card1" +docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` + ## Linux ### Setup Environment @@ -76,7 +101,7 @@ Note: for iGPU, please install the client GPU driver. b. Add user to group: video, render. -``` +```sh sudo usermod -aG render username sudo usermod -aG video username ``` @@ -85,7 +110,7 @@ Note: re-login to enable it. c. Check -``` +```sh sudo apt install clinfo sudo clinfo -l ``` @@ -103,7 +128,6 @@ Platform #0: Intel(R) OpenCL HD Graphics 2. Install Intel® oneAPI Base toolkit. - a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). Recommend to install to default folder: **/opt/intel/oneapi**. @@ -112,7 +136,7 @@ Following guide use the default folder as example. If you use other folder, plea b. Check -``` +```sh source /opt/intel/oneapi/setvars.sh sycl-ls @@ -131,21 +155,25 @@ Output (example): 2. Build locally: -``` +Note: +- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +```sh mkdir -p build cd build source /opt/intel/oneapi/setvars.sh -#for FP16 -#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference +# For FP16: +#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON -#for FP32 +# Or, for FP32: cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -#build example/main only +# Build example/main only #cmake --build . --config Release --target main -#build all binary +# Or, build all binary cmake --build . --config Release -v cd .. @@ -153,14 +181,10 @@ cd .. or -``` +```sh ./examples/sycl/build.sh ``` -Note: - -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - ### Run 1. Put model file to folder **models** @@ -177,10 +201,10 @@ source /opt/intel/oneapi/setvars.sh Run without parameter: -``` +```sh ./build/bin/ls-sycl-device -or +# or running the "main" executable and look at the output log: ./build/bin/main ``` @@ -209,13 +233,13 @@ found 4 SYCL devices: Set device ID = 0 by **GGML_SYCL_DEVICE=0** -``` +```sh GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 ``` or run by script: -``` -./examples/sycl/run-llama2.sh +```sh +./examples/sycl/run_llama2.sh ``` Note: diff --git a/README.md b/README.md index e6ed1d429..af1f09fa0 100644 --- a/README.md +++ b/README.md @@ -393,28 +393,28 @@ Building the program with BLAS support may lead to some performance improvements Check [BLIS.md](docs/BLIS.md) for more information. +- #### SYCL + SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + + llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + + For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). + - #### Intel oneMKL + Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md). + - Using manual oneAPI installation: By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: ```bash mkdir build cd build - source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-runtime docker image, only required for manual installation + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON cmake --build . --config Release ``` - Using oneAPI docker image: - If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-runtime](https://hub.docker.com/r/intel/oneapi-runtime) - - ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON - cmake --build . --config Release - ``` - - Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. @@ -601,14 +601,48 @@ Building the program with BLAS support may lead to some performance improvements You can get a list of platforms and devices from the `clinfo -l` command, etc. -- #### SYCL +- #### Vulkan - SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + **With docker**: - llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + You don't need to install Vulkan SDK. It will be installed inside the container. - For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). + ```sh + # Build the image + docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile . + # Then, use it: + docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 + ``` + + **Without docker**: + + Firstly, you need to make sure you installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + + For example, on Ubuntu 22.04 (jammy), use the command below: + + ```bash + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + apt update -y + apt-get install -y vulkan-sdk + # To verify the installation, use the command below: + vulkaninfo + ``` + + Then, build llama.cpp using the cmake command below: + + ```bash + mkdir -p build + cd build + cmake .. -DLLAMA_VULKAN=1 + cmake --build . --config Release + # Test the output binary (with "-ngl 33" to offload all layers to GPU) + ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 + + # You should see in the output, ggml_vulkan detected your GPU. For example: + # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 + ``` ### Prepare Data & Run From b05102fe8cfa9893851c6bf6efd15cdc20b6afa2 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 2 Feb 2024 08:39:48 +0000 Subject: [PATCH 600/664] Tidy ggml-sycl (#5261) * Tidy some code in ggml-sycl * Remove blank space * Remove std::printf comments --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 4ee2eed38..ac75f8e16 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -1366,6 +1366,7 @@ namespace dpct } #else return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); #endif // DPCT_USM_LEVEL_NONE } @@ -1667,7 +1668,7 @@ namespace dpct using Ty = typename DataType::T2; Ty s_h; if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only) - detail::dpct_memcpy(q, (void *)&s_h, (void *)s, sizeof(T), device_to_host) + detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host) .wait(); else s_h = *reinterpret_cast(s); @@ -1691,6 +1692,20 @@ namespace dpct int ldb, const void *beta, void *c, int ldc) { #ifndef __INTEL_MKL__ + GGML_UNUSED(q); + GGML_UNUSED(a_trans); + GGML_UNUSED(b_trans); + GGML_UNUSED(m); + GGML_UNUSED(n); + GGML_UNUSED(k); + GGML_UNUSED(alpha); + GGML_UNUSED(a); + GGML_UNUSED(lda); + GGML_UNUSED(b); + GGML_UNUSED(ldb); + GGML_UNUSED(beta); + GGML_UNUSED(c); + GGML_UNUSED(ldc); throw std::runtime_error("The oneAPI Math Kernel Library (oneMKL) Interfaces " "Project does not support this API."); #else @@ -1830,7 +1845,7 @@ namespace dpct template T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask, - int logical_sub_group_size = 32) + unsigned int logical_sub_group_size = 32) { unsigned int id = g.get_local_linear_id(); unsigned int start_index = @@ -2160,6 +2175,7 @@ namespace dpct } #else return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); #endif // DPCT_USM_LEVEL_NONE } @@ -3302,7 +3318,7 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo std::ofstream logfile; logfile.open(filename); // printf("local buf element %d\n", total_elements); - for(int i=0; ibackend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; - const int compute_capability = g_device_caps[id].cc; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check #else @@ -12691,7 +12700,7 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { continue; } - int can_access_peer; + // int can_access_peer; // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other)); // if (can_access_peer) { // if (enable_peer_access) { @@ -12716,7 +12725,6 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; - const int64_t nrows0 = ggml_nrows(src0); const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; @@ -13812,13 +13820,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); - const dpct::memcpy_direction src1_kind = - src1->backend == GGML_BACKEND_CPU ? dpct::host_to_device - : dpct::device_to_device; - const dpct::memcpy_direction dst_kind = dst->backend == GGML_BACKEND_CPU - ? dpct::device_to_host - : dpct::device_to_device; - for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; From 2d40085c26794e29c434480b9e06738e89e5686f Mon Sep 17 00:00:00 2001 From: Mirror Azure <54669636+MirrorAzure@users.noreply.github.com> Date: Fri, 2 Feb 2024 14:39:09 +0300 Subject: [PATCH 601/664] py : add check for '.attn.masked_bias' layers to GPT2model (#5281) --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 4ebab07b3..a6ffd128b 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1138,7 +1138,7 @@ class GPT2Model(Model): for name, data_torch in self.get_tensors(): # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")): + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")): continue if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): From e437b37fd0b2b97e6c6ff1045ec7f901faa6498a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 Feb 2024 14:23:40 +0200 Subject: [PATCH 602/664] scripts : parse wtype in server-llm.sh (#5167) * scripts : parse wtype in server-llm.sh * scripts : fix check for wfile --- scripts/server-llm.sh | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 7bf0929bb..0b83cdbbc 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -141,6 +141,28 @@ for wt in "${wtypes[@]}"; do wfiles+=("") done +# map wtype input to index +if [[ ! -z "$wtype" ]]; then + iw=-1 + is=0 + for wt in "${wtypes[@]}"; do + # uppercase + uwt=$(echo "$wt" | tr '[:lower:]' '[:upper:]') + if [[ "$uwt" == "$wtype" ]]; then + iw=$is + break + fi + is=$((is+1)) + done + + if [[ $iw -eq -1 ]]; then + printf "[-] Invalid weight type: %s\n" "$wtype" + exit 1 + fi + + wtype="$iw" +fi + # sample repos repos=( "https://huggingface.co/TheBloke/Llama-2-7B-GGUF" @@ -252,8 +274,10 @@ for file in $model_files; do printf " %2d) %s %s\n" $iw "$have" "$file" done +wfile="${wfiles[$wtype]}" + # ask for weights type until provided and available -while [[ -z "$wtype" ]]; do +while [[ -z "$wfile" ]]; do printf "\n" read -p "[+] Select weight type: " wtype wfile="${wfiles[$wtype]}" From 191221178f51b6e81122c5bda0fd79620e547d07 Mon Sep 17 00:00:00 2001 From: kalomaze <66376113+kalomaze@users.noreply.github.com> Date: Fri, 2 Feb 2024 08:15:30 -0600 Subject: [PATCH 603/664] perplexity : fix KL divergence calculations on Windows (#5273) --- examples/perplexity/perplexity.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 8d2204969..4b08145cd 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -457,14 +457,14 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par std::ofstream logits_stream; if (!params.logits_file.empty()) { - logits_stream.open(params.logits_file.c_str()); + logits_stream.open(params.logits_file.c_str(), std::ios::binary); if (!logits_stream.is_open()) { fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str()); return {}; } fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str()); logits_stream.write("_logits_", 8); - logits_stream.write((const char *)&n_ctx, sizeof(n_ctx)); + logits_stream.write(reinterpret_cast(&n_ctx), sizeof(n_ctx)); } auto tim1 = std::chrono::high_resolution_clock::now(); From a305dba8ff642e57f538f42010868fe0bc5262a1 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Sat, 3 Feb 2024 08:11:37 +0000 Subject: [PATCH 604/664] Fix im2col with 32fp (#5286) --- ggml-sycl.cpp | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ac75f8e16..51445b5e7 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -8247,7 +8247,8 @@ static void clamp_f32(const float * x, float * dst, const float min, const float dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } -static void im2col_f32_f16(const float *x, sycl::half *dst, int offset_delta, +template +static void im2col_kernel(const float *x, T *dst, int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW, int s0, int s1, int p0, int p1, int d0, int d1, @@ -11019,7 +11020,8 @@ static void soft_max_f32_sycl(const float *x, const float *y, float *dst, }); } -static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, +template +static void im2col_sycl(const float *x, T *dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, int offset_delta, int s0, int s1, int p0, int p1, int d0, int d1, @@ -11036,7 +11038,7 @@ static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - im2col_f32_f16(x, dst, offset_delta, IW, IH, OW, KW, KH, + im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1, item_ct1); }); @@ -12424,7 +12426,7 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; @@ -12447,8 +12449,11 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - im2col_f32_f16_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, - IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + if (dst->type == GGML_TYPE_F16) { + im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } else { + im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } (void) src0; (void) src0_dd; From 6a66c5071a74a96c4f52cf1015a092acd18c3714 Mon Sep 17 00:00:00 2001 From: BADR Date: Sat, 3 Feb 2024 12:20:26 +0100 Subject: [PATCH 605/664] readme : add tenere in the ui tools list (#5284) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index af1f09fa0..4a9bdf314 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,7 @@ as the main playground for developing new features for the [ggml](https://github - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [iohub/collama](https://github.com/iohub/coLLaMA) +- [pythops/tenere](https://github.com/pythops/tenere) --- From 1ec3332ade60aeb1494ace2211cf1a966db6d770 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 3 Feb 2024 06:22:06 -0500 Subject: [PATCH 606/664] YaRN : store rope scaling type as int32_t in memory (#5285) * YaRN : store rope scaling type as int32_t in memory * llama : store mapped names as const char * --- common/common.h | 3 +-- llama.cpp | 24 ++++++++++++------------ llama.h | 2 +- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/common/common.h b/common/common.h index 24a99d728..62de25d6a 100644 --- a/common/common.h +++ b/common/common.h @@ -75,8 +75,7 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment - // pinging @cebtenzzre + int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters struct llama_sampling_params sparams; diff --git a/llama.cpp b/llama.cpp index 6bf7f9efb..4787a92fe 100644 --- a/llama.cpp +++ b/llama.cpp @@ -208,7 +208,7 @@ enum llm_arch { LLM_ARCH_UNKNOWN, }; -static std::map LLM_ARCH_NAMES = { +static std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GPT2, "gpt2" }, @@ -285,7 +285,7 @@ enum llm_kv { LLM_KV_TOKENIZER_RWKV, }; -static std::map LLM_KV_NAMES = { +static std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, @@ -346,7 +346,7 @@ struct LLM_KV { llm_arch arch; std::string operator()(llm_kv kv) const { - return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str()); + return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]); } }; @@ -747,13 +747,13 @@ struct LLM_TN { // gguf helpers // -static std::map LLAMA_ROPE_SCALING_TYPES = { +static std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_NONE, "none" }, { LLAMA_ROPE_SCALING_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_YARN, "yarn" }, }; -static int8_t llama_rope_scaling_type_from_string(const std::string & name) { +static int32_t llama_rope_scaling_type_from_string(const std::string & name) { for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { if (kv.second == name) { return kv.first; @@ -1415,6 +1415,7 @@ static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; + bool rope_finetuned; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; @@ -1434,8 +1435,7 @@ struct llama_hparams { float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; - int8_t rope_scaling_type_train : 3; - bool rope_finetuned : 1; + int32_t rope_scaling_type_train; float f_clamp_kqv; float f_max_alibi_bias; @@ -2701,7 +2701,7 @@ struct llama_model_loader { // load LLaMA models // -static std::string llama_model_arch_name(llm_arch arch) { +static const char * llama_model_arch_name(llm_arch arch) { auto it = LLM_ARCH_NAMES.find(arch); if (it == LLM_ARCH_NAMES.end()) { return "unknown"; @@ -3310,11 +3310,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { const auto & hparams = model.hparams; const auto & vocab = model.vocab; - const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); - LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); + LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch)); LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); @@ -3336,7 +3336,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); - LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str()); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); @@ -10735,7 +10735,7 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { return snprintf(buf, buf_size, "%s %s %s", - llama_model_arch_name(model->arch).c_str(), + llama_model_arch_name(model->arch), llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } diff --git a/llama.h b/llama.h index 9a60e9bfb..cec4158bc 100644 --- a/llama.h +++ b/llama.h @@ -213,7 +213,7 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model From 52bb63c7082c859c3f1dfc527227e6a95b299c7c Mon Sep 17 00:00:00 2001 From: Michael Klimenko Date: Sat, 3 Feb 2024 12:23:37 +0100 Subject: [PATCH 607/664] refactor : switch to emplace_back to avoid extra object (#5291) --- common/common.cpp | 8 ++--- examples/llama-bench/llama-bench.cpp | 34 +++++++++++----------- examples/main/main.cpp | 4 +-- examples/perplexity/perplexity.cpp | 8 ++--- examples/quantize-stats/quantize-stats.cpp | 4 +-- examples/quantize/quantize.cpp | 4 +-- examples/server/server.cpp | 8 ++--- tests/test-llama-grammar.cpp | 2 +- 8 files changed, 36 insertions(+), 36 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index ce739b15c..3302caa20 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -515,7 +515,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); + params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") { if (++i >= argc) { @@ -527,7 +527,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") { if (++i >= argc) { @@ -664,7 +664,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.antiprompt.push_back(argv[i]); + params.antiprompt.emplace_back(argv[i]); } else if (arg == "-ld" || arg == "--logdir") { if (++i >= argc) { invalid_param = true; @@ -880,7 +880,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index e36c061a2..ddb0ba064 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -948,46 +948,46 @@ struct markdown_printer : public printer { void print_header(const cmd_params & params) override { // select fields to print - fields.push_back("model"); - fields.push_back("size"); - fields.push_back("params"); - fields.push_back("backend"); + fields.emplace_back("model"); + fields.emplace_back("size"); + fields.emplace_back("params"); + fields.emplace_back("backend"); bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS"; if (!is_cpu_backend) { - fields.push_back("n_gpu_layers"); + fields.emplace_back("n_gpu_layers"); } if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) { - fields.push_back("n_threads"); + fields.emplace_back("n_threads"); } if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) { - fields.push_back("n_batch"); + fields.emplace_back("n_batch"); } if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) { - fields.push_back("type_k"); + fields.emplace_back("type_k"); } if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) { - fields.push_back("type_v"); + fields.emplace_back("type_v"); } if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) { - fields.push_back("main_gpu"); + fields.emplace_back("main_gpu"); } if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { - fields.push_back("split_mode"); + fields.emplace_back("split_mode"); } if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.push_back("mul_mat_q"); + fields.emplace_back("mul_mat_q"); } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { - fields.push_back("no_kv_offload"); + fields.emplace_back("no_kv_offload"); } if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { - fields.push_back("tensor_split"); + fields.emplace_back("tensor_split"); } if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { - fields.push_back("use_mmap"); + fields.emplace_back("use_mmap"); } - fields.push_back("test"); - fields.push_back("t/s"); + fields.emplace_back("test"); + fields.emplace_back("t/s"); fprintf(fout, "|"); for (const auto & field : fields) { diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 1c6138d23..0ed4d79f9 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -352,12 +352,12 @@ int main(int argc, char ** argv) { // in instruct mode, we inject a prefix and a suffix to each input by the user if (params.instruct) { params.interactive_first = true; - params.antiprompt.push_back("### Instruction:\n\n"); + params.antiprompt.emplace_back("### Instruction:\n\n"); } // similar for chatml mode else if (params.chatml) { params.interactive_first = true; - params.antiprompt.push_back("<|im_start|>user\n"); + params.antiprompt.emplace_back("<|im_start|>user\n"); } // enable interactive mode if interactive start is specified diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 4b08145cd..b2c131d4c 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -881,7 +881,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { size_t li = hs_cur.common_prefix; for (int s = 0; s < 4; ++s) { for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) { - eval_pairs.push_back(std::make_pair(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1])); + eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]); } ++li; } @@ -1159,13 +1159,13 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0; size_t li = n_base1 - 1; for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) { - eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[0][j+1])); + eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]); } const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix; const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0; li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1; for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) { - eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[1][j+1])); + eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]); } } compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); @@ -1524,7 +1524,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params size_t li = cur_task.common_prefix; for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { - eval_pairs.push_back(std::make_pair(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1])); + eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]); } ++li; } diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 6d5f213dc..1d05f1391 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -257,13 +257,13 @@ int main(int argc, char ** argv) { invalid_param = true; break; } - params.include_layers.push_back(argv[i]); + params.include_layers.emplace_back(argv[i]); } else if (arg == "-L" || arg == "--exclude-layer") { if (++i >= argc) { invalid_param = true; break; } - params.exclude_layers.push_back(argv[i]); + params.exclude_layers.emplace_back(argv[i]); } else if (arg == "-t" || arg == "--type") { if (++i >= argc) { invalid_param = true; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index a9673f0d4..85f403ffc 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -208,13 +208,13 @@ int main(int argc, char ** argv) { } } else if (strcmp(argv[arg_idx], "--include-weights") == 0) { if (arg_idx < argc-1) { - included_weights.push_back(argv[++arg_idx]); + included_weights.emplace_back(argv[++arg_idx]); } else { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) { if (arg_idx < argc-1) { - excluded_weights.push_back(argv[++arg_idx]); + excluded_weights.emplace_back(argv[++arg_idx]); } else { usage(argv[0]); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ea77125ea..a9f8cb369 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1884,7 +1884,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - sparams.api_keys.push_back(argv[i]); + sparams.api_keys.emplace_back(argv[i]); } else if (arg == "--api-key-file") { @@ -2160,7 +2160,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); + params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") @@ -2176,7 +2176,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") @@ -2318,7 +2318,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 78fc41117..16ebe753f 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -105,7 +105,7 @@ int main() for (auto rule : expected_rules) { - parsed_grammar.rules.push_back({}); + parsed_grammar.rules.emplace_back(); for (auto element : rule) { parsed_grammar.rules.back().push_back(element); From e920ed393d989ed35625ddaf182ebb52cda07fcd Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 3 Feb 2024 18:15:00 +0100 Subject: [PATCH 608/664] Vulkan Intel Fixes, Optimizations and Debugging Flags (#5301) * Fix Vulkan on Intel ARC Optimize matmul for Intel ARC Add Vulkan dequant test * Add Vulkan debug and validate flags to Make and CMakeLists.txt * Enable asynchronous transfers in Vulkan backend * Fix flake8 * Disable Vulkan async backend functions for now * Also add Vulkan run tests command to Makefile and CMakeLists.txt --- CMakeLists.txt | 20 + Makefile | 12 + ggml-vulkan-shaders.hpp | 10922 +++------------------------------- ggml-vulkan.cpp | 420 +- ggml_vk_generate_shaders.py | 213 +- 5 files changed, 1257 insertions(+), 10330 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 1ee455b3a..c156c4824 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -100,6 +100,10 @@ option(LLAMA_HIPBLAS "llama: use hipBLAS" option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_VULKAN "llama: use Vulkan" OFF) +option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF) +option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF) +option(LLAMA_VULKAN_VALIDATE "llama: enable Vulkan validation" OFF) +option(LLAMA_VULKAN_RUN_TESTS "llama: run Vulkan tests" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) @@ -431,6 +435,22 @@ if (LLAMA_VULKAN) add_compile_definitions(GGML_USE_VULKAN) + if (LLAMA_VULKAN_CHECK_RESULTS) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_CHECK_RESULTS) + endif() + + if (LLAMA_VULKAN_DEBUG) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_DEBUG) + endif() + + if (LLAMA_VULKAN_VALIDATE) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_VALIDATE) + endif() + + if (LLAMA_VULKAN_RUN_TESTS) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_RUN_TESTS) + endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan) else() message(WARNING "Vulkan not found") diff --git a/Makefile b/Makefile index bf9e085de..a55d15888 100644 --- a/Makefile +++ b/Makefile @@ -457,6 +457,18 @@ ifdef LLAMA_VULKAN_CHECK_RESULTS MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS endif +ifdef LLAMA_VULKAN_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_DEBUG +endif + +ifdef LLAMA_VULKAN_VALIDATE + MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE +endif + +ifdef LLAMA_VULKAN_RUN_TESTS + MK_CPPFLAGS += -DGGML_VULKAN_RUN_TESTS +endif + ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h $(CXX) $(CXXFLAGS) -c $< -o $@ endif # LLAMA_VULKAN diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 195410c02..e5e7a8414 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -890,156 +890,6 @@ const uint64_t cpy_f32_f32_len = 2472; unsigned char dequant_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x81,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x4f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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-0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q4_K_len = 5776; - -unsigned char dequant_q4_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xb1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -6669,1134 +3848,10 @@ unsigned char dequant_q4_K_fp32_data[] = { 0x37,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q4_K_fp32_len = 5940; +const uint64_t dequant_q4_K_len = 5940; unsigned char dequant_q5_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7a,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q5_K_len = 5888; - -unsigned char dequant_q5_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xa0,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -12162,366 +6653,10 @@ unsigned char dequant_q5_K_fp32_data[] = { 0x8a,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q5_K_fp32_len = 5988; +const uint64_t dequant_q5_K_len = 5988; unsigned char dequant_q6_K_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x0a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, 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dequant_q6_K_fp32_data[] = { 0x06,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q6_K_fp32_len = 4296; +const uint64_t dequant_q6_K_len = 4296; unsigned char dequant_q8_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -14798,500 +8158,6 @@ const uint64_t gelu_f32_len = 1484; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x68,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x6e,0x00,0x00,0x00, -0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x72,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,0x75,0x00,0x00,0x00, -0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x72,0x00,0x00,0x00, -0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x78,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t get_rows_f16_f32_fp32_len = 1932; - -unsigned char get_rows_f16_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, @@ -15455,21 +8321,19 @@ unsigned char get_rows_f16_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_fp32_len = 1948; +const uint64_t get_rows_f16_len = 1948; -unsigned char get_rows_q4_0_data[] = { +unsigned char get_rows_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x97,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, +0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, -0x7d,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15488,297 +8352,75 @@ unsigned char get_rows_q4_0_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x56,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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-0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_0_len = 2356; - -unsigned char get_rows_q4_0_f32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x9a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, -0x7d,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 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0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00, 0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00, @@ -15813,258 +8455,41 @@ unsigned char get_rows_q4_0_f32_data[] = { 0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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-0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x50,0x00,0x00,0x00, -0x8c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00, -0x8c,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x85,0x00,0x00,0x00, -0x8e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x8a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00, -0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_0_f32_len = 2404; +0x44,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x16,0x00,0x00,0x00, +0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x86,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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char get_rows_q4_0_fp32_data[] = { +unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -16264,425 +8689,11 @@ unsigned char get_rows_q4_0_fp32_data[] = { 0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_0_fp32_len = 2372; +const uint64_t get_rows_q4_0_len = 2372; -unsigned char get_rows_q4_1_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x94,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_1_f32_len = 2456; - -unsigned char get_rows_q4_1_f32_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -16690,7 +8701,7 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x7c,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -16714,91 +8725,92 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x5a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x96,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -17093,503 +9098,11 @@ unsigned char get_rows_q4_1_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_1_fp32_len = 2440; +const uint64_t get_rows_q4_1_len = 2440; -unsigned char get_rows_q5_0_data[] = { +unsigned char get_rows_q4_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 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-0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -17597,7 +9110,7 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -17617,106 +9130,95 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 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0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -17758,80 +9260,54 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5e,0x00,0x00,0x00, -0x5f,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00, +0x5d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 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0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -18074,485 +9550,11 @@ unsigned char get_rows_q5_0_fp32_data[] = { 0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q5_0_fp32_len = 2884; +const uint64_t get_rows_q5_0_len = 2884; -unsigned char get_rows_q5_1_data[] = { +unsigned char get_rows_q5_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xb4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -18560,7 +9562,7 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -18580,101 +9582,106 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -18695,7 +9702,7 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -18716,77 +9723,80 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00, 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-0x2e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, -0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0xb3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q5_1_f32_fp32_len = 2780; +0x52,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00, +0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x20,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x66,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x67,0x00,0x00,0x00, +0x68,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00, +0x6b,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 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get_rows_q8_0_data[] = { +unsigned char get_rows_q5_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00, -0x73,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -19365,247 +10196,81 @@ unsigned char get_rows_q8_0_f32_data[] = { 0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, +0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4d,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5a,0x00,0x00,0x00, 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2296; + +unsigned char get_rows_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x76,0x00,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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matmul_f16_aligned_l_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index b1e0006bb..14fb89e09 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1,6 +1,6 @@ #include "ggml-vulkan.h" -#ifdef VK_RUN_TESTS +#ifdef GGML_VULKAN_RUN_TESTS #include #endif @@ -255,6 +255,7 @@ static size_t vk_staging_offset; static vk_buffer vk_sync_staging; static vk_context * vk_ctx; +static vk_context * vk_transfer_ctx; static bool vk_disable; @@ -264,7 +265,7 @@ size_t vk_output_tensor; #endif static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; #endif GGML_ASSERT(parameter_count > 0); @@ -368,7 +369,7 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s } static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; #endif // Check if gc already contains pipeline before adding it @@ -413,14 +414,14 @@ static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uin } static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; #endif pipeline.descriptor_set_idx = 0; } static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; #endif if (q.cmd_buffers.size() > q.cmd_buffer_idx) { @@ -442,7 +443,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { } static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_submission()" << std::endl; #endif vk_submission s; @@ -453,14 +454,14 @@ static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_sequence_1()" << std::endl; #endif return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; } static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; #endif if (ctx->seqs.empty()) { @@ -536,7 +537,7 @@ static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { } static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_find_queue_family_index()" << std::endl; #endif const uint32_t qfsize = queue_family_props.size(); @@ -578,7 +579,7 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector= vk_gc.tl_semaphores.size()) { @@ -642,7 +643,7 @@ static vk::Event ggml_vk_create_event() { } static void ggml_vk_queue_cleanup(vk_queue& q) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_queue_cleanup()" << std::endl; #endif // Requires command buffers to be done @@ -652,7 +653,7 @@ static void ggml_vk_queue_cleanup(vk_queue& q) { } static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; #endif GGML_ASSERT(size > 0); @@ -743,7 +744,7 @@ static void ggml_vk_destroy_buffer(vk_buffer& buf) { if (buf.size == 0) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; #endif @@ -757,7 +758,7 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { } static void ggml_vk_sync_buffers(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_sync_buffers()" << std::endl; #endif const std::vector mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } }; @@ -773,7 +774,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { } static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_wait_events()" << std::endl; #endif if (events.empty()) { @@ -810,7 +811,7 @@ static bool ggml_vk_build_shader(ggml_type type) { } static void ggml_vk_load_shaders() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_load_shaders()" << std::endl; #endif @@ -849,36 +850,6 @@ static void ggml_vk_load_shaders() { vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - // Build dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - - // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); } else { vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); @@ -901,36 +872,6 @@ static void ggml_vk_load_shaders() { vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - // Build dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_fp32_len, f32_to_f16_fp32_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_fp32_len, dequant_f16_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_fp32_len, dequant_q4_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_fp32_len, dequant_q4_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_fp32_len, dequant_q5_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_fp32_len, dequant_q5_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_fp32_len, dequant_q8_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_fp32_len, dequant_q2_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_fp32_len, dequant_q3_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_fp32_len, dequant_q4_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_fp32_len, dequant_q5_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_fp32_len, dequant_q6_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - - // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_fp32_len, get_rows_f16_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_fp32_len, get_rows_q4_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_fp32_len, get_rows_q4_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_fp32_len, get_rows_q5_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_fp32_len, get_rows_q5_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_fp32_len, get_rows_q8_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_fp32_len, get_rows_f16_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_fp32_len, get_rows_q4_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_fp32_len, get_rows_q4_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_fp32_len, get_rows_q5_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_fp32_len, get_rows_q5_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_fp32_len, get_rows_q8_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); } vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); @@ -945,6 +886,36 @@ static void ggml_vk_load_shaders() { vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + // dequant shaders + vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); + + vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + + // get_rows + vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); @@ -983,7 +954,7 @@ static void ggml_vk_load_shaders() { } void ggml_vk_init() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_init()" << std::endl; #endif static bool initialized = false; @@ -999,17 +970,17 @@ void ggml_vk_init() { vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector layers = { -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE "VK_LAYER_KHRONOS_validation", #endif }; const std::vector extensions = { -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE "VK_EXT_validation_features", #endif }; vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; vk::ValidationFeaturesEXT validation_features = { features_enable, @@ -1120,7 +1091,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions.push_back("VK_KHR_16bit_storage"); -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif @@ -1154,6 +1125,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; vk_fence = vk_device.device.createFence({}); vk_ctx = nullptr; + vk_transfer_ctx = nullptr; vk_disable = false; @@ -1166,7 +1138,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; } static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif switch (type) { @@ -1190,7 +1162,7 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { } static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif switch (type) { @@ -1219,7 +1191,7 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; static vk_buffer ggml_vk_pool_malloc(size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; #endif int best_i = -1; @@ -1253,7 +1225,7 @@ static vk_buffer ggml_vk_pool_malloc(size_t size) { } static void ggml_vk_pool_free(vk_buffer& buffer) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; #endif for (int i = 0; i < MAX_VK_BUFFERS; ++i) { @@ -1286,7 +1258,7 @@ static vk_buffer ggml_vk_create_buffer_temp(size_t size) { } static void * ggml_vk_host_malloc(size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); @@ -1309,7 +1281,7 @@ static void ggml_vk_host_free(void* ptr) { if (ptr == nullptr) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; #endif vk_buffer* buf = nullptr; @@ -1363,7 +1335,7 @@ static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, s const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; #endif std::vector descriptor_buffer_infos; @@ -1398,7 +1370,7 @@ static void ggml_vk_end_submission(vk_submission& s, std::vector w } static void ggml_vk_ctx_end(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl; #endif if (ctx->s == nullptr) { @@ -1410,7 +1382,7 @@ static void ggml_vk_ctx_end(vk_context * ctx) { } static void ggml_vk_ctx_begin(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; #endif if (ctx->s != nullptr) { @@ -1441,7 +1413,7 @@ static void ensure_sync_staging_buffer(size_t size) { } static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; #endif GGML_ASSERT(!ggml_is_contiguous(tensor)); @@ -1548,7 +1520,7 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; #endif // Buffer is already mapped @@ -1582,7 +1554,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "STAGING" << std::endl; #endif @@ -1619,14 +1591,14 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size } static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; #endif return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); } static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; #endif // Buffer is already mapped @@ -1653,14 +1625,14 @@ static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * } static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; #endif ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); } static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; #endif GGML_ASSERT(width > 0); @@ -1693,7 +1665,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "STAGING" << std::endl; #endif @@ -1722,7 +1694,7 @@ static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t o } static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; #endif if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { @@ -1746,7 +1718,7 @@ static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_ } static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; #endif VkBufferCopy bc{ src_offset, dst_offset, size }; @@ -1755,7 +1727,7 @@ static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t } static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; #endif VkBufferCopy bc{ src_offset, dst_offset, size }; @@ -1771,7 +1743,7 @@ static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * } static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; #endif vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); @@ -1785,7 +1757,7 @@ static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, siz } static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; #endif const uint64_t ne0 = src->ne[0]; @@ -1815,7 +1787,7 @@ static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offs } static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; #endif const uint64_t ne0 = dst->ne[0]; @@ -1841,24 +1813,24 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs } static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"; #endif if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " = 4" << std::endl; #endif return 4; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " = 1" << std::endl; #endif return 1; } static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif if (m <= 32 || n <= 32) { @@ -1871,41 +1843,41 @@ static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { } static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; #endif if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; @@ -1914,30 +1886,30 @@ static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, in GGML_ASSERT(false); } - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; } static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif + ggml_vk_sync_buffers(ctx); if (split_k == 1) { - ggml_vk_sync_buffers(ctx); const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); return; @@ -1945,10 +1917,6 @@ static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer GGML_ASSERT(batch_stride_d == m * n); - // Synchronize the two submissions - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.fillBuffer(split_k_buffer.buffer.buffer, 0, split_k_buffer.size, 0); - ggml_vk_sync_buffers(ctx); const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); @@ -1980,7 +1948,7 @@ static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { } static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; #endif @@ -2002,7 +1970,7 @@ static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, } static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2186,7 +2154,7 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co } static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2366,7 +2334,7 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 } static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2455,7 +2423,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor } static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2561,7 +2529,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr } static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { @@ -2774,7 +2742,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * template static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -3095,7 +3063,7 @@ static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor } } -#ifdef VK_RUN_TESTS +#ifdef GGML_VULKAN_RUN_TESTS static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { return; @@ -3129,7 +3097,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 template static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; #endif const size_t x_ne = m * k * batch; @@ -3520,7 +3488,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) } static void ggml_vk_test_transfer(size_t ne, bool pinned) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct @@ -3600,10 +3568,103 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { free(y); } } + +static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; +#endif + const size_t x_sz = sizeof(float) * ne; + const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; + const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); + float * x = (float *) malloc(x_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); + + for (size_t i = 0; i < ne; i++) { + x[i] = rand() / (float)RAND_MAX; + } + + std::vector hist_cur(1 << 4, 0); + + vk_pipeline& p = vk_pipeline_dequant[quant]; + + switch(quant) { + case GGML_TYPE_Q4_0: + ggml_quantize_q4_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_1: + ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_0: + ggml_quantize_q5_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_1: + ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q8_0: + ggml_quantize_q8_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q2_K: + ggml_quantize_q2_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q3_K: + ggml_quantize_q3_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_K: + ggml_quantize_q4_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_K: + ggml_quantize_q5_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q6_K: + ggml_quantize_q6_K(x, qx, ne, ne, hist_cur.data()); + break; + default: + GGML_ASSERT(false); + } + + ggml_vk_pipeline_allocate_descriptor_sets(p, 1); + + ggml_vk_buffer_write(&qx_buf, 0, qx, qx_sz); + + vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + ggml_vk_ctx_begin(ctx); + const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(ctx); + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + auto end = std::chrono::high_resolution_clock::now(); + + double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(&x_buf, 0, x_chk, x_sz_f16); + + double avg_err = 0.0; + for (size_t i = 0; i < ne; i++) { + avg_err += std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + } + + std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err / ne << std::endl; + + ggml_vk_destroy_buffer(x_buf); + ggml_vk_destroy_buffer(qx_buf); + + free(x); + free(qx); + free(x_chk); +} #endif static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl; #endif ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu; @@ -3627,7 +3688,7 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph } void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU @@ -3746,15 +3807,26 @@ void ggml_vk_preallocate_buffers() { if (vk_disable) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; #endif -#if defined(VK_RUN_TESTS) +#if defined(GGML_VULKAN_RUN_TESTS) vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); ggml_vk_test_transfer(8192 * 1000, false); ggml_vk_test_transfer(8192 * 1000, true); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q6_K); + const std::vector vals { 8, 8, 8, 100, 46, 576, @@ -3845,7 +3917,7 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; #endif vk_semaphore_idx = 0; @@ -4068,7 +4140,7 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) return true; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; #endif @@ -4111,7 +4183,7 @@ void ggml_vk_graph_cleanup() { if (vk_disable) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif for (auto& buffer : vk_gc.temp_buffers) { @@ -4150,7 +4222,7 @@ void ggml_vk_graph_cleanup() { } static void ggml_vk_cleanup() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cleanup()" << std::endl; #endif ggml_vk_destroy_buffer(vk_prealloc_x); @@ -4234,7 +4306,7 @@ GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl; #endif ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; @@ -4254,7 +4326,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b } GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -4267,7 +4339,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -4323,7 +4395,7 @@ GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buff } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); @@ -4467,7 +4539,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_t } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -4475,19 +4547,19 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_write_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + ggml_vk_buffer_write_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); UNUSED(backend); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -4495,32 +4567,32 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_read_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + ggml_vk_buffer_read_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); UNUSED(backend); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_copy_async(vk_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + ggml_vk_buffer_copy_async(vk_transfer_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); return true; } @@ -4530,28 +4602,28 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_synchronize()" << std::endl; #endif - if(vk_ctx == nullptr) { + if(vk_transfer_ctx == nullptr) { return; } - ggml_vk_ctx_end(vk_ctx); + ggml_vk_ctx_end(vk_transfer_ctx); - for (auto& cpy : vk_ctx->in_memcpys) { + for (auto& cpy : vk_transfer_ctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(vk_ctx, vk_fence); + ggml_vk_submit(vk_transfer_ctx, vk_fence); VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); vk_device.device.resetFences({ vk_fence }); - for (auto& cpy : vk_ctx->out_memcpys) { + for (auto& cpy : vk_transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - vk_ctx = nullptr; + vk_transfer_ctx = nullptr; UNUSED(backend); } diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 67981a751..4abb0383f 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -157,19 +157,10 @@ struct block_q6_K # Dequant functions shader_f16_dequant_func = """ -#define DEQUANT_FUNC f16vec2 v = f16vec2(data_a[ib + 0], data_a[ib + 1]); -""" -shader_f16_dequant_func_compat = """ #define DEQUANT_FUNC vec2 v = vec2(data_a[ib + 0], data_a[ib + 1]); """ shader_q4_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ -v = (v - 8.0hf)*d; -""" -shader_q4_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const uint vui = uint(data_a[ib].qs[iqs]); \ vec2 v = vec2(vui & 0xF, vui >> 4); \ @@ -177,13 +168,6 @@ v = (v - 8.0f)*d; """ shader_q4_1_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const float16_t m = data_a[ib].m; \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ -v = v*d + m; -""" -shader_q4_1_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ const uint vui = uint(data_a[ib].qs[iqs]); \ @@ -192,14 +176,6 @@ v = v*d + m; """ shader_q5_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ -const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = (v - 16.0hf) * d; -""" -shader_q5_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ @@ -209,14 +185,6 @@ v = (v - 16.0f) * d; """ shader_q5_1_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const float16_t m = data_a[ib].m; \ -const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = v*d + m; -""" -shader_q5_1_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ @@ -226,11 +194,6 @@ v = v*d + m; """ shader_q8_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -f16vec2 v = f16vec2(data_a[ib].qs[iqs], data_a[ib].qs[iqs + 1]); \ -v = v * d; -""" -shader_q8_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \ v = v * d; @@ -2110,7 +2073,7 @@ lock = asyncio.Lock() shader_fnames = [] -async def string_to_spv(name, code, defines, fp16): +async def string_to_spv(name, code, defines, fp16=True): f = NamedTemporaryFile(mode="w", delete=False) f.write(code) f.flush() @@ -2200,64 +2163,6 @@ async def main(): tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - # Build dequant shaders - tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}, fp16)) - - for i in range(0, VK_NUM_TYPES): - stream.clear() - - stream.extend((dequant_head, shader_int8_ext, shader_float_type)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q2_K: - stream.extend((shader_q2_K_defines, dequant_q2_K_body)) - elif i == GGML_TYPE_Q3_K: - stream.extend((shader_q3_K_defines, dequant_q3_K_body)) - elif i == GGML_TYPE_Q4_K: - stream.extend((shader_q4_K_defines, dequant_q4_K_body)) - elif i == GGML_TYPE_Q5_K: - stream.extend((shader_q5_K_defines, dequant_q5_K_body)) - elif i == GGML_TYPE_Q6_K: - stream.extend((shader_q6_K_defines, dequant_q6_K_body)) - else: - continue - - tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}, fp16)) - - # get_rows - for i in range(0, VK_NUM_TYPES): - stream.clear() - stream.extend((generic_head, shader_int8_ext, shader_float_type)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, get_rows_body)) - else: - continue - - tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}, fp16)) - tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # Shaders where precision is needed, so no fp16 version # mul mat vec @@ -2266,17 +2171,17 @@ async def main(): stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_f16_defines, shader_f16_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q2_K: stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body)) elif i == GGML_TYPE_Q3_K: @@ -2290,43 +2195,101 @@ async def main(): else: continue - tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}, fp16)) + tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) - tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) + # Dequant shaders + for i in range(0, VK_NUM_TYPES): + stream.clear() + + stream.extend((dequant_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q2_K: + stream.extend((shader_q2_K_defines, dequant_q2_K_body)) + elif i == GGML_TYPE_Q3_K: + stream.extend((shader_q3_K_defines, dequant_q3_K_body)) + elif i == GGML_TYPE_Q4_K: + stream.extend((shader_q4_K_defines, dequant_q4_K_body)) + elif i == GGML_TYPE_Q5_K: + stream.extend((shader_q5_K_defines, dequant_q5_K_body)) + elif i == GGML_TYPE_Q6_K: + stream.extend((shader_q6_K_defines, dequant_q6_K_body)) + else: + continue + + tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"})) + + tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {})) + + # get_rows + for i in range(0, VK_NUM_TYPES): + stream.clear() + stream.extend((generic_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, get_rows_body)) + else: + continue + + tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"})) + + tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) # Norms - tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}, True)) - tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}, True)) - tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) + tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) await asyncio.gather(*tasks) From 60ecf099eddfe70fec797ef6790572e452054add Mon Sep 17 00:00:00 2001 From: Martin Schwaighofer Date: Sun, 28 Jan 2024 12:59:43 +0100 Subject: [PATCH 609/664] add Vulkan support to Nix flake --- .devops/nix/package.nix | 21 +++++++++++++++++---- flake.nix | 1 + 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index a868a9a61..ad23f7dd7 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -13,18 +13,22 @@ cudaPackages, darwin, rocmPackages, + vulkan-headers, + vulkan-loader, clblast, useBlas ? builtins.all (x: !x) [ useCuda useMetalKit useOpenCL useRocm + useVulkan ], useCuda ? config.cudaSupport, useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, useMpi ? false, # Increases the runtime closure size by ~700M useOpenCL ? false, useRocm ? config.rocmSupport, + useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake }@inputs: @@ -48,7 +52,8 @@ let ++ lib.optionals useMetalKit [ "MetalKit" ] ++ lib.optionals useMpi [ "MPI" ] ++ lib.optionals useOpenCL [ "OpenCL" ] - ++ lib.optionals useRocm [ "ROCm" ]; + ++ lib.optionals useRocm [ "ROCm" ] + ++ lib.optionals useVulkan [ "Vulkan" ]; pnameSuffix = strings.optionalString (suffices != [ ]) @@ -108,6 +113,11 @@ let hipblas rocblas ]; + + vulkanBuildInputs = [ + vulkan-headers + vulkan-loader + ]; in effectiveStdenv.mkDerivation ( @@ -164,7 +174,8 @@ effectiveStdenv.mkDerivation ( ++ optionals useCuda cudaBuildInputs ++ optionals useMpi [ mpi ] ++ optionals useOpenCL [ clblast ] - ++ optionals useRocm rocmBuildInputs; + ++ optionals useRocm rocmBuildInputs + ++ optionals useVulkan vulkanBuildInputs; cmakeFlags = [ @@ -178,6 +189,7 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) (cmakeBool "LLAMA_MPI" useMpi) + (cmakeBool "LLAMA_VULKAN" useVulkan) ] ++ optionals useCuda [ ( @@ -218,6 +230,7 @@ effectiveStdenv.mkDerivation ( useMpi useOpenCL useRocm + useVulkan ; shell = mkShell { @@ -242,11 +255,11 @@ effectiveStdenv.mkDerivation ( # Configurations we don't want even the CI to evaluate. Results in the # "unsupported platform" messages. This is mostly a no-op, because # cudaPackages would've refused to evaluate anyway. - badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + badPlatforms = optionals (useCuda || useOpenCL || useVulkan) lib.platforms.darwin; # Configurations that are known to result in build failures. Can be # overridden by importing Nixpkgs with `allowBroken = true`. - broken = (useMetalKit && !effectiveStdenv.isDarwin); + broken = (useMetalKit && !effectiveStdenv.isDarwin) || (useVulkan && effectiveStdenv.isDarwin); description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; homepage = "https://github.com/ggerganov/llama.cpp/"; diff --git a/flake.nix b/flake.nix index a776ba024..ad2f9b295 100644 --- a/flake.nix +++ b/flake.nix @@ -157,6 +157,7 @@ mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; + vulkan = config.packages.default.override { useVulkan = true; }; } // lib.optionalAttrs (system == "x86_64-linux") { rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; From 3cc5ed353c07201d8d5b98b0a4713ab633da6d04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 3 Feb 2024 20:14:59 +0100 Subject: [PATCH 610/664] make: fix nvcc optimization flags for host code (#5309) --- Makefile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index a55d15888..40b16e0ea 100644 --- a/Makefile +++ b/Makefile @@ -109,6 +109,7 @@ MK_NVCCFLAGS += -O3 else MK_CFLAGS += -O3 MK_CXXFLAGS += -O3 +MK_NVCCFLAGS += -O3 endif # clock_gettime came in POSIX.1b (1993) @@ -365,7 +366,7 @@ ifdef LLAMA_CUBLAS MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o - MK_NVCCFLAGS = -use_fast_math + MK_NVCCFLAGS += -use_fast_math ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT From 3c0d25c4756742ebf15ad44700fabc0700c638bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 3 Feb 2024 20:15:13 +0100 Subject: [PATCH 611/664] make: add nvcc info print (#5310) --- Makefile | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 40b16e0ea..21d5e15ba 100644 --- a/Makefile +++ b/Makefile @@ -553,8 +553,11 @@ $(info I CFLAGS: $(CFLAGS)) $(info I CXXFLAGS: $(CXXFLAGS)) $(info I NVCCFLAGS: $(NVCCFLAGS)) $(info I LDFLAGS: $(LDFLAGS)) -$(info I CC: $(shell $(CC) --version | head -n 1)) -$(info I CXX: $(shell $(CXX) --version | head -n 1)) +$(info I CC: $(shell $(CC) --version | head -n 1)) +$(info I CXX: $(shell $(CXX) --version | head -n 1)) +ifdef LLAMA_CUBLAS +$(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) +endif # LLAMA_CUBLAS $(info ) # From 277fad30c60ef3559dc2d01b19d05e659d40a824 Mon Sep 17 00:00:00 2001 From: Welby Seely Date: Sat, 3 Feb 2024 23:18:51 -0500 Subject: [PATCH 612/664] cmake : use set() for LLAMA_WIN_VER (#5298) option() is specifically for booleans. Fixes #5158 --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c156c4824..8c04e4c19 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -79,7 +79,7 @@ if (NOT MSVC) endif() if (WIN32) - option(LLAMA_WIN_VER "llama: Windows Version" 0x602) + set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version") endif() # 3rd party libs From 5ed26e1fc9fab4ce96ecf2d84183fe45bdcab0d4 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 4 Feb 2024 10:39:58 +0200 Subject: [PATCH 613/664] Adding some imatrix tools (#5302) * imatrix: adding --combine and --continue-from * imatrix: be able to start from a specific chunk --------- Co-authored-by: Iwan Kawrakow --- examples/imatrix/imatrix.cpp | 116 +++++++++++++++++++++++++++++++++-- 1 file changed, 112 insertions(+), 4 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index ea06fcdbf..bc9f6fa68 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -36,6 +36,8 @@ public: void set_parameters(StatParams&& params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); void save_imatrix() const; + bool load_imatrix(const char * file_name, bool add); + static bool load_imatrix(const char * file_name, std::unordered_map& imatrix); private: std::unordered_map m_stats; StatParams m_params; @@ -189,6 +191,57 @@ void IMatrixCollector::save_imatrix(const char * fname) const { } } +bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map& imatrix_data) { + std::ifstream in(imatrix_file, std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__,imatrix_file); + return false; + } + int n_entries; + in.read((char*)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file); + return false; + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file); + return false; + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto& e = imatrix_data[std::move(name)]; + int ncall; + in.read((char*)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n",__func__,i); + imatrix_data = {}; + return false; + } + e.values.resize(nval); + in.read((char*)e.values.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n",__func__,i); + imatrix_data = {}; + return false; + } + e.ncall = ncall; + } + return true; +} + +bool IMatrixCollector::load_imatrix(const char * file_name, bool add) { + if (!add) { + m_stats.clear(); + } + return load_imatrix(file_name, m_stats); +} + static IMatrixCollector g_collector; static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { @@ -269,7 +322,7 @@ static void process_logits( } } -static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl) { +static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) { const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); @@ -282,6 +335,15 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + if (from_chunk > 0) { + if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) { + fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk); + return false; + } + fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx); + tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx); + } + if (int(tokens.size()) < 2*n_ctx) { fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx, n_ctx); @@ -402,7 +464,10 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool int main(int argc, char ** argv) { StatParams sparams; + std::string prev_result_file; + std::string combine_files; bool compute_ppl = true; + int from_chunk = 0; std::vector args; args.push_back(argv[0]); int iarg = 1; @@ -423,6 +488,13 @@ int main(int argc, char ** argv) { compute_ppl = false; } else if (arg == "--keep-imatrix") { sparams.keep_every = std::stoi(argv[++iarg]); + } else if (arg == "--continue-from") { + prev_result_file = argv[++iarg]; + } else if (arg == "--combine") { + combine_files = argv[++iarg]; + } + else if (arg == "--from-chunk") { + from_chunk = std::stoi(argv[++iarg]); } else { args.push_back(argv[iarg]); } @@ -436,14 +508,50 @@ int main(int argc, char ** argv) { } } + g_collector.set_parameters(std::move(sparams)); + + if (!combine_files.empty()) { + std::vector files; + size_t pos = 0; + while (true) { + auto new_pos = combine_files.find(',', pos); + if (new_pos != std::string::npos) { + files.emplace_back(combine_files.substr(pos, new_pos - pos)); + pos = new_pos + 1; + } else { + files.emplace_back(combine_files.substr(pos)); + break; + } + } + if (files.size() < 2) { + fprintf(stderr, "You must provide at least two comma separated files to use --combine\n"); + return 1; + } + printf("Combining the following %d files\n", int(files.size())); + for (auto& file : files) { + printf(" %s\n", file.c_str()); + if (!g_collector.load_imatrix(file.c_str(), true)) { + fprintf(stderr, "Failed to load %s\n", file.c_str()); + return 1; + } + } + g_collector.save_imatrix(); + return 0; + } + + if (!prev_result_file.empty()) { + if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) { + fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str()); + return 1; + } + } + gpt_params params; params.n_batch = 512; if (!gpt_params_parse(args.size(), args.data(), params)) { return 1; } - g_collector.set_parameters(std::move(sparams)); - params.logits_all = true; params.n_batch = std::min(params.n_batch, params.n_ctx); @@ -495,7 +603,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s\n", get_system_info(params).c_str()); } - bool OK = compute_imatrix(ctx, params, compute_ppl); + bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); if (!OK) { return 1; } From 9392ebd49ea5ae236a55b47cbf6a13247e8a3b8c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 4 Feb 2024 00:17:24 +0000 Subject: [PATCH 614/664] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/07f6395285469419cf9d078f59b5b49993198c00' (2024-01-11) → 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/b0d36bd0a420ecee3bc916c91886caca87c894e9?dir=lib' (2023-12-30) → 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/ae5c332cbb5827f6b1f02572496b141021de335f' (2024-01-25) → 'github:NixOS/nixpkgs/b8b232ae7b8b144397fdb12d20f592e5e7c1a64d' (2024-01-31) --- flake.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/flake.lock b/flake.lock index 95e41f333..8cfc78273 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1704982712, - "narHash": "sha256-2Ptt+9h8dczgle2Oo6z5ni5rt/uLMG47UFTR1ry/wgg=", + "lastModified": 1706830856, + "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "07f6395285469419cf9d078f59b5b49993198c00", + "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1706191920, - "narHash": "sha256-eLihrZAPZX0R6RyM5fYAWeKVNuQPYjAkCUBr+JNvtdE=", + "lastModified": 1706732774, + "narHash": "sha256-hqJlyJk4MRpcItGYMF+3uHe8HvxNETWvlGtLuVpqLU0=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "ae5c332cbb5827f6b1f02572496b141021de335f", + "rev": "b8b232ae7b8b144397fdb12d20f592e5e7c1a64d", "type": "github" }, "original": { @@ -37,11 +37,11 @@ "nixpkgs-lib": { "locked": { "dir": "lib", - "lastModified": 1703961334, - "narHash": "sha256-M1mV/Cq+pgjk0rt6VxoyyD+O8cOUiai8t9Q6Yyq4noY=", + "lastModified": 1706550542, + "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "b0d36bd0a420ecee3bc916c91886caca87c894e9", + "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652", "type": "github" }, "original": { From 4833ac209da6a427de64f97e8f403dcdc5de6bc3 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 5 Feb 2024 07:08:24 +0000 Subject: [PATCH 615/664] [SYCL] Fix cpy with dims of 3 (#5289) * Fix cpy with dims of 3 * rm asserts --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 194 +++++++++++++++++++++++++++++--------------------- 1 file changed, 114 insertions(+), 80 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 51445b5e7..a03df4c65 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -7693,6 +7693,13 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) { *dsti = *xi; } +static void cpy_1_f16_f32(const char * cxi, char * cdsti) { + const sycl::half *xi = (const sycl::half *)cxi; + float *dsti = (float *)cdsti; + + *dsti = *xi; +} + static void cpy_1_i16_i16(const char * cxi, char * cdsti) { const int16_t *xi = (const int16_t *)cxi; int16_t *dsti = (int16_t *)cdsti; @@ -7709,9 +7716,9 @@ static void cpy_1_i32_i32(const char * cxi, char * cdsti) { template static void cpy_f32_f16(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, - const sycl::nd_item<3> &item_ct1) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2); @@ -7721,15 +7728,17 @@ static void cpy_f32_f16(const char * cx, char * cdst, const int ne, // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = i - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = i - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -7823,9 +7832,9 @@ static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { template static void cpy_f32_q(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, - const sycl::nd_item<3> &item_ct1) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2)) * qk; @@ -7834,15 +7843,17 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne, return; } - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = (i - i02*ne01*ne00 - i01*ne00); - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; cpy_blck(cx + x_offset, cdst + dst_offset); } @@ -10599,10 +10610,12 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10615,8 +10628,8 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10624,10 +10637,12 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10640,8 +10655,8 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10649,10 +10664,12 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK8_0 == 0); @@ -10661,17 +10678,20 @@ static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK4_0 == 0); @@ -10680,17 +10700,20 @@ static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK4_1 == 0); @@ -10699,17 +10722,20 @@ static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10722,8 +10748,8 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10731,10 +10757,12 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10747,8 +10775,8 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10756,10 +10784,12 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10772,8 +10802,8 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -13910,19 +13940,23 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - GGML_ASSERT(src0->ne[3] == 1); + const int64_t ne02 = src0->ne[2]; + const int64_t nb00 = src0->nb[0]; const int64_t nb01 = src0->nb[1]; const int64_t nb02 = src0->nb[2]; + const int64_t nb03 = src0->nb[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; - GGML_ASSERT(src1->ne[3] == 1); + const int64_t ne12 = src1->ne[2]; + const int64_t nb10 = src1->nb[0]; const int64_t nb11 = src1->nb[1]; const int64_t nb12 = src1->nb[2]; + const int64_t nb13 = src1->nb[3]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; @@ -13934,21 +13968,21 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { - ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { - ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); From 5d55b0cd827bb0fcfedfa329a82bd5d6ef2c93ca Mon Sep 17 00:00:00 2001 From: chiranko <96988916+chiranko@users.noreply.github.com> Date: Mon, 5 Feb 2024 15:41:38 +0800 Subject: [PATCH 616/664] readme : add CodeShell models to the supported models list (#5330) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 4a9bdf314..a6fe34629 100644 --- a/README.md +++ b/README.md @@ -107,6 +107,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** From 4be04c8965578edc09194fab769b4b922b8444f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9D=D0=B8=D1=8F=D0=B7=20=D0=93=D0=B0=D1=80=D0=B8=D1=84?= =?UTF-8?q?=D0=B7=D1=8F=D0=BD=D0=BE=D0=B2?= <112617865+garrnizon@users.noreply.github.com> Date: Mon, 5 Feb 2024 10:43:57 +0300 Subject: [PATCH 617/664] scripts : add non-interactive server-llm.sh (#5303) * Update server-llm.sh Add flag --non-interactive that allows run script without asking a permission * Update scripts/server-llm.sh --------- Co-authored-by: Georgi Gerganov --- scripts/server-llm.sh | 73 ++++++++++++++++++++++++------------------- 1 file changed, 40 insertions(+), 33 deletions(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 0b83cdbbc..062b70496 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -47,6 +47,7 @@ if ! command -v make &> /dev/null; then fi # parse arguments +is_interactive=1 port=8888 repo="" wtype="" @@ -66,15 +67,16 @@ verbose=0 function print_usage { printf "Usage:\n" - printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" - printf " --port: port number, default is 8888\n" - printf " --repo: path to a repo containing GGUF model files\n" - printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" - printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" - printf " --gpu-id: gpu id, default is 0\n" - printf " --n-parallel: number of parallel requests, default is 8\n" - printf " --n-kv: KV cache size, default is 4096\n" - printf " --verbose: verbose output\n\n" + printf " ./server-llm.sh [-interactive] [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" + printf " --non-interactive: run without asking a permision to run\n" + printf " --port: port number, default is 8888\n" + printf " --repo: path to a repo containing GGUF model files\n" + printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" + printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" + printf " --gpu-id: gpu id, default is 0\n" + printf " --n-parallel: number of parallel requests, default is 8\n" + printf " --n-kv: KV cache size, default is 4096\n" + printf " --verbose: verbose output\n\n" printf "Example:\n\n" printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' } @@ -82,6 +84,10 @@ function print_usage { while [[ $# -gt 0 ]]; do key="$1" case $key in + --non-interactive) + is_interactive=0 + shift + ;; --port) port="$2" shift @@ -176,31 +182,32 @@ repos=( "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF" "https://huggingface.co/TheBloke/CausalLM-7B-GGUF" ) +if [ $is_interactive -eq 1 ]; then + printf "\n" + printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" + printf " Based on the options that follow, the script might download a model file\n" + printf " from the internet, which can be a few GBs in size. The script will also\n" + printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" + printf "\n" + printf " Upon success, an HTTP server will be started and it will serve the selected\n" + printf " model using llama.cpp for demonstration purposes.\n" + printf "\n" + printf " Please note:\n" + printf "\n" + printf " - All new data will be stored in the current folder\n" + printf " - The server will be listening on all network interfaces\n" + printf " - The server will run with default settings which are not always optimal\n" + printf " - Do not judge the quality of a model based on the results from this script\n" + printf " - Do not use this script to benchmark llama.cpp\n" + printf " - Do not use this script in production\n" + printf " - This script is only for demonstration purposes\n" + printf "\n" + printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" + printf "\n" + printf " Press Enter to continue ...\n\n" -printf "\n" -printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" -printf " Based on the options that follow, the script might download a model file\n" -printf " from the internet, which can be a few GBs in size. The script will also\n" -printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" -printf "\n" -printf " Upon success, an HTTP server will be started and it will serve the selected\n" -printf " model using llama.cpp for demonstration purposes.\n" -printf "\n" -printf " Please note:\n" -printf "\n" -printf " - All new data will be stored in the current folder\n" -printf " - The server will be listening on all network interfaces\n" -printf " - The server will run with default settings which are not always optimal\n" -printf " - Do not judge the quality of a model based on the results from this script\n" -printf " - Do not use this script to benchmark llama.cpp\n" -printf " - Do not use this script in production\n" -printf " - This script is only for demonstration purposes\n" -printf "\n" -printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" -printf "\n" -printf " Press Enter to continue ...\n\n" - -read + read +fi if [[ -z "$repo" ]]; then printf "[+] No repo provided from the command line\n" From 30679d438d5225b3aecf5cec6482cbc9f8f87ba5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 5 Feb 2024 09:48:03 +0200 Subject: [PATCH 618/664] scripts : fix typos, cleanup (#5303) --- scripts/server-llm.sh | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 062b70496..30bbac321 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -14,16 +14,17 @@ # - Might be unstable! # # Usage: -# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] +# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive] # -# --port: port number, default is 8888 -# --repo: path to a repo containing GGUF model files -# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input -# --backend: cpu, cuda, metal, opencl, depends on the OS -# --gpu-id: gpu id, default is 0 -# --n-parallel: number of parallel requests, default is 8 -# --n-kv: KV cache size, default is 4096 -# --verbose: verbose output +# --port: port number, default is 8888 +# --repo: path to a repo containing GGUF model files +# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input +# --backend: cpu, cuda, metal, opencl, depends on the OS +# --gpu-id: gpu id, default is 0 +# --n-parallel: number of parallel requests, default is 8 +# --n-kv: KV cache size, default is 4096 +# --verbose: verbose output +# --non-interactive: run without asking a permission to run # # Example: # @@ -67,8 +68,7 @@ verbose=0 function print_usage { printf "Usage:\n" - printf " ./server-llm.sh [-interactive] [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" - printf " --non-interactive: run without asking a permision to run\n" + printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n" printf " --port: port number, default is 8888\n" printf " --repo: path to a repo containing GGUF model files\n" printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" @@ -77,6 +77,7 @@ function print_usage { printf " --n-parallel: number of parallel requests, default is 8\n" printf " --n-kv: KV cache size, default is 4096\n" printf " --verbose: verbose output\n\n" + printf " --non-interactive: run without asking a permission to run\n" printf "Example:\n\n" printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' } From e6f81775323f6f4e4a30abf022a6028fa86b79ac Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Mon, 5 Feb 2024 17:00:47 +0900 Subject: [PATCH 619/664] common : add dynamic temperature parameters to main example cli (#5295) * added dynamic temp params in main * added help text --- common/common.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 3302caa20..8c1a60583 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -399,6 +399,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } sparams.penalty_present = std::stof(argv[i]); + } else if (arg == "--dynatemp-range") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.dynatemp_range = std::stof(argv[i]); + } else if (arg == "--dynatemp-exp") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.dynatemp_exponent = std::stof(argv[i]); } else if (arg == "--mirostat") { if (++i >= argc) { invalid_param = true; @@ -942,6 +954,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat); printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present); printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq); + printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range); + printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent); printf(" --mirostat N use Mirostat sampling.\n"); printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat); From a2d60c9158435ae9a6f14632f07f1acf7a3becef Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Mon, 5 Feb 2024 08:10:22 +0000 Subject: [PATCH 620/664] server : allow to get default generation settings for completion (#5307) --- examples/server/README.md | 16 +++++++++++++++- examples/server/server.cpp | 7 ++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index fe934dab1..d8e7c313e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -264,7 +264,21 @@ Notice that each `probs` is an array of length `n_probs`. It also accepts all the options of `/completion` except `stream` and `prompt`. -- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- **GET** `/props`: Return current server settings. + +### Result JSON + +```json +{ + "assistant_name": "", + "user_name": "", + "default_generation_settings": { ... } +} +``` + +- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. +- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a9f8cb369..8000fee5c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -334,6 +334,7 @@ struct llama_server_context // slots / clients std::vector slots; + json default_generation_settings_for_props; llama_server_queue queue_tasks; llama_server_response queue_results; @@ -430,6 +431,9 @@ struct llama_server_context slots.push_back(slot); } + default_generation_settings_for_props = get_formated_generation(slots.front()); + default_generation_settings_for_props["seed"] = -1; + batch = llama_batch_init(n_ctx, 0, params.n_parallel); // empty system prompt @@ -2614,7 +2618,8 @@ int main(int argc, char **argv) res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", llama.name_user.c_str() }, - { "assistant_name", llama.name_assistant.c_str() } + { "assistant_name", llama.name_assistant.c_str() }, + { "default_generation_settings", llama.default_generation_settings_for_props } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); From 6fdfa2ecc684000a25a4ad91823bc82a6652b645 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 10:46:06 +0200 Subject: [PATCH 621/664] iq2_xxs: tune quantization (#5320) We get slightly better PPL, and we cut quantization time in nearly half. The trick is to 1st quantize without forcing points onto the E8-lattice. We can then use a narrower search range around the block scale that we got that way. Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 58 ++++++--------------------------------------------- 1 file changed, 6 insertions(+), 52 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 8236385bc..014c0525a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -9048,8 +9048,6 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict int8_t L[32]; int8_t Laux[32]; float waux[32]; - bool is_on_grid[4]; - bool is_on_grid_aux[4]; uint8_t block_signs[4]; uint32_t q2[2*(QK_K/32)]; @@ -9099,10 +9097,11 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict memset(L, 0, 32); continue; } + float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight); + float eff_max = scale*kMaxQ; float best = 0; - float scale = max/(2*kMaxQ-1); - for (int is = -9; is <= 9; ++is) { - float id = (2*kMaxQ-1+is*0.1f)/max; + for (int is = -6; is <= 6; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/eff_max; float this_scale = 1/id; for (int k = 0; k < 4; ++k) { for (int i = 0; i < 8; ++i) { @@ -9112,9 +9111,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict uint16_t u = 0; for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); int grid_index = kmap_q2xs[u]; - is_on_grid_aux[k] = true; if (grid_index < 0) { - is_on_grid_aux[k] = false; const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); } @@ -9128,16 +9125,12 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { scale = sumqx/sumq2; best = scale*sumqx; - for (int i = 0; i < 32; ++i) L[i] = Laux[i]; - for (int k = 0; k < 4; ++k) is_on_grid[k] = is_on_grid_aux[k]; + memcpy(L, Laux, 32); } } - int n_not_ongrid = 0; - for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid; - if (n_not_ongrid > 0 && scale > 0) { + if (scale > 0) { float id = 1/scale; for (int k = 0; k < 4; ++k) { - if (is_on_grid[k]) continue; uint16_t u = 0; for (int i = 0; i < 8; ++i) { int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); @@ -9193,49 +9186,10 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict float d = max_scale/31; y[ibl].d = GGML_FP32_TO_FP16(d); float id = 1/d; - float sumqx = 0, sumq2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { int l = nearest_int(0.5f*(id*scales[ib]-1)); l = MAX(0, MIN(15, l)); q2[2*ib+1] |= ((uint32_t)l << 28); - const float * xb = xbl + 32*ib; - const float * qw = quant_weights + QK_K*ibl + 32*ib; - for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); - const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib); - const float db = d * (1 + 2*l); - uint32_t u = 0; - for (int k = 0; k < 4; ++k) { - const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127); - const float * xk = xb + 8*k; - const float * wk = weight + 8*k; - const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); - float best_mse = 0; int best_index = aux8[k]; - for (int j = 0; j < 8; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - best_mse += wk[j] * diff * diff; - } - for (int idx = 0; idx < 256; ++idx) { - grid = (const uint8_t *)(kgrid_q2xs + idx); - float mse = 0; - for (int j = 0; j < 8; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - mse += wk[j] * diff * diff; - } - if (mse < best_mse) { - best_mse = mse; best_index = idx; - } - } - u |= (best_index << 8*k); - grid = (const uint8_t *)(kgrid_q2xs + best_index); - //grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); - for (int j = 0; j < 8; ++j) { - float q = db * grid[j] * signs[j]; - sumqx += wk[j] * q * xk[j]; - sumq2 += wk[j] * q * q; - } - } - q2[2*ib] = u; - if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); } memcpy(y[ibl].qs, q2, QK_K/4); } From 7e1ae372f36d98fa66b1d778c5862904b4d80c88 Mon Sep 17 00:00:00 2001 From: Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Date: Mon, 5 Feb 2024 17:04:06 +0800 Subject: [PATCH 622/664] py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci --- convert-hf-to-gguf.py | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a6ffd128b..5e343742d 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1416,8 +1416,32 @@ class InternLM2Model(Model): self.gguf_writer.add_add_space_prefix(add_prefix) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + old_eos = special_vocab.special_token_ids["eos"] + if "chat" in os.path.basename(self.dir_model.absolute()): + # For the chat model, we replace the eos with '<|im_end|>'. + special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer) + print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ +in chat mode so that the conversation can end normally.") + special_vocab.add_to_gguf(self.gguf_writer) + def _try_get_sft_eos(self, tokenizer): + unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]') + im_end_list = tokenizer.encode('<|im_end|>') + assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1) + if len(unused_145_list) == 1: + eos_token = unused_145_list[0] + if len(im_end_list) == 1: + eos_token = im_end_list[0] + return eos_token + + def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + def set_gguf_parameters(self): self.gguf_writer.add_name("InternLM2") self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) @@ -1486,8 +1510,9 @@ class InternLM2Model(Model): qkv = data_torch qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] - q = rearrange(q, " o g n i -> o (g n i)").T - k = rearrange(k, " o g n i -> o (g n i)").T + # The model weights of q and k equire additional reshape. + q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads) + k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads) v = rearrange(v, " o g n i -> o (g n i)").T self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) From 89503dcb5f764a5cc7093db1f395f5121876a2cc Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 12:32:27 +0200 Subject: [PATCH 623/664] iq3_xxs: quards for the no-imatrix situation (#5334) Co-authored-by: Iwan Kawrakow --- llama.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index 4787a92fe..65e399adc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9456,8 +9456,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q4_K; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) { - new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; @@ -9496,9 +9496,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } - //else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { - // if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K; - //} + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) { + new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K From abb61944a5f64dec62c893ed0db10790169b672a Mon Sep 17 00:00:00 2001 From: "Dr. Tom Murphy VII Ph.D" <499244+tom7@users.noreply.github.com> Date: Mon, 5 Feb 2024 06:13:57 -0500 Subject: [PATCH 624/664] ggml : avoid duplicating function calls using MIN/MAX macros (#5325) * Avoid duplicating function calls when using MIN/MAX macros. Since these copy "a" and "b" they ask the compiler to evaluate one of them twice. The compiler doesn't have a problem with removing the duplication in something like MAX(0, x + 2), but in some cases we're calling functions, and those calls just happen twice. By explicitly evaluating at the expression we get smaller and faster code without duplicate calls. See ggml_rope_yarn_corr_dims in Compiler Explorer: https://godbolt.org/z/Ee4KMrvKh Code behaves exactly the same. * Update ggml.c --------- Co-authored-by: Georgi Gerganov --- ggml.c | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/ggml.c b/ggml.c index ee994c875..b9ec0c981 100644 --- a/ggml.c +++ b/ggml.c @@ -2470,7 +2470,8 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { size_t max_size = 0; for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) { - max_size = MAX(max_size, ggml_nbytes(tensor)); + size_t bytes = ggml_nbytes(tensor); + max_size = MAX(max_size, bytes); } return max_size; @@ -11887,8 +11888,10 @@ GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims - dims[0] = MAX(0, floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base))); - dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base))); + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)); + dims[0] = MAX(0, start); + dims[1] = MIN(n_dims - 1, end); } static void ggml_compute_forward_rope_f32( From c6b395535a6874d749ef47c33eacd466cb252cd5 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 14:09:47 +0200 Subject: [PATCH 625/664] ggml : make use of ggml-quants.h possible in C++ code (#5338) * Make use of ggml-quants.h possible in C++ code * One cannot possibly be defining static_assert in a C++ compilation --------- Co-authored-by: Iwan Kawrakow --- ggml-impl.h | 2 + ggml-quants.h | 117 +++++++++++++++++++++++++++----------------------- 2 files changed, 65 insertions(+), 54 deletions(-) diff --git a/ggml-impl.h b/ggml-impl.h index 2c58075ac..19df66bce 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -19,6 +19,7 @@ extern "C" { // fall back to the _Static_assert C11 keyword. // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 +#ifndef __cplusplus #ifndef static_assert #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) #define static_assert(cond, msg) _Static_assert(cond, msg) @@ -26,6 +27,7 @@ extern "C" { #define static_assert(cond, msg) struct global_scope_noop_trick #endif #endif +#endif // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) diff --git a/ggml-quants.h b/ggml-quants.h index 5c9f63bd9..bfdf3c997 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -191,70 +191,74 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +#ifdef __cplusplus +extern "C" { +#endif + // Quantization -void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); -void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k); -void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k); -void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k); -void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k); -void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k); +void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k); +void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k); +void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k); +void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k); +void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k); +void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k); -void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k); -void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k); -void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k); -void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); -void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); -void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); -void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k); +void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k); +void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k); +void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k); +void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k); +void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); +void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); -void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_1(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_1(const float * restrict x, void * restrict y, int k); +void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q2_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q3_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); -void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k); +void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization -void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); -void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k); -void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k); -void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k); -void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k); -//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k); +void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k); -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k); -void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k); -void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k); -void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k); -void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); -void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k); -void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k); -void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k); +void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") @@ -276,3 +280,8 @@ void iq2xs_init_impl(int grid_size); void iq2xs_free_impl(int grid_size); void iq3xs_init_impl(int grid_size); void iq3xs_free_impl(int grid_size); + +#ifdef __cplusplus +} +#endif + From 78b00dda6c0d62c34f5371d47718defff6ed2b22 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 Feb 2024 15:55:10 +0100 Subject: [PATCH 626/664] README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov --- README.md | 49 ++++++++++++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index a6fe34629..bb6c49338 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) -Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ +Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ ### Hot topics @@ -58,18 +58,20 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ## Description -The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook +The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide +variety of hardware - locally and in the cloud. -- Plain C/C++ implementation without dependencies -- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks +- Plain C/C++ implementation without any dependencies +- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks - AVX, AVX2 and AVX512 support for x86 architectures -- Mixed F16 / F32 precision -- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support -- CUDA, Metal, OpenCL, SYCL GPU backend support +- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use +- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) +- Vulkan, SYCL, and (partial) OpenCL backend support +- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity -The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022). -Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves -as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library. +Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has +improved significantly thanks to many contributions. It is the main playground for developing new features for the +[ggml](https://github.com/ggerganov/ggml) library. **Supported platforms:** @@ -77,11 +79,14 @@ as the main playground for developing new features for the [ggml](https://github - [X] Linux - [X] Windows (via CMake) - [X] Docker +- [X] FreeBSD **Supported models:** - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 +- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [X] Falcon - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) @@ -95,7 +100,6 @@ as the main playground for developing new features for the [ggml](https://github - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) -- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) @@ -104,15 +108,14 @@ as the main playground for developing new features for the [ggml](https://github - [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) -- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [GPT-2](https://huggingface.co/gpt2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** -- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) -- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava) +- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) +- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) @@ -137,14 +140,22 @@ as the main playground for developing new features for the [ggml](https://github **UI:** +Unless otherwise noted these projects are open-source with permissive licensing: + +- [iohub/collama](https://github.com/iohub/coLLaMA) +- [janhq/jan](https://github.com/janhq/jan) (AGPL) - [nat/openplayground](https://github.com/nat/openplayground) -- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) -- [withcatai/catai](https://github.com/withcatai/catai) -- [semperai/amica](https://github.com/semperai/amica) +- [LMStudio](https://lmstudio.ai/) (proprietary) +- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) +- [ollama/ollama](https://github.com/ollama/ollama) +- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) -- [iohub/collama](https://github.com/iohub/coLLaMA) -- [pythops/tenere](https://github.com/pythops/tenere) +- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [semperai/amica](https://github.com/semperai/amica) +- [withcatai/catai](https://github.com/withcatai/catai) --- From 098f6d737b65134cf220d12b9b706e8cfc5e4610 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 Feb 2024 19:33:00 +0100 Subject: [PATCH 627/664] make: Use ccache for faster compilation (#5318) * make: Use ccache for faster compilation --- CMakeLists.txt | 4 +- Makefile | 169 ++++++++++++++++++++++++++++++++++--------------- 2 files changed, 121 insertions(+), 52 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 8c04e4c19..427015be5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -809,9 +809,9 @@ if (LLAMA_CCACHE) if (LLAMA_CCACHE_FOUND) set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache) set(ENV{CCACHE_SLOPPINESS} time_macros) - message(STATUS "Using ccache") + message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.") else() - message(STATUS "Warning: ccache not found - consider installing it or use LLAMA_CCACHE=OFF") + message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF") endif () endif() diff --git a/Makefile b/Makefile index 21d5e15ba..ba73f0637 100644 --- a/Makefile +++ b/Makefile @@ -112,6 +112,18 @@ MK_CXXFLAGS += -O3 MK_NVCCFLAGS += -O3 endif +ifndef LLAMA_NO_CCACHE +CCACHE := $(shell which ccache) +ifdef CCACHE +export CCACHE_SLOPPINESS = time_macros +$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.) +CC := $(CCACHE) $(CC) +CXX := $(CCACHE) $(CXX) +else +$(info I ccache not found. Consider installing it for faster compilation.) +endif # CCACHE +endif # LLAMA_NO_CCACHE + # clock_gettime came in POSIX.1b (1993) # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional # posix_memalign came in POSIX.1-2001 / SUSv3 @@ -374,9 +386,9 @@ ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG ifdef LLAMA_CUDA_NVCC - NVCC = $(LLAMA_CUDA_NVCC) + NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) else - NVCC = nvcc + NVCC = $(CCACHE) nvcc endif #LLAMA_CUDA_NVCC ifdef CUDA_DOCKER_ARCH MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) @@ -483,7 +495,7 @@ ifdef LLAMA_HIPBLAS ROCM_PATH ?= /opt/rocm GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif - HIPCC ?= $(ROCM_PATH)/bin/hipcc + HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 @@ -607,97 +619,135 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + find examples pocs -type f -name "*.o" -delete # # Examples # +# $< is the first prerequisite, i.e. the source file. +# Explicitly compile this to an object file so that it can be cached with ccache. +# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead. + +# Helper function that replaces .c, .cpp, and .cu file endings with .o: +GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) + main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @echo infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp) + $(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS) baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift @@ -705,7 +755,7 @@ swift: examples/batched.swift endif common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh - @sh scripts/build-info.sh $(CC) > $@.tmp + @sh scripts/build-info.sh "$(CC)" > $@.tmp @if ! cmp -s $@.tmp $@; then \ mv $@.tmp $@; \ else \ @@ -722,7 +772,8 @@ build-info.o: common/build-info.cpp tests: $(TEST_TARGETS) benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) run-benchmark-matmult: benchmark-matmult ./$@ @@ -730,58 +781,76 @@ run-benchmark-matmult: benchmark-matmult .PHONY: run-benchmark-matmult swift vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-c.o: tests/test-c.c llama.h $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) From 906cff55c2848fda091d888a1585915ec0c9ea9e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 6 Feb 2024 07:47:22 +0200 Subject: [PATCH 628/664] py : handle byte tokens in `get_token_type` (#5341) * py : handle byte tokens in `get_token_type` * py : fix empty bytes arg --- convert.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/convert.py b/convert.py index 75c100118..4a2847a27 100755 --- a/convert.py +++ b/convert.py @@ -515,10 +515,14 @@ class HfVocab: # Yield token text, score, and type yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, self.special_ids # Reuse already stored special IDs + token_id, token_text, self.special_ids # Reuse already stored special IDs ) - def get_token_type(self, token_id: int, special_ids: set[int]) -> gguf.TokenType: + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + # Determine token type based on whether it's a special token return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL @@ -530,7 +534,7 @@ class HfVocab: def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: if text in self.specials: - toktype = self.get_token_type(self.specials[text], self.special_ids) + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) score = self.get_token_score(self.specials[text]) else: toktype = gguf.TokenType.USER_DEFINED From 4ffc7a17d4e80c5f3f905139cb570ed9b6934fcb Mon Sep 17 00:00:00 2001 From: Niall Coates <1349685+Niall-@users.noreply.github.com> Date: Tue, 6 Feb 2024 08:16:23 +0000 Subject: [PATCH 629/664] server : various fixes for the prompt field in /completion (#5300) server : fix deadlock when prompt array contains strings and numbers server : removed an unnecessary generation when generating multi-prompts server : removed an unnecessary assert --- examples/server/server.cpp | 34 +++++++++++++++++++++++++++------- 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8000fee5c..fc7e723a1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1163,13 +1163,30 @@ struct llama_server_context task.multitask_id = multitask_id; // when a completion task's prompt array is not a singleton, we split it into multiple requests - if (task.data.count("prompt") && task.data.at("prompt").size() > 1) - { - split_multiprompt_task(task_id, task); - } - // otherwise, it's a single-prompt task, we actually queue it - queue_tasks.post(task); + // if there's numbers in the prompt array it will be treated as an array of tokens + if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { + bool numbers = false; + for (const auto& e : task.data.at("prompt")) { + if (e.is_number()) { + numbers = true; + break; + } + } + + // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers, + // it will completely stall the server. I don't know where the bug for this is. + // + // if there are numbers, it needs to be treated like a single prompt, + // queue_tasks handles a mix of strings and numbers just fine. + if (numbers) { + queue_tasks.post(task); + } else { + split_multiprompt_task(task_id, task); + } + } else { + queue_tasks.post(task); + } } // for multiple images processing @@ -1251,7 +1268,10 @@ struct llama_server_context void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) { int prompt_count = multiprompt_task.data.at("prompt").size(); - assert(prompt_count > 1); + if (prompt_count <= 1) { + send_error(multiprompt_task, "error while handling multiple prompts"); + return; + } // generate all the ID for subtask std::vector subtask_ids(prompt_count); From 31e790322133a4b1d0684527ea446e765e8a96cf Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Tue, 6 Feb 2024 04:20:00 -0500 Subject: [PATCH 630/664] server : add `dynatemp_range` and `dynatemp_exponent` (#5352) * server: added `dynatemp_range` and `dynatemp_exponent` * Update README.md --------- Co-authored-by: Michael Coppola --- examples/server/README.md | 4 ++++ examples/server/server.cpp | 46 +++++++++++++++++++++----------------- 2 files changed, 29 insertions(+), 21 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index d8e7c313e..46d8f85ae 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -137,6 +137,10 @@ node index.js `temperature`: Adjust the randomness of the generated text (default: 0.8). + `dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled). + + `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0). + `top_k`: Limit the next token selection to the K most probable tokens (default: 40). `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fc7e723a1..e48a1da75 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -524,27 +524,29 @@ struct llama_server_context slot->oaicompat_model = ""; } - slot->params.stream = json_value(data, "stream", false); - slot->params.cache_prompt = json_value(data, "cache_prompt", false); - slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); - slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); - slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); - slot->params.seed = json_value(data, "seed", default_params.seed); - slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); - slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot->params.stream = json_value(data, "stream", false); + slot->params.cache_prompt = json_value(data, "cache_prompt", false); + slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); + slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); + slot->params.seed = json_value(data, "seed", default_params.seed); + slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); // infill if (data.count("input_prefix") != 0) @@ -1002,6 +1004,8 @@ struct llama_server_context {"model", params.model_alias}, {"seed", slot.params.seed}, {"temperature", slot.sparams.temp}, + {"dynatemp_range", slot.sparams.dynatemp_range}, + {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, From 8a79c591de9b7ff3242a94f68b7fb5a17ed8c2be Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Tue, 6 Feb 2024 04:20:59 -0500 Subject: [PATCH 631/664] server : include total "num_slots" in props endpoint (#5349) --- examples/server/server.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e48a1da75..d86d7e04a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -432,6 +432,7 @@ struct llama_server_context } default_generation_settings_for_props = get_formated_generation(slots.front()); + default_generation_settings_for_props["num_slots"] = params.n_parallel; default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); From 2c516611f1d0f1e5e9754f8ea1cf97cb1b17bf2c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 Feb 2024 14:44:06 +0100 Subject: [PATCH 632/664] CUDA: mul_mat_vec_q for batch sizes > 1 (#5351) --- ggml-cuda.cu | 240 +++++++++++++++++++++------------------------------ 1 file changed, 98 insertions(+), 142 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 3242a0b4a..95161b3f4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5310,41 +5310,50 @@ template static __global__ void #endif // __CUDA_ARCH__ >= CC_VOLTA } -template -static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) { +template +static __global__ void mul_mat_vec_q( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) { + + const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; + const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row >= nrows) { + if (row >= nrows_x) { return; } - const int blocks_per_row = ncols / qk; + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; const int blocks_per_warp = vdr * WARP_SIZE / qi; // partial sum for each thread - float tmp = 0.0f; + float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f}; const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) { - const int ibx = row*blocks_per_row + i; // x block index + for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) { + const int ibx = row*blocks_per_row_x + i; // x block index const int iby = i * (qk/QK8_1); // y block index that aligns with ibx const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int - tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs); +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { + tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs); + } } // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + for (int j = 0; j < ncols_y; ++j) { + tmp[j] = warp_reduce_sum(tmp[j]); - if (threadIdx.x == 0) { - dst[row] = tmp; + if (threadIdx.x == 0) { + dst[j*nrows_x + row] = tmp[j]; + } } } @@ -6816,121 +6825,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa <<>>(vx, y, dst, ncols, nrows); } -static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK4_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} +template +static void mul_mat_vec_q_cuda( + const void * vx, const void * vy, float * dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { -static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK4_1 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} + GGML_ASSERT(ncols_x % qk == 0); + GGML_ASSERT(ncols_y <= 8); -static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK5_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK5_1 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK8_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); + switch (ncols_y) { + case 1: + mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 2: + mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 3: + mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 4: + mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 5: + mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 6: + mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 7: + mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 8: + mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + default: + GGML_ASSERT(false); + // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + } } static void ggml_mul_mat_q4_0_q8_1_cuda( @@ -8578,50 +8522,61 @@ static void ggml_cuda_op_mul_mat_vec_q( const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, cudaStream_t stream) { - GGML_ASSERT(ggml_nrows(src1) == 1); - const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q4_1: - mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_0: - mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_1: - mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q8_0: - mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q2_K: - mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q3_K: - mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q4_K: - mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_K: - mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q6_K: - mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ2_XXS: - mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ2_XS: - mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ3_XXS: - mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; default: GGML_ASSERT(false); @@ -9945,17 +9900,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 #ifdef GGML_CUDA_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else - const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1; + const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); #endif // GGML_CUDA_FORCE_DMMV if (use_mul_mat_vec_q) { - // NOTE: this kernel does not support ggml_nrows(src1) > 1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (use_mul_mat_q) { + if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); } else { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); From 2e9c0bd6b301155ce749e162527fc55e9fb5b832 Mon Sep 17 00:00:00 2001 From: BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:06:48 -0500 Subject: [PATCH 633/664] readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362) --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index bb6c49338..cc87ac797 100644 --- a/README.md +++ b/README.md @@ -105,11 +105,14 @@ improved significantly thanks to many contributions. It is the main playground f - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) -- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) +- [X] [StableLM models](https://huggingface.co/stabilityai) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) +- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) - [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118) +- [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** @@ -119,6 +122,7 @@ improved significantly thanks to many contributions. It is the main playground f - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) +- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) **Bindings:** From f57fadc009cbff741a1961cb7896c47d73978d2c Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 6 Feb 2024 17:28:02 +0200 Subject: [PATCH 634/664] Slight quantization improvement for Q4_K and Q5_K (#5361) * Q4_K: slightly better quantization * Q5_K: slightly better quantization --------- Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 75 +++++++++++++++++++++++---------------------------- 1 file changed, 33 insertions(+), 42 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 014c0525a..101d3e783 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -2381,19 +2381,20 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri uint8_t L[QK_K]; uint8_t Laux[32]; + uint8_t Ls[QK_K/32]; + uint8_t Lm[QK_K/32]; float weights[32]; - float mins[QK_K/32]; - float scales[QK_K/32]; + float sw[QK_K/32]; + float mins[QK_K/32]; + float scales[QK_K/32]; for (int i = 0; i < nb; i++) { float sum_x2 = 0; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; - float sigma2 = sum_x2/QK_K; + float sigma2 = 2*sum_x2/QK_K; float av_x = sqrtf(sigma2); - float max_scale = 0; // as we are deducting the min, scales are always positive - float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { if (quant_weights) { const float * qw = quant_weights + QK_K*i + 32*j; @@ -2401,25 +2402,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri } else { for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); } + float sumw = 0; + for (int l = 0; l < 32; ++l) sumw += weights[l]; + sw[j] = sumw; scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); - //scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false); - float scale = scales[j]; - if (scale > max_scale) { - max_scale = scale; - } - float min = mins[j]; - if (min > max_min) { - max_min = min; - } } - float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; - float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw); + float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw); for (int j = 0; j < QK_K/32; ++j) { - uint8_t ls = nearest_int(inv_scale*scales[j]); - uint8_t lm = nearest_int(inv_min*mins[j]); - ls = MIN(63, ls); - lm = MIN(63, lm); + uint8_t ls = Ls[j]; + uint8_t lm = Lm[j]; if (j < 4) { y[i].scales[j] = ls; y[i].scales[j+4] = lm; @@ -2429,8 +2422,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri y[i].scales[j-0] |= ((lm >> 4) << 6); } } - y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); - y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + y[i].d = GGML_FP32_TO_FP16(d_block); + y[i].dmin = GGML_FP32_TO_FP16(m_block); uint8_t sc, m; for (int j = 0; j < QK_K/32; ++j) { @@ -2688,20 +2681,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri const int nb = n_per_row / QK_K; uint8_t L[QK_K]; - float mins[QK_K/32]; - float scales[QK_K/32]; - float weights[32]; uint8_t Laux[32]; + uint8_t Ls[QK_K/32]; + uint8_t Lm[QK_K/32]; + float mins[QK_K/32]; + float scales[QK_K/32]; + float sw[QK_K/32]; + float weights[32]; for (int i = 0; i < nb; i++) { float sum_x2 = 0; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; - float sigma2 = sum_x2/QK_K; + float sigma2 = 2*sum_x2/QK_K; float av_x = sqrtf(sigma2); - float max_scale = 0; // as we are deducting the min, scales are always positive - float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { if (quant_weights) { const float * qw = quant_weights + QK_K*i + 32*j; @@ -2709,22 +2703,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri } else { for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); } + float sumw = 0; + for (int l = 0; l < 32; ++l) sumw += weights[l]; + sw[j] = sumw; + scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); - float scale = scales[j]; - if (scale > max_scale) { - max_scale = scale; - } - float min = mins[j]; - if (min > max_min) { - max_min = min; - } } - float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; - float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw); + float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw); + for (int j = 0; j < QK_K/32; ++j) { - uint8_t ls = nearest_int(inv_scale*scales[j]); - uint8_t lm = nearest_int(inv_min*mins[j]); + uint8_t ls = Ls[j]; + uint8_t lm = Lm[j]; ls = MIN(63, ls); lm = MIN(63, lm); if (j < 4) { @@ -2736,8 +2727,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri y[i].scales[j-0] |= ((lm >> 4) << 6); } } - y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); - y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + y[i].d = GGML_FP32_TO_FP16(d_block); + y[i].dmin = GGML_FP32_TO_FP16(m_block); uint8_t sc, m; for (int j = 0; j < QK_K/32; ++j) { From b08f22c882a1443e6b97081f3ce718a4d1a741f8 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 6 Feb 2024 19:00:16 +0200 Subject: [PATCH 635/664] Update README.md (#5366) Add some links to quantization related PRs --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index cc87ac797..34f2021f9 100644 --- a/README.md +++ b/README.md @@ -736,9 +736,21 @@ Several quantization methods are supported. They differ in the resulting model d | 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) -- recent k-quants improvements +- recent k-quants improvements and new i-quants - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) + - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) + - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) + - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) + - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) + - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) + - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) + - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) + - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) + - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) + - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) + - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) + - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) ### Perplexity (measuring model quality) From 17c97fb0620448b37516a3f53fea6c482b0a30a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 Feb 2024 18:43:06 +0100 Subject: [PATCH 636/664] CUDA: mul_mat_vec_q max. batch size 8 -> 4 (#5370) --- ggml-cuda.cu | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 95161b3f4..3b828375e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6831,7 +6831,7 @@ static void mul_mat_vec_q_cuda( const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); - GGML_ASSERT(ncols_y <= 8); + GGML_ASSERT(ncols_y <= 4); const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); @@ -6853,22 +6853,22 @@ static void mul_mat_vec_q_cuda( mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); break; - case 5: - mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 6: - mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 7: - mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 8: - mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; + // case 5: + // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 6: + // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 7: + // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 8: + // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; default: GGML_ASSERT(false); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> @@ -9909,7 +9909,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); From 213d1439fadefe182f69c5f7e8dd3b4b6572ebcb Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Tue, 6 Feb 2024 18:08:38 +0000 Subject: [PATCH 637/664] server : remove model.json endpoint (#5371) --- examples/server/completion.js.hpp | 448 +++++++++++++++------------ examples/server/public/completion.js | 3 +- examples/server/server.cpp | 11 - 3 files changed, 244 insertions(+), 218 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index fe5f81228..f5e696e17 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -236,214 +236,250 @@ unsigned char completion_js[] = { 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 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0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, + 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, + 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, + 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; -unsigned int completion_js_len = 5346; +unsigned int completion_js_len = 5782; diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index baaec1d60..ab38a7b40 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => { // Get the model info from the server. This is useful for getting the context window and so on. export const llamaModelInfo = async () => { if (!generation_settings) { - generation_settings = await fetch("/model.json").then(r => r.json()); + const props = await fetch("/props").then(r => r.json()); + generation_settings = props.default_generation_settings; } return generation_settings; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d86d7e04a..9481ce6b1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -990,11 +990,6 @@ struct llama_server_context queue_results.send(res); } - json get_model_props() - { - return get_formated_generation(slots[0]); - } - json get_formated_generation(llama_client_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); @@ -2895,12 +2890,6 @@ int main(int argc, char **argv) } }); - svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) - { - const json data = llama.get_model_props(); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json; charset=utf-8"); }); From f68664ac241a6b5c233d8f1051eef20929b06008 Mon Sep 17 00:00:00 2001 From: Sang-Kil Park Date: Wed, 7 Feb 2024 13:28:00 +0900 Subject: [PATCH 638/664] convert : fix TypeError on GPT-2 vocab.json (#5288) --- convert.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index 4a2847a27..323e8058d 100755 --- a/convert.py +++ b/convert.py @@ -334,9 +334,9 @@ class Params: class BpeVocab: def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) - try: + if isinstance(self.bpe_tokenizer.get('model'), dict): self.vocab = self.bpe_tokenizer["model"]["vocab"] - except KeyError: + else: self.vocab = self.bpe_tokenizer added_tokens: dict[str, int] if fname_added_tokens is not None: From f3e2b4fa3f81a410ecb7dec929c259ef8d8dbb7d Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Wed, 7 Feb 2024 01:15:19 -0500 Subject: [PATCH 639/664] server : update `/props` with "total_slots" value (#5373) * include total "num_slots" in default_generation_settings_for_props * cleanup total_slots return value in /props endpoint * update /props endpoint docs with total_slots * remove num_slots from default_generation_settings_for_props * update /props endpoint section --- examples/server/README.md | 4 +++- examples/server/server.cpp | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 46d8f85ae..1db7cdf21 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -276,13 +276,15 @@ Notice that each `probs` is an array of length `n_probs`. { "assistant_name": "", "user_name": "", - "default_generation_settings": { ... } + "default_generation_settings": { ... }, + "total_slots": 1 } ``` - `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. +- `total_slots` - the total number of slots for process requests (defined by `--parallel` option) - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9481ce6b1..eceda30d0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -432,7 +432,6 @@ struct llama_server_context } default_generation_settings_for_props = get_formated_generation(slots.front()); - default_generation_settings_for_props["num_slots"] = params.n_parallel; default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); @@ -2639,7 +2638,8 @@ int main(int argc, char **argv) json data = { { "user_name", llama.name_user.c_str() }, { "assistant_name", llama.name_assistant.c_str() }, - { "default_generation_settings", llama.default_generation_settings_for_props } + { "default_generation_settings", llama.default_generation_settings_for_props }, + { "total_slots", llama.params.n_parallel } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); From 316c7faf7740fa98ea68f1445f4505810f706b9e Mon Sep 17 00:00:00 2001 From: runfuture Date: Wed, 7 Feb 2024 14:15:56 +0800 Subject: [PATCH 640/664] llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm --- convert-hf-to-gguf.py | 49 ++++++++++ gguf-py/gguf/constants.py | 21 +++++ llama.cpp | 190 +++++++++++++++++++++++++++++++++++++- 3 files changed, 259 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5e343742d..829d68368 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -22,6 +22,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf +from convert import HfVocab + # check for any of the given keys in the dictionary and return the value of the first key found def get_key_opts(d, keys): @@ -205,6 +207,8 @@ class Model: return OrionModel if model_architecture == "InternLM2ForCausalLM": return InternLM2Model + if model_architecture == "MiniCPMForCausalLM": + return MiniCPMModel return Model def _is_model_safetensors(self) -> bool: @@ -258,6 +262,8 @@ class Model: return gguf.MODEL_ARCH.ORION if arch == "InternLM2ForCausalLM": return gguf.MODEL_ARCH.INTERNLM2 + if arch == "MiniCPMForCausalLM": + return gguf.MODEL_ARCH.MINICPM raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -402,6 +408,31 @@ class Model: special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_hf(self): + path = self.dir_model + added_tokens_path = self.dir_model + vocab = HfVocab( + path, added_tokens_path if added_tokens_path.exists() else None + ) + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + class GPTNeoXModel(Model): def set_gguf_parameters(self): @@ -1041,6 +1072,24 @@ class MixtralModel(Model): self._set_vocab_sentencepiece() +class MiniCPMModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + self.gguf_writer.add_name("MiniCPM") + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + + def set_vocab(self): + self._set_vocab_hf() + + class QwenModel(Model): @staticmethod def token_bytes_to_string(b): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index ed8e26f83..1cfd41c0b 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -104,6 +104,7 @@ class MODEL_ARCH(IntEnum): CODESHELL = auto() ORION = auto() INTERNLM2 = auto() + MINICPM = auto() class MODEL_TENSOR(IntEnum): @@ -156,6 +157,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", MODEL_ARCH.INTERNLM2: "internlm2", + MODEL_ARCH.MINICPM: "minicpm", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -464,6 +466,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.MINICPM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], # TODO } diff --git a/llama.cpp b/llama.cpp index 65e399adc..f3c5146d1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -205,6 +205,7 @@ enum llm_arch { LLM_ARCH_CODESHELL, LLM_ARCH_ORION, LLM_ARCH_INTERNLM2, + LLM_ARCH_MINICPM, LLM_ARCH_UNKNOWN, }; @@ -228,6 +229,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, }; enum llm_kv { @@ -690,6 +692,29 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_MINICPM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1390,6 +1415,7 @@ enum e_model { MODEL_UNKNOWN, MODEL_0_5B, MODEL_1B, + MODEL_2B, MODEL_3B, MODEL_4B, MODEL_7B, @@ -2748,6 +2774,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { case MODEL_1B: return "1B"; + case MODEL_2B: return "2B"; case MODEL_3B: return "3B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; @@ -2887,6 +2914,13 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_MINICPM: + { + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_2B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_FALCON: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -3524,13 +3558,16 @@ static bool llm_load_tensors( switch (model.arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: + case LLM_ARCH_MINICPM: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + if (model.arch != LLM_ARCH_MINICPM){ + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } } for (int i = 0; i < n_layer; ++i) { @@ -6781,6 +6818,153 @@ struct llm_build_context { return gf; } + // ref: https://arxiv.org/abs/2203.03466 + // https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738 + // based on the original build_llama() function + struct ggml_cgraph * build_minicpm() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + const int64_t n_embd = hparams.n_embd; + //TODO: if the model varies, these parameters need to be read from the model + const int64_t n_embd_base = 256; + const float scale_embd = 12.0f; + const float scale_depth = 1.4f; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // scale the input embeddings + inpL = ggml_scale(ctx0, inpL, scale_embd); + cb(inpL, "inp_scaled", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + // scale_res - scale the hidden states for residual connection + const float scale_res = scale_depth/sqrtf(float(n_layer)); + cur = ggml_scale(ctx0, cur, scale_res); + cb(cur, "hidden_scaled", -1); + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + // scale the hidden states for residual connection + cur = ggml_scale(ctx0, cur, scale_res); + cb(cur, "hidden_scaled_ffn", -1); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head scaling + const float scale_lmhead = float(n_embd_base)/float(n_embd); + cur = ggml_scale(ctx0, cur, scale_lmhead); + cb(cur, "lmhead_scaling", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.tok_embd, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( @@ -6943,6 +7127,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_internlm2(); } break; + case LLM_ARCH_MINICPM: + { + result = llm.build_minicpm(); + } break; default: GGML_ASSERT(false); } From 9a697d842bc0cfce8268ebd2ba703ffc1c904f98 Mon Sep 17 00:00:00 2001 From: Ben Williams Date: Tue, 6 Feb 2024 22:16:48 -0800 Subject: [PATCH 641/664] readme : update ui list (#5354) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 34f2021f9..672512d18 100644 --- a/README.md +++ b/README.md @@ -149,6 +149,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [iohub/collama](https://github.com/iohub/coLLaMA) - [janhq/jan](https://github.com/janhq/jan) (AGPL) - [nat/openplayground](https://github.com/nat/openplayground) +- [Faraday](https://faraday.dev/) (proprietary) - [LMStudio](https://lmstudio.ai/) (proprietary) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) From ed0bf32290ee5b30ffad5becd99cbecef74aedd7 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 7 Feb 2024 06:21:30 +0000 Subject: [PATCH 642/664] readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md --- README.md | 127 +++++++++++++++-------------------------------------- SHA256SUMS | 40 ----------------- 2 files changed, 36 insertions(+), 131 deletions(-) delete mode 100644 SHA256SUMS diff --git a/README.md b/README.md index 672512d18..0509b0ba1 100644 --- a/README.md +++ b/README.md @@ -33,17 +33,14 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
  • Build
  • BLAS Build
    • -
  • Prepare Data & Run
    • +
  • Prepare and Quantize
    • +
  • Run the quantized model
  • Memory/Disk Requirements
  • Quantization
  • Interactive mode
  • Constrained output with grammars
    • -
  • Instruction mode with Alpaca
    • -
  • Using OpenLLaMA
    • -
  • Using GPT4All
    • -
  • Using Pygmalion 7B & Metharme 7B
    • -
  • Obtaining the Facebook LLaMA original model and Stanford Alpaca model data
    • -
  • Verifying the model files
    • +
  • Instruct mode
    • +
  • Obtaining and using the Facebook LLaMA 2 model
  • Seminal papers and background on the models
  • Perplexity (measuring model quality)
  • Android
    • @@ -83,20 +80,16 @@ improved significantly thanks to many contributions. It is the main playground f **Supported models:** +Typically finetunes of the base models below are supported as well. + - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 -- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [X] Falcon -- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) -- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) -- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) -- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy) -- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b) -- [X] [WizardLM](https://github.com/nlpxucan/WizardLM) - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) @@ -166,7 +159,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: Here is a typical run using LLaMA v2 13B on M2 Ultra: -```java +``` $ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e I llama.cpp build info: I UNAME_S: Darwin @@ -250,7 +243,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8 ## Usage -Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model. +Here are the end-to-end binary build and model conversion steps for most supported models. ### Get the Code @@ -635,7 +628,7 @@ Building the program with BLAS support may lead to some performance improvements **Without docker**: - Firstly, you need to make sure you installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) For example, on Ubuntu 22.04 (jammy), use the command below: @@ -648,6 +641,8 @@ Building the program with BLAS support may lead to some performance improvements vulkaninfo ``` + Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. + Then, build llama.cpp using the cmake command below: ```bash @@ -662,34 +657,42 @@ Building the program with BLAS support may lead to some performance improvements # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 ``` -### Prepare Data & Run +### Prepare and Quantize + +To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. ```bash -# obtain the original LLaMA model weights and place them in ./models +# obtain the official LLaMA model weights and place them in ./models ls ./models -65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model +llama-2-7b tokenizer_checklist.chk tokenizer.model # [Optional] for models using BPE tokenizers ls ./models -65B 30B 13B 7B vocab.json + vocab.json +# [Optional] for PyTorch .bin models like Mistral-7B +ls ./models + # install Python dependencies python3 -m pip install -r requirements.txt -# convert the 7B model to ggml FP16 format -python3 convert.py models/7B/ +# convert the model to ggml FP16 format +python3 convert.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/7B/ --vocabtype bpe +python convert.py models/mymodel/ --vocabtype bpe -# quantize the model to 4-bits (using q4_0 method) -./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0 +# quantize the model to 4-bits (using Q4_K_M method) +./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M -# update the gguf filetype to current if older version is unsupported by another application -./quantize ./models/7B/ggml-model-q4_0.gguf ./models/7B/ggml-model-q4_0-v2.gguf COPY +# update the gguf filetype to current version if older version is now unsupported +./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +``` +### Run the quantized model -# run the inference -./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 +```bash +# start inference on a gguf model +./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 ``` When running the larger models, make sure you have enough disk space to store all the intermediate files. @@ -710,7 +713,7 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. -| Model | Original size | Quantized size (4-bit) | +| Model | Original size | Quantized size (Q4_0) | |------:|--------------:|-----------------------:| | 7B | 13 GB | 3.9 GB | | 13B | 24 GB | 7.8 GB | @@ -826,9 +829,9 @@ The `grammars/` folder contains a handful of sample grammars. To write your own, For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. -### Instruction mode with Alpaca +### Instruct mode -1. First, download the `ggml` Alpaca model into the `./models` folder +1. First, download and place the `ggml` model into the `./models` folder 2. Run the `main` tool like this: ``` @@ -854,50 +857,6 @@ cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach. > ``` -### Using [OpenLLaMA](https://github.com/openlm-research/open_llama) - -OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights. - -- Download the [3B](https://huggingface.co/openlm-research/open_llama_3b), [7B](https://huggingface.co/openlm-research/open_llama_7b), or [13B](https://huggingface.co/openlm-research/open_llama_13b) model from Hugging Face. -- Convert the model to ggml FP16 format using `python convert.py ` - -### Using [GPT4All](https://github.com/nomic-ai/gpt4all) - -*Note: these instructions are likely obsoleted by the GGUF update* - -- Obtain the `tokenizer.model` file from LLaMA model and put it to `models` -- Obtain the `added_tokens.json` file from Alpaca model and put it to `models` -- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B` -- It is distributed in the old `ggml` format which is now obsoleted -- You have to convert it to the new format using `convert.py`: - -```bash -python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin -``` - -- You can now use the newly generated `models/gpt4all-7B/ggml-model-q4_0.bin` model in exactly the same way as all other models - -- The newer GPT4All-J model is not yet supported! - -### Using Pygmalion 7B & Metharme 7B - -- Obtain the [LLaMA weights](#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data) -- Obtain the [Pygmalion 7B](https://huggingface.co/PygmalionAI/pygmalion-7b/) or [Metharme 7B](https://huggingface.co/PygmalionAI/metharme-7b) XOR encoded weights -- Convert the LLaMA model with [the latest HF convert script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) -- Merge the XOR files with the converted LLaMA weights by running the [xor_codec](https://huggingface.co/PygmalionAI/pygmalion-7b/blob/main/xor_codec.py) script -- Convert to `ggml` format using the `convert.py` script in this repo: -```bash -python3 convert.py pygmalion-7b/ --outtype q4_1 -``` -> The Pygmalion 7B & Metharme 7B weights are saved in [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) precision. If you wish to convert to `ggml` without quantizating, please specify the `--outtype` as `f32` instead of `f16`. - - -### Obtaining the Facebook LLaMA original model and Stanford Alpaca model data - -- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.** -- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository. -- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data. - ### Obtaining and using the Facebook LLaMA 2 model - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. @@ -909,20 +868,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1 - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) -### Verifying the model files - -Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files. -- The following python script will verify if you have all possible latest files in your self-installed `./models` subdirectory: - -```bash -# run the verification script -./scripts/verify-checksum-models.py -``` - -- On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed `./models` subdirectory: - - On Linux: `sha256sum --ignore-missing -c SHA256SUMS` - - on macOS: `shasum -a 256 --ignore-missing -c SHA256SUMS` - ### Seminal papers and background on the models If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: diff --git a/SHA256SUMS b/SHA256SUMS deleted file mode 100644 index ca4d5a4a5..000000000 --- a/SHA256SUMS +++ /dev/null @@ -1,40 +0,0 @@ -700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth -666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847 models/7B/ggml-model-f16.bin -ec2f2d1f0dfb73b72a4cbac7fa121abbe04c37ab327125a38248f930c0f09ddf models/7B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_1.bin -7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json -745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth -d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth -2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808 models/13B/ggml-model-f16.bin -fad169e6f0f575402cf75945961cb4a8ecd824ba4da6be2af831f320c4348fa5 models/13B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_1.bin -4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f models/13B/params.json -e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/consolidated.00.pth -4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff models/30B/consolidated.01.pth -24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378 models/30B/consolidated.02.pth -1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b models/30B/consolidated.03.pth -7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37 models/30B/ggml-model-f16.bin -d2a441403944819492ec8c2002cc36fa38468149bfb4b7b4c52afc7bd9a7166d models/30B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_1.bin -2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb models/30B/params.json -135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe models/65B/consolidated.00.pth -9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde models/65B/consolidated.01.pth -e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770 models/65B/consolidated.02.pth -73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e models/65B/consolidated.03.pth -882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225 models/65B/consolidated.04.pth -a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/consolidated.05.pth -72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth -d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth -60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0 models/65B/ggml-model-f16.bin -cde053439fa4910ae454407e2717cc46cc2c2b4995c00c93297a2b52e790fa92 models/65B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_1.bin -999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json -9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 models/tokenizer.model From ee1628bdfea8b0079fed0140ac2f00ef1b465b57 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 7 Feb 2024 07:54:50 +0100 Subject: [PATCH 643/664] Basic Vulkan Multi-GPU implementation (#5321) * Initial Vulkan multi-gpu implementation Move most global variables into backend context * Add names to backend device functions * Add further missing cleanup code * Reduce code duplication in tensor split layer assignment * generalize LLAMA_SPLIT_LAYER for all backends, do not expose device count and memory in llama.h * Only do device info print in the beginning and initialize one backend for cpu assist Add missing cleanup code * Rework backend memory management to make sure devices and buffers get properly allocated and freed * Rename cpu assist free function --------- Co-authored-by: slaren --- common/common.cpp | 8 +- ggml-vulkan.cpp | 2639 ++++++++++++++++++++++++++------------------- ggml-vulkan.h | 23 +- ggml.c | 14 +- llama.cpp | 69 +- 5 files changed, 1587 insertions(+), 1166 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 8c1a60583..e0082a823 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -46,6 +46,10 @@ #define GGML_USE_CUBLAS_SYCL #endif +#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN) +#define GGML_USE_CUBLAS_SYCL_VULKAN +#endif + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -660,8 +664,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.tensor_split[i] = 0.0f; } } -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n"); +#ifndef GGML_USE_CUBLAS_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL } else if (arg == "--no-mmap") { params.use_mmap = false; diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 14fb89e09..9e2846ee4 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -15,6 +15,7 @@ #include #include #include +#include #include "ggml.h" #include "ggml-backend-impl.h" @@ -37,6 +38,8 @@ #define GGML_VK_MAX_NODES 8192 +#define MAX_VK_BUFFERS 256 + #ifndef K_QUANTS_PER_ITERATION #define K_QUANTS_PER_ITERATION 1 #else @@ -53,15 +56,68 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA } \ } while (0) -struct vk_buffer { +struct ggml_backend_vk_context; + +struct vk_queue { + uint32_t queue_family_index; + vk::Queue queue; + vk::CommandPool pool; + uint32_t cmd_buffer_idx; + std::vector cmd_buffers; + + vk::PipelineStageFlags stage_flags; +}; + +struct vk_device { + vk::PhysicalDevice physical_device; + vk::PhysicalDeviceProperties properties; + std::string name; + uint64_t max_memory_allocation_size; + bool fp16; + vk::Device device; + uint32_t vendor_id; + vk_queue compute_queue; + vk_queue transfer_queue; + bool single_queue; + uint32_t descriptor_set_mode; + uint32_t subgroup_size; + bool uma; + + ~vk_device() { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "destroy device " << name << std::endl; +#endif + device.destroy(); + } +}; + +struct vk_buffer_struct { vk::Buffer buffer; vk::DeviceMemory device_memory; vk::MemoryPropertyFlags memory_property_flags; void * ptr; size_t size = 0; - uint32_t qf_owner; + + ggml_backend_vk_context * ctx; + + std::shared_ptr device; + + ~vk_buffer_struct() { + if (size == 0) { + return; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl; +#endif + + device->device.freeMemory(device_memory); + device->device.destroyBuffer(buffer); + } }; +typedef std::shared_ptr vk_buffer; +typedef std::weak_ptr vk_buffer_ref; + struct vk_subbuffer { vk_buffer buffer; uint64_t offset; @@ -70,6 +126,7 @@ struct vk_subbuffer { struct vk_pipeline { std::string name; + vk::ShaderModule shader_module; vk::DescriptorSetLayout dsl; std::vector descriptor_pools; std::vector descriptor_sets; @@ -82,16 +139,6 @@ struct vk_pipeline { uint32_t align; }; -struct vk_queue { - uint32_t queue_family_index; - vk::Queue queue; - vk::CommandPool pool; - uint32_t cmd_buffer_idx; - std::vector cmd_buffers; - - vk::PipelineStageFlags stage_flags; -}; - struct vk_semaphore { vk::Semaphore s; uint64_t value; @@ -105,20 +152,6 @@ struct vk_submission { typedef std::vector vk_sequence; -struct vk_device { - vk::PhysicalDevice physical_device; - vk::PhysicalDeviceProperties properties; - uint64_t max_memory_allocation_size; - bool fp16; - vk::Device device; - uint32_t vendor_id; - vk_queue compute_queue; - vk_queue transfer_queue; - uint32_t descriptor_set_mode; - uint32_t subgroup_size; - bool uma; -}; - struct vk_op_push_constants { uint32_t KX; uint32_t KY; @@ -190,13 +223,13 @@ struct ggml_tensor_extra_gpu { size_t ctx_idx; - vk_buffer buffer_gpu; + vk_buffer_ref buffer_gpu; uint64_t offset; void reset() { ready = false; ctx_idx = 0; - buffer_gpu.size = 0; + buffer_gpu.reset(); offset = 0; } }; @@ -210,69 +243,96 @@ struct ggml_vk_garbage_collector { std::vector contexts; }; -typedef void (*ggml_vk_func_t)(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); +struct ggml_backend_vk_context { + std::string name; -vk::Instance vk_instance; -vk_device vk_device; -vk_pipeline vk_pipeline_matmul_f32_l, vk_pipeline_matmul_f32_m, vk_pipeline_matmul_f32_s; -vk_pipeline vk_pipeline_matmul_f32_aligned_l, vk_pipeline_matmul_f32_aligned_m, vk_pipeline_matmul_f32_aligned_s; -vk_pipeline vk_pipeline_matmul_f16_l, vk_pipeline_matmul_f16_m, vk_pipeline_matmul_f16_s; -vk_pipeline vk_pipeline_matmul_f16_aligned_l, vk_pipeline_matmul_f16_aligned_m, vk_pipeline_matmul_f16_aligned_s; -vk_pipeline vk_pipeline_matmul_f16_f32_l, vk_pipeline_matmul_f16_f32_m, vk_pipeline_matmul_f16_f32_s; -vk_pipeline vk_pipeline_matmul_f16_f32_aligned_l, vk_pipeline_matmul_f16_f32_aligned_m, vk_pipeline_matmul_f16_f32_aligned_s; -vk_pipeline vk_pipeline_matmul_split_k_reduce; -vk_pipeline vk_pipeline_dequant[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_mul_mat_vec_p021_f16_f32; -vk_pipeline vk_pipeline_mul_mat_vec_nc_f16_f32; -vk_pipeline vk_pipeline_get_rows[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_get_rows_f32[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_mul_f32; -vk_pipeline vk_pipeline_add_f32; -vk_pipeline vk_pipeline_scale_f32; -vk_pipeline vk_pipeline_sqr_f32; -vk_pipeline vk_pipeline_clamp_f32; -vk_pipeline vk_pipeline_cpy_f32_f32, vk_pipeline_cpy_f32_f16, vk_pipeline_cpy_f16_f16; -vk_pipeline vk_pipeline_norm_f32; -vk_pipeline vk_pipeline_rms_norm_f32; -vk_pipeline vk_pipeline_gelu_f32; -vk_pipeline vk_pipeline_silu_f32; -vk_pipeline vk_pipeline_relu_f32; -vk_pipeline vk_pipeline_diag_mask_inf_f32; -vk_pipeline vk_pipeline_soft_max_f32; -vk_pipeline vk_pipeline_rope_f32, vk_pipeline_rope_f16; -vk_pipeline vk_pipeline_rope_neox_f32, vk_pipeline_rope_neox_f16; + std::weak_ptr device; + vk_pipeline pipeline_matmul_f32_l, pipeline_matmul_f32_m, pipeline_matmul_f32_s; + vk_pipeline pipeline_matmul_f32_aligned_l, pipeline_matmul_f32_aligned_m, pipeline_matmul_f32_aligned_s; + vk_pipeline pipeline_matmul_f16_l, pipeline_matmul_f16_m, pipeline_matmul_f16_s; + vk_pipeline pipeline_matmul_f16_aligned_l, pipeline_matmul_f16_aligned_m, pipeline_matmul_f16_aligned_s; + vk_pipeline pipeline_matmul_f16_f32_l, pipeline_matmul_f16_f32_m, pipeline_matmul_f16_f32_s; + vk_pipeline pipeline_matmul_f16_f32_aligned_l, pipeline_matmul_f16_f32_aligned_m, pipeline_matmul_f16_f32_aligned_s; + vk_pipeline pipeline_matmul_split_k_reduce; + vk_pipeline pipeline_dequant[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; + vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; + vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; + vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_add_f32; + vk_pipeline pipeline_scale_f32; + vk_pipeline pipeline_sqr_f32; + vk_pipeline pipeline_clamp_f32; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_norm_f32; + vk_pipeline pipeline_rms_norm_f32; + vk_pipeline pipeline_gelu_f32; + vk_pipeline pipeline_silu_f32; + vk_pipeline pipeline_relu_f32; + vk_pipeline pipeline_diag_mask_inf_f32; + vk_pipeline pipeline_soft_max_f32; + vk_pipeline pipeline_rope_f32, pipeline_rope_f16; + vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; -static size_t vk_semaphore_idx, vk_event_idx; -static ggml_vk_garbage_collector vk_gc; -static std::vector> vk_pinned_memory; -static size_t vk_prealloc_size_qx, vk_prealloc_size_qy, vk_prealloc_size_x, vk_prealloc_size_y, vk_prealloc_size_split_k; -static vk_buffer vk_prealloc_qx, vk_prealloc_qy, vk_prealloc_x, vk_prealloc_y, vk_prealloc_split_k; -static vk::Fence vk_fence; -static vk_buffer vk_staging; -static size_t vk_staging_size; -static size_t vk_staging_offset; -static vk_buffer vk_sync_staging; + size_t semaphore_idx, event_idx; + ggml_vk_garbage_collector gc; + std::vector> pinned_memory; + size_t prealloc_size_qx, prealloc_size_qy, prealloc_size_x, prealloc_size_y, prealloc_size_split_k; + vk_buffer prealloc_qx, prealloc_qy, prealloc_x, prealloc_y, prealloc_split_k; + vk::Fence fence; + vk_buffer staging; + size_t staging_size; + size_t staging_offset; + vk_buffer sync_staging; -static vk_context * vk_ctx; -static vk_context * vk_transfer_ctx; + vk_buffer buffer_pool[MAX_VK_BUFFERS]; -static bool vk_disable; + vk_context * compute_ctx; + vk_context * transfer_ctx; + + bool disable; + bool initialized; + + size_t idx; +}; + +struct vk_instance { + vk::Instance instance; + + std::vector device_indices; + + std::shared_ptr devices[GGML_VK_MAX_DEVICES]; + ggml_backend_t backends[GGML_VK_MAX_DEVICES]; + ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES]; + ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES]; + bool initialized[GGML_VK_MAX_DEVICES]; +}; #ifdef GGML_VULKAN_CHECK_RESULTS -size_t vk_skip_checks; -size_t vk_output_tensor; +static size_t vk_skip_checks; +static size_t vk_output_tensor; + +static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name); +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor); +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor); #endif -static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { +typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +static bool vk_instance_initialized = false; +static vk_instance vk_instance; + +GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); + +static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; #endif GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - vk_pipeline pipeline; - pipeline.name = name; pipeline.parameter_count = parameter_count; pipeline.push_constant_size = push_constant_size; @@ -280,7 +340,7 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s pipeline.align = align; vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); - vk::ShaderModule shader_module = vk_device.device.createShaderModule(shader_module_create_info); + pipeline.shader_module = ctx->device.lock()->device.createShaderModule(shader_module_create_info); std::vector dsl_binding; std::vector dsl_binding_flags; @@ -301,17 +361,17 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s {}, dsl_binding); descriptor_set_layout_create_info.setPNext(&dslbfci); - pipeline.dsl = vk_device.device.createDescriptorSetLayout(descriptor_set_layout_create_info); + pipeline.dsl = ctx->device.lock()->device.createDescriptorSetLayout(descriptor_set_layout_create_info); // Check if device supports multiple descriptors per pool - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { const uint32_t alloc_count = 2; // Try allocating multiple sets from one pool // This fails on AMD for some reason, so add a fall back to allocating one pool per set vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); - vk::DescriptorPool pool = vk_device.device.createDescriptorPool(descriptor_pool_create_info); + vk::DescriptorPool pool = ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { @@ -319,24 +379,24 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s } try { vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); } catch(vk::OutOfPoolMemoryError const&) { - vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; + ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; } - vk_device.device.destroyDescriptorPool(pool); + ctx->device.lock()->device.destroyDescriptorPool(pool); } - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); - pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); } pipeline.descriptor_set_idx = 0; vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr); - pipeline.layout = vk_device.device.createPipelineLayout(pipeline_layout_create_info); + pipeline.layout = ctx->device.lock()->device.createPipelineLayout(pipeline_layout_create_info); std::vector specialization_entries(specialization_constants.size()); @@ -356,41 +416,45 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( vk::PipelineShaderStageCreateFlags(), vk::ShaderStageFlagBits::eCompute, - shader_module, + pipeline.shader_module, entrypoint.c_str(), &specialization_info); vk::ComputePipelineCreateInfo compute_pipeline_create_info( vk::PipelineCreateFlags(), pipeline_shader_create_info, pipeline.layout); - pipeline.pipeline = vk_device.device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + pipeline.pipeline = ctx->device.lock()->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - return pipeline; + ctx->gc.pipelines.push_back(&pipeline); } -static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { +static void ggml_vk_destroy_pipeline(ggml_backend_vk_context * ctx, vk_pipeline * pipeline) { + for (auto& pool : pipeline->descriptor_pools) { + ctx->device.lock()->device.destroyDescriptorPool(pool); + } + pipeline->descriptor_pools.clear(); + pipeline->descriptor_sets.clear(); + pipeline->descriptor_set_idx = 0; + + ctx->device.lock()->device.destroyDescriptorSetLayout(pipeline->dsl); + + ctx->device.lock()->device.destroyPipelineLayout(pipeline->layout); + + ctx->device.lock()->device.destroyShaderModule(pipeline->shader_module); + + ctx->device.lock()->device.destroyPipeline(pipeline->pipeline); +} + +static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; + std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; #endif - // Check if gc already contains pipeline before adding it - bool gc_found = false; - for (auto * pl : vk_gc.pipelines) { - if (&pipeline == pl) { - gc_found = true; - break; - } - } - - if (!gc_found) { - vk_gc.pipelines.push_back(&pipeline); - } - if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) { // Enough descriptors are available return; } - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size(); std::vector layouts(alloc_count); @@ -398,29 +462,29 @@ static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uin layouts[i] = pipeline.dsl; } vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data()); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end()); } else { for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) { vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); - pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); pipeline.descriptor_sets.push_back(sets[0]); } } } -static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { +static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; + std::cerr << "ggml_pipeline_cleanup(" << pipeline.name << ")" << std::endl; #endif pipeline.descriptor_set_idx = 0; } -static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { +static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; #endif @@ -433,7 +497,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { q.pool, vk::CommandBufferLevel::ePrimary, 1); - const std::vector cmd_buffers = vk_device.device.allocateCommandBuffers(command_buffer_alloc_info); + const std::vector cmd_buffers = ctx->device.lock()->device.allocateCommandBuffers(command_buffer_alloc_info); auto buf = cmd_buffers.front(); q.cmd_buffers.push_back(buf); @@ -442,24 +506,17 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { return buf; } -static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { +static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_submission()" << std::endl; #endif vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(q); + s.buffer = ggml_vk_create_cmd_buffer(ctx, q); s.wait_semaphores = std::move(wait_semaphores); s.signal_semaphores = std::move(signal_semaphores); return s; } -static vk_sequence ggml_vk_create_sequence_1(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_sequence_1()" << std::endl; -#endif - return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; -} - static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; @@ -578,89 +635,89 @@ static uint32_t ggml_vk_find_queue_family_index(std::vectordevice.lock()->device.createCommandPool(command_pool_create_info_compute); q.cmd_buffer_idx = 0; - q.queue = vk_device.device.getQueue(queue_family_index, queue_index); + q.queue = ctx->device.lock()->device.getQueue(queue_family_index, queue_index); q.stage_flags = stage_flags; - - return q; } -static vk_context * ggml_vk_create_context(vk_queue& q) { +static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_context()" << std::endl; #endif - vk_gc.contexts.emplace_back(); - vk_context * result = &vk_gc.contexts[vk_gc.contexts.size() - 1]; + ctx->gc.contexts.emplace_back(); + vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1]; memset((void *) result, 0, sizeof(vk_context)); - result->idx = vk_gc.contexts.size() - 1; + result->idx = ctx->gc.contexts.size() - 1; result->q = &q; return result; } -static vk_semaphore * ggml_vk_create_binary_semaphore() { +static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; #endif vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); - vk_gc.semaphores.push_back({ semaphore, 0 }); - return &vk_gc.semaphores[vk_gc.semaphores.size() - 1]; + vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + ctx->gc.semaphores.push_back({ semaphore, 0 }); + return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; } -static vk_semaphore * ggml_vk_create_timeline_semaphore() { +static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; #endif - if (vk_semaphore_idx >= vk_gc.tl_semaphores.size()) { + if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) { vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); - vk_gc.tl_semaphores.push_back({ semaphore, 0 }); + vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); } - return &vk_gc.tl_semaphores[vk_semaphore_idx++]; + return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; } -static vk::Event ggml_vk_create_event() { - if (vk_event_idx >= vk_gc.events.size()) { - vk_gc.events.push_back(vk_device.device.createEvent({})); +static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { + if (ctx->event_idx >= ctx->gc.events.size()) { + ctx->gc.events.push_back(ctx->device.lock()->device.createEvent({})); } - return vk_gc.events[vk_event_idx++]; + return ctx->gc.events[ctx->event_idx++]; } -static void ggml_vk_queue_cleanup(vk_queue& q) { +static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_queue_cleanup()" << std::endl; #endif // Requires command buffers to be done - vk_device.device.resetCommandPool(q.pool); + ctx->device.lock()->device.resetCommandPool(q.pool); q.cmd_buffer_idx = 0; } -static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; #endif - GGML_ASSERT(size > 0); + vk_buffer buf = std::make_shared(); - vk_buffer buf; + if (size == 0) { + buf->size = 0; + return buf; + } - buf.size = size; + buf->size = size; vk::BufferCreateInfo buffer_create_info{ vk::BufferCreateFlags(), size, @@ -670,11 +727,11 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ nullptr, }; - buf.buffer = vk_device.device.createBuffer(buffer_create_info); + buf->buffer = ctx->device.lock()->device.createBuffer(buffer_create_info); - vk::MemoryRequirements mem_req = vk_device.device.getBufferMemoryRequirements(buf.buffer); + vk::MemoryRequirements mem_req = ctx->device.lock()->device.getBufferMemoryRequirements(buf->buffer); - vk::PhysicalDeviceMemoryProperties mem_props = vk_device.physical_device.getMemoryProperties(); + vk::PhysicalDeviceMemoryProperties mem_props = ctx->device.lock()->physical_device.getMemoryProperties(); uint32_t memory_type_index = UINT32_MAX; @@ -691,30 +748,36 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ } try { - buf.device_memory = vk_device.device.allocateMemory({ mem_req.size, memory_type_index }); + buf->device_memory = ctx->device.lock()->device.allocateMemory({ mem_req.size, memory_type_index }); } catch (const vk::SystemError& e) { // Out of Host/Device memory, clean up buffer - vk_device.device.destroyBuffer(buf.buffer); - buf.size = 0; + ctx->device.lock()->device.destroyBuffer(buf->buffer); + buf->size = 0; throw e; } - buf.memory_property_flags = req_flags; - buf.ptr = nullptr; + buf->memory_property_flags = req_flags; + buf->ptr = nullptr; if (req_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - buf.ptr = vk_device.device.mapMemory(buf.device_memory, 0, VK_WHOLE_SIZE); + buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); } - vk_device.device.bindBufferMemory(buf.buffer, buf.device_memory, 0); + ctx->device.lock()->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); - buf.qf_owner = VK_QUEUE_FAMILY_IGNORED; + buf->ctx = ctx; + + buf->device = ctx->device.lock(); + +#ifdef GGML_VULKAN_DEBUG + std::cerr << "Created buffer " << buf->buffer << std::endl; +#endif return buf; } -static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { try { - return ggml_vk_create_buffer(size, req_flags); + return ggml_vk_create_buffer(ctx, size, req_flags); } catch (const vk::SystemError& e) { std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -722,14 +785,14 @@ static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlag } } -static vk_buffer ggml_vk_create_buffer_device(size_t size) { +static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) { vk_buffer buf; try { - buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } catch (const vk::SystemError& e) { - if (vk_device.uma) { + if (ctx->device.lock()->uma) { // Fall back to host memory type - buf = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + buf = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -741,16 +804,7 @@ static vk_buffer ggml_vk_create_buffer_device(size_t size) { } static void ggml_vk_destroy_buffer(vk_buffer& buf) { - if (buf.size == 0) { - return; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; -#endif - - buf.size = 0; - vk_device.device.freeMemory(buf.device_memory); - vk_device.device.destroyBuffer(buf.buffer); + buf.reset(); } static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { @@ -773,7 +827,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { ); } -static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { +static void ggml_vk_wait_events(vk_context * ctx, std::vector&& events) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_wait_events()" << std::endl; #endif @@ -781,10 +835,10 @@ static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vectors->buffer.waitEvents( events, - src_stages, - dst_stages, + ctx->q->stage_flags, + ctx->q->stage_flags, {}, {}, {} @@ -810,15 +864,15 @@ static bool ggml_vk_build_shader(ggml_type type) { } } -static void ggml_vk_load_shaders() { +static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_load_shaders()" << std::endl; + std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl; #endif // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; - std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + std::initializer_list warptile_l = { 128, 128, 128, 16, ctx->device.lock()->subgroup_size * 2, 64, 2, 4, 4, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_m = { 128, 64, 64, 16, ctx->device.lock()->subgroup_size, 32, 2, 4, 2, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_s = { ctx->device.lock()->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, ctx->device.lock()->subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -828,145 +882,208 @@ static void ggml_vk_load_shaders() { uint32_t m_align = 64; uint32_t s_align = 32; - if (vk_device.fp16) { - vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + if (ctx->device.lock()->fp16) { + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); } else { - vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); } - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); // dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), { 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F16 ], "dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); - vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - vk_pipeline_mul_mat_vec_nc_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - vk_pipeline_norm_f32 = ggml_vk_create_pipeline("norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_rms_norm_f32 = ggml_vk_create_pipeline("rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_cpy_f32_f32 = ggml_vk_create_pipeline("cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_cpy_f32_f16 = ggml_vk_create_pipeline("cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_cpy_f16_f16 = ggml_vk_create_pipeline("cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_add_f32 = ggml_vk_create_pipeline("add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_mul_f32 = ggml_vk_create_pipeline("mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_scale_f32 = ggml_vk_create_pipeline("scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_sqr_f32 = ggml_vk_create_pipeline("sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_clamp_f32 = ggml_vk_create_pipeline("clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_gelu_f32 = ggml_vk_create_pipeline("gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_silu_f32 = ggml_vk_create_pipeline("silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_relu_f32 = ggml_vk_create_pipeline("relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_diag_mask_inf_f32 = ggml_vk_create_pipeline("diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_soft_max_f32 = ggml_vk_create_pipeline("soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_rope_f32 = ggml_vk_create_pipeline("rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_f16 = ggml_vk_create_pipeline("rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_neox_f32 = ggml_vk_create_pipeline("rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_neox_f16 = ggml_vk_create_pipeline("rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); } -void ggml_vk_init() { +static void ggml_vk_print_gpu_info(size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_init()" << std::endl; + std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl; #endif - static bool initialized = false; + GGML_ASSERT(vk_instance.initialized); - if (initialized) { - return; + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); } - initialized = true; + vk::PhysicalDevice physical_device = devices[dev_num]; + std::vector ext_props = physical_device.enumerateDeviceExtensionProperties(); - const char* GGML_VULKAN_DEVICE = getenv("GGML_VULKAN_DEVICE"); - int dev_num = (GGML_VULKAN_DEVICE == NULL ? 0 : atoi(GGML_VULKAN_DEVICE)); + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + physical_device.getProperties2(&props2); + + const size_t subgroup_size = subgroup_props.subgroupSize; + const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + + bool fp16_storage = false; + bool fp16_compute = false; + + for (auto properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } + } + + const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + + bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + + vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures(); + + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + vkGetPhysicalDeviceFeatures2(physical_device, &device_features2); + + fp16 = fp16 && vk12_features.shaderFloat16; + + std::string device_name = props2.properties.deviceName.data(); + std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; + + if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { + std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; + } +} + +void ggml_vk_instance_init() { + if (vk_instance_initialized) { + return; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_instance_init()" << std::endl; +#endif vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector layers = { @@ -989,12 +1106,55 @@ void ggml_vk_init() { validation_features.setPNext(nullptr); instance_create_info.setPNext(&validation_features); -std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; + std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; #endif - vk_instance = vk::createInstance(instance_create_info); + vk_instance.instance = vk::createInstance(instance_create_info); - vk_device.physical_device = vk_instance.enumeratePhysicalDevices()[dev_num]; - std::vector ext_props = vk_device.physical_device.enumerateDeviceExtensionProperties(); + memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES); + + size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size(); + + // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan + char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES"); + if (devices_env != nullptr) { + std::string devices(devices_env); + std::replace(devices.begin(), devices.end(), ',', ' '); + + std::stringstream ss(devices); + size_t tmp; + while (ss >> tmp) { + if(tmp >= num_available_devices) { + std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl; + throw std::runtime_error("Invalid Vulkan device index"); + } + vk_instance.device_indices.push_back(tmp); + } + } else { + vk_instance.device_indices.push_back(0); + } + + vk_instance_initialized = true; +} + +void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl; +#endif + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); + } + + vk_instance.devices[idx] = std::make_shared(); + ctx->device = vk_instance.devices[idx]; + ctx->device.lock()->physical_device = devices[dev_num]; + std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); bool maintenance4_support = false; @@ -1014,18 +1174,18 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; if (maintenance4_support) { subgroup_props.pNext = &props4; } - vk_device.physical_device.getProperties2(&props2); - vk_device.properties = props2.properties; + ctx->device.lock()->physical_device.getProperties2(&props2); + ctx->device.lock()->properties = props2.properties; if (maintenance4_support) { - vk_device.max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + ctx->device.lock()->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); } else { - vk_device.max_memory_allocation_size = props3.maxMemoryAllocationSize; + ctx->device.lock()->max_memory_allocation_size = props3.maxMemoryAllocationSize; } - vk_device.vendor_id = vk_device.properties.vendorID; - vk_device.subgroup_size = subgroup_props.subgroupSize; - vk_device.uma = vk_device.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + ctx->device.lock()->vendor_id = ctx->device.lock()->properties.vendorID; + ctx->device.lock()->subgroup_size = subgroup_props.subgroupSize; + ctx->device.lock()->uma = ctx->device.lock()->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; bool fp16_storage = false; bool fp16_compute = false; @@ -1039,31 +1199,31 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; } const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != NULL; + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; - vk_device.fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + ctx->device.lock()->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - std::vector queue_family_props = vk_device.physical_device.getQueueFamilyProperties(); + std::vector queue_family_props = ctx->device.lock()->physical_device.getQueueFamilyProperties(); // Try to find a non-graphics compute queue and transfer-focused queues const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); const float priorities[] = { 1.0f, 1.0f }; - const bool single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + ctx->device.lock()->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; std::vector device_queue_create_infos; if (compute_queue_family_index != transfer_queue_family_index) { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!single_queue) { + } else if(!ctx->device.lock()->single_queue) { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); } else { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); } vk::DeviceCreateInfo device_create_info; std::vector device_extensions; - vk::PhysicalDeviceFeatures device_features = vk_device.physical_device.getFeatures(); + vk::PhysicalDeviceFeatures device_features = ctx->device.lock()->physical_device.getFeatures(); VkPhysicalDeviceFeatures2 device_features2; device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; @@ -1080,13 +1240,13 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; vk11_features.pNext = &vk12_features; - vkGetPhysicalDeviceFeatures2(vk_device.physical_device, &device_features2); + vkGetPhysicalDeviceFeatures2(ctx->device.lock()->physical_device, &device_features2); - vk_device.fp16 = vk_device.fp16 && vk12_features.shaderFloat16; + ctx->device.lock()->fp16 = ctx->device.lock()->fp16 && vk12_features.shaderFloat16; if (!vk11_features.storageBuffer16BitAccess) { - std::cerr << "ggml_vulkan: device does not support 16-bit storage" << std::endl; - GGML_ASSERT(false); + std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; + throw std::runtime_error("Unsupported device"); } device_extensions.push_back("VK_KHR_16bit_storage"); @@ -1095,10 +1255,11 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif - if (vk_device.fp16) { + if (ctx->device.lock()->fp16) { device_extensions.push_back("VK_KHR_shader_float16_int8"); } - std::cerr << "ggml_vulkan: Using " << vk_device.properties.deviceName << " | uma: " << vk_device.uma << " | fp16: " << vk_device.fp16 << " | warp size: " << vk_device.subgroup_size << std::endl; + ctx->device.lock()->name = ctx->device.lock()->properties.deviceName.data(); + device_create_info = { vk::DeviceCreateFlags(), device_queue_create_infos, @@ -1106,28 +1267,32 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions }; device_create_info.setPNext(&device_features2); - vk_device.device = vk_device.physical_device.createDevice(device_create_info); + ctx->device.lock()->device = ctx->device.lock()->physical_device.createDevice(device_create_info); - vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; + ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; // Shaders - ggml_vk_load_shaders(); + ggml_vk_load_shaders(ctx); // Queues - vk_device.compute_queue = ggml_vk_create_queue(compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - if (!single_queue) { + ggml_vk_create_queue(ctx, ctx->device.lock()->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); + if (!ctx->device.lock()->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - vk_device.transfer_queue = ggml_vk_create_queue(transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + ggml_vk_create_queue(ctx, ctx->device.lock()->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); } else { - vk_device.transfer_queue = vk_device.compute_queue; + // TODO: Use pointer or reference to avoid copy + ctx->device.lock()->transfer_queue = ctx->device.lock()->compute_queue; } - vk_fence = vk_device.device.createFence({}); + ctx->fence = ctx->device.lock()->device.createFence({}); - vk_ctx = nullptr; - vk_transfer_ctx = nullptr; + ctx->compute_ctx = nullptr; + ctx->transfer_ctx = nullptr; - vk_disable = false; + ctx->disable = false; + ctx->initialized = true; + + ctx->idx = idx; #ifdef GGML_VULKAN_CHECK_RESULTS const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); @@ -1137,7 +1302,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; #endif } -static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { +static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif @@ -1158,10 +1323,10 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { return nullptr; } - return &vk_pipeline_dequant[type]; + return &ctx->pipeline_dequant[type]; } -static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { +static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif @@ -1182,15 +1347,10 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { return nullptr; } - return &vk_pipeline_dequant_mul_mat_vec_f32[type]; + return &ctx->pipeline_dequant_mul_mat_vec_f32[type]; } -// buffer pool for vulkan -#define MAX_VK_BUFFERS 256 - -static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; - -static vk_buffer ggml_vk_pool_malloc(size_t size) { +static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; #endif @@ -1199,98 +1359,95 @@ static vk_buffer ggml_vk_pool_malloc(size_t size) { int worst_i = -1; size_t worst_size = 0; //largest unused buffer seen so far for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer &b = g_vk_buffer_pool[i]; - if (b.size > 0 && b.size >= size && b.size < best_size) { + vk_buffer &b = ctx->buffer_pool[i]; + if (b != nullptr && b->size >= size && b->size < best_size) { best_i = i; - best_size = b.size; + best_size = b->size; } - if (b.size > 0 && b.size > worst_size) { + if (b != nullptr && b->size > worst_size) { worst_i = i; - worst_size = b.size; + worst_size = b->size; } } if(best_i != -1) { //found the smallest buffer that fits our needs - vk_buffer b = g_vk_buffer_pool[best_i]; - g_vk_buffer_pool[best_i].size = 0; + vk_buffer b = ctx->buffer_pool[best_i]; + ctx->buffer_pool[best_i].reset(); return b; } if(worst_i != -1) { //no buffer that fits our needs, resize largest one to save memory - vk_buffer& b = g_vk_buffer_pool[worst_i]; + vk_buffer& b = ctx->buffer_pool[worst_i]; ggml_vk_destroy_buffer(b); } - return ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } -static void ggml_vk_pool_free(vk_buffer& buffer) { +static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; + std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl; #endif for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer& b = g_vk_buffer_pool[i]; - if (b.size == 0) { + vk_buffer& b = ctx->buffer_pool[i]; + if (b == nullptr) { b = buffer; - // Set owning queue family index to ignored to avoid synchronization on next use - b.qf_owner = VK_QUEUE_FAMILY_IGNORED; return; } } - fprintf(stderr, "WARNING: vk buffer pool full, increase MAX_VK_BUFFERS\n"); + std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl; ggml_vk_destroy_buffer(buffer); } // Returns an available temporary buffer that may only be used temporarily, it will be reused -static vk_buffer ggml_vk_create_buffer_temp(size_t size) { +static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) { // Try to find existing temp buffer with enough capacity - for (auto& buffer : vk_gc.temp_buffers) { - if (buffer.size >= size) { + for (auto& buffer : ctx->gc.temp_buffers) { + if (buffer->size >= size) { return buffer; } } // Otherwise create new buffer - vk_buffer buf = ggml_vk_pool_malloc(size); - vk_gc.temp_buffers.push_back(buf); + vk_buffer buf = ggml_vk_pool_malloc(ctx, size); + ctx->gc.temp_buffers.push_back(buf); return buf; } -static void * ggml_vk_host_malloc(size_t size) { +static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif - vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + vk_buffer buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - if(!(buf.memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { + if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", size/1024.0/1024.0); - buf.size = 0; - vk_device.device.freeMemory(buf.device_memory); - vk_device.device.destroyBuffer(buf.buffer); + ctx->device.lock()->device.freeMemory(buf->device_memory); + ctx->device.lock()->device.destroyBuffer(buf->buffer); return nullptr; } - vk_pinned_memory.push_back(std::make_tuple(buf.ptr, size, buf)); + ctx->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf)); - return buf.ptr; + return buf->ptr; } -static void ggml_vk_host_free(void* ptr) { +static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) { if (ptr == nullptr) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; #endif - vk_buffer* buf = nullptr; + vk_buffer buf; size_t index; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]); if (ptr >= addr && ptr < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); + buf = std::get<2>(ctx->pinned_memory[i]); index = i; break; } @@ -1300,28 +1457,28 @@ static void ggml_vk_host_free(void* ptr) { return; } - ggml_vk_destroy_buffer(*buf); + ggml_vk_destroy_buffer(buf); - vk_pinned_memory.erase(vk_pinned_memory.begin() + index); + ctx->pinned_memory.erase(ctx->pinned_memory.begin() + index); } -static void ggml_vk_host_get(const void * ptr, vk_buffer *& buf, size_t& buf_offset) { +static void ggml_vk_host_get(ggml_backend_vk_context * ctx, const void * ptr, vk_buffer& buf, size_t& buf_offset) { buf = nullptr; buf_offset = 0; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]); if (ptr >= addr && ptr < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); + buf = std::get<2>(ctx->pinned_memory[i]); buf_offset = ((const uint8_t *)ptr) - addr; break; } } } -static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) { +static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_queue& q, bool one_time = true) { vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(q); + s.buffer = ggml_vk_create_cmd_buffer(ctx, q); if (one_time) { s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); } else { @@ -1331,7 +1488,7 @@ static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) return s; } -static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { +static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); @@ -1344,22 +1501,22 @@ static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, s GGML_ASSERT(buffers.size() == pipeline.parameter_count); vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++]; for (uint32_t i = 0; i < pipeline.parameter_count; i++) { - descriptor_buffer_infos.push_back({buffers[i].buffer.buffer, buffers[i].offset, buffers[i].size}); + descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size}); } for (uint32_t i = 0; i < pipeline.parameter_count; i++) { write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); } - vk_device.device.updateDescriptorSets(write_descriptor_sets, {}); + ctx->device.lock()->device.updateDescriptorSets(write_descriptor_sets, {}); - ctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); - ctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); - ctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, + subctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); + subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); + subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, pipeline.layout, 0, { descriptor_set }, {}); - ctx->s->buffer.dispatch(wg0, wg1, wg2); + subctx->s->buffer.dispatch(wg0, wg1, wg2); } static void ggml_vk_end_submission(vk_submission& s, std::vector wait_semaphores, std::vector signal_semaphores) { @@ -1381,16 +1538,16 @@ static void ggml_vk_ctx_end(vk_context * ctx) { ctx->s = nullptr; } -static void ggml_vk_ctx_begin(vk_context * ctx) { +static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; #endif - if (ctx->s != nullptr) { - ggml_vk_ctx_end(ctx); + if (subctx->s != nullptr) { + ggml_vk_ctx_end(subctx); } - ctx->seqs.push_back({ ggml_vk_begin_submission(*ctx->q) }); - ctx->s = ctx->seqs[ctx->seqs.size() - 1].data(); + subctx->seqs.push_back({ ggml_vk_begin_submission(ctx, *subctx->q) }); + subctx->s = subctx->seqs[subctx->seqs.size() - 1].data(); } static size_t ggml_vk_align_size(size_t width, size_t align) { @@ -1405,14 +1562,14 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect } } -static void ensure_sync_staging_buffer(size_t size) { - if (vk_sync_staging.size < size) { - ggml_vk_destroy_buffer(vk_sync_staging); - vk_sync_staging = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); +static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) { + if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) { + ggml_vk_destroy_buffer(ctx->sync_staging); + ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } -static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { +static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; #endif @@ -1423,9 +1580,9 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf; size_t buf_offset; - ggml_vk_host_get(tensor->data, buf, buf_offset); + ggml_vk_host_get(ctx, tensor->data, buf, buf_offset); const uint64_t ne0 = tensor->ne[0]; const uint64_t ne1 = tensor->ne[1]; @@ -1471,21 +1628,21 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } // Staging buffer required - vk_buffer * staging = &vk_staging; - size_t staging_offset = vk_staging_offset; + vk_buffer staging = ctx->staging; + size_t staging_offset = ctx->staging_offset; const size_t copy_size = ts*ne/bs; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; staging_offset = 0; } else { GGML_ASSERT(false); @@ -1494,23 +1651,23 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size VkBufferCopy buf_copy{ staging_offset, offset, copy_size }; - ggml_vk_sync_buffers(ctx); - vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + ggml_vk_sync_buffers(subctx); + vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); for (uint64_t i3 = 0; i3 < ne3; i3++) { for (uint64_t i2 = 0; i2 < ne2; i2++) { // Find longest contiguous slice if (ne1*nb1 == dstnb2) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); } else { for (uint64_t i1 = 0; i1 < ne1; i1++) { if (ne0*nb0/bs == dstnb1) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); } else { const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; for (uint64_t i0 = 0; i0 < ne0; i0++) { - deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys); } } } @@ -1519,19 +1676,22 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } } -static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { +static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; #endif + // Make sure ctx owns the buffer + GGML_ASSERT(dst->ctx == ctx); + // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf = nullptr; size_t buf_offset; - ggml_vk_host_get(src, buf, buf_offset); + ggml_vk_host_get(ctx, src, buf, buf_offset); if (buf != nullptr) { // Memory is pinned, use as staging buffer @@ -1550,8 +1710,8 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } #ifdef GGML_VULKAN_DEBUG @@ -1559,14 +1719,14 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size #endif // Staging buffer required - vk_buffer * staging = &vk_staging; - size_t staging_offset = vk_staging_offset; + vk_buffer staging = ctx->staging; + size_t staging_offset = ctx->staging_offset; const size_t copy_size = width*height; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; staging_offset = 0; } else { GGML_ASSERT(false); @@ -1578,26 +1738,26 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size offset, copy_size}; - ggml_vk_sync_buffers(ctx); - vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + ggml_vk_sync_buffers(subctx); + vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); if (width == spitch) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &subctx->in_memcpys); } else { for (size_t i = 0; i < height; i++) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); } } } -static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { +static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; #endif - return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); + return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging); } -static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { +static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; #endif @@ -1609,39 +1769,42 @@ static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); } } else { - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, spitch, width, height, true); - ggml_vk_ctx_end(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true); + ggml_vk_ctx_end(subctx); - for (auto& cpy : ctx->in_memcpys) { + for (auto& cpy : subctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); } } -static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { +static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; #endif - ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); + ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1); } -static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { +static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; #endif GGML_ASSERT(width > 0); GGML_ASSERT(height > 0); - GGML_ASSERT(src->size > 0); + GGML_ASSERT(src != nullptr); + // Make sure ctx owns the buffer + GGML_ASSERT(src->ctx == ctx); + // Check if dst is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf = nullptr; size_t buf_offset; - ggml_vk_host_get(dst, buf, buf_offset); + ggml_vk_host_get(ctx, dst, buf, buf_offset); std::vector slices(1); if (width == spitch && width == dpitch) { @@ -1660,8 +1823,8 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ if (buf != nullptr) { // Memory is pinned, use as staging buffer - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); return; } @@ -1670,30 +1833,30 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ #endif // Fall back to staging buffer - vk_buffer * staging = &vk_staging; + vk_buffer staging = ctx->staging; const size_t copy_size = dpitch * height; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; } else { GGML_ASSERT(false); } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); - deferred_memcpy(dst, staging->ptr, copy_size, &ctx->out_memcpys); + deferred_memcpy(dst, staging->ptr, copy_size, &subctx->out_memcpys); } -static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t size, bool sync_staging = false) { - return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst, size, size, size, 1, sync_staging); +static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) { + return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst, size, size, size, 1, sync_staging); } -static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { +static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; #endif @@ -1702,61 +1865,88 @@ static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_ memcpy(dst, (uint8_t *) src->ptr + offset, size); } else { - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ggml_vk_buffer_read_async(ctx, src, offset, dst, size, true); - ggml_vk_ctx_end(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true); + ggml_vk_ctx_end(subctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : ctx->out_memcpys) { + for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } } } -static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; #endif + // Make sure both buffers are on same ctx + GGML_ASSERT(src->ctx == dst->ctx); + VkBufferCopy bc{ src_offset, dst_offset, size }; vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); } -static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { + if (src->ctx == dst->ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; + std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl; #endif - VkBufferCopy bc{ src_offset, dst_offset, size }; + // Copy within the device + ggml_backend_vk_context * ctx = src->ctx; - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); - ggml_vk_buffer_copy_async(ctx, dst, dst_offset, src, src_offset, size); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); - vk_device.device.resetFences({ vk_fence }); + VkBufferCopy bc{ src_offset, dst_offset, size }; + + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); + } else { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl; +#endif + // Copy device to device + ggml_backend_vk_context * src_ctx = src->ctx; + ggml_backend_vk_context * dst_ctx = dst->ctx; + + ggml_vk_ensure_sync_staging_buffer(src_ctx, size); + ggml_vk_ensure_sync_staging_buffer(dst_ctx, size); + + // Copy to src staging buffer + ggml_vk_buffer_copy(src_ctx->sync_staging, 0, src, src_offset, size); + // memcpy to dst staging buffer + memcpy(dst_ctx->sync_staging->ptr, src_ctx->sync_staging->ptr, size); + // Copy to dst buffer + ggml_vk_buffer_copy(dst, dst_offset, dst_ctx->sync_staging, 0, size); + } } -static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { +static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; #endif - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); - ggml_vk_ctx_end(ctx); + // Make sure ctx owns the buffer + GGML_ASSERT(dst->ctx == ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_memset waitForFences"); - vk_device.device.resetFences({ vk_fence }); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); + ggml_vk_ctx_end(subctx); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); } -static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { +static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; #endif @@ -1773,20 +1963,20 @@ static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offs const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3); if (nb0 == ts && nb1 == row_length) { - return ggml_vk_buffer_write_async(ctx, dst, offset, x, i1*nb1); + return ggml_vk_buffer_write_async(ctx, subctx, dst, offset, x, i1*nb1); } if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) { - return ggml_vk_buffer_write_2d_async(ctx, dst, offset, x, nb1, row_length, i1); + return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, x, nb1, row_length, i1); } GGML_ASSERT(i3 == 0); GGML_ASSERT(i2 == 0); GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src)); - return ggml_vk_buffer_write_nc_async(ctx, dst, offset, src); + return ggml_vk_buffer_write_nc_async(ctx, subctx, dst, offset, src); } -static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { +static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; #endif @@ -1804,10 +1994,10 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs const size_t row_length = ts*ne0/bs; if (ggml_is_contiguous(dst)) { - return ggml_vk_buffer_read_async(ctx, src, offset, dst->data, ne1*nb1*ne2*ne3); + return ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst->data, ne1*nb1*ne2*ne3); } if (nb0 == ts) { - return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); + return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); } GGML_ASSERT(false); } @@ -1829,89 +2019,89 @@ static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { return 1; } -static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { +static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, int m, int n) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif if (m <= 32 || n <= 32) { - return vk_pipeline_matmul_f32_aligned_s.align; + return ctx->pipeline_matmul_f32_aligned_s.align; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { - return vk_pipeline_matmul_f32_aligned_m.align; + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { + return ctx->pipeline_matmul_f32_aligned_m.align; } - return vk_pipeline_matmul_f32_aligned_l.align; + return ctx->pipeline_matmul_f32_aligned_l.align; } -static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { +static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; #endif if (bit16_x && bit16_y) { - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; + return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; + return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; + return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_l : &ctx->pipeline_matmul_f16_f32_l; } if (!bit16_x && bit16_y) { GGML_ASSERT(false); } - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; + return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; + return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; + return aligned ? &ctx->pipeline_matmul_f32_aligned_l : &ctx->pipeline_matmul_f32_l; } -static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { +static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif - ggml_vk_sync_buffers(ctx); + ggml_vk_sync_buffers(subctx); if (split_k == 1) { const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; - ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); return; } @@ -1919,10 +2109,10 @@ static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); - ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); + ggml_vk_sync_buffers(subctx); const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -1932,32 +2122,32 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { +static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return &vk_pipeline_cpy_f32_f32; + return &ctx->pipeline_cpy_f32_f32; } if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return &vk_pipeline_cpy_f32_f16; + return &ctx->pipeline_cpy_f32_f16; } if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return &vk_pipeline_cpy_f16_f16; + return &ctx->pipeline_cpy_f16_f16; } std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; GGML_ASSERT(false); } -static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { +static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; - std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; + std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); const int dst_type_size = ggml_type_size(buffer_type); const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2]; - const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; + const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; const vk_op_cpy_push_constants pc = { (uint32_t)ne, @@ -1965,11 +2155,11 @@ static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], 1 , (uint32_t)tensor->ne[0] , nb2, 0, }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); } -static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -1998,17 +2188,17 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qx = nullptr; + vk_buffer d_Qx; size_t qx_buf_offset = 0; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; } @@ -2031,12 +2221,12 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ne01, ne11)); + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, ne01, ne11)); const bool aligned = ne10 == kpad; const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(true, !f16_f32_kernel, ne01, ne11, aligned); + vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(ctx, true, !f16_f32_kernel, ne01, ne11, aligned); const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); @@ -2044,30 +2234,30 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); - vk_buffer* d_X; + vk_buffer d_X; uint64_t x_buf_offset = 0; - vk_buffer* d_Y; + vk_buffer d_Y; uint64_t y_buf_offset = 0; if (load_x) { - d_Qx = &vk_prealloc_qx; + d_Qx = ctx->prealloc_qx; } else if (!src0_uma) { - d_Qx = &extra_src0->buffer_gpu; + d_Qx = extra_src0->buffer_gpu.lock(); qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if (!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { - d_X = &vk_prealloc_x; + d_X = ctx->prealloc_x; GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); } else { d_X = d_Qx; @@ -2075,7 +2265,7 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co GGML_ASSERT(qx_sz == x_sz); // NOLINT } if (qy_needs_dequant) { - d_Y = &vk_prealloc_y; + d_Y = ctx->prealloc_y; GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); } else { d_Y = d_Qy; @@ -2087,49 +2277,49 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co vk_pipeline * to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(src0->type); + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne12 * ne13); if (qx_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); } if (qy_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } if (split_k > 1) { - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, ne12 * ne13); } if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, dst->type, false); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, dst->type, false); } else if (load_x || qx_needs_dequant) { if (load_x) { // copy data to device - ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); - vk_staging_offset = qx_sz * ne02 * ne03; + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + ctx->staging_offset = qx_sz * ne02 * ne03; } if (qx_needs_dequant) { const std::vector pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_0, { { *d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { *d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } } if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, dst->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, dst->type); } else if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } uint32_t stride_batch_x = ne00*ne01; @@ -2144,16 +2334,16 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co } // compute - ggml_vk_matmul(ctx, *pipeline, { *d_X, x_buf_offset, x_sz * ne02 * ne03 }, { *d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { *d_D, d_buf_offset, d_sz * ne12 * ne13 }, { vk_prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data); - ggml_vk_buffer_read_async(ctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); } } -static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2184,17 +2374,17 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qx = nullptr; + vk_buffer d_Qx; size_t qx_buf_offset = 0; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; } @@ -2214,42 +2404,42 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_ne = ne11 * ne10; const uint64_t d_ne = ne11 * ne01; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_X; + vk_buffer d_X; uint64_t x_buf_offset = 0; - vk_buffer* d_Y; + vk_buffer d_Y; uint64_t y_buf_offset = 0; if (load_x) { - d_Qx = &vk_prealloc_qx; + d_Qx = ctx->prealloc_qx; } else if(!src1_uma) { - d_Qx = &extra_src0->buffer_gpu; + d_Qx = extra_src0->buffer_gpu.lock(); qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if(!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { - d_X = &vk_prealloc_x; + d_X = ctx->prealloc_x; } else { d_X = d_Qx; x_buf_offset = qx_buf_offset; GGML_ASSERT(qx_sz == x_sz); } if (qy_needs_dequant) { - d_Y = &vk_prealloc_y; + d_Y = ctx->prealloc_y; } else { d_Y = d_Qy; y_buf_offset = qy_buf_offset; @@ -2259,39 +2449,39 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 vk_pipeline * to_fp16_vk_0 = nullptr; vk_pipeline* to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(src0->type); + vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); // Allocate descriptor sets if (qx_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } - ggml_vk_pipeline_allocate_descriptor_sets(*dmmv, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *dmmv, ne12 * ne13); if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, src0->type); + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, src0->type); } else if (load_x) { // copy data to device - ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); } if (y_non_contig) { GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, src1->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, src1->type); } else if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } for (uint64_t i13 = 0; i13 < ne13; i13++) { @@ -2306,34 +2496,34 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; - const uint64_t y_buffer_offset = (y_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t y_buffer_offset = (y_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t y_shader_offset = y_offset - y_buffer_offset; - const uint64_t d_buffer_offset = (d_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_offset - d_buffer_offset; if (!y_non_contig && qy_needs_dequant) { const std::vector pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_1, { { *d_Qy, qy_offset, qy_sz }, { *d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); } // compute const std::array pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))}; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *dmmv, { { *d_X, x_offset, x_sz }, { *d_Y, y_buffer_offset, y_sz + y_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne); } } } } -static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2362,13 +2552,13 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2378,51 +2568,51 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor const uint64_t y_ne = ne10 * ne11 * ne12; const uint64_t d_ne = ne01 * ne11 * ne12; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx = &extra_src0->buffer_gpu; + vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); const uint64_t qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if (!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); } // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_p021_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); } // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_p021_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); } } -static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2454,13 +2644,13 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2475,43 +2665,43 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * const uint64_t qy_sz = ggml_nbytes(src1); const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx = &extra_src0->buffer_gpu; + vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); const uint64_t qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); } // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_nc_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); } // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_nc_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); } } @@ -2528,22 +2718,22 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU); } -static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - ggml_vk_mul_mat_vec_p021_f16_f32(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst); } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - ggml_vk_mul_mat_vec_nc_f16_f32(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst); } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_q_f16(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst); } else { - ggml_vk_mul_mat_q_f16(ctx, src0, src1, dst); + ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst); } } -static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat const uint64_t ne0 = dst->ne[0]; const uint64_t ne1 = dst->ne[1]; @@ -2579,9 +2769,9 @@ static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - const vk_buffer* src_buf = &extra_src0->buffer_gpu; + const vk_buffer src_buf = extra_src0->buffer_gpu.lock(); const uint64_t src_offset = extra_src0->offset; - vk_buffer* dst_buf = &extra->buffer_gpu; + vk_buffer dst_buf = extra->buffer_gpu.lock(); const uint64_t dst_offset = extra->offset; std::vector copies; @@ -2606,78 +2796,79 @@ static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); - (void) src1; + GGML_UNUSED(ctx); + GGML_UNUSED(src1); } -static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { +static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_add_f32; + return &ctx->pipeline_add_f32; } return nullptr; case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); if (dst->type == GGML_TYPE_F16) { - return &vk_pipeline_get_rows[src0->type]; + return &ctx->pipeline_get_rows[src0->type]; } if (dst->type == GGML_TYPE_F32) { - return &vk_pipeline_get_rows_f32[src0->type]; + return &ctx->pipeline_get_rows_f32[src0->type]; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_mul_f32; + return &ctx->pipeline_mul_f32; } return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_scale_f32; + return &ctx->pipeline_scale_f32; } return nullptr; case GGML_OP_SQR: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_sqr_f32; + return &ctx->pipeline_sqr_f32; } return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_clamp_f32; + return &ctx->pipeline_clamp_f32; } return nullptr; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - return ggml_vk_get_cpy_pipeline(src0->type, dst->type); + return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_norm_f32; + return &ctx->pipeline_norm_f32; } return nullptr; case GGML_OP_RMS_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rms_norm_f32; + return &ctx->pipeline_rms_norm_f32; } return nullptr; case GGML_OP_UNARY: switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_SILU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_silu_f32; + return &ctx->pipeline_silu_f32; } break; case GGML_UNARY_OP_GELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_gelu_f32; + return &ctx->pipeline_gelu_f32; } break; case GGML_UNARY_OP_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_relu_f32; + return &ctx->pipeline_relu_f32; } break; default: @@ -2686,12 +2877,12 @@ static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml return nullptr; case GGML_OP_DIAG_MASK_INF: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_diag_mask_inf_f32; + return &ctx->pipeline_diag_mask_inf_f32; } return nullptr; case GGML_OP_SOFT_MAX: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_soft_max_f32; + return &ctx->pipeline_soft_max_f32; } return nullptr; case GGML_OP_ROPE: @@ -2706,17 +2897,17 @@ static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rope_neox_f32; + return &ctx->pipeline_rope_neox_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &vk_pipeline_rope_neox_f16; + return &ctx->pipeline_rope_neox_f16; } } else { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rope_f32; + return &ctx->pipeline_rope_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &vk_pipeline_rope_f16; + return &ctx->pipeline_rope_f16; } } return nullptr; @@ -2735,13 +2926,8 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } } -#ifdef GGML_VULKAN_CHECK_RESULTS -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); -static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor); -#endif - template -static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { @@ -2768,7 +2954,7 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm const uint64_t nb2 = dst->nb[2]; const uint64_t nb3 = dst->nb[3]; - vk_pipeline * pipeline = ggml_vk_op_get_pipeline(src0, src1, dst, op); + vk_pipeline * pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -2782,7 +2968,7 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm GGML_ASSERT(false); } - op_func(ctx, src0, src1, dst); + op_func(ctx, subctx, src0, src1, dst); return; } @@ -2790,19 +2976,19 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - vk_buffer * d_X = nullptr; + vk_buffer d_X = nullptr; size_t x_buf_offset = 0; - vk_buffer * d_Y = nullptr; + vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_X, x_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); src0_uma = d_X != nullptr; if (use_src1) { - ggml_vk_host_get(src1->data, d_Y, y_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } } @@ -2810,30 +2996,31 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma; const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma; - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, vk_device.properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, vk_device.properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; + vk_buffer d_D = extra->buffer_gpu.lock(); + // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > extra_src1->buffer_gpu.size) { + if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } - vk_buffer* d_D = &extra->buffer_gpu; GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = (extra->offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + uint64_t d_buf_offset = (extra->offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT if (transfer_src0) { - d_X = &vk_prealloc_qx; + d_X = ctx->prealloc_qx; } else if(!src0_uma) { - d_X = &extra_src0->buffer_gpu; + d_X = extra_src0->buffer_gpu.lock(); x_buf_offset = extra_src0->offset; GGML_ASSERT(d_X != nullptr); } if (transfer_src1) { - d_Y = &vk_prealloc_qy; + d_Y = ctx->prealloc_qy; } else if (use_src1 && !src1_uma) { - d_Y = &extra_src1->buffer_gpu; + d_Y = extra_src1->buffer_gpu.lock(); y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); } @@ -2856,16 +3043,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm // copy src0 to device if (transfer_src0) { - ggml_vk_h2d_tensor_2d(ctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); - vk_staging_offset = x_sz * ne02 * ne03; + ggml_vk_h2d_tensor_2d(ctx, subctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); + ctx->staging_offset = x_sz * ne02 * ne03; } if (transfer_src1) { - ggml_vk_h2d_tensor_2d(ctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); } // Single call if dimension 2 is contiguous if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, 1); switch (dst->op) { case GGML_OP_NORM: @@ -2896,24 +3083,24 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (!use_src1 && op == GGML_OP_SOFT_MAX) { // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_Y, y_buf_offset, y_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) { - ggml_vk_d2h_tensor_2d(ctx, d_D, 0, dst); + ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); } else if(dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_buffer_read_async(ctx, d_D, 0, d, d_sz); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); } } else { - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne02 * ne03); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: @@ -2940,60 +3127,60 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (!use_src1 && op == GGML_OP_SOFT_MAX) { // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_Y, y_buf_offset + y_offset, y_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); } } } } } -static void ggml_vk_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_get_rows(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_add(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_mul(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_scale(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); } -static void ggml_vk_sqr(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } -static void ggml_vk_clamp(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); } -static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; const int src0_type_size = ggml_type_size(src0->type); const int dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = (extra->offset % vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CPY, { + const uint32_t d_offset = (extra->offset % ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, @@ -3001,30 +3188,30 @@ static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor }); } -static void ggml_vk_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); +static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); } -static void ggml_vk_rms_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } -static void ggml_vk_unary(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } -static void ggml_vk_diag_mask_inf(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); } -static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -3047,19 +3234,19 @@ static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_ if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); } else { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); } } -static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { // If backend is CPU, data from src0 has to be copied off the device if (dst->backend == GGML_BACKEND_CPU) { ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - vk_buffer * d_D = &extra_src0->buffer_gpu; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, 0, dst->data, d_D->size); + vk_buffer d_D = extra_src0->buffer_gpu.lock(); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, dst->data, d_D->size); } } @@ -3096,7 +3283,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 } template -static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { +static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; #endif @@ -3108,39 +3295,39 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size std::string shname; if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_s; + p = &ctx->pipeline_matmul_f32_aligned_s; shname = "F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_s; + p = &ctx->pipeline_matmul_f16_f32_aligned_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_s; + p = &ctx->pipeline_matmul_f16_aligned_s; shname = "F16_ALIGNED_S"; } else { GGML_ASSERT(false); } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_m; + p = &ctx->pipeline_matmul_f32_aligned_m; shname = "F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_m; + p = &ctx->pipeline_matmul_f16_f32_aligned_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_m; + p = &ctx->pipeline_matmul_f16_aligned_m; shname = "F16_ALIGNED_M"; } else { GGML_ASSERT(false); } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_l; + p = &ctx->pipeline_matmul_f32_aligned_l; shname = "F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_l; + p = &ctx->pipeline_matmul_f16_f32_aligned_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_l; + p = &ctx->pipeline_matmul_f16_aligned_l; shname = "F16_ALIGNED_L"; } else { GGML_ASSERT(false); @@ -3154,56 +3341,56 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (k != kpad) { if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_s; + p = &ctx->pipeline_matmul_f32_s; shname = "F32_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_s; + p = &ctx->pipeline_matmul_f16_f32_s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_s; + p = &ctx->pipeline_matmul_f16_s; shname = "F16_S"; } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_m; + p = &ctx->pipeline_matmul_f32_m; shname = "F32_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_m; + p = &ctx->pipeline_matmul_f16_f32_m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_m; + p = &ctx->pipeline_matmul_f16_m; shname = "F16_M"; } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_l; + p = &ctx->pipeline_matmul_f32_l; shname = "F32_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_l; + p = &ctx->pipeline_matmul_f16_f32_l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_l; + p = &ctx->pipeline_matmul_f16_l; shname = "F16_L"; } } } - ggml_vk_pipeline_allocate_descriptor_sets(*p, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, *p, num_it); if (split_k > 1) { - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, num_it); - if (vk_prealloc_split_k.size < sizeof(float) * d_ne * split_k) { + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer - if (vk_prealloc_split_k.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_split_k); + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer_check(sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); } } - vk_buffer d_X = ggml_vk_create_buffer_check(sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_Y = ggml_vk_create_buffer_check(sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_D = ggml_vk_create_buffer_check(sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); @@ -3228,26 +3415,26 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size } } - ggml_vk_buffer_write(&d_X, 0, x, sizeof(X_TYPE) * k * m * batch); - ggml_vk_buffer_write(&d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); + ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch); + ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx); - ggml_vk_matmul(ctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(vk_prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); - ggml_vk_ctx_end(ctx); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_matmul(ctx, subctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_ctx_end(subctx); } auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double time = std::chrono::duration_cast(end-begin).count() / 1000.0; // copy dst to host - ggml_vk_buffer_read(&d_D, 0, d, sizeof(float) * d_ne); + ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne); float * d_chk = (float *) malloc(sizeof(float) * d_ne); @@ -3285,14 +3472,14 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size src1_ggml->data = y; tensor_ggml->data = d_chk; - vk_disable = true; + ctx->disable = true; ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - vk_disable = false; + ctx->disable = false; ggml_free(ggml_ctx); @@ -3325,7 +3512,7 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (split_k > 1) { float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); - ggml_vk_buffer_read(&vk_prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); std::cerr << "d_buf0: " << std::endl << std::endl; ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); @@ -3345,15 +3532,15 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size free(d_chk); - ggml_vk_queue_cleanup(vk_device.transfer_queue); - ggml_vk_queue_cleanup(vk_device.compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); ggml_vk_destroy_buffer(d_X); ggml_vk_destroy_buffer(d_Y); ggml_vk_destroy_buffer(d_D); - ggml_vk_pipeline_cleanup(*p); - ggml_vk_pipeline_cleanup(vk_pipeline_matmul_split_k_reduce); + ggml_pipeline_cleanup(*p); + ggml_pipeline_cleanup(ctx->pipeline_matmul_split_k_reduce); free(x); free(y); @@ -3392,7 +3579,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1 } } -static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) { +static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_t ne1, size_t ne2, size_t ne3) { const size_t ne = ne0 * ne1 * ne2 * ne3; ggml_init_params iparams = { @@ -3406,7 +3593,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3); // NOLINT ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3); - float * data = (float *) ggml_vk_host_malloc(ggml_nbytes(tensor)); + float * data = (float *) ggml_vk_host_malloc(ctx, ggml_nbytes(tensor)); tensor->data = data; float * result_data = (float *) malloc(ggml_nbytes(tensor)); @@ -3426,19 +3613,19 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; } - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); - vk_buffer buffer = ggml_vk_create_buffer_check(ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); - ggml_vk_h2d_tensor_2d(ctx, &buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); + ggml_vk_h2d_tensor_2d(ctx, subctx, buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - ggml_vk_buffer_read(&buffer, 0, result_data, ggml_nbytes(tensor)); + ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor)); double avg_err = 0.0; int first_err_i0 = -1; @@ -3483,22 +3670,22 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) ggml_vk_destroy_buffer(buffer); - ggml_vk_host_free(data); + ggml_vk_host_free(ctx, data); free(result_data); } -static void ggml_vk_test_transfer(size_t ne, bool pinned) { +static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct - vk_buffer buffer = ggml_vk_create_buffer_check(sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); float * x; float * y; if (pinned) { - x = (float *) ggml_vk_host_malloc(sizeof(float) * ne); - y = (float *) ggml_vk_host_malloc(sizeof(float) * ne); + x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne); + y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne); } else { x = (float *) malloc(sizeof(float) * ne); y = (float *) malloc(sizeof(float) * ne); @@ -3508,42 +3695,42 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { x[i] = rand() / (float)RAND_MAX; } - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_buffer_write_async(ctx, &buffer, 0, x, sizeof(float) * ne); + ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne); - for (auto& cpy : ctx->in_memcpys) { + for (auto& cpy : subctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx->in_memcpys.clear(); + subctx->in_memcpys.clear(); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_to_gpu = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_ctx_begin(ctx); + ggml_vk_ctx_begin(ctx, subctx); begin = std::chrono::high_resolution_clock::now(); - ggml_vk_buffer_read_async(ctx, &buffer, 0, y, sizeof(float) * ne); + ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : ctx->out_memcpys) { + for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx->out_memcpys.clear(); + subctx->out_memcpys.clear(); end = std::chrono::high_resolution_clock::now(); @@ -3561,15 +3748,15 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { ggml_vk_destroy_buffer(buffer); if (pinned) { - ggml_vk_host_free(x); - ggml_vk_host_free(y); + ggml_vk_host_free(ctx, x); + ggml_vk_host_free(ctx, y); } else { free(x); free(y); } } -static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { +static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; #endif @@ -3578,8 +3765,8 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); float * x = (float *) malloc(x_sz); void * qx = malloc(qx_sz); - vk_buffer qx_buf = ggml_vk_create_buffer_check(qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer x_buf = ggml_vk_create_buffer_check(x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); for (size_t i = 0; i < ne; i++) { @@ -3588,7 +3775,7 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { std::vector hist_cur(1 << 4, 0); - vk_pipeline& p = vk_pipeline_dequant[quant]; + vk_pipeline& p = ctx->pipeline_dequant[quant]; switch(quant) { case GGML_TYPE_Q4_0: @@ -3625,27 +3812,26 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { GGML_ASSERT(false); } - ggml_vk_pipeline_allocate_descriptor_sets(p, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, p, 1); - ggml_vk_buffer_write(&qx_buf, 0, qx, qx_sz); + ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); - ggml_vk_ctx_end(ctx); + ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_buffer_read(&x_buf, 0, x_chk, x_sz_f16); + ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16); double avg_err = 0.0; for (size_t i = 0; i < ne; i++) { @@ -3687,15 +3873,15 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph return nullptr; } -void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ +static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; + std::cerr << "ggml_ctx->preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU)); - if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { return; } @@ -3735,16 +3921,16 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ const uint32_t y_ne = ne10 * ne11; const uint32_t d_ne = ne20 * ne21; - const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; + const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; - if (extra->buffer_gpu.size == 0) { + if (extra->buffer_gpu.expired()) { // Workaround for CPU backend BLAS matmul calls - extra->buffer_gpu = ggml_vk_create_buffer_temp(d_sz); + extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz); } switch (node->op) { @@ -3779,23 +3965,23 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ } break; case GGML_OP_MUL_MAT: - if (vk_prealloc_size_qx < qx_sz) { - vk_prealloc_size_qx = qx_sz; + if (ctx->prealloc_size_qx < qx_sz) { + ctx->prealloc_size_qx = qx_sz; } - if (vk_prealloc_size_qy < qy_sz) { - vk_prealloc_size_qy = qy_sz; + if (ctx->prealloc_size_qy < qy_sz) { + ctx->prealloc_size_qy = qy_sz; } - if (vk_prealloc_size_x < x_sz) { - vk_prealloc_size_x = x_sz; + if (ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; } - if (vk_prealloc_size_y < y_sz) { - vk_prealloc_size_y = y_sz; + if (ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; } - if (vk_prealloc_size_split_k < split_k_size) { - vk_prealloc_size_split_k = split_k_size; + if (ctx->prealloc_size_split_k < split_k_size) { + ctx->prealloc_size_split_k = split_k_size; } - if (vk_staging_size < x_sz + y_sz) { - vk_staging_size = x_sz + y_sz; + if (ctx->staging_size < x_sz + y_sz) { + ctx->staging_size = x_sz + y_sz; } break; default: @@ -3803,29 +3989,29 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ } } -void ggml_vk_preallocate_buffers() { - if (vk_disable) { +static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { + if (ctx->disable) { return; } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; - std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; + std::cerr << "ggml_ctx->preallocate_buffers()" << std::endl; + std::cerr << "qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << std::endl; #endif #if defined(GGML_VULKAN_RUN_TESTS) - vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - ggml_vk_test_transfer(8192 * 1000, false); - ggml_vk_test_transfer(8192 * 1000, true); + ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ggml_vk_test_transfer(ctx, 8192 * 1000, false); + ggml_vk_test_transfer(ctx, 8192 * 1000, true); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_1); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_1); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q8_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q2_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q3_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q6_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q6_K); const std::vector vals { 8, 8, 8, @@ -3852,76 +4038,76 @@ void ggml_vk_preallocate_buffers() { }; const size_t num_it = 1; for (size_t i = 0; i < vals.size(); i += 3) { - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); std::cerr << std::endl; } GGML_ASSERT(false); #endif - if (vk_prealloc_size_qx > 0 && vk_prealloc_qx.size < vk_prealloc_size_qx) { + if (ctx->prealloc_qx == nullptr || (ctx->prealloc_size_qx > 0 && ctx->prealloc_qx->size < ctx->prealloc_size_qx)) { // Resize buffer - if (vk_prealloc_qx.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_qx); + if (ctx->prealloc_qx != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_qx); } - vk_prealloc_qx = ggml_vk_create_buffer_device(vk_prealloc_size_qx); + ctx->prealloc_qx = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qx); } - if (vk_prealloc_size_qy > 0 && vk_prealloc_qy.size < vk_prealloc_size_qy) { + if (ctx->prealloc_qy == nullptr || (ctx->prealloc_size_qy > 0 && ctx->prealloc_qy->size < ctx->prealloc_size_qy)) { // Resize buffer - if (vk_prealloc_qy.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_qy); + if (ctx->prealloc_qy != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_qy); } - vk_prealloc_qy = ggml_vk_create_buffer_device(vk_prealloc_size_qy); + ctx->prealloc_qy = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qy); } - if (vk_prealloc_size_x > 0 && vk_prealloc_x.size < vk_prealloc_size_x) { + if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) { // Resize buffer - if (vk_prealloc_x.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_x); + if (ctx->prealloc_x != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_x); } - vk_prealloc_x = ggml_vk_create_buffer_device(vk_prealloc_size_x); + ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x); } - if (vk_prealloc_size_y > 0 && vk_prealloc_y.size < vk_prealloc_size_y) { + if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) { // Resize buffer - if (vk_prealloc_y.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_y); + if (ctx->prealloc_y != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_y); } - vk_prealloc_y = ggml_vk_create_buffer_device(vk_prealloc_size_y); + ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y); } - if (vk_prealloc_size_split_k > 0 && vk_prealloc_split_k.size < vk_prealloc_size_split_k) { + if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) { // Resize buffer - if (vk_prealloc_split_k.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_split_k); + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer_device(vk_prealloc_size_split_k); + ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k); } - if (vk_staging_size > 0 && vk_staging.size < vk_staging_size) { + if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) { // Resize buffer - if (vk_staging.size > 0) { - ggml_vk_destroy_buffer(vk_staging); + if (ctx->staging != nullptr) { + ggml_vk_destroy_buffer(ctx->staging); } - vk_staging = ggml_vk_create_buffer_check(vk_staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } -void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ +static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU); - if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { + if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; #endif - vk_semaphore_idx = 0; - vk_staging_offset = 0; + ctx->semaphore_idx = 0; + ctx->staging_offset = 0; const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; @@ -3969,44 +4155,44 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ return; } - if (vk_ctx == nullptr) { - vk_ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(vk_ctx); + if (ctx->compute_ctx == nullptr) { + ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, ctx->compute_ctx); } switch (node->op) { case GGML_OP_REPEAT: - ggml_vk_repeat(vk_ctx, src0, src1, node); + ggml_vk_repeat(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_GET_ROWS: - ggml_vk_get_rows(vk_ctx, src0, src1, node); + ggml_vk_get_rows(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ADD: - ggml_vk_add(vk_ctx, src0, src1, node); + ggml_vk_add(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL: - ggml_vk_mul(vk_ctx, src0, src1, node); + ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_SCALE: - ggml_vk_scale(vk_ctx, src0, node); + ggml_vk_scale(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_SQR: - ggml_vk_sqr(vk_ctx, src0, node); + ggml_vk_sqr(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_CLAMP: - ggml_vk_clamp(vk_ctx, src0, node); + ggml_vk_clamp(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - ggml_vk_cpy(vk_ctx, src0, node); + ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_RESHAPE: @@ -4014,15 +4200,15 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NONE: - ggml_vk_nop(vk_ctx, src0, node); + ggml_vk_nop(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_NORM: - ggml_vk_norm(vk_ctx, src0, node); + ggml_vk_norm(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_RMS_NORM: - ggml_vk_rms_norm(vk_ctx, src0, node); + ggml_vk_rms_norm(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_UNARY: @@ -4030,26 +4216,26 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_RELU: - ggml_vk_unary(vk_ctx, src0, node); + ggml_vk_unary(ctx, ctx->compute_ctx, src0, node); break; default: return; } break; case GGML_OP_DIAG_MASK_INF: - ggml_vk_diag_mask_inf(vk_ctx, src0, node); + ggml_vk_diag_mask_inf(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(vk_ctx, src0, src1, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ROPE: - ggml_vk_rope(vk_ctx, src0, src1, node); + ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL_MAT: - ggml_vk_mul_mat(vk_ctx, src0, src1, node); + ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); break; default: @@ -4057,7 +4243,7 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ } extra->ready = true; - extra->ctx_idx = vk_ctx->idx; + extra->ctx_idx = ctx->compute_ctx->idx; #ifdef GGML_VULKAN_CHECK_RESULTS // Force context reset on each node so that each tensor ends up in its own context @@ -4066,18 +4252,18 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ #endif if (node->backend == GGML_BACKEND_CPU || last_node) { - ggml_vk_ctx_end(vk_ctx); - vk_ctx->exit_tensor = node; - vk_ctx = nullptr; + ggml_vk_ctx_end(ctx->compute_ctx); + ctx->compute_ctx->exit_tensor = node; + ctx->compute_ctx = nullptr; } } -bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor){ +static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ const bool any_on_device = tensor->backend == GGML_BACKEND_GPU || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); - if (vk_disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { return false; } @@ -4145,33 +4331,33 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) #endif #ifdef GGML_VULKAN_CHECK_RESULTS - ggml_vk_check_results_0(params, tensor); + ggml_vk_check_results_0(ctx, params, tensor); #endif GGML_ASSERT(extra->ready); - vk_context& ctx = vk_gc.contexts[extra->ctx_idx]; + vk_context& subctx = ctx->gc.contexts[extra->ctx_idx]; // Only run if ctx hasn't been submitted yet - if (!ctx.seqs.empty()) { + if (!subctx.seqs.empty()) { // Do staging buffer copies - for (auto& cpy : ctx.in_memcpys) { + for (auto& cpy : subctx.in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(&ctx, vk_fence); + ggml_vk_submit(&subctx, ctx->fence); } - if (tensor == ctx.exit_tensor) { - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + if (tensor == subctx.exit_tensor) { + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); // Do staging buffer copies - for (auto& cpy : ctx.out_memcpys) { + for (auto& cpy : subctx.out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx.in_memcpys.clear(); - ctx.out_memcpys.clear(); + subctx.in_memcpys.clear(); + subctx.out_memcpys.clear(); } extra->ready = false; @@ -4179,90 +4365,204 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) return true; } -void ggml_vk_graph_cleanup() { - if (vk_disable) { +// Clean up after graph processing is done +static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { + if (ctx->disable) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif - for (auto& buffer : vk_gc.temp_buffers) { - ggml_vk_pool_free(buffer); + for (auto& buffer : ctx->gc.temp_buffers) { + ggml_vk_pool_free(ctx, buffer); } - vk_gc.temp_buffers.clear(); + ctx->gc.temp_buffers.clear(); - for (auto * pipeline : vk_gc.pipelines) { - ggml_vk_pipeline_cleanup(*pipeline); - } - vk_gc.pipelines.clear(); - - ggml_vk_queue_cleanup(vk_device.compute_queue); - ggml_vk_queue_cleanup(vk_device.transfer_queue); - - for (size_t i = 0; i < vk_gc.semaphores.size(); i++) { - vk_device.device.destroySemaphore({ vk_gc.semaphores[i].s }); - } - vk_gc.semaphores.clear(); - - for (size_t i = 0; i < vk_gc.tl_semaphores.size(); i++) { - vk_device.device.destroySemaphore({ vk_gc.tl_semaphores[i].s }); - } - vk_gc.tl_semaphores.clear(); - - vk_event_idx = 0; - - for (auto& event : vk_gc.events) { - vk_device.device.resetEvent(event); + for (auto * pipeline : ctx->gc.pipelines) { + ggml_pipeline_cleanup(*pipeline); } - vk_staging_offset = 0; + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); - vk_ctx = nullptr; - vk_gc.contexts.clear(); + for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { + ctx->device.lock()->device.destroySemaphore({ ctx->gc.semaphores[i].s }); + } + ctx->gc.semaphores.clear(); + + for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { + ctx->device.lock()->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); + } + ctx->gc.tl_semaphores.clear(); + ctx->semaphore_idx = 0; + + ctx->event_idx = 0; + + for (auto& event : ctx->gc.events) { + ctx->device.lock()->device.resetEvent(event); + } + + ctx->staging_offset = 0; + + ctx->compute_ctx = nullptr; + ctx->transfer_ctx = nullptr; + ctx->gc.contexts.clear(); } -static void ggml_vk_cleanup() { +// Clean up on backend free +static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cleanup()" << std::endl; + std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl; #endif - ggml_vk_destroy_buffer(vk_prealloc_x); - ggml_vk_destroy_buffer(vk_prealloc_y); - ggml_vk_destroy_buffer(vk_prealloc_split_k); - ggml_vk_destroy_buffer(vk_staging); - ggml_vk_destroy_buffer(vk_sync_staging); + ggml_vk_graph_cleanup(ctx); - vk_prealloc_size_x = 0; - vk_prealloc_size_y = 0; - vk_prealloc_size_split_k = 0; - vk_staging_size = 0; + ggml_vk_destroy_buffer(ctx->prealloc_qx); + ggml_vk_destroy_buffer(ctx->prealloc_qy); + ggml_vk_destroy_buffer(ctx->prealloc_x); + ggml_vk_destroy_buffer(ctx->prealloc_y); + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + ggml_vk_destroy_buffer(ctx->staging); + ggml_vk_destroy_buffer(ctx->sync_staging); - for (auto& event : vk_gc.events) { - vk_device.device.destroyEvent(event); + for (auto& buffer : ctx->buffer_pool) { + ggml_vk_destroy_buffer(buffer); } - vk_gc.events.clear(); + + ctx->prealloc_size_qx = 0; + ctx->prealloc_size_qy = 0; + ctx->prealloc_size_x = 0; + ctx->prealloc_size_y = 0; + ctx->prealloc_size_split_k = 0; + ctx->staging_size = 0; + + for (auto& event : ctx->gc.events) { + ctx->device.lock()->device.destroyEvent(event); + } + ctx->gc.events.clear(); + + for (auto* pipeline : ctx->gc.pipelines) { + ggml_vk_destroy_pipeline(ctx, pipeline); + } + ctx->gc.pipelines.clear(); + + ctx->device.lock()->device.destroyFence(ctx->fence); + + ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->compute_queue.pool); + if (!ctx->device.lock()->single_queue) { + ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->transfer_queue.pool); + } +} + +GGML_CALL int ggml_vk_get_device_count() { + ggml_vk_instance_init(); + + return vk_instance.device_indices.size(); +} + +GGML_CALL void ggml_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + vk::PhysicalDeviceProperties props; + devices[device].getProperties(&props); + + snprintf(description, description_size, "%s", props.deviceName.data()); +} + +// CPU assist interface + +void ggml_vk_init_cpu_assist() { + ggml_vk_instance_init(); + + std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl; + + for (size_t i = 0; i < ggml_vk_get_device_count(); i++) { + ggml_vk_print_gpu_info(i); + } + // Initialize the first backend to make sure CPU matrix multiplications can be offloaded. + ggml_backend_vk_init(0); +} + +void ggml_vk_preallocate_buffers_graph_cpu_assist(ggml_tensor * node) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_preallocate_buffers_graph(ctx, node); +} + +void ggml_vk_preallocate_buffers_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_preallocate_buffers(ctx); +} + +void ggml_vk_build_graph_cpu_assist(ggml_tensor * node, bool last_node) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_build_graph(ctx, node, last_node); +} + +bool ggml_vk_compute_forward_cpu_assist(ggml_compute_params * params, ggml_tensor * tensor){ + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return false; + } + + return ggml_vk_compute_forward(ctx, params, tensor); +} + +void ggml_vk_graph_cleanup_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_graph_cleanup(ctx); +} + +void ggml_vk_free_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized || vk_instance.backends[0] == nullptr) { + return; + } + + ggml_backend_vk_free(vk_instance.backends[0]); } // backend interface #define UNUSED GGML_UNUSED -struct ggml_backend_vk_context { - std::string name; -}; - // device backend static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT struct ggml_backend_vk_buffer_context { + ggml_backend_vk_context * ctx; vk_buffer dev_buffer; ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; size_t temp_tensor_extra_index = 0; std::string name; - ggml_backend_vk_buffer_context(vk_buffer dev_buffer) : + ggml_backend_vk_buffer_context(ggml_backend_vk_context * ctx, vk_buffer&& dev_buffer, std::string& name) : + ctx(ctx), dev_buffer(dev_buffer), - name(GGML_VK_NAME) { + name(name) { } ~ggml_backend_vk_buffer_context() { @@ -4294,6 +4594,9 @@ GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { } GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl; +#endif ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_vk_destroy_buffer(ctx->dev_buffer); delete ctx; @@ -4313,6 +4616,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra(); if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) { + GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra; extra->buffer_gpu = extra_view->buffer_gpu; extra->offset = extra_view->offset + tensor->view_offs; @@ -4331,11 +4635,13 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_write(&extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(buffer); + ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size); } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { @@ -4344,31 +4650,35 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(buffer); + ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size); } GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_vk_buffer_copy(&src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + vk_buffer src_buf = src_extra->buffer_gpu.lock(); + vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); + + ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src)); return true; } return false; - - UNUSED(buffer); } GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - ggml_vk_buffer_memset(&ctx->dev_buffer, 0, value, buffer->size); + ggml_vk_buffer_memset(ctx->ctx, ctx->dev_buffer, 0, value, buffer->size); } static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { @@ -4386,6 +4696,7 @@ static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { // vk buffer type struct ggml_backend_vk_buffer_type_context { std::string name; + ggml_backend_vk_context * ctx; }; GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -4398,25 +4709,22 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer( #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif - vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size); - ggml_backend_vk_buffer_context * ctx = new ggml_backend_vk_buffer_context(dev_buffer); + ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name); - return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, ctx, size); - - UNUSED(buft); + return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size); } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_device.properties.limits.minStorageBufferOffsetAlignment; - - UNUSED(buft); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - return vk_device.max_memory_allocation_size; - - UNUSED(buft); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->ctx->device.lock()->max_memory_allocation_size; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { @@ -4426,9 +4734,14 @@ GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_ } GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_vk(backend); + if (!ggml_backend_is_vk(backend)) { + return false; + } - UNUSED(buft); + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + return buft_ctx->ctx->idx == ctx->idx; } static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { @@ -4441,20 +4754,16 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { /* .is_host = */ NULL, }; -GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type() { - static ggml_backend_buffer_type ggml_backend_vk_buffer_type; +GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t idx) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_buffer_type(" << idx << ")" << std::endl; +#endif - static bool ggml_backend_vk_buffer_type_initialized = false; + GGML_ASSERT(idx < vk_instance.device_indices.size()); - if (!ggml_backend_vk_buffer_type_initialized) { - ggml_backend_vk_buffer_type = { - /* .iface = */ ggml_backend_vk_buffer_type_interface, - /* .context = */ new ggml_backend_vk_buffer_type_context{GGML_VK_NAME}, - }; - ggml_backend_vk_buffer_type_initialized = true; - } + ggml_backend_vk_init(idx); - return &ggml_backend_vk_buffer_type; + return &vk_instance.buffer_types[idx]; } // host buffer type @@ -4472,13 +4781,19 @@ GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buff } GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_vk_host_free(buffer->context); +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl; +#endif + ggml_vk_host_free(&vk_instance.contexts[0], buffer->context); } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; +#endif void * ptr = nullptr; try { - ptr = ggml_vk_host_malloc(size); + ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size); } catch (vk::SystemError& e) { std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -4495,7 +4810,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu } GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_device.properties.limits.minMemoryMapAlignment; + return vk_instance.contexts[0].device.lock()->properties.limits.minMemoryMapAlignment; UNUSED(buft); } @@ -4514,127 +4829,150 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .context = */ nullptr, }; + if (!vk_instance.contexts[0].initialized) { + // Fall back to CPU + return ggml_backend_cpu_buffer_type(); + } + return &ggml_backend_vk_buffer_type_host; } // backend GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) { - ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - return vk_ctx->name.c_str(); + return ctx->name.c_str(); } GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { - ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl; +#endif - delete vk_ctx; + size_t idx = ctx->idx; + + ggml_vk_cleanup(ctx); + + // Release device + vk_instance.devices[ctx->idx].reset(); + ctx->initialized = false; + + vk_instance.initialized[idx] = false; + vk_instance.backends[idx] = nullptr; + memset(&vk_instance.buffer_types[idx], 0, sizeof(ggml_backend_buffer_type)); delete backend; } GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_vk_buffer_type(); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - UNUSED(backend); + GGML_ASSERT(ctx->initialized); + + return ggml_backend_vk_buffer_type(ctx->idx); } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_write_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(backend); + ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_read_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(backend); + ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif - if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_copy_async(vk_transfer_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + vk_buffer src_buf = src_extra->buffer_gpu.lock(); + vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); + + ggml_vk_buffer_copy_async(ctx->transfer_ctx, src_buf, src_extra->offset, dst_buf, dst_extra->offset, ggml_nbytes(src)); return true; } return false; - - UNUSED(backend); } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_synchronize()" << std::endl; #endif - if(vk_transfer_ctx == nullptr) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if(ctx->transfer_ctx == nullptr) { return; } - ggml_vk_ctx_end(vk_transfer_ctx); + ggml_vk_ctx_end(ctx->transfer_ctx); - for (auto& cpy : vk_transfer_ctx->in_memcpys) { + for (auto& cpy : ctx->transfer_ctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(vk_transfer_ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(ctx->transfer_ctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : vk_transfer_ctx->out_memcpys) { + for (auto& cpy : ctx->transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - vk_transfer_ctx = nullptr; - - UNUSED(backend); + ctx->transfer_ctx = nullptr; } GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - // ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]); } - ggml_vk_preallocate_buffers(); + ggml_vk_preallocate_buffers(ctx); int last_node = cgraph->n_nodes - 1; @@ -4644,7 +4982,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == last_node); + ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node); } ggml_compute_params params = {}; @@ -4657,19 +4995,19 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml continue; } - bool ok = ggml_vk_compute_forward(¶ms, node); + bool ok = ggml_vk_compute_forward(ctx, ¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } #ifdef GGML_VULKAN_CHECK_RESULTS else { - ggml_vk_check_results_1(¶ms, node); + ggml_vk_check_results_1(ctx, ¶ms, node); } #endif GGML_ASSERT(ok); } - ggml_vk_graph_cleanup(); + ggml_vk_graph_cleanup(ctx); return true; @@ -4734,7 +5072,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } return false; } break; - // case GGML_OP_DUP: + case GGML_OP_DUP: // case GGML_OP_REPEAT: // { // ggml_type src0_type = op->src[0]->type; @@ -4786,18 +5124,30 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .supports_op = */ ggml_backend_vk_supports_op, }; -GGML_CALL ggml_backend_t ggml_backend_vk_init() { - ggml_vk_init(); // TODO: remove from ggml.c +GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { + if (vk_instance.initialized[idx]) { + return vk_instance.backends[idx]; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_init(" << idx << ")" << std::endl; +#endif - ggml_backend_vk_context * ctx = new ggml_backend_vk_context { - /* .name = */ GGML_VK_NAME, + ggml_backend_vk_context * ctx = &vk_instance.contexts[idx]; + ggml_vk_init(ctx, idx); + ctx->name = GGML_VK_NAME + std::to_string(idx); + vk_instance.buffer_types[idx] = { + /* .iface = */ ggml_backend_vk_buffer_type_interface, + /* .context = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx }, }; + vk_instance.initialized[idx] = true; ggml_backend_t vk_backend = new ggml_backend { /* .interface = */ ggml_backend_vk_interface, - /* .context = */ ctx + /* .context = */ &vk_instance.contexts[ctx->idx], }; + vk_instance.backends[idx] = vk_backend; + return vk_backend; } @@ -4805,20 +5155,47 @@ GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_vk_name; } +GGML_CALL int ggml_backend_vk_get_device_count() { + return ggml_vk_get_device_count(); +} + +GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_get_device_description(device, description, description_size); +} + +GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { + GGML_ASSERT(device < vk_instance.device_indices.size()); + + vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; + + vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); + + for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { + if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { + *total = heap.size; + *free = heap.size; + break; + } + } +} + // backend registry GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) { - ggml_backend_t vk_backend = ggml_backend_vk_init(); + ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data); return vk_backend; UNUSED(params); - UNUSED(user_data); } extern "C" GGML_CALL int ggml_backend_vk_reg_devices(); GGML_CALL int ggml_backend_vk_reg_devices() { - ggml_backend_register(GGML_VK_NAME, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(), nullptr); - return 1; + for (auto idx : vk_instance.device_indices) { + char name[128]; + snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, idx); + ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(idx), (void *) (intptr_t) idx); + } + return vk_instance.device_indices.size(); } // checks @@ -4874,7 +5251,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d } } -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { +static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; if (tensor->backend == GGML_BACKEND_GPU) { @@ -4883,7 +5260,7 @@ static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -4944,7 +5321,7 @@ void * comp_result; size_t comp_size; size_t comp_nb[GGML_MAX_DIMS]; size_t check_counter = 0; -static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -4966,7 +5343,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * /*.no_alloc =*/ false, }; - struct ggml_context * ctx = ggml_init(iparams); + struct ggml_context * ggml_ctx = ggml_init(iparams); struct ggml_tensor * src0_clone = nullptr; struct ggml_tensor * src1_clone = nullptr; @@ -4979,7 +5356,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * void * src1_buffer; if (src0 != nullptr) { - src0_clone = ggml_dup_tensor(ctx, src0); + src0_clone = ggml_dup_tensor(ggml_ctx, src0); src0_size = ggml_nbytes(src0); @@ -4995,7 +5372,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5005,10 +5382,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu.size) { - src0_size = extra->buffer_gpu.size - offset; + if (offset + src0_size >= extra->buffer_gpu->size) { + src0_size = extra->buffer_gpu->size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5016,13 +5393,13 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src0, "src0"); + ggml_vk_print_tensor(ctx, src0, "src0"); } ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone); } if (src1 != nullptr) { - src1_clone = ggml_dup_tensor(ctx, src1); + src1_clone = ggml_dup_tensor(ggml_ctx, src1); src1_size = ggml_nbytes(src1); @@ -5038,7 +5415,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5048,10 +5425,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu.size) { - src1_size = extra->buffer_gpu.size - offset; + if (offset + src1_size >= extra->buffer_gpu->size) { + src1_size = extra->buffer_gpu->size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5059,7 +5436,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src1, "src1"); + ggml_vk_print_tensor(ctx, src1, "src1"); std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl; std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; if (src1->src[0] != nullptr) { @@ -5082,51 +5459,51 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (tensor->op == GGML_OP_MUL_MAT) { - tensor_clone = ggml_mul_mat(ctx, src0_clone, src1_clone); + tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_MUL) { - tensor_clone = ggml_mul(ctx, src0_clone, src1_clone); + tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_SCALE) { - tensor_clone = ggml_scale(ctx, src0_clone, ((float *)tensor->op_params)[0]); + tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]); } else if (tensor->op == GGML_OP_SQR) { - tensor_clone = ggml_sqr(ctx, src0_clone); + tensor_clone = ggml_sqr(ggml_ctx, src0_clone); } else if (tensor->op == GGML_OP_CLAMP) { - tensor_clone = ggml_clamp(ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else if (tensor->op == GGML_OP_ADD) { - tensor_clone = ggml_add(ctx, src0_clone, src1_clone); + tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_NORM) { - tensor_clone = ggml_norm(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_RMS_NORM) { - tensor_clone = ggml_rms_norm(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ctx, src0_clone, src1_clone, *(float *)tensor->op_params); + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); } else { - tensor_clone = ggml_soft_max(ctx, src0_clone); + tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { - tensor_clone = ggml_diag_mask_inf(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + const int n_ggml_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ggml_ctx = ((int32_t *) tensor->op_params)[4]; float freq_base = ((float *) tensor->op_params)[5]; float freq_scale = ((float *) tensor->op_params)[6]; float ext_factor = ((float *) tensor->op_params)[7]; float attn_factor = ((float *) tensor->op_params)[8]; float beta_fast = ((float *) tensor->op_params)[9]; float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_custom(ctx, src0_clone, src1_clone, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else if (tensor->op == GGML_OP_UNARY) { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_SILU: - tensor_clone = ggml_silu(ctx, src0_clone); + tensor_clone = ggml_silu(ggml_ctx, src0_clone); break; case GGML_UNARY_OP_GELU: - tensor_clone = ggml_gelu(ctx, src0_clone); + tensor_clone = ggml_gelu(ggml_ctx, src0_clone); break; case GGML_UNARY_OP_RELU: - tensor_clone = ggml_relu(ctx, src0_clone); + tensor_clone = ggml_relu(ggml_ctx, src0_clone); break; default: std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; @@ -5134,40 +5511,40 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { if (src1 == nullptr) { - tensor_clone = ggml_dup(ctx, src0_clone); + tensor_clone = ggml_dup(ggml_ctx, src0_clone); tensor_clone->type = tensor->type; } else { - tensor_clone = ggml_cpy(ctx, src0_clone, src1_clone); + tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone); } } else if (tensor->op == GGML_OP_CONT) { - tensor_clone = ggml_cont_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); } else if (tensor->op == GGML_OP_RESHAPE) { - tensor_clone = ggml_reshape_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); } else if (tensor->op == GGML_OP_VIEW) { - tensor_clone = ggml_view_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); + tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); } else if (tensor->op == GGML_OP_PERMUTE) { int32_t * params = (int32_t *)tensor->op_params; - tensor_clone = ggml_permute(ctx, src0_clone, params[0], params[1], params[2], params[3]); + tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]); } else if (tensor->op == GGML_OP_TRANSPOSE) { - tensor_clone = ggml_transpose(ctx, src0_clone); + tensor_clone = ggml_transpose(ggml_ctx, src0_clone); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ASSERT(false); } // Disable vulkan here to avoid the hooks in ggml.c - vk_disable = true; + ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ctx); + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_clone); - ggml_graph_compute_with_ctx(ctx, cgraph, 8); + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - vk_disable = false; + ctx->disable = false; ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(tensor_clone, "tensor_clone"); + ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone"); } comp_size = ggml_nbytes(tensor_clone); @@ -5183,10 +5560,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * free(src1_buffer); } - ggml_free(ctx); + ggml_free(ggml_ctx); } -void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -5208,11 +5585,11 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu.size) { - tensor_size = extra->buffer_gpu.size - (extra->offset); + if (extra->offset + tensor_size >= extra->buffer_gpu->size) { + tensor_size = extra->buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; @@ -5339,4 +5716,10 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) free(tensor_data); } } + +void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + ggml_vk_check_results_0(ctx, params, tensor); +} #endif diff --git a/ggml-vulkan.h b/ggml-vulkan.h index eb8a148e2..9645126b4 100644 --- a/ggml-vulkan.h +++ b/ggml-vulkan.h @@ -8,24 +8,29 @@ extern "C" { #endif #define GGML_VK_NAME "Vulkan" +#define GGML_VK_MAX_DEVICES 16 -GGML_API void ggml_vk_init(void); +GGML_API void ggml_vk_init_cpu_assist(void); -GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node); -GGML_API void ggml_vk_preallocate_buffers(void); -GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node); -GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node); +GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void); +GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node); +GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor); #ifdef GGML_VULKAN_CHECK_RESULTS -void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor); +void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor); #endif -GGML_API void ggml_vk_graph_cleanup(void); +GGML_API void ggml_vk_graph_cleanup_cpu_assist(void); +GGML_API void ggml_vk_free_cpu_assist(void); // backend API -GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void); +GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num); GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend); +GGML_API GGML_CALL int ggml_backend_vk_get_device_count(void); +GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); -GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); diff --git a/ggml.c b/ggml.c index b9ec0c981..f783a6fd3 100644 --- a/ggml.c +++ b/ggml.c @@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { #elif defined(GGML_USE_CLBLAST) ggml_cl_init(); #elif defined(GGML_USE_VULKAN) - ggml_vk_init(); + ggml_vk_init_cpu_assist(); #elif defined(GGML_USE_SYCL) ggml_init_sycl(); #endif @@ -14850,10 +14850,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); #elif defined(GGML_USE_VULKAN) - const bool skip_cpu = ggml_vk_compute_forward(params, tensor); + const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor); #ifdef GGML_VULKAN_CHECK_RESULTS if (skip_cpu) { - ggml_vk_check_results_1(params, tensor); + ggml_vk_check_results_1_cpu_assist(params, tensor); } #endif if (skip_cpu) { @@ -17269,12 +17269,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { #ifdef GGML_USE_VULKAN for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]); } - ggml_vk_preallocate_buffers(); + ggml_vk_preallocate_buffers_cpu_assist(); for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1); } #endif @@ -17330,7 +17330,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { } #ifdef GGML_USE_VULKAN - ggml_vk_graph_cleanup(); + ggml_vk_graph_cleanup_cpu_assist(); #endif // performance stats (graph) diff --git a/llama.cpp b/llama.cpp index f3c5146d1..c45ae1d50 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1355,7 +1355,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { #elif defined(GGML_USE_CUBLAS) buft = ggml_backend_cuda_buffer_type(gpu); #elif defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(); + buft = ggml_backend_vk_buffer_type(gpu); #elif defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(gpu); #elif defined(GGML_USE_CLBLAST) @@ -1392,6 +1392,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g GGML_UNUSED(tensor_split); } +static size_t llama_get_device_count() { +#if defined(GGML_USE_CUBLAS) + return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_VULKAN) + return ggml_backend_vk_get_device_count(); +#else + return 1; +#endif +} + +static size_t llama_get_device_memory(int device) { +#if defined(GGML_USE_CUBLAS) + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(device, &total, &free); + return free; +#elif defined(GGML_USE_VULKAN) + size_t total; + size_t free; + ggml_backend_vk_get_device_memory(device, &total, &free); + return free; +#else + return 1; + GGML_UNUSED(device); +#endif +} + // // globals // @@ -1763,6 +1790,10 @@ struct llama_context { ggml_backend_free(backend); } +#ifdef GGML_USE_VULKAN + ggml_vk_free_cpu_assist(); +#endif + ggml_backend_buffer_free(buf_input); ggml_free(ctx_input); } @@ -3436,22 +3467,18 @@ static bool llm_load_tensors( model.buft_layer[i] = llama_default_buffer_type_cpu(true); } -#ifdef GGML_USE_CUBLAS if (split_mode == LLAMA_SPLIT_LAYER) { // calculate the split points - int device_count = ggml_backend_cuda_get_device_count(); + int device_count = llama_get_device_count(); bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); - float splits[GGML_CUDA_MAX_DEVICES]; + std::vector splits(device_count); if (all_zero) { // default split, by free memory for (int i = 0; i < device_count; ++i) { - size_t total; - size_t free; - ggml_backend_cuda_get_device_memory(i, &total, &free); - splits[i] = free; + splits[i] = llama_get_device_memory(i); } } else { - std::copy(tensor_split, tensor_split + device_count, splits); + std::copy(tensor_split, tensor_split + device_count, splits.begin()); } // sum and normalize the splits to get the split points @@ -3467,19 +3494,17 @@ static bool llm_load_tensors( // assign the repeating layers to the devices according to the splits int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); for (int64_t i = i_gpu_start; i < n_layer; ++i) { - int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits; + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); } // assign the output layer if (n_gpu_layers > n_layer) { - int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits; + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); model.buft_output = llama_default_buffer_type_offload(layer_gpu); } else { model.buft_output = llama_default_buffer_type_cpu(true); } - } else -#endif - { + } else { ggml_backend_buffer_type_t split_buft; if (split_mode == LLAMA_SPLIT_ROW) { split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); @@ -10483,6 +10508,8 @@ size_t llama_max_devices(void) { return GGML_CUDA_MAX_DEVICES; #elif defined(GGML_USE_SYCL) return GGML_SYCL_MAX_DEVICES; +#elif defined(GGML_USE_VULKAN) + return GGML_VK_MAX_DEVICES; #else return 1; #endif @@ -10690,13 +10717,15 @@ struct llama_context * llama_new_context_with_model( } #elif defined(GGML_USE_VULKAN) if (model->n_gpu_layers > 0) { - ggml_backend_t backend = ggml_backend_vk_init(); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; + for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) { + ggml_backend_t backend = ggml_backend_vk_init(device); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); } - ctx->backends.push_back(backend); } #elif defined(GGML_USE_SYCL) if (model->n_gpu_layers > 0) { From 0ef46da632c32faa1a538e5dc180994e8bbb46e1 Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Wed, 7 Feb 2024 02:17:25 -0600 Subject: [PATCH 644/664] llava-cli : always tokenize special tokens (#5382) * llava-cli: tokenize special tokens in prompt * llava-cli: use the escape CLI argument, remove incomplete separate escaping process --- examples/llava/llava-cli.cpp | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 6ac70ba69..031e9806d 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -34,7 +34,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); + std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true); eval_tokens(ctx_llama, embd_inp, n_batch, n_past); return true; } @@ -152,20 +152,8 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ size_t image_pos = prompt.find(""); if (image_pos != std::string::npos) { // new templating mode: Provide the full prompt including system message and use as a placeholder for the image - system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); - // We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string - size_t pos = 0; - while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) { - user_prompt.replace(pos, 2, "\n"); - pos += 1; // Advance past the replaced newline - } - while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) { - system_prompt.replace(pos, 2, "\n"); - pos += 1; // Advance past the replaced newline - } - printf("system_prompt: %s\n", system_prompt.c_str()); printf("user_prompt: %s\n", user_prompt.c_str()); } else { From 10afa6f1d11ebc9fcc1085f468170002cbf6e2b5 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 7 Feb 2024 18:16:55 +0800 Subject: [PATCH 645/664] [SYCL] update install make by w64devkit (#5297) --- README-sycl.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 7aa4274a9..e3a8e726e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -311,15 +311,13 @@ Output (example): a. Download & install cmake for Windows: https://cmake.org/download/ -b. Download & install make for Windows provided by mingw-w64 +b. Download & install mingw-w64 make for Windows provided by w64devkit -- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases. +- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z). +- Extract `w64devkit` on your pc. -- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**. - -- Add the **bin** folder path in the Windows system PATH environment. +- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`. ### Build locally: From aa7ab99be29b633263803f2e185265734c2d9427 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 7 Feb 2024 12:40:26 +0100 Subject: [PATCH 646/664] CUDA: fixed mmvq kernel for bs 2,3,4 and -sm row (#5386) --- ggml-cuda.cu | 66 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 39 insertions(+), 27 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 3b828375e..db9da2459 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5313,7 +5313,7 @@ template static __global__ void template static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) { + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) { const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; @@ -5352,7 +5352,7 @@ static __global__ void mul_mat_vec_q( tmp[j] = warp_reduce_sum(tmp[j]); if (threadIdx.x == 0) { - dst[j*nrows_x + row] = tmp[j]; + dst[j*nrows_dst + row] = tmp[j]; } } } @@ -6828,7 +6828,7 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa template static void mul_mat_vec_q_cuda( const void * vx, const void * vy, float * dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_y <= 4); @@ -6839,40 +6839,40 @@ static void mul_mat_vec_q_cuda( switch (ncols_y) { case 1: mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 2: mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 3: mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 4: mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; // case 5: // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 6: // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 7: // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 8: // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; default: GGML_ASSERT(false); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; } } @@ -8391,7 +8391,7 @@ static void ggml_cuda_op_mul_mat_q( CUDA_CHECK(cudaGetDevice(&id)); // the main device has a larger memory buffer to hold the results from all GPUs - // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into + // nrows_dst == nrows of the matrix that the kernel writes into const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; switch (src0->type) { @@ -8525,58 +8525,70 @@ static void ggml_cuda_op_mul_mat_vec_q( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + const int64_t ne10 = src1->ne[0]; + GGML_ASSERT(ne10 % QK8_1 == 0); + + const int64_t ne0 = dst->ne[0]; + + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the kernel writes into + const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; + switch (src0->type) { case GGML_TYPE_Q4_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q4_1: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_1: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q8_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q2_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q3_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q4_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q6_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ2_XXS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ2_XS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ3_XXS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; default: GGML_ASSERT(false); @@ -9909,7 +9921,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); From b906596bb775b17656c2e51d5ab1b347faab6860 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kamil=20Tom=C5=A1=C3=ADk?= Date: Wed, 7 Feb 2024 19:44:52 +0100 Subject: [PATCH 647/664] Add Ava in the list of llama.cpp UIs (#4362) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0509b0ba1..7e1187349 100644 --- a/README.md +++ b/README.md @@ -150,6 +150,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [ollama/ollama](https://github.com/ollama/ollama) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) - [semperai/amica](https://github.com/semperai/amica) From 8c933b70c21e05b685d476d0a1f36b34cbda7365 Mon Sep 17 00:00:00 2001 From: Ebey Abraham Date: Wed, 7 Feb 2024 21:11:30 +0000 Subject: [PATCH 648/664] fix typo in readme (#5399) Co-authored-by: Ebey Abraham --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7e1187349..66166c01b 100644 --- a/README.md +++ b/README.md @@ -680,7 +680,7 @@ python3 -m pip install -r requirements.txt python3 convert.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocabtype bpe +python convert.py models/mymodel/ --vocab-type bpe # quantize the model to 4-bits (using Q4_K_M method) ./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M From c4fbb6717c684196bd13b72d21747557130914e8 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Wed, 7 Feb 2024 22:39:23 +0100 Subject: [PATCH 649/664] CMAKE_OSX_ARCHITECTURES for MacOS cross compilation (#5393) Co-authored-by: Jared Van Bortel --- CMakeLists.txt | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 427015be5..a544f2da6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -850,7 +850,9 @@ endif() set(ARCH_FLAGS "") -if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) +if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) message(STATUS "ARM detected") if (MSVC) add_compile_definitions(__ARM_NEON) @@ -876,7 +878,9 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) +elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) message(STATUS "x86 detected") if (MSVC) # instruction set detection for MSVC only From 8504d2d0da8cc7a1f2eee0e9e56949f960510b75 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 8 Feb 2024 09:46:47 +0200 Subject: [PATCH 650/664] tests : .gitignore obj files --- tests/.gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/.gitignore b/tests/.gitignore index 092dce742..9427cf13d 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -1,3 +1,3 @@ * !*.* -test-c.o +*.o From 26d4efd11e48908e14e2ee9471a7fc4c57079a1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 8 Feb 2024 09:46:30 +0100 Subject: [PATCH 651/664] sampling: fix top_k <= 0 (#5388) * sampling: fix top_k <= 0 * Update llama.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- common/sampling.cpp | 2 +- llama.cpp | 4 ++++ tests/test-sampling.cpp | 2 ++ 3 files changed, 7 insertions(+), 1 deletion(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index e8675a8c0..844ad7c53 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -132,7 +132,7 @@ static void sampler_queue( const float temp = params.temp; const float dynatemp_range = params.dynatemp_range; const float dynatemp_exponent = params.dynatemp_exponent; - const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const int32_t top_k = params.top_k; const float top_p = params.top_p; const float min_p = params.min_p; const float tfs_z = params.tfs_z; diff --git a/llama.cpp b/llama.cpp index c45ae1d50..f8f5796a4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8585,6 +8585,10 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can // } const int64_t t_start_sample_us = ggml_time_us(); + + if (k <= 0) { + k = candidates->size; + } k = std::max(k, (int) min_keep); k = std::min(k, (int) candidates->size); diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index c3b3d6629..6374958fe 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -235,6 +235,8 @@ int main(void) { test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1); test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3); + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4); + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0); test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0); test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f); From a6e514a85f0fda38ff78ec91782877ea3d19ed98 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 8 Feb 2024 09:58:19 +0100 Subject: [PATCH 652/664] llava: fix typo/formatting in README.md (#5405) This commit fixes a typo in the README.md file for the llava example which is causing the formatting to look a little off: Clone llava-v15-7b`` and clip-vit-large-patch14-336`` locally Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 323c5fdd0..295181a34 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -21,7 +21,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## Model conversion -- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally: +- Clone `llava-v15-7b` and `clip-vit-large-patch14-336` locally: ```sh git clone https://huggingface.co/liuhaotian/llava-v1.5-7b From 4aa43fab569215a13495a7f1a0f8afc541b16d03 Mon Sep 17 00:00:00 2001 From: runfuture Date: Thu, 8 Feb 2024 18:36:19 +0800 Subject: [PATCH 653/664] llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint --- convert-hf-to-gguf.py | 63 +++++++++++++++++++++++++++++++++++++++++-- llama.cpp | 2 ++ 2 files changed, 63 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 829d68368..0d4ea03b4 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1078,17 +1078,76 @@ class MiniCPMModel(Model): self.gguf_writer.add_name("MiniCPM") self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) def set_vocab(self): self._set_vocab_hf() + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + n_head = self.hparams.get("num_attention_heads") + n_kv_head = self.hparams.get("num_key_value_heads") + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # HF models permute some of the tensors, so we need to undo that + if name.endswith(("q_proj.weight")): + data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight")): + data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + class QwenModel(Model): @staticmethod diff --git a/llama.cpp b/llama.cpp index f8f5796a4..552e0d02e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2947,6 +2947,8 @@ static void llm_load_hparams( } break; case LLM_ARCH_MINICPM: { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { case 40: model.type = e_model::MODEL_2B; break; default: model.type = e_model::MODEL_UNKNOWN; From b7b74cef36a93ae01e0b9af8986d131761742d0e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 8 Feb 2024 11:36:54 +0100 Subject: [PATCH 654/664] fix trailing whitespace (#5407) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 552e0d02e..89acafbc3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8587,7 +8587,7 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can // } const int64_t t_start_sample_us = ggml_time_us(); - + if (k <= 0) { k = candidates->size; } From ff4ff05c5ff4311c05a8ce1f984c7d8def4f07a5 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 8 Feb 2024 15:20:03 +0100 Subject: [PATCH 655/664] llava : add missing .py, and fix paths in README.md (#5414) This commit adds the missing .py extension to the convert-image-encoder-to-gguf script. It also fixes the paths for the `model` and `mmproj` options in the example llava-cli command. Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 295181a34..721d5e613 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -14,7 +14,7 @@ Build with cmake or run `make llava-cli` to build it. After building, run: `./llava-cli` to see the usage. For example: ```sh -./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg +./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. @@ -38,7 +38,7 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b 3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: From 6e99f2a04f1871d637dd77eb4d81de31a5510253 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Thu, 8 Feb 2024 22:39:10 +0530 Subject: [PATCH 656/664] Fix f16_sycl cpy call from Arc (#5411) * fix f16_sycl cpy call * rm old logic * add fp16 build CI * use macro * format fix --- .github/workflows/build.yml | 41 +++++++++++++++++++++++++++++++++++++ ggml-sycl.cpp | 8 +++++--- 2 files changed, 46 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f4c374ce5..ed292d6b8 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -184,6 +184,47 @@ jobs: cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx .. cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-sycl-fp16: + runs-on: ubuntu-22.04 + + continue-on-error: true + + steps: + - uses: actions/checkout@v2 + + - name: add oneAPI to apt + shell: bash + run: | + cd /tmp + wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main" + + - name: install oneAPI dpcpp compiler + shell: bash + run: | + sudo apt update + sudo apt install intel-oneapi-compiler-dpcpp-cpp + + - name: install oneAPI MKL library + shell: bash + run: | + sudo apt install intel-oneapi-mkl-devel + + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Build + id: cmake_build + run: | + source /opt/intel/oneapi/setvars.sh + mkdir build + cd build + cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON .. + cmake --build . --config Release -j $(nproc) + # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know # how to debug it. # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124 diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a03df4c65..dd562a898 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -12148,7 +12148,8 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( const int64_t src1_ncols, const int64_t src1_padded_row_size, const dpct::queue_ptr &stream) { - const int64_t ne00 = src0->ne[0]; + GGML_TENSOR_BINARY_OP_LOCALS + const int64_t row_diff = row_high - row_low; // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics @@ -12167,8 +12168,9 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( } else { src1_dfloat = src1_dfloat_a.alloc(ne00); ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat, - ne00, ne00, 1, sizeof(float), 0, 0, ne00, 1, - sizeof(sycl::half), 0, 0, stream); + ne00, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, + nb13, stream); } } #else From 41f308f58edc2a04bcf9e245100b0a9b10e9a0fb Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 8 Feb 2024 21:33:03 +0100 Subject: [PATCH 657/664] llama : do not print "offloading layers" message in CPU-only builds (#5416) --- llama.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index 89acafbc3..db7d1c1cd 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4209,8 +4209,7 @@ static bool llm_load_tensors( ctx_bufs.emplace_back(ctx, buf); } - // print memory requirements - { + if (llama_supports_gpu_offload()) { const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); @@ -4222,10 +4221,11 @@ static bool llm_load_tensors( const int max_offloadable_layers = hparams.n_layer + 1; LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); + } - for (ggml_backend_buffer_t buf : model.bufs) { - LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); - } + // print memory requirements + for (ggml_backend_buffer_t buf : model.bufs) { + LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); } // populate tensors_by_name From 8e6a9d2de0096af7120606c74ee2f26684e87b41 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 8 Feb 2024 21:56:40 +0100 Subject: [PATCH 658/664] CUDA: more warps for mmvq on NVIDIA (#5394) --- ggml-cuda.cu | 133 +++++++++++++++++++++++++++++++++------------------ 1 file changed, 86 insertions(+), 47 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index db9da2459..5053757e6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5310,22 +5310,26 @@ template static __global__ void #endif // __CUDA_ARCH__ >= CC_VOLTA } -template +#define MMVQ_NWARPS_NVIDIA 4 +#define MMVQ_NWARPS_AMD_RDNA2 1 +#define MMVQ_NWARPS_AMD_OLD 4 + +template +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) // tells the compiler to use as many registers as it wants +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) { const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; - const int row = blockIdx.x*blockDim.y + threadIdx.y; - - if (row >= nrows_x) { - return; - } + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + const int row = blockIdx.x; const int blocks_per_row_x = ncols_x / qk; const int blocks_per_col_y = nrows_y / QK8_1; - const int blocks_per_warp = vdr * WARP_SIZE / qi; + const int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi; // partial sum for each thread float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f}; @@ -5333,12 +5337,12 @@ static __global__ void mul_mat_vec_q( const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) { + for (int i = tid / (qi/vdr); i < blocks_per_row_x; i += blocks_per_iter) { const int ibx = row*blocks_per_row_x + i; // x block index const int iby = i * (qk/QK8_1); // y block index that aligns with ibx - const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int + const int iqs = vdr * (tid % (qi/vdr)); // x block quant index when casting the quants to int #pragma unroll for (int j = 0; j < ncols_y; ++j) { @@ -5346,9 +5350,25 @@ static __global__ void mul_mat_vec_q( } } + __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y_template != 0 ? ncols_y_template : 8][WARP_SIZE]; + if (threadIdx.y > 0) { +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { + tmp_shared[threadIdx.y-1][j][threadIdx.x] = tmp[j]; + } + } + __syncthreads(); + if (threadIdx.y > 0) { + return; + } + // sum up partial sums and write back result #pragma unroll for (int j = 0; j < ncols_y; ++j) { +#pragma unroll + for (int i = 0; i < nwarps-1; ++i) { + tmp[j] += tmp_shared[i][j][threadIdx.x]; + } tmp[j] = warp_reduce_sum(tmp[j]); if (threadIdx.x == 0) { @@ -6833,46 +6853,65 @@ static void mul_mat_vec_q_cuda( GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_y <= 4); - const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - switch (ncols_y) { - case 1: - mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 2: - mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 3: - mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 4: - mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - // case 5: - // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; - // case 6: - // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; - // case 7: - // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; - // case 8: - // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + int nwarps; + if (g_device_caps[id].cc >= CC_OFFSET_AMD) { + nwarps = g_device_caps[id].cc >= CC_RDNA2 ? MMVQ_NWARPS_AMD_RDNA2 : MMVQ_NWARPS_AMD_OLD; + } else { + nwarps = MMVQ_NWARPS_NVIDIA; + } + + const dim3 block_nums(nrows_x, 1, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); + + switch (nwarps) { + case 1: switch(ncols_y) { + case 1: + mul_mat_vec_q<1, 1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 2: + mul_mat_vec_q<1, 2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 3: + mul_mat_vec_q<1, 3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 4: + mul_mat_vec_q<1, 4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + default: + GGML_ASSERT(false); + break; + } break; + case 4: switch(ncols_y) { + case 1: + mul_mat_vec_q<4, 1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 2: + mul_mat_vec_q<4, 2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 3: + mul_mat_vec_q<4, 3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 4: + mul_mat_vec_q<4, 4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + default: + GGML_ASSERT(false); + break; + } break; + default: GGML_ASSERT(false); - // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; } } From 44fbe34360dd760f9e68b4271f21533436397f84 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Fri, 9 Feb 2024 06:52:33 +0100 Subject: [PATCH 659/664] Fix Vulkan crash on APUs with very little device memory (#5424) * Fix Vulkan crash on APUs with very little device memory * Fix debug output function names --- ggml-vulkan.cpp | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 9e2846ee4..254f648a6 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -744,6 +744,8 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz } if (memory_type_index >= mem_props.memoryTypeCount) { + ctx->device.lock()->device.destroyBuffer(buf->buffer); + buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); } @@ -3875,7 +3877,7 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_ctx->preallocate_buffers_graph(" << node << ")" << std::endl; + std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) @@ -3994,8 +3996,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { return; } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_ctx->preallocate_buffers()" << std::endl; - std::cerr << "qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << std::endl; + std::cerr << "ggml_vk_preallocate_buffers(qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; #endif #if defined(GGML_VULKAN_RUN_TESTS) ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); From b2f87cb64db47d799b6f3656855c9caf9792ab2a Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Fri, 9 Feb 2024 10:56:43 +0100 Subject: [PATCH 660/664] ggml : fix `error C2078: too many initializers` for MSVC ARM64 (#5404) --- ggml-quants.c | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 101d3e783..1031e3761 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -268,6 +268,17 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) #if defined(__ARM_NEON) + +#ifdef _MSC_VER + +#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) } + +#else + +#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) } + +#endif + #if !defined(__aarch64__) // 64-bit compatibility @@ -8698,10 +8709,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(const int n, float * restrict s, const void * res for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { q8b = ggml_vld1q_s8_x4(q8); q8 += 64; memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); - const uint32x4_t aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; - const uint32x4_t aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; - const uint32x4_t aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; - const uint32x4_t aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); q3 += 16; q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); From e4124c24775f2cb5b3d7acc93bf9dc5471c172ef Mon Sep 17 00:00:00 2001 From: Marko Tasic Date: Fri, 9 Feb 2024 11:17:00 +0100 Subject: [PATCH 661/664] readme : add JavaScript/Wasm repo (#5415) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 66166c01b..0b4efdd33 100644 --- a/README.md +++ b/README.md @@ -124,6 +124,7 @@ Typically finetunes of the base models below are supported as well. - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) +- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) From e5ca3937c685d6e012ac4db40555d6ec100ff03c Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Fri, 9 Feb 2024 10:48:06 +0000 Subject: [PATCH 662/664] llama : do not cap thread count when MoE on CPU (#5419) * Not capping thread count when MoE inference is running on CPU * Whitespace --- llama.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index db7d1c1cd..0566b087b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7285,7 +7285,9 @@ static int llama_decode_internal( // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well // we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering // with the BLAS calls. need a better solution - if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) { + // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is + // being processed then Accelerate/BLAS will not be involved, so capping would limit performance. + if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) { n_threads = std::min(4, n_threads); } From 7c777fcd5dd4af7079e33390cf6a19c328a2666f Mon Sep 17 00:00:00 2001 From: Riley Stewart Date: Fri, 9 Feb 2024 02:49:49 -0800 Subject: [PATCH 663/664] server : fix prompt caching for repeated prompts (#5420) --- examples/server/server.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index eceda30d0..8d668f798 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1592,10 +1592,6 @@ struct llama_server_context LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } - LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); - - llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); - slot.cache_tokens = prompt_tokens; if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) @@ -1609,6 +1605,10 @@ struct llama_server_context } } + LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); + + llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); + LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, From e00d2a62dd1441e3b089570ec06d05c18800d368 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 9 Feb 2024 14:00:59 +0100 Subject: [PATCH 664/664] llava : add requirements.txt and update README.md (#5428) * llava: add requirements.txt and update README.md This commit adds a `requirements.txt` file to the `examples/llava` directory. This file contains the required Python packages to run the scripts in the `examples/llava` directory. The motivation of this to make it easier for users to run the scripts in `examples/llava`. This will avoid users from having to possibly run into missing package issues if the packages are not installed on their system. Signed-off-by: Daniel Bevenius * llava: fix typo in llava-surgery.py output Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 12 +++++++++--- examples/llava/llava-surgery.py | 2 +- examples/llava/requirements.txt | 3 +++ 3 files changed, 13 insertions(+), 4 deletions(-) create mode 100644 examples/llava/requirements.txt diff --git a/examples/llava/README.md b/examples/llava/README.md index 721d5e613..19f1a50a2 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -29,19 +29,25 @@ git clone https://huggingface.co/liuhaotian/llava-v1.5-7b git clone https://huggingface.co/openai/clip-vit-large-patch14-336 ``` -2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: +2. Install the required Python packages: + +```sh +pip install -r examples/llava/requirements.txt +``` + +3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b ``` -3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: +4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: ```sh python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: ```sh python ./convert.py ../llava-v1.5-7b diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava-surgery.py index 515f6b58d..0a61efdfe 100644 --- a/examples/llava/llava-surgery.py +++ b/examples/llava/llava-surgery.py @@ -42,5 +42,5 @@ if len(clip_tensors) > 0: torch.save(checkpoint, path) print("Done!") -print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.") +print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.") diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt new file mode 100644 index 000000000..f80f727a7 --- /dev/null +++ b/examples/llava/requirements.txt @@ -0,0 +1,3 @@ +-r ../../requirements/requirements-convert.txt +pillow~=10.2.0 +torch~=2.1.1
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changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 611ed3f4d..3c49d645c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -44,7 +44,7 @@ endif() # general option(LLAMA_STATIC "llama: static link libraries" OFF) -option(LLAMA_NATIVE "llama: enable -march=native flag" ON) +option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) option(LLAMA_LTO "llama: enable link time optimization" OFF) # debug From 4ff1046d75e64f0e556d8dcd930ea25c23eb8b18 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 16:22:30 +0200 Subject: [PATCH 025/664] gguf : print error for GGUFv1 files (#3908) --- ggml.c | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ggml.c b/ggml.c index d5a49d8e4..605a27940 100644 --- a/ggml.c +++ b/ggml.c @@ -18884,6 +18884,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); + if (ctx->header.version == 1) { + fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } + if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); fclose(file); From d6069051de7165a4e06662c89257f5d2905bb156 Mon Sep 17 00:00:00 2001 From: Oleksii Maryshchenko Date: Thu, 2 Nov 2023 18:10:39 +0100 Subject: [PATCH 026/664] cuda : use CUDA memory pool with async memory allocation/deallocation when available (#3903) * Using cuda memory pools for async alloc/dealloc. * If cuda device doesnt support memory pool than use old implementation. * Removed redundant cublasSetStream --------- Co-authored-by: Oleksii Maryshchenko --- ggml-cuda.cu | 130 ++++++++++++++++++++++++++++++--------------------- 1 file changed, 78 insertions(+), 52 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e46295126..58b58f331 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -181,11 +181,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cudaError_t err_ = (err); \ if (err_ != cudaSuccess) { \ - int id; \ - cudaGetDevice(&id); \ + int dev_id; \ + cudaGetDevice(&dev_id); \ fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ cudaGetErrorString(err_)); \ - fprintf(stderr, "current device: %d\n", id); \ + fprintf(stderr, "current device: %d\n", dev_id); \ exit(1); \ } \ } while (0) @@ -195,11 +195,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cublasStatus_t err_ = (err); \ if (err_ != CUBLAS_STATUS_SUCCESS) { \ - int id; \ - cudaGetDevice(&id); \ + int dev_id; \ + cudaGetDevice(&dev_id); \ fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \ err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \ - fprintf(stderr, "current device: %d\n", id); \ + fprintf(stderr, "current device: %d\n", dev_id); \ exit(1); \ } \ } while (0) @@ -465,6 +465,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MAX_STREAMS 8 static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr }; +static cudaMemPool_t g_cudaMemPools[GGML_CUDA_MAX_DEVICES] = { nullptr }; struct ggml_tensor_extra_gpu { void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -5772,6 +5773,16 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { return ptr; } +static void * ggml_cuda_pool_malloc_async(size_t size, size_t * actual_size, int id, cudaStream_t stream) { + if (g_cudaMemPools[id] == nullptr) { + return ggml_cuda_pool_malloc(size, actual_size); + } + void *ptr; + CUDA_CHECK(cudaMallocFromPoolAsync(&ptr, size, g_cudaMemPools[id], stream)); + *actual_size = size; + return ptr; +} + static void ggml_cuda_pool_free(void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); int id; @@ -5790,6 +5801,13 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { } +static void ggml_cuda_pool_free_async(void * ptr, size_t actual_size, int id, cudaStream_t stream) { + if (g_cudaMemPools[id] == nullptr) { + return ggml_cuda_pool_free(ptr, actual_size); + } + CUDA_CHECK(cudaFreeAsync(ptr, stream)); +} + void ggml_init_cublas() { static bool initialized = false; @@ -5844,6 +5862,13 @@ void ggml_init_cublas() { // create cublas handle CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id])); CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH)); + + // configure memory pool + cudaError_t err = cudaDeviceGetMemPool(&g_cudaMemPools[id], id); + if (err == cudaSuccess) { + size_t treshold = UINT64_MAX; + CUDA_CHECK(cudaMemPoolSetAttribute(g_cudaMemPools[id], cudaMemPoolAttrReleaseThreshold, &treshold)); + } } // configure logging to stdout @@ -6437,7 +6462,7 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = row_diff*ne00; - src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as); + src0_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src0_as, id, stream); to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream); } const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16; @@ -6448,13 +6473,12 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = src1_ncols*ne10; - src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); + src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src1_as, id, stream); to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); } const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16; - - size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); + size_t dst_f16_as = 0; + half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(row_diff*src1_ncols * sizeof(half), &dst_f16_as, id, stream); const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; @@ -6472,14 +6496,15 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream); - ggml_cuda_pool_free(dst_f16, dst_as); - - if (src0_as != 0) { - ggml_cuda_pool_free(src0_as_f16, src0_as); + if (dst_f16_as != 0) { + ggml_cuda_pool_free_async(dst_f16, dst_f16_as, id, stream); } + if (src0_as != 0) { + ggml_cuda_pool_free_async(src0_as_f16, src0_as, id, stream); + } if (src1_as != 0) { - ggml_cuda_pool_free(src1_as_f16, src1_as); + ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, stream); } } else { @@ -6489,7 +6514,7 @@ inline void ggml_cuda_op_mul_mat_cublas( if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT + src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc_async(row_diff*ne00 * sizeof(float), &src0_as, id, stream); // NOLINT to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream); } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32; @@ -6506,7 +6531,7 @@ inline void ggml_cuda_op_mul_mat_cublas( &beta, dst_dd_i, ldc)); if (src0_as != 0) { - ggml_cuda_pool_free(src0_ddq_as_f32, src0_as); + ggml_cuda_pool_free_async(src0_ddq_as_f32, src0_as, id, stream); } } @@ -6929,21 +6954,22 @@ static void ggml_cuda_op_mul_mat( src0_dd[id] = (char *) src0_extra->data_device[id]; } else { const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + src0_dd[id] = (char *) ggml_cuda_pool_malloc_async(ggml_nbytes(src0), &src0_as[id], id, stream); } if (src1_on_device && src1_is_contiguous) { src1_ddf[id] = (float *) src1_extra->data_device[id]; } else { - src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + src1_ddf[id] = (float *) ggml_cuda_pool_malloc_async(ggml_nbytes(src1), &src1_asf[id], id, stream); } if (convert_src1_to_q8_1) { - src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + const size_t size_dst_ddq = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs; + src1_ddq[id] = (char *) ggml_cuda_pool_malloc_async(size_dst_ddq, &src1_asq[id], id, stream); if (src1_on_device && src1_is_contiguous) { quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); - CUDA_CHECK(cudaGetLastError()); + // CUDA_CHECK(cudaGetLastError()); } } @@ -6951,7 +6977,7 @@ static void ggml_cuda_op_mul_mat( dst_dd[id] = (float *) dst_extra->data_device[id]; } else { const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]); + dst_dd[id] = (float *) ggml_cuda_pool_malloc_async(size_dst_ddf, &dst_as[id], id, stream); } } @@ -7077,24 +7103,6 @@ static void ggml_cuda_op_mul_mat( } } - for (int64_t id = 0; id < g_device_count; ++id) { - CUDA_CHECK(ggml_cuda_set_device(id)); - - // free buffers again when done - if (src0_as[id] > 0) { - ggml_cuda_pool_free(src0_dd[id], src0_as[id]); - } - if (src1_asf[id] > 0) { - ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); - } - if (src1_asq[id] > 0) { - ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]); - } - if (dst_as[id] > 0) { - ggml_cuda_pool_free(dst_dd[id], dst_as[id]); - } - } - // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; @@ -7112,6 +7120,21 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(g_main_device)); CUDA_CHECK(cudaDeviceSynchronize()); } + + for (int64_t id = 0; id < g_device_count; ++id) { + if (src0_as[id] > 0) { + ggml_cuda_pool_free_async(src0_dd[id], src0_as[id], id, g_cudaStreams[id][0]); + } + if (src1_asf[id] > 0) { + ggml_cuda_pool_free_async(src1_ddf[id], src1_asf[id], id, g_cudaStreams[id][0]); + } + if (src1_asq[id] > 0) { + ggml_cuda_pool_free_async(src1_ddq[id], src1_asq[id], id, g_cudaStreams[id][0]); + } + if (dst_as[id] > 0) { + ggml_cuda_pool_free_async(dst_dd[id], dst_as[id], id, g_cudaStreams[id][0]); + } + } } static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7298,11 +7321,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const GGML_ASSERT(to_fp16_cuda != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); + half * src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne1 * sizeof(half), &src1_as, id, main_stream); to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); + half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &dst_as, id, main_stream); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -7349,10 +7372,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const } else { // use cublasGemmBatchedEx const int ne23 = ne12*ne13; - - void ** ptrs_as = nullptr; + // allocate device memory for pointers size_t ptrs_s = 0; - ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s); + void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( @@ -7365,7 +7387,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dst->nb[2], dst->nb[3], r2, r3); CUDA_CHECK(cudaGetLastError()); - CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, @@ -7375,16 +7396,21 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ne23, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - - ggml_cuda_pool_free(ptrs_as, ptrs_s); + // free device memory for pointers + if (ptrs_s != 0) { + ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream); + } } #endif const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); - - ggml_cuda_pool_free(src1_as_f16, src1_as); - ggml_cuda_pool_free(dst_f16, dst_as); + if (src1_as != 0) { + ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, main_stream); + } + if (dst_as != 0) { + ggml_cuda_pool_free_async(dst_f16, dst_as, id, main_stream); + } } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { From c7743fe1c1cbda5a886362aa371480360580fdf0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 20:32:11 +0200 Subject: [PATCH 027/664] cuda : fix const ptrs warning causing ROCm build issues (#3913) --- ggml-cuda.cu | 37 +++++++++++++++++++++++-------------- 1 file changed, 23 insertions(+), 14 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 58b58f331..06c28f565 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7248,7 +7248,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor __global__ void k_compute_batched_ptrs( const half * src0_as_f16, const half * src1_as_f16, half * dst_f16, - void ** ptrs, + const void ** ptrs_src, void ** ptrs_dst, int ne12, int ne13, int ne23, int nb02, int nb03, @@ -7265,9 +7265,9 @@ __global__ void k_compute_batched_ptrs( int i03 = i13 / r3; int i02 = i12 / r2; - ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* nb2/2 + i13* nb3/2; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; } static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7372,14 +7372,20 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const } else { // use cublasGemmBatchedEx const int ne23 = ne12*ne13; - // allocate device memory for pointers - size_t ptrs_s = 0; - void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream); + + const void ** ptrs_src = nullptr; + void ** ptrs_dst = nullptr; + + size_t ptrs_src_s = 0; + size_t ptrs_dst_s = 0; + + ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream); + ptrs_dst = ( void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( src0_as_f16, src1_as_f16, dst_f16, - ptrs_as, + ptrs_src, ptrs_dst, ne12, ne13, ne23, nb02, nb03, @@ -7390,15 +7396,18 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float), - &beta_f16, ( void ** ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01, + &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half), + (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + &beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01, ne23, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - // free device memory for pointers - if (ptrs_s != 0) { - ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream); + + if (ptrs_src_s != 0) { + ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream); + } + if (ptrs_dst_s != 0) { + ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream); } } #endif From 224e7d5b14cbabab7ae45c64db2cfde979c8455d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 20:44:12 +0200 Subject: [PATCH 028/664] readme : add notice about #3912 --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index b56ecaec7..9c9e36ad0 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,6 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) -[![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) @@ -11,8 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- LLaVA support: https://github.com/ggerganov/llama.cpp/pull/3436 -- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252) +- ⚠️ **Upcoming change that might break functionality. Help with testing is needed:** https://github.com/ggerganov/llama.cpp/pull/3912 ---- From 51b2fc11f7f605fff49725a4540e9a6ef7b51b70 Mon Sep 17 00:00:00 2001 From: Andrei Date: Thu, 2 Nov 2023 15:40:31 -0400 Subject: [PATCH 029/664] cmake : fix relative path to git submodule index (#3915) --- common/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 0150114e3..ac594b2ca 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -11,7 +11,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") if(NOT IS_DIRECTORY "${GIT_DIR}") file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) - set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}") + set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}") endif() set(GIT_INDEX "${GIT_DIR}/index") From 629f917cd6b96ba1274c49a8aab163b1b189229d Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Thu, 2 Nov 2023 13:58:22 -0600 Subject: [PATCH 030/664] cuda : add ROCM aliases for CUDA pool stuff (#3918) --- ggml-cuda.cu | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 06c28f565..baf02df2b 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -39,6 +39,10 @@ #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess +#define cudaDeviceGetMemPool hipDeviceGetMemPool +#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold +#define cudaMemPoolSetAttribute hipMemPoolSetAttribute +#define cudaMemPool_t hipMemPool_t #define cudaDeviceProp hipDeviceProp_t #define cudaDeviceSynchronize hipDeviceSynchronize #define cudaError_t hipError_t @@ -48,6 +52,7 @@ #define cudaEvent_t hipEvent_t #define cudaEventDestroy hipEventDestroy #define cudaFree hipFree +#define cudaFreeAsync hipFreeAsync #define cudaFreeHost hipHostFree #define cudaGetDevice hipGetDevice #define cudaGetDeviceCount hipGetDeviceCount @@ -55,6 +60,7 @@ #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError #define cudaMalloc hipMalloc +#define cudaMallocFromPoolAsync hipMallocFromPoolAsync #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) #define cudaMemcpy hipMemcpy #define cudaMemcpy2DAsync hipMemcpy2DAsync From 3fdbe6b66b7b5c6ad3b2f245cbad1517c27ff776 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Fri, 3 Nov 2023 02:31:58 -0400 Subject: [PATCH 031/664] llama : change yarn_ext_factor placeholder to -1 (#3922) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index bb60044b4..cc0211ceb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7982,7 +7982,7 @@ struct llama_context_params llama_context_default_params() { /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, - /*.yarn_ext_factor =*/ NAN, + /*.yarn_ext_factor =*/ -1.0f, /*.yarn_attn_factor =*/ 1.0f, /*.yarn_beta_fast =*/ 32.0f, /*.yarn_beta_slow =*/ 1.0f, @@ -8125,7 +8125,7 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none } - if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set' + if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set' cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f; } From 05816027d649f977468fc804cdb54e99eac246d1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 3 Nov 2023 09:24:00 +0200 Subject: [PATCH 032/664] common : YAYF (yet another YARN fix) (#3925) ggml-ci --- common/common.h | 44 ++++++++++++++++++++++---------------------- llama.h | 10 +++++----- 2 files changed, 27 insertions(+), 27 deletions(-) diff --git a/common/common.h b/common/common.h index 72a49b890..9ad625633 100644 --- a/common/common.h +++ b/common/common.h @@ -43,29 +43,29 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 16; // number of tokens to draft during speculative decoding - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_parallel = 1; // number of parallel sequences to decode - int32_t n_sequences = 1; // number of sequences to decode - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_beams = 0; // if non-zero then use beam search of given width. - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor - float yarn_ext_factor = NAN; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor - float yarn_beta_fast = 32.0f;// YaRN low correction dim - float yarn_beta_slow = 1.0f; // YaRN high correction dim - int32_t yarn_orig_ctx = 0; // YaRN original context length + int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 16; // number of tokens to draft during speculative decoding + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs + int32_t n_beams = 0; // if non-zero then use beam search of given width. + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f; // YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters diff --git a/llama.h b/llama.h index 3f1becd76..e8dc04bb5 100644 --- a/llama.h +++ b/llama.h @@ -175,11 +175,11 @@ extern "C" { }; struct llama_context_params { - uint32_t seed; // RNG seed, -1 for random - uint32_t n_ctx; // text context, 0 = from model - uint32_t n_batch; // prompt processing maximum batch size - uint32_t n_threads; // number of threads to use for generation - uint32_t n_threads_batch; // number of threads to use for batch processing + uint32_t seed; // RNG seed, -1 for random + uint32_t n_ctx; // text context, 0 = from model + uint32_t n_batch; // prompt processing maximum batch size + uint32_t n_threads; // number of threads to use for generation + uint32_t n_threads_batch; // number of threads to use for batch processing int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 From 8f961abdc4e134c83bf8c2ad618ab256b4cae0f9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 3 Nov 2023 09:41:17 +0200 Subject: [PATCH 033/664] speculative : change default p_accept to 0.5 + CLI args (#3919) ggml-ci --- common/common.cpp | 14 ++++++++++++++ common/common.h | 8 ++++++-- examples/speculative/speculative.cpp | 8 +++++--- 3 files changed, 25 insertions(+), 5 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index e938dee16..20cc4a081 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -403,6 +403,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_sequences = std::stoi(argv[i]); + } else if (arg == "--p-accept" || arg == "-pa") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.p_accept = std::stof(argv[i]); + } else if (arg == "--p-split" || arg == "-ps") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.p_split = std::stof(argv[i]); } else if (arg == "-m" || arg == "--model") { if (++i >= argc) { invalid_param = true; @@ -778,6 +790,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); + printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept); + printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); diff --git a/common/common.h b/common/common.h index 9ad625633..dd6b002eb 100644 --- a/common/common.h +++ b/common/common.h @@ -44,6 +44,7 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = -1; // RNG seed + int32_t n_threads = get_num_physical_cores(); int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_predict = -1; // new tokens to predict @@ -54,6 +55,8 @@ struct gpt_params { int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode + float p_accept = 0.5f; // speculative decoding accept probability + float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors @@ -66,7 +69,8 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment + // pinging @cebtenzzre // // sampling parameters struct llama_sampling_params sparams; @@ -90,7 +94,7 @@ struct gpt_params { int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line // (which is more convenient to use for plotting) // - bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt + bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 798684f66..3a8e27811 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -37,9 +37,11 @@ int main(int argc, char ** argv) { // max number of parallel drafting sequences (i.e. tree branches) const int n_seq_dft = params.n_parallel; - // TODO: make this configurable - const float p_accept = 0.80f; - const float p_split = 0.10f; + // probability threshold for accepting a token from the draft model + const float p_accept = params.p_accept; + + // probability threshold for splitting a draft branch (only for n_seq_dft > 1) + const float p_split = params.p_split; #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); From abb77e7319aabc0b5cfb7c22da690a692489b6b7 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 3 Nov 2023 12:13:09 +0100 Subject: [PATCH 034/664] ggml-cuda : move row numbers to x grid dim in mmv kernels (#3921) --- ggml-cuda.cu | 53 ++++++++++++++++++++++++++-------------------------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index baf02df2b..bdbcca0ca 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -989,7 +989,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; @@ -1093,7 +1093,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; @@ -1197,7 +1197,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1451,7 +1451,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; @@ -4261,7 +4261,7 @@ template static __global__ void template static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) { - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row >= nrows) { return; @@ -4301,7 +4301,7 @@ template static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { // qk = quantized weights per x block // qr = number of quantized weights per data value in x block - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row >= nrows) { return; @@ -4874,7 +4874,8 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4883,7 +4884,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4892,7 +4893,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4901,7 +4902,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4910,7 +4911,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4920,7 +4921,7 @@ static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q2_k<<>>(vx, y, dst, ncols, nrows); } @@ -4929,7 +4930,7 @@ static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); } @@ -4938,7 +4939,7 @@ static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); } @@ -4953,7 +4954,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); } @@ -4961,7 +4962,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK4_0 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4970,7 +4971,7 @@ static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK4_1 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4979,7 +4980,7 @@ static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK5_0 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4988,7 +4989,7 @@ static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK5_1 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4997,7 +4998,7 @@ static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK8_0 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5006,7 +5007,7 @@ static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5015,7 +5016,7 @@ static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5024,7 +5025,7 @@ static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5033,7 +5034,7 @@ static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5042,7 +5043,7 @@ static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5061,7 +5062,7 @@ static void convert_fp32_to_fp16_cuda(const void * vx, half * y, const int k, cu static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec<1, 1, convert_f16> <<>>(vx, y, dst, ncols, nrows); From 5ba37461711095c0284233dbd14f0d9010cdbf56 Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Fri, 3 Nov 2023 13:00:31 -0500 Subject: [PATCH 035/664] ggml-metal: fix yarn rope (#3937) --- ggml-metal.m | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml-metal.m b/ggml-metal.m index b33a3cb8f..acdb83843 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1403,7 +1403,8 @@ void ggml_metal_graph_compute( const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[3]; + // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); From d9b33fe95bd257b36c84ee5769cc048230067d6f Mon Sep 17 00:00:00 2001 From: Peter Sugihara Date: Fri, 3 Nov 2023 12:18:18 -0700 Subject: [PATCH 036/664] metal : round up to 16 to fix MTLDebugComputeCommandEncoder assertion (#3938) --- ggml-metal.m | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index acdb83843..78ae4485d 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1017,7 +1017,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:MAX(16, nth/32*sizeof(float)) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1348,7 +1348,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:MAX(16, nth*sizeof(float)) atIndex:0]; const int64_t nrows = ggml_nrows(src0); From f28af0d81aa1010afa5de74cf627dcb04bea3157 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 4 Nov 2023 16:20:34 -0600 Subject: [PATCH 037/664] gguf-py: Support 01.AI Yi models (#3943) --- gguf-py/gguf/gguf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 727b4e554..a2271d225 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -393,6 +393,7 @@ class TensorNameMap: "layers.{bid}.attention_norm", # llama-pth "encoder.layer.{bid}.attention.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.input_layernorm", # persimmon + "model.layers.{bid}.ln1", # yi ), # Attention norm 2 @@ -464,6 +465,7 @@ class TensorNameMap: "layers.{bid}.ffn_norm", # llama-pth "encoder.layer.{bid}.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon + "model.layers.{bid}.ln2", # yi ), # Feed-forward up From 48ade94538fa509465d71023e49d07aab0ec8cd5 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 5 Nov 2023 08:12:13 +0100 Subject: [PATCH 038/664] cuda : revert CUDA pool stuff (#3944) * Revert "cuda : add ROCM aliases for CUDA pool stuff (#3918)" This reverts commit 629f917cd6b96ba1274c49a8aab163b1b189229d. * Revert "cuda : use CUDA memory pool with async memory allocation/deallocation when available (#3903)" This reverts commit d6069051de7165a4e06662c89257f5d2905bb156. ggml-ci --- ggml-cuda.cu | 131 ++++++++++++++++++++------------------------------- 1 file changed, 50 insertions(+), 81 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index bdbcca0ca..dc14f2f5d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -39,10 +39,6 @@ #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess -#define cudaDeviceGetMemPool hipDeviceGetMemPool -#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold -#define cudaMemPoolSetAttribute hipMemPoolSetAttribute -#define cudaMemPool_t hipMemPool_t #define cudaDeviceProp hipDeviceProp_t #define cudaDeviceSynchronize hipDeviceSynchronize #define cudaError_t hipError_t @@ -52,7 +48,6 @@ #define cudaEvent_t hipEvent_t #define cudaEventDestroy hipEventDestroy #define cudaFree hipFree -#define cudaFreeAsync hipFreeAsync #define cudaFreeHost hipHostFree #define cudaGetDevice hipGetDevice #define cudaGetDeviceCount hipGetDeviceCount @@ -60,7 +55,6 @@ #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError #define cudaMalloc hipMalloc -#define cudaMallocFromPoolAsync hipMallocFromPoolAsync #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) #define cudaMemcpy hipMemcpy #define cudaMemcpy2DAsync hipMemcpy2DAsync @@ -187,11 +181,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cudaError_t err_ = (err); \ if (err_ != cudaSuccess) { \ - int dev_id; \ - cudaGetDevice(&dev_id); \ + int id; \ + cudaGetDevice(&id); \ fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ cudaGetErrorString(err_)); \ - fprintf(stderr, "current device: %d\n", dev_id); \ + fprintf(stderr, "current device: %d\n", id); \ exit(1); \ } \ } while (0) @@ -201,11 +195,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cublasStatus_t err_ = (err); \ if (err_ != CUBLAS_STATUS_SUCCESS) { \ - int dev_id; \ - cudaGetDevice(&dev_id); \ + int id; \ + cudaGetDevice(&id); \ fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \ err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \ - fprintf(stderr, "current device: %d\n", dev_id); \ + fprintf(stderr, "current device: %d\n", id); \ exit(1); \ } \ } while (0) @@ -471,7 +465,6 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MAX_STREAMS 8 static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr }; -static cudaMemPool_t g_cudaMemPools[GGML_CUDA_MAX_DEVICES] = { nullptr }; struct ggml_tensor_extra_gpu { void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -5780,16 +5773,6 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { return ptr; } -static void * ggml_cuda_pool_malloc_async(size_t size, size_t * actual_size, int id, cudaStream_t stream) { - if (g_cudaMemPools[id] == nullptr) { - return ggml_cuda_pool_malloc(size, actual_size); - } - void *ptr; - CUDA_CHECK(cudaMallocFromPoolAsync(&ptr, size, g_cudaMemPools[id], stream)); - *actual_size = size; - return ptr; -} - static void ggml_cuda_pool_free(void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); int id; @@ -5808,13 +5791,6 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { } -static void ggml_cuda_pool_free_async(void * ptr, size_t actual_size, int id, cudaStream_t stream) { - if (g_cudaMemPools[id] == nullptr) { - return ggml_cuda_pool_free(ptr, actual_size); - } - CUDA_CHECK(cudaFreeAsync(ptr, stream)); -} - void ggml_init_cublas() { static bool initialized = false; @@ -5869,13 +5845,6 @@ void ggml_init_cublas() { // create cublas handle CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id])); CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH)); - - // configure memory pool - cudaError_t err = cudaDeviceGetMemPool(&g_cudaMemPools[id], id); - if (err == cudaSuccess) { - size_t treshold = UINT64_MAX; - CUDA_CHECK(cudaMemPoolSetAttribute(g_cudaMemPools[id], cudaMemPoolAttrReleaseThreshold, &treshold)); - } } // configure logging to stdout @@ -6469,7 +6438,7 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = row_diff*ne00; - src0_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src0_as, id, stream); + src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as); to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream); } const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16; @@ -6480,12 +6449,13 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = src1_ncols*ne10; - src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src1_as, id, stream); + src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); } const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16; - size_t dst_f16_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(row_diff*src1_ncols * sizeof(half), &dst_f16_as, id, stream); + + size_t dst_as = 0; + half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; @@ -6503,15 +6473,14 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream); - if (dst_f16_as != 0) { - ggml_cuda_pool_free_async(dst_f16, dst_f16_as, id, stream); - } + ggml_cuda_pool_free(dst_f16, dst_as); if (src0_as != 0) { - ggml_cuda_pool_free_async(src0_as_f16, src0_as, id, stream); + ggml_cuda_pool_free(src0_as_f16, src0_as); } + if (src1_as != 0) { - ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, stream); + ggml_cuda_pool_free(src1_as_f16, src1_as); } } else { @@ -6521,7 +6490,7 @@ inline void ggml_cuda_op_mul_mat_cublas( if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc_async(row_diff*ne00 * sizeof(float), &src0_as, id, stream); // NOLINT + src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream); } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32; @@ -6538,7 +6507,7 @@ inline void ggml_cuda_op_mul_mat_cublas( &beta, dst_dd_i, ldc)); if (src0_as != 0) { - ggml_cuda_pool_free_async(src0_ddq_as_f32, src0_as, id, stream); + ggml_cuda_pool_free(src0_ddq_as_f32, src0_as); } } @@ -6961,22 +6930,21 @@ static void ggml_cuda_op_mul_mat( src0_dd[id] = (char *) src0_extra->data_device[id]; } else { const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_cuda_pool_malloc_async(ggml_nbytes(src0), &src0_as[id], id, stream); + src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]); } if (src1_on_device && src1_is_contiguous) { src1_ddf[id] = (float *) src1_extra->data_device[id]; } else { - src1_ddf[id] = (float *) ggml_cuda_pool_malloc_async(ggml_nbytes(src1), &src1_asf[id], id, stream); + src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); } if (convert_src1_to_q8_1) { - const size_t size_dst_ddq = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs; - src1_ddq[id] = (char *) ggml_cuda_pool_malloc_async(size_dst_ddq, &src1_asq[id], id, stream); + src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); if (src1_on_device && src1_is_contiguous) { quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); - // CUDA_CHECK(cudaGetLastError()); + CUDA_CHECK(cudaGetLastError()); } } @@ -6984,7 +6952,7 @@ static void ggml_cuda_op_mul_mat( dst_dd[id] = (float *) dst_extra->data_device[id]; } else { const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_cuda_pool_malloc_async(size_dst_ddf, &dst_as[id], id, stream); + dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]); } } @@ -7110,6 +7078,24 @@ static void ggml_cuda_op_mul_mat( } } + for (int64_t id = 0; id < g_device_count; ++id) { + CUDA_CHECK(ggml_cuda_set_device(id)); + + // free buffers again when done + if (src0_as[id] > 0) { + ggml_cuda_pool_free(src0_dd[id], src0_as[id]); + } + if (src1_asf[id] > 0) { + ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); + } + if (src1_asq[id] > 0) { + ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]); + } + if (dst_as[id] > 0) { + ggml_cuda_pool_free(dst_dd[id], dst_as[id]); + } + } + // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; @@ -7127,21 +7113,6 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(g_main_device)); CUDA_CHECK(cudaDeviceSynchronize()); } - - for (int64_t id = 0; id < g_device_count; ++id) { - if (src0_as[id] > 0) { - ggml_cuda_pool_free_async(src0_dd[id], src0_as[id], id, g_cudaStreams[id][0]); - } - if (src1_asf[id] > 0) { - ggml_cuda_pool_free_async(src1_ddf[id], src1_asf[id], id, g_cudaStreams[id][0]); - } - if (src1_asq[id] > 0) { - ggml_cuda_pool_free_async(src1_ddq[id], src1_asq[id], id, g_cudaStreams[id][0]); - } - if (dst_as[id] > 0) { - ggml_cuda_pool_free_async(dst_dd[id], dst_as[id], id, g_cudaStreams[id][0]); - } - } } static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7328,11 +7299,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const GGML_ASSERT(to_fp16_cuda != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne1 * sizeof(half), &src1_as, id, main_stream); + half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &dst_as, id, main_stream); + half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -7386,8 +7357,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const size_t ptrs_src_s = 0; size_t ptrs_dst_s = 0; - ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream); - ptrs_dst = ( void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream); + ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( @@ -7400,6 +7371,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dst->nb[2], dst->nb[3], r2, r3); CUDA_CHECK(cudaGetLastError()); + CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, @@ -7411,22 +7383,19 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_GEMM_DEFAULT_TENSOR_OP)); if (ptrs_src_s != 0) { - ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream); + ggml_cuda_pool_free(ptrs_src, ptrs_src_s); } if (ptrs_dst_s != 0) { - ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream); + ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s); } } #endif const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); - if (src1_as != 0) { - ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, main_stream); - } - if (dst_as != 0) { - ggml_cuda_pool_free_async(dst_f16, dst_as, id, main_stream); - } + + ggml_cuda_pool_free(src1_as_f16, src1_as); + ggml_cuda_pool_free(dst_f16, dst_as); } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { From a7fac013cf1cc7bbc0160a226aa2412e9f22e78a Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Sun, 5 Nov 2023 07:46:44 +0000 Subject: [PATCH 039/664] ci : use intel sde when ci cpu doesn't support avx512 (#3949) --- .github/workflows/build.yml | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 5af497a3c..bc295d52d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -288,6 +288,7 @@ jobs: OPENBLAS_VERSION: 0.3.23 OPENCL_VERSION: 2023.04.17 CLBLAST_VERSION: 1.6.0 + SDE_VERSION: 9.21.1-2023-04-24 strategy: matrix: @@ -383,11 +384,23 @@ jobs: - name: Test id: cmake_test - if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # Test AVX-512 only when possible + if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512 run: | cd build ctest -C Release --verbose --timeout 900 + - name: Test (Intel SDE) + id: cmake_test_sde + if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation + run: | + curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/777395/sde-external-${env:SDE_VERSION}-win.tar.xz" + # for some weird reason windows tar doesn't like sde tar.xz + 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz + 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar + $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) + cd build + & $sde -future -- ctest -C Release --verbose --timeout 900 + - name: Determine tag name id: tag shell: bash From c41ea36eaa3548776de4cb3d5d49b925cd3fc0f2 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Sun, 5 Nov 2023 08:03:09 +0000 Subject: [PATCH 040/664] cmake : MSVC instruction detection (fixed up #809) (#3923) * Add detection code for avx * Only check hardware when option is ON * Modify per code review sugguestions * Build locally will detect CPU * Fixes CMake style to use lowercase like everywhere else * cleanup * fix merge * linux/gcc version for testing * msvc combines avx2 and fma into /arch:AVX2 so check for both * cleanup * msvc only version * style * Update FindSIMD.cmake --------- Co-authored-by: Howard Su Co-authored-by: Jeremy Dunn --- CMakeLists.txt | 8 +++- cmake/FindSIMD.cmake | 100 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 106 insertions(+), 2 deletions(-) create mode 100644 cmake/FindSIMD.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt index 3c49d645c..7b4eb1840 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -10,7 +10,7 @@ endif() set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) -if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) +if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) set(LLAMA_STANDALONE ON) # configure project version @@ -44,7 +44,7 @@ endif() # general option(LLAMA_STATIC "llama: static link libraries" OFF) -option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) +option(LLAMA_NATIVE "llama: enable -march=native flag" ON) option(LLAMA_LTO "llama: enable link time optimization" OFF) # debug @@ -510,6 +510,10 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) message(STATUS "x86 detected") if (MSVC) + # instruction set detection for MSVC only + if (LLAMA_NATIVE) + include(cmake/FindSIMD.cmake) + endif () if (LLAMA_AVX512) add_compile_options($<$:/arch:AVX512>) add_compile_options($<$:/arch:AVX512>) diff --git a/cmake/FindSIMD.cmake b/cmake/FindSIMD.cmake new file mode 100644 index 000000000..33377ec44 --- /dev/null +++ b/cmake/FindSIMD.cmake @@ -0,0 +1,100 @@ +include(CheckCSourceRuns) + +set(AVX_CODE " + #include + int main() + { + __m256 a; + a = _mm256_set1_ps(0); + return 0; + } +") + +set(AVX512_CODE " + #include + int main() + { + __m512i a = _mm512_set_epi8(0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0); + __m512i b = a; + __mmask64 equality_mask = _mm512_cmp_epi8_mask(a, b, _MM_CMPINT_EQ); + return 0; + } +") + +set(AVX2_CODE " + #include + int main() + { + __m256i a = {0}; + a = _mm256_abs_epi16(a); + __m256i x; + _mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code + return 0; + } +") + +set(FMA_CODE " + #include + int main() + { + __m256 acc = _mm256_setzero_ps(); + const __m256 d = _mm256_setzero_ps(); + const __m256 p = _mm256_setzero_ps(); + acc = _mm256_fmadd_ps( d, p, acc ); + return 0; + } +") + +macro(check_sse type flags) + set(__FLAG_I 1) + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) + foreach (__FLAG ${flags}) + if (NOT ${type}_FOUND) + set(CMAKE_REQUIRED_FLAGS ${__FLAG}) + check_c_source_runs("${${type}_CODE}" HAS_${type}_${__FLAG_I}) + if (HAS_${type}_${__FLAG_I}) + set(${type}_FOUND TRUE CACHE BOOL "${type} support") + set(${type}_FLAGS "${__FLAG}" CACHE STRING "${type} flags") + endif() + math(EXPR __FLAG_I "${__FLAG_I}+1") + endif() + endforeach() + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + + if (NOT ${type}_FOUND) + set(${type}_FOUND FALSE CACHE BOOL "${type} support") + set(${type}_FLAGS "" CACHE STRING "${type} flags") + endif() + + mark_as_advanced(${type}_FOUND ${type}_FLAGS) +endmacro() + +# flags are for MSVC only! +check_sse("AVX" " ;/arch:AVX") +if (NOT ${AVX_FOUND}) + set(LLAMA_AVX OFF) +else() + set(LLAMA_AVX ON) +endif() + +check_sse("AVX2" " ;/arch:AVX2") +check_sse("FMA" " ;/arch:AVX2") +if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND})) + set(LLAMA_AVX2 OFF) +else() + set(LLAMA_AVX2 ON) +endif() + +check_sse("AVX512" " ;/arch:AVX512") +if (NOT ${AVX512_FOUND}) + set(LLAMA_AVX512 OFF) +else() + set(LLAMA_AVX512 ON) +endif() From 3d48f42efcd05381221654376e9f6f69d76af739 Mon Sep 17 00:00:00 2001 From: Meng Zhang Date: Sun, 5 Nov 2023 04:40:08 -0800 Subject: [PATCH 041/664] llama : mark LLM_ARCH_STARCODER as full offload supported (#3945) as done in https://github.com/ggerganov/llama.cpp/pull/3827 --- llama.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index cc0211ceb..e16539000 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5164,11 +5164,12 @@ static int llama_decode_internal( // If all tensors can be run on the GPU then using more than 1 thread is detrimental. const bool full_offload_supported = - model.arch == LLM_ARCH_LLAMA || - model.arch == LLM_ARCH_BAICHUAN || - model.arch == LLM_ARCH_FALCON || - model.arch == LLM_ARCH_REFACT || - model.arch == LLM_ARCH_MPT; + model.arch == LLM_ARCH_LLAMA || + model.arch == LLM_ARCH_BAICHUAN || + model.arch == LLM_ARCH_FALCON || + model.arch == LLM_ARCH_REFACT || + model.arch == LLM_ARCH_MPT || + model.arch == LLM_ARCH_STARCODER; const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3; if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) { From 132d25b8a62ea084447e0014a0112c1b371fb3f8 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sun, 5 Nov 2023 10:08:57 -0500 Subject: [PATCH 042/664] cuda : fix disabling device with --tensor-split 1,0 (#3951) Co-authored-by: slaren --- ggml-cuda.cu | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index dc14f2f5d..9f873035a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6893,6 +6893,8 @@ static void ggml_cuda_op_mul_mat( int64_t row_low[GGML_CUDA_MAX_DEVICES]; int64_t row_high[GGML_CUDA_MAX_DEVICES]; + int used_devices = 0; + for (int64_t id = 0; id < g_device_count; ++id) { // by default, use all rows row_low[id] = 0; @@ -6920,6 +6922,8 @@ static void ggml_cuda_op_mul_mat( continue; } + used_devices++; + const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device; const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device; @@ -6958,12 +6962,12 @@ static void ggml_cuda_op_mul_mat( // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data - if (split && g_device_count > 1) { + if (split && used_devices > 1) { CUDA_CHECK(ggml_cuda_set_device(g_main_device)); CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0])); } - const int64_t src1_col_stride = split && g_device_count > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; + const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; @@ -7079,6 +7083,9 @@ static void ggml_cuda_op_mul_mat( } for (int64_t id = 0; id < g_device_count; ++id) { + if ((!split && id != g_main_device) || row_low[id] == row_high[id]) { + continue; + } CUDA_CHECK(ggml_cuda_set_device(id)); // free buffers again when done @@ -7103,6 +7110,9 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(g_main_device)); for (int64_t id = 0; id < g_device_count; ++id) { + if (row_low[id] == row_high[id]) { + continue; + } for (int64_t is = 0; is < is_max; ++is) { CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0)); } @@ -7400,7 +7410,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = - (src0->backend == GGML_BACKEND_GPU) && + (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && (src1->backend == GGML_BACKEND_GPU) && ( dst->backend == GGML_BACKEND_GPU); From bb60fd0bf6bb270744d86dd45b3a95af01b7de45 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A1i=20Ho=C3=A0ng=20T=C3=A2m?= <75922889+RoyalHeart@users.noreply.github.com> Date: Sun, 5 Nov 2023 23:15:27 +0700 Subject: [PATCH 043/664] server : fix typo for --alias shortcut from -m to -a (#3958) --- examples/server/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 715007735..089ebe2d1 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -7,7 +7,7 @@ Command line options: - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). -- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. +- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. - `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. From d9ccce2e339ca0396560d18b8637f2c848d72a08 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 5 Nov 2023 10:06:06 -0700 Subject: [PATCH 044/664] Allow common process_escapes to handle \x sequences (#3928) * Allow common process_escapes to handle \x sequences * Fix edge case when second hex digit is NUL --- common/common.cpp | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 20cc4a081..37e3ace8a 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -90,6 +90,19 @@ void process_escapes(std::string& input) { case '\'': input[output_idx++] = '\''; break; case '\"': input[output_idx++] = '\"'; break; case '\\': input[output_idx++] = '\\'; break; + case 'x': + // Handle \x12, etc + if (input_idx + 2 < input_len) { + const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; + char *err_p = nullptr; + const long val = std::strtol(x, &err_p, 16); + if (err_p == x + 2) { + input_idx += 2; + input[output_idx++] = char(val); + break; + } + // Intentionally fall through to default. + } default: input[output_idx++] = '\\'; input[output_idx++] = input[input_idx]; break; } From 2833a6f63c1b87c7f4ac574bcf7a15a2f3bf3ede Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 5 Nov 2023 18:45:16 +0100 Subject: [PATCH 045/664] ggml-cuda : fix f16 mul mat (#3961) * ggml-cuda : fix f16 mul mat ggml-ci * silence common.cpp warning (bonus) --- common/common.cpp | 2 +- ggml-cuda.cu | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 37e3ace8a..6a7114200 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -101,8 +101,8 @@ void process_escapes(std::string& input) { input[output_idx++] = char(val); break; } - // Intentionally fall through to default. } + // fall through default: input[output_idx++] = '\\'; input[output_idx++] = input[input_idx]; break; } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9f873035a..2d9ffffbf 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7414,6 +7414,8 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 (src1->backend == GGML_BACKEND_GPU) && ( dst->backend == GGML_BACKEND_GPU); + const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + int64_t min_compute_capability = INT_MAX; for (int64_t id = 0; id < g_device_count; ++id) { if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { @@ -7435,13 +7437,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { // KQ single-batch ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { From 381efbf480959bb6d1e247a8b0c2328f22e350f8 Mon Sep 17 00:00:00 2001 From: Damian Stewart Date: Mon, 6 Nov 2023 22:36:23 +0100 Subject: [PATCH 046/664] llava : expose as a shared library for downstream projects (#3613) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * wip llava python bindings compatibility * add external llava API * add base64 in-prompt image support * wip refactor image loading * refactor image load out of llava init * cleanup * further cleanup; move llava-cli into its own file and rename * move base64.hpp into common/ * collapse clip and llava libraries * move llava into its own subdir * wip * fix bug where base64 string was not removed from the prompt * get libllava to output in the right place * expose llava methods in libllama.dylib * cleanup memory usage around clip_image_* * cleanup and refactor *again* * update headerdoc * build with cmake, not tested (WIP) * Editorconfig * Editorconfig * Build with make * Build with make * Fix cyclical depts on Windows * attempt to fix build on Windows * attempt to fix build on Windows * Upd TODOs * attempt to fix build on Windows+CUDA * Revert changes in cmake * Fix according to review comments * Support building as a shared library * address review comments --------- Co-authored-by: M. Yusuf Sarıgöz Co-authored-by: Jared Van Bortel --- .gitignore | 2 +- Makefile | 7 +- common/CMakeLists.txt | 1 + common/base64.hpp | 392 +++++++++++++++++++++++++++++++++ examples/llava/CMakeLists.txt | 46 +++- examples/llava/README.md | 7 +- examples/llava/clip.cpp | 86 +++++--- examples/llava/clip.h | 41 +++- examples/llava/llava-cli.cpp | 315 ++++++++++++++++++++++++++ examples/llava/llava-utils.h | 147 ------------- examples/llava/llava.cpp | 280 ++++++++++++----------- examples/llava/llava.h | 50 +++++ examples/server/CMakeLists.txt | 2 +- 13 files changed, 1022 insertions(+), 354 deletions(-) create mode 100644 common/base64.hpp create mode 100644 examples/llava/llava-cli.cpp delete mode 100644 examples/llava/llava-utils.h create mode 100644 examples/llava/llava.h diff --git a/.gitignore b/.gitignore index 50cbd0b47..708e8582e 100644 --- a/.gitignore +++ b/.gitignore @@ -46,7 +46,7 @@ models-mnt /infill /libllama.so /llama-bench -/llava +/llava-cli /main /metal /perplexity diff --git a/Makefile b/Makefile index 300c1e6c7..f2d4fd031 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf llama-bench llava baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o # Binaries only useful for tests @@ -617,7 +617,10 @@ convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggm llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ $(LDFLAGS) -Wno-cast-qual + +llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index ac594b2ca..4f930bdc5 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -41,6 +41,7 @@ endif() set(TARGET common) add_library(${TARGET} STATIC + base64.hpp common.h common.cpp sampling.h diff --git a/common/base64.hpp b/common/base64.hpp new file mode 100644 index 000000000..563247a6e --- /dev/null +++ b/common/base64.hpp @@ -0,0 +1,392 @@ +/* +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR +OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +For more information, please refer to +*/ + +#ifndef PUBLIC_DOMAIN_BASE64_HPP_ +#define PUBLIC_DOMAIN_BASE64_HPP_ + +#include +#include +#include +#include + +class base64_error : public std::runtime_error +{ +public: + using std::runtime_error::runtime_error; +}; + +class base64 +{ +public: + enum class alphabet + { + /** the alphabet is detected automatically */ + auto_, + /** the standard base64 alphabet is used */ + standard, + /** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/ + url_filename_safe + }; + + enum class decoding_behavior + { + /** if the input is not padded, the remaining bits are ignored */ + moderate, + /** if a padding character is encounter decoding is finished */ + loose + }; + + /** + Encodes all the elements from `in_begin` to `in_end` to `out`. + + @warning The source and destination cannot overlap. The destination must be able to hold at least + `required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator. + + @tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than + 8 bits + @tparam Output_iterator the destination; the elements written to it are from the type `char` + @param in_begin the beginning of the source + @param in_end the ending of the source + @param out the destination iterator + @param alphabet which alphabet should be used + @returns the iterator to the next element past the last element copied + @throws see `Input_iterator` and `Output_iterator` + */ + template + static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out, + alphabet alphabet = alphabet::standard) + { + constexpr auto pad = '='; + const char* alpha = alphabet == alphabet::url_filename_safe + ? "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_" + : "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; + + while (in_begin != in_end) { + std::uint8_t i0 = 0, i1 = 0, i2 = 0; + + // first character + i0 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[i0 >> 2 & 0x3f]; + ++out; + + // part of first character and second + if (in_begin != in_end) { + i1 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)]; + ++out; + } else { + *out = alpha[(i0 & 0x3) << 4]; + ++out; + + // last padding + *out = pad; + ++out; + + // last padding + *out = pad; + ++out; + + break; + } + + // part of second character and third + if (in_begin != in_end) { + i2 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)]; + ++out; + } else { + *out = alpha[(i1 & 0xf) << 2]; + ++out; + + // last padding + *out = pad; + ++out; + + break; + } + + // rest of third + *out = alpha[i2 & 0x3f]; + ++out; + } + + return out; + } + /** + Encodes a string. + + @param str the string that should be encoded + @param alphabet which alphabet should be used + @returns the encoded base64 string + @throws see base64::encode() + */ + static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard) + { + std::string result; + + result.reserve(required_encode_size(str.length()) + 1); + + encode(str.begin(), str.end(), std::back_inserter(result), alphabet); + + return result; + } + /** + Encodes a char array. + + @param buffer the char array + @param size the size of the array + @param alphabet which alphabet should be used + @returns the encoded string + */ + static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard) + { + std::string result; + + result.reserve(required_encode_size(size) + 1); + + encode(buffer, buffer + size, std::back_inserter(result), alphabet); + + return result; + } + /** + Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`, + in other words: inplace decoding is possible. + + @warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`, + otherwise the behavior depends on the output iterator. + + @tparam Input_iterator the source; the returned elements are cast to `char` + @tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t` + @param in_begin the beginning of the source + @param in_end the ending of the source + @param out the destination iterator + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the iterator to the next element past the last element copied + @throws base64_error depending on the set behavior + @throws see `Input_iterator` and `Output_iterator` + */ + template + static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out, + alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + //constexpr auto pad = '='; + std::uint8_t last = 0; + auto bits = 0; + + while (in_begin != in_end) { + auto c = *in_begin; + ++in_begin; + + if (c == '=') { + break; + } + + auto part = _base64_value(alphabet, c); + + // enough bits for one byte + if (bits + 6 >= 8) { + *out = (last << (8 - bits)) | (part >> (bits - 2)); + ++out; + + bits -= 2; + } else { + bits += 6; + } + + last = part; + } + + // check padding + if (behavior != decoding_behavior::loose) { + while (in_begin != in_end) { + auto c = *in_begin; + ++in_begin; + + if (c != '=') { + throw base64_error("invalid base64 character."); + } + } + } + + return out; + } + /** + Decodes a string. + + @param str the base64 encoded string + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the decoded string + @throws see base64::decode() + */ + static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + std::string result; + + result.reserve(max_decode_size(str.length())); + + decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior); + + return result; + } + /** + Decodes a string. + + @param buffer the base64 encoded buffer + @param size the size of the buffer + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the decoded string + @throws see base64::decode() + */ + static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + std::string result; + + result.reserve(max_decode_size(size)); + + decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior); + + return result; + } + /** + Decodes a string inplace. + + @param[in,out] str the base64 encoded string + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @throws base64::decode_inplace() + */ + static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin()); + } + /** + Decodes a char array inplace. + + @param[in,out] str the string array + @param size the length of the array + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the pointer to the next element past the last element decoded + @throws base64::decode_inplace() + */ + static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + return decode(str, str + size, str, alphabet, behavior); + } + /** + Returns the required decoding size for a given size. The value is calculated with the following formula: + + $$ + \lceil \frac{size}{4} \rceil \cdot 3 + $$ + + @param size the size of the encoded input + @returns the size of the resulting decoded buffer; this the absolute maximum + */ + static std::size_t max_decode_size(std::size_t size) noexcept + { + return (size / 4 + (size % 4 ? 1 : 0)) * 3; + } + /** + Returns the required encoding size for a given size. The value is calculated with the following formula: + + $$ + \lceil \frac{size}{3} \rceil \cdot 4 + $$ + + @param size the size of the decoded input + @returns the size of the resulting encoded buffer + */ + static std::size_t required_encode_size(std::size_t size) noexcept + { + return (size / 3 + (size % 3 ? 1 : 0)) * 4; + } + +private: + static std::uint8_t _base64_value(alphabet& alphabet, char c) + { + if (c >= 'A' && c <= 'Z') { + return c - 'A'; + } else if (c >= 'a' && c <= 'z') { + return c - 'a' + 26; + } else if (c >= '0' && c <= '9') { + return c - '0' + 52; + } + + // comes down to alphabet + if (alphabet == alphabet::standard) { + if (c == '+') { + return 62; + } else if (c == '/') { + return 63; + } + } else if (alphabet == alphabet::url_filename_safe) { + if (c == '-') { + return 62; + } else if (c == '_') { + return 63; + } + } // auto detect + else { + if (c == '+') { + alphabet = alphabet::standard; + + return 62; + } else if (c == '/') { + alphabet = alphabet::standard; + + return 63; + } else if (c == '-') { + alphabet = alphabet::url_filename_safe; + + return 62; + } else if (c == '_') { + alphabet = alphabet::url_filename_safe; + + return 63; + } + } + + throw base64_error("invalid base64 character."); + } +}; + +#endif // !PUBLIC_DOMAIN_BASE64_HPP_ diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 03d32c26e..8ea3e5c83 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -1,14 +1,36 @@ -set(TARGET clip) -add_library(${TARGET} clip.cpp clip.h) -install(TARGETS ${TARGET} LIBRARY) -target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) -if (NOT MSVC) - target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h +add_library(llava OBJECT + llava.cpp + llava.h + clip.cpp + clip.h + ) + +target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT}) + +target_include_directories(llava PUBLIC .) +target_include_directories(llava PUBLIC ../..) +target_include_directories(llava PUBLIC ../../common) + +target_compile_features(llava PRIVATE cxx_std_11) + +add_library(llava_static STATIC $) +if (BUILD_SHARED_LIBS) + set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD) + add_library(llava_shared SHARED $) + target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT}) + install(TARGETS llava_shared LIBRARY) endif() -set(TARGET llava) -add_executable(${TARGET} llava.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +if (NOT MSVC) + target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h + endif() +if(TARGET BUILD_INFO) + add_dependencies(llava BUILD_INFO) +endif() + +set(TARGET llava-cli) +add_executable(llava-cli llava-cli.cpp) +install(TARGETS llava-cli RUNTIME) +target_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(llava PRIVATE cxx_std_11) diff --git a/examples/llava/README.md b/examples/llava/README.md index fc3446b60..323c5fdd0 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -9,12 +9,12 @@ models are available. After API is confirmed, more models will be supported / uploaded. ## Usage -Build with cmake or run `make llava` to build it. +Build with cmake or run `make llava-cli` to build it. -After building, run: `./llava` to see the usage. For example: +After building, run: `./llava-cli` to see the usage. For example: ```sh -./llava -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg +./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. @@ -51,7 +51,6 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director ## TODO -- [ ] Support server mode. - [ ] Support non-CPU backend for the image encoding part. - [ ] Support different sampling methods. - [ ] Support more model variants. diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 61932e659..3c909c7d3 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -680,26 +680,44 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return new_clip; } -clip_image_u8 * make_clip_image_u8() { return new clip_image_u8(); } - +clip_image_u8 * make_clip_image_u8() { + auto img = new clip_image_u8(); + return img; +} clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); } -bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { - int nx, ny, nc; - auto data = stbi_load(fname, &nx, &ny, &nc, 3); - if (!data) { - fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname); - return false; - } +void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; } +void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; } +static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) { img->nx = nx; img->ny = ny; img->size = nx * ny * 3; img->data = new uint8_t[img->size](); memcpy(img->data, data, img->size); +} +bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { + int nx, ny, nc; + auto data = stbi_load(fname, &nx, &ny, &nc, 3); + if (!data) { + fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); + return false; + } + build_clip_img_from_data(data, nx, ny, img); stbi_image_free(data); + return true; +} +bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) { + int nx, ny, nc; + auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); + if (!data) { + fprintf(stderr, "%s: failed to decode image bytes\n", __func__); + return false; + } + build_clip_img_from_data(data, nx, ny, img); + stbi_image_free(data); return true; } @@ -714,39 +732,40 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 - clip_image_u8 temp; // we will keep the input image data here temporarily + clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily if (pad2square && img->nx != img->ny) { int longer_side = std::max(img->nx, img->ny); - temp.nx = longer_side; - temp.ny = longer_side; - temp.size = 3 * longer_side * longer_side; - temp.data = new uint8_t[temp.size](); + temp->nx = longer_side; + temp->ny = longer_side; + temp->size = 3 * longer_side * longer_side; + temp->data = new uint8_t[temp->size](); uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA // fill with background color - for (size_t i = 0; i < temp.size; i++) { - temp.data[i] = bc[i % 3]; + for (size_t i = 0; i < temp->size; i++) { + temp->data[i] = bc[i % 3]; } // copy from the input image for (int y = 0; y < img->ny; y++) { for (int x = 0; x < img->nx; x++) { const int i = 3 * (y * img->nx + x); - const int j = 3 * (y * temp.nx + x); - temp.data[j] = img->data[i]; - temp.data[j+1] = img->data[i+1]; - temp.data[j+2] = img->data[i+2]; + const int j = 3 * (y * temp->nx + x); + temp->data[j] = img->data[i]; + temp->data[j+1] = img->data[i+1]; + temp->data[j+2] = img->data[i+2]; } } } else { - temp.nx = img->nx; - temp.ny = img->ny; - temp.size = img->size; - temp.data = img->data; + temp->nx = img->nx; + temp->ny = img->ny; + temp->size = img->size; + temp->data = new uint8_t[temp->size](); + *temp->data = *img->data; // copy } - const int nx = temp.nx; - const int ny = temp.ny; + const int nx = temp->nx; + const int ny = temp->ny; const int nx2 = ctx->vision_model.hparams.image_size; const int ny2 = ctx->vision_model.hparams.image_size; @@ -785,10 +804,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip const int j10 = 3 * (y1 * nx + x0) + c; const int j11 = 3 * (y1 * nx + x1) + c; - const float v00 = temp.data[j00]; - const float v01 = temp.data[j01]; - const float v10 = temp.data[j10]; - const float v11 = temp.data[j11]; + const float v00 = temp->data[j00]; + const float v01 = temp->data[j01]; + const float v10 = temp->data[j10]; + const float v11 = temp->data[j11]; const float v0 = v00 * (1.0f - dx) + v01 * dx; const float v1 = v10 * (1.0f - dx) + v11 * dx; @@ -803,6 +822,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip } } } + clip_image_u8_free(temp); return true; } @@ -1049,16 +1069,16 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i return true; } -int clip_n_mmproj_embd(struct clip_ctx * ctx) { +int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->vision_model.mm_2_b->ne[0]; } -int clip_n_patches(struct clip_ctx * ctx) { +int clip_n_patches(const struct clip_ctx * ctx) { auto & params = ctx->vision_model.hparams; return (params.image_size / params.patch_size) * (params.image_size / params.patch_size); } -size_t clip_embd_nbytes(struct clip_ctx * ctx) { +size_t clip_embd_nbytes(const struct clip_ctx * ctx) { return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); } diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 3d7261e29..f11df85de 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -1,7 +1,22 @@ #ifndef CLIP_H #define CLIP_H -#include "ggml.h" +#include +#include + +#ifdef LLAMA_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef LLAMA_BUILD +# define CLIP_API __declspec(dllexport) +# else +# define CLIP_API __declspec(dllimport) +# endif +# else +# define CLIP_API __attribute__ ((visibility ("default"))) +# endif +#else +# define CLIP_API +#endif struct clip_ctx; @@ -20,19 +35,20 @@ struct clip_vision_hparams { float eps; }; -struct clip_ctx * clip_model_load(const char * fname, const int verbosity); +/** load mmproj model */ +CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity); +/** free mmproj model */ +CLIP_API void clip_free(struct clip_ctx * ctx); -void clip_free(struct clip_ctx * ctx); - -size_t clip_embd_nbytes(struct clip_ctx * ctx); -int clip_n_patches(struct clip_ctx * ctx); -int clip_n_mmproj_embd(struct clip_ctx * ctx); +size_t clip_embd_nbytes(const struct clip_ctx * ctx); +int clip_n_patches(const struct clip_ctx * ctx); +int clip_n_mmproj_embd(const struct clip_ctx * ctx); // RGB uint8 image struct clip_image_u8 { int nx; int ny; - uint8_t * data; + uint8_t * data = NULL; size_t size; }; @@ -41,7 +57,7 @@ struct clip_image_u8 { struct clip_image_f32 { int nx; int ny; - float * data; + float * data = NULL; size_t size; }; @@ -57,7 +73,12 @@ struct clip_image_f32_batch { struct clip_image_u8 * make_clip_image_u8(); struct clip_image_f32 * make_clip_image_f32(); -bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); +CLIP_API void clip_image_u8_free(clip_image_u8 * img); +CLIP_API void clip_image_f32_free(clip_image_f32 * img); +CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); +/** interpret bytes as an image file with length bytes_length, and use the result to populate img */ +CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); + bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square); bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp new file mode 100644 index 000000000..19374c67f --- /dev/null +++ b/examples/llava/llava-cli.cpp @@ -0,0 +1,315 @@ +#include "ggml.h" +#include "common.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" + +#include "base64.hpp" + +#include +#include +#include + +static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) { + int N = (int) tokens.size(); + for (int i = 0; i < N; i += n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); + return false; + } + *n_past += n_eval; + } + return true; +} + +static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { + std::vector tokens; + tokens.push_back(id); + return eval_tokens(ctx_llama, tokens, 1, n_past); +} + +static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ + std::string str2 = str; + std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); + eval_tokens(ctx_llama, embd_inp, n_batch, n_past); + return true; +} + +// TODO: use common/sampling.h +static llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { + auto & sparams = params.sparams; + + // out of user input, sample next token + const float temp = sparams.temp; + const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k; + const float top_p = sparams.top_p; + const float tfs_z = sparams.tfs_z; + const float typical_p = sparams.typical_p; + // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n; + // const float repeat_penalty = sparams.repeat_penalty; + // const float alpha_presence = sparams.presence_penalty; + // const float alpha_frequency = sparams.frequency_penalty; + const int mirostat = sparams.mirostat; + const float mirostat_tau = sparams.mirostat_tau; + const float mirostat_eta = sparams.mirostat_eta; + // const bool penalize_nl = sparams.penalize_nl; + + llama_token id = 0; + { + auto logits = llama_get_logits(ctx_llama); + auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama)); + + // Apply params.logit_bias map + for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + std::vector candidates; + candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx_llama, &candidates_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temp(ctx_llama, &candidates_p, temp); + id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temp(ctx_llama, &candidates_p, temp); + id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1); + llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1); + llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1); + llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1); + llama_sample_temp(ctx_llama, &candidates_p, temp); + id = llama_sample_token(ctx_llama, &candidates_p); + } + } + } + + return id; +} + +static const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) { + int id = sample_id(ctx_llama, params); + static std::string ret; + if (id == llama_token_eos(llama_get_model(ctx_llama))) { + ret = ""; + } else { + ret = llama_token_to_piece(ctx_llama, id); + } + eval_id(ctx_llama, id, n_past); + return ret.c_str(); +} + +static const char* IMG_BASE64_TAG_BEGIN = ""; + +static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) { + begin_out = prompt.find(IMG_BASE64_TAG_BEGIN); + end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out); +} + +static bool prompt_contains_image(const std::string& prompt) { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + return (begin != std::string::npos); +} + +// replaces the base64 image tag in the prompt with `replacement` +static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) { + size_t img_base64_str_start, img_base64_str_end; + find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end); + if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) { + fprintf(stderr, "%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + return NULL; + } + + auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN); + auto base64_bytes_count = img_base64_str_end - base64_bytes_start; + auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count ); + + auto required_bytes = base64::required_encode_size(base64_str.size()); + auto img_bytes = std::vector(required_bytes); + base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin()); + + auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); + if (!embed) { + fprintf(stderr, "%s: could not load image from base64 string.\n", __func__); + return NULL; + } + + return embed; +} + +static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + if (begin == std::string::npos || end == std::string::npos) { + return prompt; + } + auto pre = prompt.substr(0, begin); + auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END)); + return pre + replacement + post; +} + +struct llava_context { + struct clip_ctx * ctx_clip = NULL; + struct llama_context * ctx_llama = NULL; + struct llama_model * model = NULL; +}; + +static void show_additional_info(int /*argc*/, char ** argv) { + printf("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); +} + +static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { + + // load and preprocess the image + llava_image_embed * embed = NULL; + auto prompt = params->prompt; + if (prompt_contains_image(prompt)) { + if (!params->image.empty()) { + printf("using base64 encoded image instead of command line image path\n"); + } + embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); + if (!embed) { + fprintf(stderr, "%s: can't load image from prompt\n", __func__); + return NULL; + } + params->prompt = remove_image_from_prompt(prompt); + } else { + embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str()); + if (!embed) { + fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str()); + return NULL; + } + } + + return embed; +} + +static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) { + int n_past = 0; + + const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; + + // llava chat format is "\nUSER:\n\nASSISTANT:" + eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, true); + llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); + eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false); + + // generate the response + + printf("\n"); + + for (int i = 0; i < max_tgt_len; i++) { + const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past); + if (strcmp(tmp, "") == 0) break; + + printf("%s", tmp); + fflush(stdout); + } + + printf("\n"); +} + + +static struct llava_context * llava_init(gpt_params * params) { + const char * clip_path = params->mmproj.c_str(); + + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + + auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + + llama_backend_init(params->numa); + + llama_model_params model_params = llama_model_default_params(); + llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); + if (model == NULL) { + fprintf(stderr , "%s: error: unable to load model\n" , __func__); + return NULL; + } + + llama_context_params ctx_params = llama_context_default_params(); + + ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings + ctx_params.n_threads = params->n_threads; + ctx_params.n_threads_batch = params->n_threads_batch == -1 ? params->n_threads : params->n_threads_batch; + + llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); + + if (ctx_llama == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return NULL; + } + + auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); + + ctx_llava->ctx_llama = ctx_llama; + ctx_llava->ctx_clip = ctx_clip; + ctx_llava->model = model; + return ctx_llava; +} + +static void llava_free(struct llava_context * ctx_llava) { + if (ctx_llava->ctx_clip) { + clip_free(ctx_llava->ctx_clip); + ctx_llava->ctx_clip = NULL; + } + + llama_free(ctx_llava->ctx_llama); + llama_free_model(ctx_llava->model); + llama_backend_free(); +} + +int main(int argc, char ** argv) { + ggml_time_init(); + + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + show_additional_info(argc, argv); + return 1; + } + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { + gpt_print_usage(argc, argv, params); + show_additional_info(argc, argv); + return 1; + } + + auto ctx_llava = llava_init(¶ms); + if (ctx_llava == NULL) { + fprintf(stderr, "%s: error: failed to init llava\n", __func__); + return 1; + } + + auto image_embed = load_image(ctx_llava, ¶ms); + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_print_timings(ctx_llava->ctx_llama); + + llava_image_embed_free(image_embed); + llava_free(ctx_llava); + return 0; +} diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h deleted file mode 100644 index 320c71967..000000000 --- a/examples/llava/llava-utils.h +++ /dev/null @@ -1,147 +0,0 @@ -#pragma once - -// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now - -#include "common.h" -#include "llama.h" - -#include -#include -#include - -inline bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) { - int n_embd = llama_n_embd(llama_get_model(ctx_llama)); - - for (int i = 0; i < N; i += n_batch) { - int n_eval = N - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; - if (llama_decode(ctx_llama, batch)) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return false; - } - *n_past += n_eval; - } - return true; -} - -inline bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) { - int N = (int) tokens.size(); - for (int i = 0; i < N; i += n_batch) { - int n_eval = (int) tokens.size() - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return false; - } - *n_past += n_eval; - } - return true; -} - -inline bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { - std::vector tokens; - tokens.push_back(id); - return eval_tokens(ctx_llama, tokens, 1, n_past); -} - -inline bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ - std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); - eval_tokens(ctx_llama, embd_inp, n_batch, n_past); - return true; -} - -// TODO: use common/sampling.h -inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { - auto & sparams = params.sparams; - - // out of user input, sample next token - const float temp = sparams.temp; - const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k; - const float top_p = sparams.top_p; - const float tfs_z = sparams.tfs_z; - const float typical_p = sparams.typical_p; - // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n; - // const float repeat_penalty = sparams.repeat_penalty; - // const float alpha_presence = sparams.presence_penalty; - // const float alpha_frequency = sparams.frequency_penalty; - const int mirostat = sparams.mirostat; - const float mirostat_tau = sparams.mirostat_tau; - const float mirostat_eta = sparams.mirostat_eta; - // const bool penalize_nl = sparams.penalize_nl; - - llama_token id = 0; - { - auto logits = llama_get_logits(ctx_llama); - auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama)); - - // Apply params.logit_bias map - for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - // TODO: Apply penalties - // float nl_logit = logits[llama_token_nl(ctx)]; - // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - // llama_sample_repetition_penalty(ctx, &candidates_p, - // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - // last_n_repeat, repeat_penalty); - // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, - // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - // last_n_repeat, alpha_frequency, alpha_presence); - // if (!penalize_nl) { - // logits[llama_token_nl(ctx)] = nl_logit; - // } - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx_llama, &candidates_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1); - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token(ctx_llama, &candidates_p); - } - } - } - - return id; -} - -inline const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) { - int id = sample_id(ctx_llama, params); - static std::string ret; - if (id == llama_token_eos(llama_get_model(ctx_llama))) { - ret = ""; - } else { - ret = llama_token_to_piece(ctx_llama, id); - } - eval_id(ctx_llama, id, n_past); - return ret.c_str(); -} diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index f0974d5bc..d10bcf2d2 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -1,164 +1,156 @@ #include "clip.h" -#include "llava-utils.h" #include "common.h" #include "llama.h" +#include "llava.h" #include #include #include -static void show_additional_info(int /*argc*/, char ** argv) { - printf("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); -} +#include "base64.hpp" -int main(int argc, char ** argv) { - ggml_time_init(); - - gpt_params params; - - if (!gpt_params_parse(argc, argv, params)) { - show_additional_info(argc, argv); - return 1; +static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { + clip_image_f32 * img_res = make_clip_image_f32(); + if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) { + fprintf(stderr, "%s: unable to preprocess image\n", __func__); + clip_image_f32_free(img_res); + return false; } - if (params.mmproj.empty() || params.image.empty()) { - gpt_print_usage(argc, argv, params); - show_additional_info(argc, argv); - return 1; - } - - const char * clip_path = params.mmproj.c_str(); - const char * img_path = params.image.c_str(); - - if (params.prompt.empty()) { - params.prompt = "describe the image in detail."; - } - - auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); - - // load and preprocess the image - clip_image_u8 img; - clip_image_f32 img_res; - - if (!clip_image_load_from_file(img_path, &img)) { - fprintf(stderr, "%s: is %s really an image file?\n", __func__, img_path); - - clip_free(ctx_clip); - return 1; - } - - if (!clip_image_preprocess(ctx_clip, &img, &img_res, /*pad2square =*/ true)) { - fprintf(stderr, "%s: unable to preprocess %s\n", __func__, img_path); - - clip_free(ctx_clip); - return 1; - } - - int n_img_pos = clip_n_patches(ctx_clip); - int n_img_embd = clip_n_mmproj_embd(ctx_clip); - - float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)); - - if (!image_embd) { - fprintf(stderr, "Unable to allocate memory for image embeddings\n"); - - return 1; - } + *n_img_pos = clip_n_patches(ctx_clip); const int64_t t_img_enc_start_us = ggml_time_us(); - if (!clip_image_encode(ctx_clip, params.n_threads, &img_res, image_embd)) { + bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); + clip_image_f32_free(img_res); + if (!encoded) { fprintf(stderr, "Unable to encode image\n"); - return 1; + return false; } + const int64_t t_img_enc_end_us = ggml_time_us(); + float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - // we get the embeddings, free up the memory required for CLIP - clip_free(ctx_clip); + printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); - llama_backend_init(params.numa); - - llama_model_params model_params = llama_model_default_params(); - model_params.n_gpu_layers = params.n_gpu_layers; - model_params.main_gpu = params.main_gpu; - model_params.tensor_split = params.tensor_split; - model_params.use_mmap = params.use_mmap; - model_params.use_mlock = params.use_mlock; - - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); - if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); - return 1; - } - - llama_context_params ctx_params = llama_context_default_params(); - - ctx_params.n_ctx = params.n_ctx < 2048 ? 2048 : params.n_ctx; // we need a longer context size to process image embeddings - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - ctx_params.seed = params.seed; - - llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); - - if (ctx_llama == NULL) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); - return 1; - } - - // make sure that the correct mmproj was used, i.e., compare apples to apples - const int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); - - if (n_img_embd != n_llama_embd) { - printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_llama_embd); - - llama_free(ctx_llama); - llama_free_model(model); - llama_backend_free(); - free(image_embd); - - return 1; - } - - // process the prompt - // llava chat format is "USER: \n\nASSISTANT:" - - int n_past = 0; - - const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict; - - eval_string(ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params.n_batch, &n_past, true); - eval_image_embd(ctx_llama, image_embd, n_img_pos, params.n_batch, &n_past); - eval_string(ctx_llama, (params.prompt + "\nASSISTANT:").c_str(), params.n_batch, &n_past, false); - - // generate the response - - printf("\n"); - printf("prompt: '%s'\n", params.prompt.c_str()); - printf("\n"); - - for (int i = 0; i < max_tgt_len; i++) { - const char * tmp = sample(ctx_llama, params, &n_past); - if (strcmp(tmp, "") == 0) break; - - printf("%s", tmp); - fflush(stdout); - } - - printf("\n"); - - { - const float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - - printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / n_img_pos); - } - - llama_print_timings(ctx_llama); - - llama_free(ctx_llama); - llama_free_model(model); - llama_backend_free(); - free(image_embd); - - return 0; + return true; +} + +bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) { + // make sure that the correct mmproj was used, i.e., compare apples to apples + int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); + auto n_image_embd = clip_n_mmproj_embd(ctx_clip); + if (n_image_embd != n_llama_embd) { + printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); + return false; + } + return true; +} + +static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { + float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)); + if (!image_embd) { + fprintf(stderr, "Unable to allocate memory for image embeddings\n"); + free(image_embd); + return false; + } + + int n_img_pos; + if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) { + fprintf(stderr, "%s: cannot encode image, aborting\n", __func__); + free(image_embd); + return false; + } + *image_embd_out = image_embd; + *n_img_pos_out = n_img_pos; + + return true; +} + +bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { + int n_embd = llama_n_embd(llama_get_model(ctx_llama)); + + for (int i = 0; i < image_embed->n_image_pos; i += n_batch) { + int n_eval = image_embed->n_image_pos - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; + if (llama_decode(ctx_llama, batch)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + *n_past += n_eval; + } + return true; +} + +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { + clip_image_u8 * img = make_clip_image_u8(); + if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { + clip_image_u8_free(img); + fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); + return NULL; + } + + float* image_embed = NULL; + int n_image_pos = 0; + bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); + if (!image_embed_result) { + clip_image_u8_free(img); + fprintf(stderr, "%s: coulnd't embed the image\n", __func__); + return NULL; + } + + clip_image_u8_free(img); + auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed)); + result->embed = image_embed; + result->n_image_pos = n_image_pos; + return result; +} + +static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) { + auto file = fopen(path, "rb"); + if (file == NULL) { + fprintf(stderr, "%s: can't read file %s\n", __func__, path); + return false; + } + + fseek(file, 0, SEEK_END); + auto fileSize = ftell(file); + fseek(file, 0, SEEK_SET); + + auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data + if (buffer == NULL) { + fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); + perror("Memory allocation error"); + fclose(file); + return false; + } + fread(buffer, 1, fileSize, file); // Read the file into the buffer + fclose(file); // Close the file + + *bytesOut = buffer; + *sizeOut = fileSize; + return true; +} + +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) { + unsigned char* image_bytes; + long image_bytes_length; + auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); + if (!loaded) { + fprintf(stderr, "%s: failed to load %s\n", __func__, image_path); + return NULL; + } + + auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length); + free(image_bytes); + + return embed; +} + +LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) { + free(embed->embed); + free(embed); } diff --git a/examples/llava/llava.h b/examples/llava/llava.h new file mode 100644 index 000000000..e08ce7883 --- /dev/null +++ b/examples/llava/llava.h @@ -0,0 +1,50 @@ +#ifndef LLAVA_H +#define LLAVA_H + +#include "ggml.h" + + +#ifdef LLAMA_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef LLAMA_BUILD +# define LLAVA_API __declspec(dllexport) +# else +# define LLAVA_API __declspec(dllimport) +# endif +# else +# define LLAVA_API __attribute__ ((visibility ("default"))) +# endif +#else +# define LLAVA_API +#endif + +struct clip_ctx; + +#ifdef __cplusplus +extern "C" { +#endif + +struct llava_image_embed { + float * embed; + int n_image_pos; +}; + +/** sanity check for clip <-> llava embed size match */ +LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip); + +/** build an image embed from image file bytes */ +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length); +/** build an image embed from a path to an image filename */ +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path); +LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); +/** free an embedding made with llava_image_embed_make_* */ + +/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ +LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past); + + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 1f0d26f77..859cd12c6 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() From 46876d2a2c92e60579dc732cdb8cbd243b06f317 Mon Sep 17 00:00:00 2001 From: Meng Zhang Date: Mon, 6 Nov 2023 22:49:08 -0800 Subject: [PATCH 047/664] cuda : supports running on CPU for GGML_USE_CUBLAS=ON build (#3946) * protyping the idea that supports running on CPU for a GGML_USE_CUBLAS=on build * doc: add comments to ggml_cublas_loaded() * fix defined(...) --- ggml-cuda.cu | 17 ++++- ggml-cuda.h | 5 ++ llama.cpp | 181 ++++++++++++++++++++++++++++++--------------------- 3 files changed, 127 insertions(+), 76 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2d9ffffbf..f87f18802 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5790,6 +5790,11 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { CUDA_CHECK(cudaFree(ptr)); } +static bool g_cublas_loaded = false; + +bool ggml_cublas_loaded(void) { + return g_cublas_loaded; +} void ggml_init_cublas() { static bool initialized = false; @@ -5803,7 +5808,12 @@ void ggml_init_cublas() { CUDA_CHECK(cudaDeviceSynchronize()); #endif - CUDA_CHECK(cudaGetDeviceCount(&g_device_count)); + if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) { + initialized = true; + g_cublas_loaded = false; + return; + } + GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES); int64_t total_vram = 0; #if defined(GGML_CUDA_FORCE_MMQ) @@ -5851,6 +5861,7 @@ void ggml_init_cublas() { // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); initialized = true; + g_cublas_loaded = true; } } @@ -7158,6 +7169,8 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src } bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { + if (!g_cublas_loaded) return false; + const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; @@ -7843,6 +7856,8 @@ void ggml_cuda_free_scratch() { } bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + if (!g_cublas_loaded) return false; + ggml_cuda_func_t func; const bool any_on_device = tensor->backend == GGML_BACKEND_GPU || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) diff --git a/ggml-cuda.h b/ggml-cuda.h index 57adc9cf3..528e66c33 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -17,7 +17,12 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 +// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`. GGML_API void ggml_init_cublas(void); + +// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`. +GGML_API bool ggml_cublas_loaded(void); + GGML_API void * ggml_cuda_host_malloc(size_t size); GGML_API void ggml_cuda_host_free(void * ptr); diff --git a/llama.cpp b/llama.cpp index e16539000..d220ff3e9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -596,19 +596,37 @@ static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * // llama helpers // +inline void * llama_host_malloc(size_t n) { #ifdef GGML_USE_CUBLAS -# define llama_host_malloc(n) ggml_cuda_host_malloc(n) -# define llama_host_free(data) ggml_cuda_host_free(data) + if (ggml_cublas_loaded()) { + return ggml_cuda_host_malloc(n); + } else { + return malloc(n); + } #elif GGML_USE_METAL -# define llama_host_malloc(n) ggml_metal_host_malloc(n) -# define llama_host_free(data) ggml_metal_host_free(data) + return ggml_metal_host_malloc(n); #elif GGML_USE_CPU_HBM -# define llama_host_malloc(n) hbw_malloc(n) -# define llama_host_free(data) if (data != NULL) hbw_free(data) + return hbw_malloc(n); #else -# define llama_host_malloc(n) malloc(n) -# define llama_host_free(data) free(data) + return malloc(n); #endif +} + +inline void llama_host_free(void * ptr) { +#ifdef GGML_USE_CUBLAS + if (ggml_cublas_loaded()) { + return ggml_cuda_host_free(ptr); + } else { + return free(ptr); + } +#elif GGML_USE_METAL + return ggml_metal_host_free(ptr); +#elif GGML_USE_CPU_HBM + return hbw_free(ptr); +#else + return free(ptr); +#endif +} #if defined(_WIN32) static std::string llama_format_win_err(DWORD err) { @@ -1200,9 +1218,11 @@ struct llama_kv_cache { } #ifdef GGML_USE_CUBLAS - ggml_cuda_free_data(k); - ggml_cuda_free_data(v); -#endif // GGML_USE_CUBLAS + if (ggml_cublas_loaded()) { + ggml_cuda_free_data(k); + ggml_cuda_free_data(v); + } +#endif } }; @@ -1302,11 +1322,15 @@ struct llama_model { } #ifdef GGML_USE_CUBLAS - for (size_t i = 0; i < tensors_by_name.size(); ++i) { - ggml_cuda_free_data(tensors_by_name[i].second); + if (ggml_cublas_loaded()) { + for (size_t i = 0; i < tensors_by_name.size(); ++i) { + ggml_cuda_free_data(tensors_by_name[i].second); + } + ggml_cuda_free_scratch(); } - ggml_cuda_free_scratch(); -#elif defined(GGML_USE_CLBLAST) +#endif + +#if defined(GGML_USE_CLBLAST) for (size_t i = 0; i < tensors_by_name.size(); ++i) { ggml_cl_free_data(tensors_by_name[i].second); } @@ -1418,23 +1442,26 @@ static bool llama_kv_cache_init( ggml_set_name(cache.v, "cache_v"); (void) n_gpu_layers; -#ifdef GGML_USE_CUBLAS - size_t vram_kv_cache = 0; - if (n_gpu_layers > (int)n_layer + 1) { - ggml_cuda_assign_buffers_no_scratch(cache.v); - LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__); - vram_kv_cache += ggml_nbytes(cache.v); +#ifdef GGML_USE_CUBLAS + if (ggml_cublas_loaded()) { + size_t vram_kv_cache = 0; + + if (n_gpu_layers > (int)n_layer + 1) { + ggml_cuda_assign_buffers_no_scratch(cache.v); + LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__); + vram_kv_cache += ggml_nbytes(cache.v); + } + if (n_gpu_layers > (int)n_layer + 2) { + ggml_cuda_assign_buffers_no_scratch(cache.k); + LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__); + vram_kv_cache += ggml_nbytes(cache.k); + } + if (vram_kv_cache > 0) { + LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); + } } - if (n_gpu_layers > (int)n_layer + 2) { - ggml_cuda_assign_buffers_no_scratch(cache.k); - LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__); - vram_kv_cache += ggml_nbytes(cache.k); - } - if (vram_kv_cache > 0) { - LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); - } -#endif // GGML_USE_CUBLAS +#endif return true; } @@ -2521,18 +2548,22 @@ static void llm_load_tensors( } (void) main_gpu; + + enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; + enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; + #ifdef GGML_USE_CUBLAS - LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); - ggml_cuda_set_main_device(main_gpu); -#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU -#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT + if (ggml_cublas_loaded()) { + LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); + ggml_cuda_set_main_device(main_gpu); + + llama_backend_offload = GGML_BACKEND_GPU; + llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT; + } #elif defined(GGML_USE_CLBLAST) - LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); -#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU -#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU -#else -#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU -#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU + LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); + llama_backend_offload = GGML_BACKEND_GPU; + llama_backend_offload_split = GGML_BACKEND_GPU; #endif // prepare memory for the weights @@ -2559,12 +2590,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2588,8 +2619,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2625,12 +2656,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2654,8 +2685,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2695,12 +2726,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2726,8 +2757,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2772,12 +2803,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2803,8 +2834,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2849,12 +2880,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2877,8 +2908,8 @@ static void llm_load_tensors( const int i_gpu_start = n_layer - n_gpu_layers; model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); @@ -2915,12 +2946,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2946,8 +2977,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2993,12 +3024,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -3022,8 +3053,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; From 54b4df8886103b436a4bb3b60f4d84824f9e8868 Mon Sep 17 00:00:00 2001 From: Matthew Tejo Date: Mon, 6 Nov 2023 23:43:59 -0800 Subject: [PATCH 048/664] Use params when loading models in llava-cli (#3976) llava-cli was loading models with default params and ignoring settings from the cli. This switches to a generic function to load the params from the cli options. --- examples/llava/llava-cli.cpp | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 19374c67f..633afd1da 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -242,18 +242,16 @@ static struct llava_context * llava_init(gpt_params * params) { llama_backend_init(params->numa); - llama_model_params model_params = llama_model_default_params(); + llama_model_params model_params = llama_model_params_from_gpt_params(*params); + llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); if (model == NULL) { fprintf(stderr , "%s: error: unable to load model\n" , __func__); return NULL; } - llama_context_params ctx_params = llama_context_default_params(); - + llama_context_params ctx_params = llama_context_params_from_gpt_params(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings - ctx_params.n_threads = params->n_threads; - ctx_params.n_threads_batch = params->n_threads_batch == -1 ? params->n_threads : params->n_threads_batch; llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); From e9c1cecb9d7d743d30b4a29ecd56a411437def0a Mon Sep 17 00:00:00 2001 From: xaedes Date: Tue, 7 Nov 2023 09:04:51 +0100 Subject: [PATCH 049/664] ggml : fix backward rope after YaRN (#3974) * fix backward process of rope rope backward process was broken after YaRN RoPE (#2268) implementation, due to missing changes in backward functions. the code for the backward process is nearly identically to the forward process: the only difference is the sign of the sin-values. to avoid future regressions remove the near-duplicate backward functions and reuse the forward code: for this a new function argument `bool forward` was added to `ggml_compute_forward_rope_f32` and `ggml_compute_forward_rope_f16`. the sin-values will be negated when forward is false. * fix finetune rope call to use correct default attn_factor of 1.0f * remove unused `ggml_rope_xpos_back` it is better to have only one `ggml_rope_back` function that accepts all rope parameters, so that `ggml_compute_backward` can propagate all parameters without having to switch between different rope_back variants. * fix comments explaining the sinus sign in ggml_forward_rope * add missing function arguments in declaration * fix function argument type in declaration --- examples/finetune/finetune.cpp | 2 +- ggml.c | 330 ++++++++------------------------- ggml.h | 5 + 3 files changed, 84 insertions(+), 253 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 649a3b7c1..fa7dbe496 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -643,7 +643,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( return ggml_rope_custom(ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, - rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f + rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; diff --git a/ggml.c b/ggml.c index 605a27940..009d5b398 100644 --- a/ggml.c +++ b/ggml.c @@ -4970,8 +4970,13 @@ struct ggml_tensor * ggml_rope_back( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, float xpos_base, bool xpos_down) { GGML_ASSERT(ggml_is_vector(b)); @@ -4988,11 +4993,15 @@ struct ggml_tensor * ggml_rope_back( struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx }; - memcpy(params + 4, &freq_base, sizeof(float)); - memcpy(params + 5, &freq_scale, sizeof(float)); - memcpy(params + 6, &xpos_base, sizeof(float)); - memcpy(params + 7, &xpos_down, sizeof(bool)); + int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; + memcpy(params + 5, &freq_base, sizeof(float)); + memcpy(params + 6, &freq_scale, sizeof(float)); + memcpy(params + 7, &ext_factor, sizeof(float)); + memcpy(params + 8, &attn_factor, sizeof(float)); + memcpy(params + 9, &beta_fast, sizeof(float)); + memcpy(params + 10, &beta_slow, sizeof(float)); + memcpy(params + 11, &xpos_base, sizeof(float)); + memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE_BACK; @@ -10974,7 +10983,8 @@ static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - struct ggml_tensor * dst) { + struct ggml_tensor * dst, + const bool forward) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -11033,6 +11043,11 @@ static void ggml_compute_forward_rope_f32( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { @@ -11049,9 +11064,9 @@ static void ggml_compute_forward_rope_f32( float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); + const float sin_theta = sinf(theta_base) * sin_sign; const float cos_block_theta = cosf(block_theta); - const float sin_block_theta = sinf(block_theta); + const float sin_block_theta = sinf(block_theta) * sin_sign; theta_base *= theta_scale; block_theta *= theta_scale; @@ -11075,6 +11090,7 @@ static void ggml_compute_forward_rope_f32( rope_yarn( theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; @@ -11105,6 +11121,7 @@ static void ggml_compute_forward_rope_f32( theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; theta_base *= theta_scale; @@ -11130,7 +11147,8 @@ static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - struct ggml_tensor * dst) { + struct ggml_tensor * dst, + const bool forward) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -11182,6 +11200,11 @@ static void ggml_compute_forward_rope_f16( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { @@ -11198,9 +11221,9 @@ static void ggml_compute_forward_rope_f16( float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); + const float sin_theta = sinf(theta_base) * sin_sign; const float cos_block_theta = cosf(block_theta); - const float sin_block_theta = sinf(block_theta); + const float sin_block_theta = sinf(block_theta) * sin_sign; theta_base *= theta_scale; block_theta *= theta_scale; @@ -11224,6 +11247,7 @@ static void ggml_compute_forward_rope_f16( rope_yarn( theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; theta_base *= theta_scale; @@ -11250,6 +11274,7 @@ static void ggml_compute_forward_rope_f16( theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; theta_base *= theta_scale; @@ -11279,11 +11304,11 @@ static void ggml_compute_forward_rope( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_f16(params, src0, src1, dst); + ggml_compute_forward_rope_f16(params, src0, src1, dst, true); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_f32(params, src0, src1, dst); + ggml_compute_forward_rope_f32(params, src0, src1, dst, true); } break; default: { @@ -11294,216 +11319,6 @@ static void ggml_compute_forward_rope( // ggml_compute_forward_rope_back -static void ggml_compute_forward_rope_back_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { - return; - } - - // y = rope(x, src1) - // dx = rope_back(dy, src1) - // src0 is dy, src1 contains options - - float freq_base; - float freq_scale; - - // these two only relevant for xPos RoPE: - float xpos_base; - bool xpos_down; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx); - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - assert(nb0 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - const bool is_neox = mode & 2; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - float theta_base = freq_scale * (float)p; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - // zeta scaling for xPos only: - float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; - if (xpos_down) zeta = 1.0f / zeta; - - theta_base *= theta_scale; - - const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = dy[0]; - const float dy1 = dy[1]; - - dx[0] = dy0*cos_theta*zeta + dy1*sin_theta*zeta; - dx[1] = - dy0*sin_theta*zeta + dy1*cos_theta*zeta; - } - } else { - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - theta_base *= theta_scale; - - const int64_t i0 = ib*n_dims + ic/2; - - const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = dy[0]; - const float dy1 = dy[n_dims/2]; - - dx[0] = dy0*cos_theta + dy1*sin_theta; - dx[n_dims/2] = - dy0*sin_theta + dy1*cos_theta; - } - } - } - } - } - } -} - -static void ggml_compute_forward_rope_back_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { - return; - } - - // y = rope(x, src1) - // dx = rope_back(dy, src1) - // src0 is dy, src1 contains options - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - assert(nb0 == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(10000.0, -2.0f/n_dims); - - const bool is_neox = mode & 2; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - float theta_base = (float)p; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - theta_base *= theta_scale; - - const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = GGML_FP16_TO_FP32(dy[0]); - const float dy1 = GGML_FP16_TO_FP32(dy[1]); - - dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta); - dx[1] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta); - } - } else { - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - theta_base *= theta_scale; - - const int64_t i0 = ib*n_dims + ic/2; - - const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = GGML_FP16_TO_FP32(dy[0]); - const float dy1 = GGML_FP16_TO_FP32(dy[n_dims/2]); - - dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta); - dx[n_dims/2] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta); - } - } - } - } - } - } -} - static void ggml_compute_forward_rope_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -11512,11 +11327,11 @@ static void ggml_compute_forward_rope_back( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_back_f16(params, src0, src1, dst); + ggml_compute_forward_rope_f16(params, src0, src1, dst, false); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_back_f32(params, src0, src1, dst); + ggml_compute_forward_rope_f32(params, src0, src1, dst, false); } break; default: { @@ -15559,17 +15374,20 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // necessary for llama if (src0->grad) { //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - float freq_base; - float freq_scale; - float xpos_base; - bool xpos_down; - memcpy(&freq_base, (int32_t *) tensor->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) tensor->op_params + 7, sizeof(bool)); + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; + + memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); + memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, @@ -15579,8 +15397,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor n_dims, mode, n_ctx, + n_orig_ctx, freq_base, freq_scale, + ext_factor, + attn_factor, + beta_fast, + beta_slow, xpos_base, xpos_down), zero_table); @@ -15590,17 +15413,20 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { if (src0->grad) { //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - float freq_base; - float freq_scale; - float xpos_base; - bool xpos_down; - memcpy(&freq_base, (int32_t *) tensor->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) tensor->op_params + 7, sizeof(bool)); + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; + + memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); + memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, @@ -15609,14 +15435,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src1, n_dims, mode, - 0, n_ctx, + n_orig_ctx, freq_base, freq_scale, - 0.0f, - 1.0f, - 0.0f, - 0.0f, + ext_factor, + attn_factor, + beta_fast, + beta_slow, xpos_base, xpos_down, false), diff --git a/ggml.h b/ggml.h index 70eb25a6b..26654fc8e 100644 --- a/ggml.h +++ b/ggml.h @@ -1372,8 +1372,13 @@ extern "C" { int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, float xpos_base, bool xpos_down); From 413503d4b92500d82b002d03c580a71a54747138 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 7 Nov 2023 19:25:32 +0200 Subject: [PATCH 050/664] make : do not add linker flags when compiling static llava lib (#3977) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index f2d4fd031..d6be254a0 100644 --- a/Makefile +++ b/Makefile @@ -618,7 +618,7 @@ llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ $(LDFLAGS) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual From 0a7c980b6f94a049cb804573df2d8092a34df8e4 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 7 Nov 2023 12:43:04 -0500 Subject: [PATCH 051/664] gguf : track writer state, free unneeded tensors, cleanup (#3871) --- gguf-py/gguf/gguf.py | 82 +++++++++++++++++++++++++++--------------- gguf-py/pyproject.toml | 2 +- 2 files changed, 54 insertions(+), 30 deletions(-) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index a2271d225..7e495cb19 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -646,18 +646,17 @@ class GGUFValueType(IntEnum): sys.exit() +class WriterState(Enum): + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + + class GGUFWriter: fout: BufferedWriter - arch: str - offset_tensor = 0 - data_alignment = GGUF_DEFAULT_ALIGNMENT - kv_data = b"" - kv_data_count = 0 - ti_data = b"" - ti_data_count = 0 - use_temp_file: bool - temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None - tensors: list[tuple[np.ndarray[Any, Any], int]] + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[np.ndarray[Any, Any]] @property def pack_prefix(self): @@ -683,27 +682,47 @@ class GGUFWriter: GGUFValueType.FLOAT64: f"{self.pack_prefix}d", GGUFValueType.BOOL: "?" , } - self.add_architecture() + self.offset_tensor = 0 + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.kv_data = b"" + self.kv_data_count = 0 + self.ti_data = b"" + self.ti_data_count = 0 self.use_temp_file = use_temp_file + self.temp_file = None self.tensors = [] endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian" print(f"This gguf file is for {endianess_str} only") + self.state = WriterState.EMPTY + + self.add_architecture() def write_header_to_file(self): + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + self.fout.write(struct.pack(" None: diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index f0741a7c2..c6cb2c37a 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.4.5" +version = "0.4.6" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From 875fb42871a0f5a88fbe31a0b5edd697b84038e4 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 8 Nov 2023 13:15:14 +0100 Subject: [PATCH 052/664] ggml-alloc : fix backend assignments of views (#3982) --- ggml-alloc.c | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 34eba3f83..b553eb7c1 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -378,9 +378,13 @@ static bool ggml_op_can_inplace(enum ggml_op op) { } } -static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view) { +static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view, bool update_backend) { assert(view->view_src != NULL && view->view_src->data != NULL); - view->backend = view->view_src->backend; + + if (update_backend) { + view->backend = view->view_src->backend; + } + view->buffer = view->view_src->buffer; view->data = (char *)view->view_src->data + view->view_offs; @@ -394,7 +398,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) struct hash_node * ht = alloc->hash_table; if (node->data == NULL) { if (ggml_is_view(node)) { - init_view(alloc, node); + init_view(alloc, node, true); } else { // see if we can reuse a parent's buffer (inplace) if (ggml_op_can_inplace(node->op)) { @@ -424,15 +428,14 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); node->view_src = view_src; view_src_hn->n_views += 1; - init_view(alloc, node); + init_view(alloc, node, false); return; } - } - else { + } else { AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); node->view_src = parent; p_hn->n_views += 1; - init_view(alloc, node); + init_view(alloc, node, false); return; } } @@ -463,7 +466,7 @@ size_t ggml_allocr_alloc_graph_n( hash_get(ht, view_src)->n_views += 1; if (node->buffer == NULL && node->data != NULL) { // view of a pre-allocated tensor, didn't call init_view() yet - init_view(alloc, node); + init_view(alloc, node, true); } } @@ -474,7 +477,7 @@ size_t ggml_allocr_alloc_graph_n( } hash_get(ht, parent)->n_children += 1; if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { - init_view(alloc, parent); + init_view(alloc, parent, true); } } } From 57ad015dc3011b046ed5a23186c86ea55f987c54 Mon Sep 17 00:00:00 2001 From: Mihai Date: Thu, 9 Nov 2023 04:00:34 +0200 Subject: [PATCH 053/664] server : add min_p param (#3877) * Update server.cpp with min_p after it was introduced in https://github.com/ggerganov/llama.cpp/pull/3841 * Use spaces instead of tabs * Update index.html.hpp after running deps.sh * Fix test - fix line ending --- examples/server/README.md | 2 + examples/server/index.html.hpp | 4396 +++++++++++++++-------------- examples/server/public/index.html | 2 + examples/server/server.cpp | 2 + 4 files changed, 2211 insertions(+), 2191 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 089ebe2d1..a6eda3b32 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -122,6 +122,8 @@ node index.js `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). + `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05). + `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity). `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 5d3bdfbdd..207412513 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -374,1189 +374,1161 @@ unsigned char index_html[] = { 0x7a, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, - 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x66, 0x73, - 0x5f, 0x7a, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, - 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, - 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, - 0x69, 0x63, 0x61, 0x6c, 0x5f, 0x70, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, - 0x20, 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min_p: 0.05, // 0 = disabled tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled @@ -744,6 +745,7 @@ ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} + ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}