From 424b6381c4daeed62e6600e0402e72f39845b58d Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 13 Oct 2023 12:23:10 +0200 Subject: [PATCH 01/95] ggml : add context enumeration functions (#3605) finetune : fix assert failure in ggml-alloc --- examples/finetune/finetune.cpp | 19 +++++++++---------- ggml.c | 34 ++++++++++++++++++++++++++++++++++ ggml.h | 3 +++ 3 files changed, 46 insertions(+), 10 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 9ae4bc198..35824cd2d 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -529,13 +529,14 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora set_param_lora(lora); // measure data size - struct ggml_allocr * alloc = NULL; - alloc = ggml_allocr_new_measure(tensor_alignment); - alloc_lora(alloc, lora); + size_t size = 0; + for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + size += GGML_PAD(ggml_nbytes(t), tensor_alignment); + } // allocate data - lora->data.resize(ggml_allocr_max_size(alloc) + tensor_alignment); - ggml_allocr_free(alloc); + struct ggml_allocr * alloc = NULL; + lora->data.resize(size + tensor_alignment); alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment); alloc_lora(alloc, lora); ggml_allocr_free(alloc); @@ -1714,11 +1715,9 @@ int main(int argc, char ** argv) { struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); // measure required memory for input tensors - alloc = ggml_allocr_new_measure(tensor_alignment); - ggml_allocr_alloc(alloc, tokens_input); - ggml_allocr_alloc(alloc, target_probs); - size_t max_input_size = ggml_allocr_max_size(alloc) + tensor_alignment; - ggml_allocr_free(alloc); + size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) + + GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) + + tensor_alignment; printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); // allocate input tensors diff --git a/ggml.c b/ggml.c index c00ab00da..630deb49d 100644 --- a/ggml.c +++ b/ggml.c @@ -5494,6 +5494,39 @@ struct ggml_tensor * ggml_view_tensor( return result; } +struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) { + struct ggml_object * obj = ctx->objects_begin; + + char * const mem_buffer = ctx->mem_buffer; + + while (obj != NULL) { + if (obj->type == GGML_OBJECT_TENSOR) { + return (struct ggml_tensor *)(mem_buffer + obj->offs); + } + + obj = obj->next; + } + + return NULL; +} + +struct ggml_tensor * ggml_get_next_tensor(struct ggml_context * ctx, struct ggml_tensor * tensor) { + struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE); + obj = obj->next; + + char * const mem_buffer = ctx->mem_buffer; + + while (obj != NULL) { + if (obj->type == GGML_OBJECT_TENSOR) { + return (struct ggml_tensor *)(mem_buffer + obj->offs); + } + + obj = obj->next; + } + + return NULL; +} + struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) { struct ggml_object * obj = ctx->objects_begin; @@ -8647,6 +8680,7 @@ void ggml_set_param( GGML_ASSERT(tensor->grad == NULL); tensor->grad = ggml_dup_tensor(ctx, tensor); + ggml_format_name(tensor->grad, "%s (grad)", tensor->name); } // ggml_compute_forward_dup diff --git a/ggml.h b/ggml.h index 3eddc44b9..6e35888e9 100644 --- a/ggml.h +++ b/ggml.h @@ -704,6 +704,9 @@ extern "C" { GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); 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_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); From 2a4bcbacead886996f175f33479d1d874a3e577f Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 13 Oct 2023 12:33:16 +0200 Subject: [PATCH 02/95] llama : remove n_threads from llama_decode_internal (#3614) This commit removes `n_threads` from the `llama_decode_internal` functions doc comment as it does not exist anymore. It looks like this parameter was removed in Commit 16bc66d9479edd5ee12ec734973554d4493c5dfa ("llama.cpp : split llama_context_params into model and context params"). Signed-off-by: Daniel Bevenius --- llama.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 7ed872237..2cd2dad7f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5721,7 +5721,6 @@ static struct ggml_cgraph * llama_build_graph( // // - lctx: llama context // - batch: batch to evaluate -// - n_threads: number of threads to use // // return 0 on success // return positive int on warning From 11dc1091f64b24ca6d643acc6d0051117ba60161 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sat, 14 Oct 2023 13:52:44 +0300 Subject: [PATCH 03/95] Honor -ngl option for Cuda offloading in llava (#3621) --- examples/llava/llava.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 14dacc780..8384d9d78 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -79,7 +79,13 @@ int main(int argc, char ** argv) { llama_backend_init(params.numa); - llama_model_params model_params = llama_model_default_params(); + 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__); From 11bff290458f12f020b588792707f76ec658a27a Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Sun, 15 Oct 2023 02:32:06 -0400 Subject: [PATCH 04/95] MPT : support GQA for replit-code-v1.5 (#3627) --- convert-mpt-hf-to-gguf.py | 2 ++ llama.cpp | 6 +++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py index 73a4932f7..19a66820d 100755 --- a/convert-mpt-hf-to-gguf.py +++ b/convert-mpt-hf-to-gguf.py @@ -98,6 +98,8 @@ 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"]) diff --git a/llama.cpp b/llama.cpp index 2cd2dad7f..5329bd828 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2839,8 +2839,8 @@ static void llm_load_tensors( 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, 3*n_embd}, 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, 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.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); @@ -5368,7 +5368,7 @@ 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; // == n_head for MPT, as there's no MQA/GQA + 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(); From 940efa95fec0b8a98c226a889d2ad839dfeeae0d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 16 Oct 2023 23:58:00 +0300 Subject: [PATCH 05/95] llava : fix tokenization to not add bos between image embeddings and user prompt (#3645) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * llava : fix tokenization to not add bos after system prompt * set seed --------- Co-authored-by: M. Yusuf Sarıgöz --- examples/llava/llava-utils.h | 4 ++-- examples/llava/llava.cpp | 12 +++++++----- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h index 79e237c86..4e71351dd 100644 --- a/examples/llava/llava-utils.h +++ b/examples/llava/llava-utils.h @@ -49,9 +49,9 @@ inline bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { 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){ +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, true); + std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); eval_tokens(ctx_llama, embd_inp, n_batch, n_past); return true; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 8384d9d78..b24cb2e6f 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -97,6 +97,7 @@ int main(int argc, char ** argv) { 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); @@ -106,7 +107,8 @@ int main(int argc, char ** argv) { } // 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)); + 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); @@ -125,14 +127,14 @@ int main(int argc, char ** argv) { const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict; - // GG: are we sure that the should be a trailing whitespace at the end of this string? - 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); + 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.c_str(), params.n_batch, &n_past); - eval_string(ctx_llama, "\nASSISTANT:", 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++) { From 281ef73c258cc1eebec8a64264240432d5878c4b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Oct 2023 09:19:28 +0300 Subject: [PATCH 06/95] k-quants : fix quantization ranges (#3646) --- k_quants.c | 30 +++++++++++------------------- 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/k_quants.c b/k_quants.c index 558f5fda8..e168a87bb 100644 --- a/k_quants.c +++ b/k_quants.c @@ -462,12 +462,9 @@ void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) { } size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - const int nb = k / QK_K; + (void)hist; // TODO: collect histograms - // TODO - collect histograms - although, at a second thought, I don't really care about them - (void)hist; - - for (int j = 0; j < nb; j += k) { + for (int j = 0; j < n; j += k) { block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K; quantize_row_q2_K_reference(src + j, y, k); } @@ -678,12 +675,9 @@ void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) { } size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - const int nb = k / QK_K; + (void)hist; // TODO: collect histograms - // TODO - collect histograms - although, at a second thought, I don't really care about them - (void)hist; - - for (int j = 0; j < nb; j += k) { + for (int j = 0; j < n; j += k) { block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K; quantize_row_q3_K_reference(src + j, y, k); } @@ -846,9 +840,9 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) { size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { assert(k % QK_K == 0); - const int nb = k / QK_K; (void)hist; // TODO: collect histograms - for (int j = 0; j < nb; j += k) { + + for (int j = 0; j < n; j += k) { block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K; quantize_row_q4_K_reference(src + j, y, k); } @@ -1052,9 +1046,9 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) { size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { assert(k % QK_K == 0); - const int nb = k / QK_K; - (void)hist; - for (int j = 0; j < nb; j += k) { + (void)hist; // TODO: collect histograms + + for (int j = 0; j < n; j += k) { block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K; quantize_row_q5_K_reference(src + j, y, k); } @@ -1200,11 +1194,9 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) { size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK_K == 0); - const int nb = k / QK_K; + (void)hist; // TODO: collect histograms - (void)hist; // TODO - - for (int j = 0; j < nb; j += k) { + for (int j = 0; j < n; j += k) { block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K; quantize_row_q6_K_reference(src + j, y, k); } From 1a159553f921a9209fed8c714494e57b3649f232 Mon Sep 17 00:00:00 2001 From: staviq Date: Tue, 17 Oct 2023 17:11:01 +0200 Subject: [PATCH 07/95] tokenizer : special token handling (#3538) * Rewrite special token handling from #1931 * shorten param name, add st verification by type * use offsets instead of copy by substr * formatting, remove copying iterator on delete * llama : normalize code-style * swift fix * print pfx/sfx if verb, main: split pfx input sfx * dont add space when using special tokens * minor : comment + spacing --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 12 +- common/common.h | 6 +- common/train.cpp | 8 +- examples/batched.swift/Sources/main.swift | 2 +- examples/main/main.cpp | 40 ++- llama.cpp | 290 +++++++++++++++++++++- llama.h | 13 +- 7 files changed, 332 insertions(+), 39 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 9c4f7df20..3e4b8a8cb 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -879,21 +879,23 @@ std::tuple llama_init_from_gpt_par std::vector llama_tokenize( const struct llama_context * ctx, const std::string & text, - bool add_bos) { - return llama_tokenize(llama_get_model(ctx), text, add_bos); + bool add_bos, + bool special) { + return llama_tokenize(llama_get_model(ctx), text, add_bos, special); } std::vector llama_tokenize( const struct llama_model * model, const std::string & text, - bool add_bos) { + bool add_bos, + bool special) { // upper limit for the number of tokens int n_tokens = text.length() + add_bos; std::vector result(n_tokens); - n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos); + n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos); + int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special); GGML_ASSERT(check == -n_tokens); } else { result.resize(n_tokens); diff --git a/common/common.h b/common/common.h index 36fd44166..08c603231 100644 --- a/common/common.h +++ b/common/common.h @@ -137,12 +137,14 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param std::vector llama_tokenize( const struct llama_context * ctx, const std::string & text, - bool add_bos); + bool add_bos, + bool special = false); std::vector llama_tokenize( const struct llama_model * model, const std::string & text, - bool add_bos); + bool add_bos, + bool special = false); // tokenizes a token into a piece // should work similar to Python's `tokenizer.id_to_piece` diff --git a/common/train.cpp b/common/train.cpp index 35a4cf9e6..972eaefe0 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -863,7 +863,7 @@ size_t tokenize_file( (int) buf.size(), out_tokens.data(), (int) out_tokens.size(), - false); + false, false); if (n_tokens < 0) { out_tokens.resize(-n_tokens); n_tokens = llama_tokenize( @@ -872,7 +872,7 @@ size_t tokenize_file( (int) buf.size(), out_tokens.data(), (int) out_tokens.size(), - false); + false, false); } if (n_tokens >= 0) { out_tokens.resize(n_tokens); @@ -966,7 +966,7 @@ size_t tokenize_file( (int) buf_sample.size(), tok_sample.data(), (int) tok_sample.size(), - false); + false, false); if (n_tokens < 0) { tok_sample.resize(-n_tokens); n_tokens = llama_tokenize(llama_get_model(lctx), @@ -974,7 +974,7 @@ size_t tokenize_file( (int) buf_sample.size(), tok_sample.data(), (int) tok_sample.size(), - false); + false, false); GGML_ASSERT(n_tokens >= 0); } GGML_ASSERT(n_tokens <= (int) tok_sample.size()); diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index 938f30512..05d1bb9d0 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -209,7 +209,7 @@ llama_print_timings(context) private func tokenize(text: String, add_bos: Bool) -> [llama_token] { let n_tokens = text.count + (add_bos ? 1 : 0) let tokens = UnsafeMutablePointer.allocate(capacity: n_tokens) - let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos) + let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false) var swiftTokens: [llama_token] = [] for i in 0 ..< tokenCount { swiftTokens.append(tokens[Int(i)]) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 55f73356f..a5fb65548 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -238,7 +238,7 @@ int main(int argc, char ** argv) { if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) { LOG("tokenize the prompt\n"); - embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos); + embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); } else { LOG("use session tokens\n"); embd_inp = session_tokens; @@ -260,10 +260,10 @@ int main(int argc, char ** argv) { if (ctx_guidance) { LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos); + guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true); LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp)); - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp)); original_prompt_len = original_inp.size(); @@ -320,8 +320,8 @@ int main(int argc, char ** argv) { } // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos); - const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false); + const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true); + const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx)); LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx)); @@ -383,6 +383,12 @@ int main(int argc, char ** argv) { if (!params.antiprompt.empty()) { for (const auto & antiprompt : params.antiprompt) { LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str()); + if (params.verbose_prompt) { + auto tmp = ::llama_tokenize(ctx, antiprompt, false, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + } + } } } @@ -392,10 +398,22 @@ int main(int argc, char ** argv) { if (!params.input_prefix.empty()) { LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str()); + if (params.verbose_prompt) { + auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + } + } } if (!params.input_suffix.empty()) { LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str()); + if (params.verbose_prompt) { + auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + } + } } } LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n", @@ -717,7 +735,7 @@ int main(int argc, char ** argv) { if (params.interactive) { if (!params.antiprompt.empty()) { // tokenize and inject first reverse prompt - const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); + const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true); embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); is_antiprompt = true; } @@ -744,8 +762,7 @@ int main(int argc, char ** argv) { std::string buffer; if (!params.input_prefix.empty()) { LOG("appending input prefix: '%s'\n", params.input_prefix.c_str()); - buffer += params.input_prefix; - printf("%s", buffer.c_str()); + printf("%s", params.input_prefix.c_str()); } // color user input only @@ -767,7 +784,6 @@ int main(int argc, char ** argv) { // append input suffix if any if (!params.input_suffix.empty()) { LOG("appending input suffix: '%s'\n", params.input_suffix.c_str()); - buffer += params.input_suffix; printf("%s", params.input_suffix.c_str()); } @@ -782,10 +798,14 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); } - const auto line_inp = ::llama_tokenize(ctx, buffer, false); + const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); + const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); + const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp)); + embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); + embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end()); // instruct mode: insert response suffix if (params.instruct) { diff --git a/llama.cpp b/llama.cpp index 5329bd828..82b7638ae 100644 --- a/llama.cpp +++ b/llama.cpp @@ -75,6 +75,7 @@ #include #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -1183,6 +1184,8 @@ struct llama_vocab { std::unordered_map token_to_id; std::vector id_to_token; + std::unordered_map special_tokens_cache; + std::map, int> bpe_ranks; // default LLaMA special tokens @@ -2125,7 +2128,7 @@ static void llm_load_hparams( } // TODO: This should probably be in llama.h -static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos); +static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false); static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch); static void llm_load_vocab( @@ -2241,6 +2244,101 @@ static void llm_load_vocab( GGUF_GET_KEY(ctx, vocab.special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID)); GGUF_GET_KEY(ctx, vocab.special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID)); GGUF_GET_KEY(ctx, vocab.special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID)); + + // build special tokens cache + { + // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type, + // and will always be correctly labeled in 'added_tokens.json' etc. + // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed + // to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer + // are special tokens. + // From testing, this appears to corelate 1:1 with special tokens. + // + + // Counting special tokens and verifying in only one direction + // is sufficient to detect difference in those two sets. + // + uint32_t special_tokens_count_by_type = 0; + uint32_t special_tokens_count_from_verification = 0; + + bool special_tokens_definition_mismatch = false; + + for (const auto & t : vocab.token_to_id) { + const auto & token = t.first; + const auto & id = t.second; + + // Count all non-normal tokens in the vocab while iterating + if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) { + special_tokens_count_by_type++; + } + + // Skip single character tokens + if (token.length() > 1) { + bool is_tokenizable = false; + + // Split token string representation in two, in all possible ways + // and check if both halves can be matched to a valid token + for (unsigned i = 1; i < token.length();) { + const auto left = token.substr(0, i); + const auto right = token.substr(i); + + // check if we didnt partition in the middle of a utf sequence + auto utf = utf8_len(left.at(left.length() - 1)); + + if (utf == 1) { + if (vocab.token_to_id.find(left) != vocab.token_to_id.end() && + vocab.token_to_id.find(right) != vocab.token_to_id.end() ) { + is_tokenizable = true; + break; + } + i++; + } else { + // skip over the rest of multibyte utf sequence + i += utf - 1; + } + } + + if (!is_tokenizable) { + // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1 + // it's faster to re-filter them here, since there are way less candidates now + + // Calculate a total "utf" length of a token string representation + size_t utf8_str_len = 0; + for (unsigned i = 0; i < token.length();) { + utf8_str_len++; + i += utf8_len(token.at(i)); + } + + // And skip the ones which are one character + if (utf8_str_len > 1) { + // At this point what we have left are special tokens only + vocab.special_tokens_cache[token] = id; + + // Count manually found special tokens + special_tokens_count_from_verification++; + + // If this manually found special token is not marked as such, flag a mismatch + if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) { + special_tokens_definition_mismatch = true; + } + } + } + } + } + + if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) { + fprintf(stderr, "%s: warning: Mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n", + __func__, + special_tokens_count_from_verification, vocab.id_to_token.size(), + special_tokens_count_by_type, vocab.id_to_token.size() + ); + } else { + fprintf(stderr, "%s: Special tokens definition check successful ( %u/%zu ).\n", + __func__, + special_tokens_count_from_verification, vocab.id_to_token.size() + ); + } + } } static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { @@ -6464,7 +6562,137 @@ private: llm_bigram_bpe::queue work_queue; }; -static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) { +typedef enum FRAGMENT_BUFFER_VARIANT_TYPE{ + FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN, + FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT +} FRAGMENT_BUFFER_VARIANT_TYPE; + +struct fragment_buffer_variant{ + fragment_buffer_variant(llama_vocab::id _token) + : + type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN), + token(_token), + raw_text(_dummy), + offset(0), + length(0){} + fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length) + : + type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT), + token((llama_vocab::id)-1), + raw_text(_raw_text), + offset(_offset), + length(_length){ + GGML_ASSERT( _offset >= 0 ); + GGML_ASSERT( _length >= 1 ); + GGML_ASSERT( offset + length <= raw_text.length() ); + } + + const FRAGMENT_BUFFER_VARIANT_TYPE type; + const llama_vocab::id token; + const std::string _dummy; + const std::string & raw_text; + const uint64_t offset; + const uint64_t length; +}; + +// #define PRETOKENIZERDEBUG + +static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) +{ + // for each special token + for (const auto & st: vocab.special_tokens_cache) { + const auto & special_token = st.first; + const auto & special_id = st.second; + + // for each text fragment + std::forward_list::iterator it = buffer.begin(); + while (it != buffer.end()) { + auto & fragment = (*it); + + // if a fragment is text ( not yet processed ) + if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { + auto * raw_text = &(fragment.raw_text); + + auto raw_text_base_offset = fragment.offset; + auto raw_text_base_length = fragment.length; + + // loop over the text + while (true) { + // find the first occurence of a given special token in this fragment + // passing offset argument only limit the "search area" but match coordinates + // are still relative to the source full raw_text + auto match = raw_text->find(special_token, raw_text_base_offset); + + // no occurences found, stop processing this fragment for a given special token + if (match == std::string::npos) break; + + // check if match is within bounds of offset <-> length + 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()); +#endif + auto source = std::distance(buffer.begin(), it); + + // if match is further than base offset + // then we have some text to the left of it + if (match > raw_text_base_offset) { + // left + const int64_t left_reminder_offset = raw_text_base_offset + 0; + const int64_t left_reminder_length = match - raw_text_base_offset; + 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()); +#endif + it++; + } + + // special token + buffer.emplace_after(it, special_id); + it++; + + // right + if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) { + const int64_t right_reminder_offset = match + special_token.length(); + const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length()); + 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()); +#endif + + it++; + + if (source == 0) { + buffer.erase_after(buffer.before_begin()); + } else { + buffer.erase_after(std::next(buffer.begin(), (source-1))); + } + + // repeat for the right side + raw_text_base_offset = right_reminder_offset; + 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()); +#endif + } else { + if (source == 0) { + buffer.erase_after(buffer.before_begin()); + } else { + buffer.erase_after(std::next(buffer.begin(), (source-1))); + } + break; + } + } + } + it++; + } + } +} + +static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) { std::vector output; // OG tokenizer behavior: @@ -6480,20 +6708,58 @@ static std::vector llama_tokenize_internal(const llama_vocab & return output; } + std::forward_list fragment_buffer; + fragment_buffer.emplace_front( raw_text, 0, raw_text.length() ); + + if (special) tokenizer_st_partition( vocab, fragment_buffer ); + switch (vocab.type) { case LLAMA_VOCAB_TYPE_SPM: { - // without adding this leading whitespace, we do not get the same results as the original tokenizer - raw_text = " " + raw_text; + for (const auto & fragment: fragment_buffer) + { + if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) + { + // without adding this leading whitespace, we do not get the same results as the original tokenizer - llm_tokenizer_spm tokenizer(vocab); - llama_escape_whitespace(raw_text); - tokenizer.tokenize(raw_text, output); + // TODO: It's likely possible to get rid of this string copy entirely + // by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer + // and passing 'add space prefix' as bool argument + // + auto raw_text = (special ? "" : " ") + 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()); +#endif + llm_tokenizer_spm tokenizer(vocab); + llama_escape_whitespace(raw_text); + tokenizer.tokenize(raw_text, output); + } + else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) + { + output.push_back(fragment.token); + } + } } break; case LLAMA_VOCAB_TYPE_BPE: { - llm_tokenizer_bpe tokenizer(vocab); - tokenizer.tokenize(raw_text, output); + for (const auto & fragment: fragment_buffer) + { + if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) + { + 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()); +#endif + llm_tokenizer_bpe tokenizer(vocab); + tokenizer.tokenize(raw_text, output); + } + else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) + { + output.push_back(fragment.token); + } + } } break; } @@ -9407,15 +9673,15 @@ llama_token llama_token_eot(const struct llama_context * ctx) { return ctx->model.vocab.special_eot_id; } - int llama_tokenize( const struct llama_model * model, const char * text, int text_len, llama_token * tokens, int n_max_tokens, - bool add_bos) { - auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos); + bool add_bos, + bool special) { + auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special); if (n_max_tokens < (int) res.size()) { // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); diff --git a/llama.h b/llama.h index a78015ada..b13f23123 100644 --- a/llama.h +++ b/llama.h @@ -511,17 +511,20 @@ extern "C" { // Tokenization // - // Convert the provided text into tokens. - // The tokens pointer must be large enough to hold the resulting tokens. - // Returns the number of tokens on success, no more than n_max_tokens - // Returns a negative number on failure - the number of tokens that would have been returned + /// @details Convert the provided text into tokens. + /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. + /// @return Returns the number of tokens on success, no more than n_max_tokens + /// @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( const struct llama_model * model, const char * text, int text_len, llama_token * tokens, int n_max_tokens, - bool add_bos); + bool add_bos, + bool special); // Token Id -> Piece. // Uses the vocabulary in the provided context. From 5fe268a4d9ce09f3a6c77239af583d3a8e49d54c Mon Sep 17 00:00:00 2001 From: ldwang Date: Tue, 17 Oct 2023 23:52:33 +0800 Subject: [PATCH 08/95] readme : add Aquila2 links (#3610) Signed-off-by: ldwang Co-authored-by: ldwang --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 60f14a1fb..56372865b 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] [WizardLM](https://github.com/nlpxucan/WizardLM) - [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft)) - [X] [Aquila-7B](https://huggingface.co/BAAI/Aquila-7B) / [AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B) +- [X] [Aquila2-7B](https://huggingface.co/BAAI/Aquila2-7B) / [AquilaChat2-7B](https://huggingface.co/BAAI/AquilaChat2-7B) / [AquilaChat2-34B](https://huggingface.co/BAAI/AquilaChat2-34B) / [Aquila2-34B](https://huggingface.co/BAAI/Aquila2-34B) - [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) From 1142013da40e98946a109b141dd858f0ed996051 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Oct 2023 19:12:46 +0300 Subject: [PATCH 09/95] save-load-state : fix example + add ci test (#3655) * save-load-state : fix example (close #3606) * ci : add test for save-load-state example ggml-ci --- ci/run.sh | 6 ++ examples/save-load-state/save-load-state.cpp | 97 ++++++++++---------- 2 files changed, 52 insertions(+), 51 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index 34c9129c1..2e3343831 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -208,6 +208,8 @@ function gg_run_open_llama_3b_v2 { (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + function check_ppl { qnt="$1" ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) @@ -296,6 +298,7 @@ function gg_sum_open_llama_3b_v2 { gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" @@ -382,6 +385,8 @@ function gg_run_open_llama_7b_v2 { (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + function check_ppl { qnt="$1" ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) @@ -470,6 +475,7 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index f9e3c98a3..38d05f4d3 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -8,10 +8,7 @@ int main(int argc, char ** argv) { gpt_params params; - llama_sampling_params & sparams = params.sampling_params; - params.seed = 42; - params.n_threads = 4; - sparams.repeat_last_n = 64; + params.prompt = "The quick brown fox"; if (!gpt_params_parse(argc, argv, params)) { @@ -25,56 +22,49 @@ int main(int argc, char ** argv) { } auto n_past = 0; - auto last_n_tokens_data = std::vector(sparams.repeat_last_n, 0); + + std::string result0; + std::string result1; // init llama_model * model; llama_context * ctx; - std::tie(model, ctx) = llama_init_from_gpt_params( params ); - if (model == nullptr) { - return 1; - } - if (ctx == nullptr) { - llama_free_model(model); + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr || ctx == nullptr) { + fprintf(stderr, "%s : failed to init\n", __func__); return 1; } + + // tokenize prompt auto tokens = llama_tokenize(ctx, params.prompt, true); - auto n_prompt_tokens = tokens.size(); - if (n_prompt_tokens < 1) { - fprintf(stderr, "%s : failed to tokenize prompt\n", __func__); - llama_free(ctx); - llama_free_model(model); - return 1; - } // evaluate prompt - llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt_tokens, n_past, 0)); + llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0)); + n_past += tokens.size(); - last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens); - n_past += n_prompt_tokens; - - const size_t state_size = llama_get_state_size(ctx); - uint8_t * state_mem = new uint8_t[state_size]; - - // Save state (rng, logits, embedding and kv_cache) to file + // save state (rng, logits, embedding and kv_cache) to file { - FILE *fp_write = fopen("dump_state.bin", "wb"); - llama_copy_state_data(ctx, state_mem); // could also copy directly to memory mapped file - fwrite(state_mem, 1, state_size, fp_write); - fclose(fp_write); + std::vector state_mem(llama_get_state_size(ctx)); + + { + 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); + } } // save state (last tokens) - const auto last_n_tokens_data_saved = std::vector(last_n_tokens_data); const auto n_past_saved = n_past; // first run - printf("\n%s", params.prompt.c_str()); + printf("\nfirst run: %s", params.prompt.c_str()); for (auto i = 0; i < params.n_predict; i++) { auto * logits = llama_get_logits(ctx); auto n_vocab = llama_n_vocab(model); + std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < n_vocab; token_id++) { @@ -83,9 +73,10 @@ int main(int argc, char ** argv) { llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; auto next_token = llama_sample_token(ctx, &candidates_p); auto next_token_str = llama_token_to_piece(ctx, next_token); - last_n_tokens_data.push_back(next_token); printf("%s", next_token_str.c_str()); + result0 += next_token_str; + if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); llama_free(ctx); @@ -103,32 +94,28 @@ int main(int argc, char ** argv) { // make new context auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params)); - // Load state (rng, logits, embedding and kv_cache) from file - { - FILE *fp_read = fopen("dump_state.bin", "rb"); - if (state_size != llama_get_state_size(ctx2)) { - fprintf(stderr, "\n%s : failed to validate state size\n", __func__); - llama_free(ctx2); - llama_free_model(model); - return 1; - } + printf("\nsecond run: %s", params.prompt.c_str()); - const size_t ret = fread(state_mem, 1, state_size, fp_read); - if (ret != state_size) { + // load state (rng, logits, embedding and kv_cache) from file + { + std::vector state_mem(llama_get_state_size(ctx2)); + + FILE * fp_read = fopen("dump_state.bin", "rb"); + + const size_t ret = fread(state_mem.data(), 1, state_mem.size(), fp_read); + if (ret != state_mem.size()) { 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); // could also read directly from memory mapped file + llama_set_state_data(ctx2, state_mem.data()); + fclose(fp_read); } - delete[] state_mem; - // restore state (last tokens) - last_n_tokens_data = last_n_tokens_data_saved; n_past = n_past_saved; // second run @@ -143,10 +130,11 @@ int main(int argc, char ** argv) { llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; auto next_token = llama_sample_token(ctx2, &candidates_p); auto next_token_str = llama_token_to_piece(ctx2, next_token); - last_n_tokens_data.push_back(next_token); printf("%s", next_token_str.c_str()); - if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) { + result1 += next_token_str; + + if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); llama_free(ctx2); llama_free_model(model); @@ -155,10 +143,17 @@ int main(int argc, char ** argv) { n_past += 1; } - printf("\n\n"); + printf("\n"); llama_free(ctx2); llama_free_model(model); + if (result0 != result1) { + fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__); + return 1; + } + + fprintf(stderr, "\n%s : success\n", __func__); + return 0; } From 3ad1e3f1a10c1f66b4f1cd7510e0977fadbc0dfd Mon Sep 17 00:00:00 2001 From: coezbek Date: Tue, 17 Oct 2023 18:51:02 +0200 Subject: [PATCH 10/95] server : documentation of JSON return value of /completion endpoint (#3632) * Added documentation of JSON return value of /completion endpoint * Update examples/server/README.md --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 42 +++++++++++++++++++++++++++++++++------ 1 file changed, 36 insertions(+), 6 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 8a079ae26..9737010d3 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -106,25 +106,25 @@ node index.js ## API Endpoints -- **POST** `/completion`: Given a prompt, it returns the predicted completion. +- **POST** `/completion`: Given a `prompt`, it returns the predicted completion. *Options:* + `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does. + `temperature`: Adjust the randomness of the generated text (default: 0.8). `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). - `n_predict`: Set the 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_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 initial prompt to retain when the model resets its internal context. - By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt. + `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. + By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt. `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. - `prompt`: Provide a prompt as a string, or as an array of strings and numbers representing tokens. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. If the prompt is a string, or an array with the first element given as a string, a space is inserted in the front like main.cpp does. - `stop`: Specify a JSON array of stopping strings. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []). @@ -158,6 +158,36 @@ 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) + *Result JSON:* + + 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) + + `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. *Options:* From e74c705e15cd228ad696c4a3cdea6d6fb4ff434c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Oct 2023 19:52:53 +0300 Subject: [PATCH 11/95] editorconfig : remove trailing spaces --- examples/server/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 9737010d3..9f0ace3d7 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -164,7 +164,7 @@ node index.js `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) + `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` @@ -186,7 +186,7 @@ node index.js `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`) + `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 a5e8c1d8c71f01d98ae2ec63a57c118664f9764d Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 17 Oct 2023 19:00:58 +0200 Subject: [PATCH 12/95] train-text-from-scratch : fix assert failure in ggml-alloc (#3618) --- .../train-text-from-scratch.cpp | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) 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 be693b3ac..1ce6cef29 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -253,13 +253,14 @@ static void init_model(struct my_llama_model * model) { set_param_model(model); // measure data size - struct ggml_allocr * alloc = NULL; - alloc = ggml_allocr_new_measure(tensor_alignment); - alloc_model(alloc, model); + size_t size = 0; + for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + size += GGML_PAD(ggml_nbytes(t), tensor_alignment); + } // allocate data - model->data.resize(ggml_allocr_max_size(alloc) + tensor_alignment); - ggml_allocr_free(alloc); + struct ggml_allocr * alloc = NULL; + model->data.resize(size + tensor_alignment); alloc = ggml_allocr_new(model->data.data(), model->data.size(), tensor_alignment); alloc_model(alloc, model); ggml_allocr_free(alloc); @@ -1094,11 +1095,9 @@ int main(int argc, char ** argv) { struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); // measure required memory for input tensors - alloc = ggml_allocr_new_measure(tensor_alignment); - ggml_allocr_alloc(alloc, tokens_input); - ggml_allocr_alloc(alloc, target_probs); - size_t max_input_size = ggml_allocr_max_size(alloc) + tensor_alignment; - ggml_allocr_free(alloc); + size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) + + GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) + + tensor_alignment; printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); // allocate input tensors From 40e5ce054f4c4fa555e4510ea5f760bb29185332 Mon Sep 17 00:00:00 2001 From: shibe2 Date: Wed, 11 Oct 2023 21:30:06 +0400 Subject: [PATCH 13/95] CLBlast: Fix temporary buffer size for f16 conversion (wsize) Fix buffer overflow. Reduce the size to fit just one 2D slice. Assert sufficient size. --- ggml-opencl.cpp | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 33d0691eb..22fd0e3a7 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1568,7 +1568,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr 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 */) { +static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) { GGML_ASSERT(fp16_support); const int64_t ne00 = src0->ne[0]; @@ -1598,6 +1598,10 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; + GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne); + GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne); + ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata; + size_t x_size; size_t y_size; size_t d_size; @@ -1634,7 +1638,6 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr // convert src1 to fp16 // TODO: use multiple threads - ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i13 * ne12 + i12); char * src1i = (char *) src1->data + i13*nb13 + i12*nb12; if (src1_cont_rows) { if (src1_cont_cols) { @@ -1897,8 +1900,8 @@ void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * } size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) { - return ggml_nelements(src1) * sizeof(ggml_fp16_t); + if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) { + return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]); } return 0; } From 8402566a7c436bfbde8e7b0461faee50298106a0 Mon Sep 17 00:00:00 2001 From: BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com> Date: Tue, 17 Oct 2023 14:13:21 -0400 Subject: [PATCH 14/95] readme : update hot-topics & models, detail windows release in usage (#3615) * Update README.md * Update README.md * Update README.md * move "Running on Windows" section below "Prepare data and run" --------- Co-authored-by: Georgi Gerganov --- README.md | 27 ++++++++++++++++++++------- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 56372865b..4fd4bd427 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 - +- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252) - ‼️ Breaking change: `rope_freq_base` and `rope_freq_scale` must be set to zero to use the model default values: [#3401](https://github.com/ggerganov/llama.cpp/pull/3401) - Parallel decoding + continuous batching support added: [#3228](https://github.com/ggerganov/llama.cpp/pull/3228) \ **Devs should become familiar with the new API** @@ -89,16 +89,17 @@ as the main playground for developing new features for the [ggml](https://github - [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 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b) +- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b) - [X] [WizardLM](https://github.com/nlpxucan/WizardLM) -- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft)) -- [X] [Aquila-7B](https://huggingface.co/BAAI/Aquila-7B) / [AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B) -- [X] [Aquila2-7B](https://huggingface.co/BAAI/Aquila2-7B) / [AquilaChat2-7B](https://huggingface.co/BAAI/AquilaChat2-7B) / [AquilaChat2-34B](https://huggingface.co/BAAI/AquilaChat2-34B) / [Aquila2-34B](https://huggingface.co/BAAI/Aquila2-34B) +- [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] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) +- [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) + **Bindings:** @@ -207,7 +208,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8 ## Usage -Here are the steps for the LLaMA-7B model. +Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model. ### Get the Code @@ -574,6 +575,18 @@ python3 convert.py models/7B/ When running the larger models, make sure you have enough disk space to store all the intermediate files. +### Running on Windows with prebuilt binaries + +You will find prebuilt Windows binaries on the release page. + +Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`) + +From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so: + +``` +.\main -m llama-2-7b.Q4_0.gguf -n 128 +``` + ### Memory/Disk Requirements 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. From e1675d133c31e1c8de2f06be7164e12c0ba6cf2c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Oct 2023 22:34:26 +0300 Subject: [PATCH 15/95] llama : avoid fprintf in favor of LLAMA_LOG (#3538) --- examples/main/main.cpp | 2 +- llama.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index a5fb65548..7313d06a0 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -799,7 +799,7 @@ int main(int argc, char ** argv) { } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); + const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp)); diff --git a/llama.cpp b/llama.cpp index 82b7638ae..37df88779 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2327,13 +2327,13 @@ static void llm_load_vocab( } if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) { - fprintf(stderr, "%s: warning: Mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n", + LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n", __func__, special_tokens_count_from_verification, vocab.id_to_token.size(), special_tokens_count_by_type, vocab.id_to_token.size() ); } else { - fprintf(stderr, "%s: Special tokens definition check successful ( %u/%zu ).\n", + LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n", __func__, special_tokens_count_from_verification, vocab.id_to_token.size() ); From cb33f43a2a9f5a5a5f8d290dd97c625d9ba97a2f Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 17 Oct 2023 22:24:50 +0200 Subject: [PATCH 16/95] fix embeddings when using CUDA (#3657) --- llama.cpp | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/llama.cpp b/llama.cpp index 37df88779..04a779e04 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5903,6 +5903,13 @@ static int llama_decode_internal( ggml_allocr_alloc_graph(lctx.alloc, gf); + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + 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); + + #ifdef GGML_USE_CUBLAS for (int i = 0; i < gf->n_leafs; i++) { ggml_tensor * node = gf->leafs[i]; @@ -5920,6 +5927,12 @@ static int llama_decode_internal( } ggml_cuda_set_mul_mat_q(cparams.mul_mat_q); + + // 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); @@ -5944,12 +5957,6 @@ static int llama_decode_internal( n_threads = 1; } - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - 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 GGML_USE_MPI const int64_t n_layer = hparams.n_layer; ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); From 1117d06607d2d885640ac501f05f0aae5494e2c5 Mon Sep 17 00:00:00 2001 From: shibe2 Date: Wed, 18 Oct 2023 16:09:22 +0400 Subject: [PATCH 17/95] opencl : fix element-wise multiplication (#3656) --- ggml-opencl.cpp | 67 ++++++++++++++----------------------------------- 1 file changed, 19 insertions(+), 48 deletions(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 22fd0e3a7..67ac20eac 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1395,75 +1395,46 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; - const int64_t ne0 = ne00 * ne01 * ne02 * ne03; 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 nb10 = src1->nb[0]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; size_t x_size; size_t d_size; - cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size); // src0 + cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0 cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted. - cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size); // dst + cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { - const int i0 = i03*ne02 + i02; - cl_event ev; // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, i0, src0, i03, i02, &ev)); + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev)); - if (nb10 == sizeof(float)) { - // Contiguous, avoid overhead from queueing many kernel runs - const int64_t i13 = i03%ne13; - const int64_t i12 = i02%ne12; - const int i1 = i13*ne12*ne11 + i12*ne11; + const int64_t i13 = i03%ne13; + const int64_t i12 = i02%ne12; + const int i1 = i13*ne12*ne11 + i12*ne11; - cl_int x_offset = 0; - cl_int y_offset = i1*ne10; - cl_int d_offset = 0; + cl_int x_offset = 0; + cl_int y_offset = i1*ne10; + cl_int d_offset = 0; - size_t global = ne00 * ne01; - cl_int ky = ne10; - CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky)); - CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); - } else { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const int64_t i13 = i03%ne13; - const int64_t i12 = i02%ne12; - const int64_t i11 = i01%ne11; - const int i1 = i13*ne12*ne11 + i12*ne11 + i11; + size_t global = ne00 * ne01; + cl_int ky = ne10 * ne11; - cl_int x_offset = i01*ne00; - cl_int y_offset = i1*ne10; - cl_int d_offset = i01*ne00; - - // compute - size_t global = ne00; - cl_int ky = ne10; - CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky)); - CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); - } - } + CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X)); + CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset)); + CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y)); + CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset)); + CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D)); + CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset)); + CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky)); + CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); CL_CHECK(clReleaseEvent(ev)); CL_CHECK(clFinish(queue)); From c67fe68e417f766970fb1feaf2e66458aa24116a Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Wed, 18 Oct 2023 07:21:48 -0500 Subject: [PATCH 18/95] metal : implement q5_0 and q5_1 kernels (#3648) * metal : implement dequantize_q5_0 * metal : block_q_n_dot_y for block_q5_0 (broken) * metal : revert unnecessary change * metal : implement dequantize_q5_1 * metal : block_q_n_dot_y for q5_1 (broken) * metal : fix block_q_n_dot_y * minor : spaces / formatting --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 47 +++++++++++++- ggml-metal.metal | 163 ++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 206 insertions(+), 4 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 87fa17216..c908106be 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -73,6 +73,8 @@ struct ggml_metal_context { 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); @@ -87,6 +89,8 @@ struct ggml_metal_context { 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); @@ -97,6 +101,8 @@ struct ggml_metal_context { 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); @@ -254,6 +260,8 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { 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); @@ -268,6 +276,8 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { 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); @@ -278,8 +288,10 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { 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_q8_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); @@ -346,6 +358,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { 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); @@ -360,6 +374,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { 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); @@ -370,8 +386,10 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { 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_q8_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); @@ -1052,6 +1070,8 @@ void ggml_metal_graph_compute( 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; @@ -1121,6 +1141,24 @@ void ggml_metal_graph_compute( nth1 = 8; [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32]; } break; + case GGML_TYPE_Q5_0: + { + GGML_ASSERT(ne02 == 1); + GGML_ASSERT(ne12 == 1); + + nth0 = 8; + nth1 = 8; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32]; + } break; + case GGML_TYPE_Q5_1: + { + GGML_ASSERT(ne02 == 1); + GGML_ASSERT(ne12 == 1); + + nth0 = 8; + nth1 = 8; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32]; + } break; case GGML_TYPE_Q8_0: { GGML_ASSERT(ne02 == 1); @@ -1201,7 +1239,8 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17]; - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 || + 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) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1233,6 +1272,8 @@ void ggml_metal_graph_compute( 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; diff --git a/ggml-metal.metal b/ggml-metal.metal index 99b9fd7a7..69fc71362 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -18,6 +18,21 @@ typedef struct { uint8_t qs[QK4_1 / 2]; // nibbles / quants } block_q4_1; +#define QK5_0 32 +typedef struct { + half d; // delta + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_0 / 2]; // nibbles / quants +} block_q5_0; + +#define QK5_1 32 +typedef struct { + half d; // delta + half m; // min + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_1 / 2]; // nibbles / quants +} block_q5_1; + #define QK8_0 32 typedef struct { half d; // delta @@ -399,8 +414,11 @@ kernel void kernel_rms_norm( // that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) { float d = qb_curr->d; + float2 acc = 0.f; + device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2); + for (int i = 0; i < 8; i+=2) { acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F) + yl[i + 1] * (qs[i / 2] & 0x0F00); @@ -417,8 +435,11 @@ inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thre inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thread float * yl, int il) { float d = qb_curr->d; float m = qb_curr->m; - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2); + float2 acc = 0.f; + + device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2); + for (int i = 0; i < 8; i+=2) { acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F) + yl[i + 1] * (qs[i / 2] & 0x0F00); @@ -428,6 +449,49 @@ inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thre return d * (acc[0] + acc[1]) + sumy * m; } +// function for calculate inner product between half a q5_0 block and 16 floats (yl), sumy is SUM(yl[i]) +// il indicates where the q5 quants begin (0 or QK5_0/4) +// we assume that the yl's have been multiplied with the appropriate scale factor +// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) +inline float block_q_n_dot_y(device const block_q5_0 * qb_curr, float sumy, thread float * yl, int il) { + float d = qb_curr->d; + + float2 acc = 0.f; + + device const uint16_t * qs = ((device const uint16_t *)qb_curr + 3 + il/2); + const uint32_t qh = *((device const uint32_t *)qb_curr->qh); + + for (int i = 0; i < 8; i+=2) { + acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)) + + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); + acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)) + + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); + } + return d * (sumy * -16.f + acc[0] + acc[1]); +} + +// function for calculate inner product between half a q5_1 block and 16 floats (yl), sumy is SUM(yl[i]) +// il indicates where the q5 quants begin (0 or QK5_1/4) +// we assume that the yl's have been multiplied with the appropriate scale factor +// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) +inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thread float * yl, int il) { + float d = qb_curr->d; + float m = qb_curr->m; + + float2 acc = 0.f; + + device const uint16_t * qs = ((device const uint16_t *)qb_curr + 4 + il/2); + const uint32_t qh = *((device const uint32_t *)qb_curr->qh); + + for (int i = 0; i < 8; i+=2) { + acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)) + + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); + acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)) + + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); + } + return d * (acc[0] + acc[1]) + sumy * m; +} + // putting them in the kernel cause a significant performance penalty #define N_DST 4 // each SIMD group works on 4 rows #define N_SIMDGROUP 2 // number of SIMD groups in a thread group @@ -525,6 +589,43 @@ kernel void kernel_mul_mv_q4_1_f32( mul_vec_q_n_f32(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg); } +kernel void kernel_mul_mv_q5_0_f32( + device const void * src0, + 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 & gqa[[buffer(17)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg); +} + +kernel void kernel_mul_mv_q5_1_f32( + device const void * src0, + 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 & gqa[[buffer(17)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg); +} + + #define NB_Q8_0 8 kernel void kernel_mul_mv_q8_0_f32( @@ -2149,6 +2250,62 @@ void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg } } +template +void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 3); + const float d = xb->d; + const float md = -16.h * xb->d; + const ushort mask = il ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = il ? 4 : 0; + + const int gh_mv = il ? 12 : 0; + const int gh_bk = il ? 0 : 4; + + for (int i = 0; i < 8; i++) { + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg[i/2][2*(i%2)+0] = d * x0 + md; + reg[i/2][2*(i%2)+1] = d * x1 + md; + } +} + +template +void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 4); + const float d = xb->d; + const float m = xb->m; + const ushort mask = il ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = il ? 4 : 0; + + const int gh_mv = il ? 12 : 0; + const int gh_bk = il ? 0 : 4; + + for (int i = 0; i < 8; i++) { + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg[i/2][2*(i%2)+0] = d * x0 + m; + reg[i/2][2*(i%2)+1] = d * x1 + m; + } +} + template 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); @@ -2490,6 +2647,8 @@ template [[host_name("kernel_get_rows_f32")]] kernel get_rows_t kernel_get_rows template [[host_name("kernel_get_rows_f16")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows; @@ -2518,6 +2677,8 @@ template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm; From 0e89203b517c95ec6675eda75d200a60d1e8921d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 18 Oct 2023 16:21:57 +0300 Subject: [PATCH 19/95] speculative : add tree-based sampling example (#3624) * sampling : one sequence per sampling context ggml-ci * speculative : add tree-based sampling support ggml-ci * speculative : reuse the n_parallel CLI param * speculative : refactor sampling * examples : fix build after sampling refactoring ggml-ci * batched : fix n_seq_id * sampling : fix malloc ggml-ci * swift : fix build ggml-ci * swift : try to fix build ggml-ci * prompts : add assistant.txt * common : add llama_batch_add() and llama_batch_clear() helpers * speculative : minor refactor ggml-ci * minor : comments + rename ggml-ci * speculative : fix off-by-one for n_drafted * speculative : fix the n_drafted fix + p constants --- Makefile | 2 +- common/common.cpp | 21 ++ common/common.h | 16 +- common/log.h | 101 ++++-- common/sampling.cpp | 201 +++++++----- common/sampling.h | 83 +++-- examples/batched-bench/batched-bench.cpp | 38 +-- examples/batched.swift/Sources/main.swift | 14 +- examples/batched/batched.cpp | 26 +- examples/embd-input/embd-input-lib.cpp | 2 +- examples/infill/infill.cpp | 44 +-- examples/llava/llava-utils.h | 2 +- examples/llava/llava.cpp | 2 +- examples/main/main.cpp | 92 ++---- examples/parallel/parallel.cpp | 70 ++-- examples/server/server.cpp | 73 ++--- examples/simple/simple.cpp | 2 +- examples/speculative/speculative.cpp | 383 ++++++++++++++-------- llama.cpp | 95 ++++-- llama.h | 17 +- prompts/assistant.txt | 31 ++ 21 files changed, 737 insertions(+), 578 deletions(-) create mode 100644 prompts/assistant.txt diff --git a/Makefile b/Makefile index 9a8faef45..04104bee8 100644 --- a/Makefile +++ b/Makefile @@ -545,7 +545,7 @@ 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 build-info.h common/log.h -COMMON_DEPS = $(COMMON_H_DEPS) common.o sampling.o +COMMON_DEPS = $(COMMON_H_DEPS) common.o sampling.o grammar-parser.o common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ diff --git a/common/common.cpp b/common/common.cpp index 3e4b8a8cb..ce14d66b8 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -820,6 +820,27 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param return cparams; } +void llama_batch_clear(struct llama_batch & batch) { + batch.n_tokens = 0; +} + +void llama_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits) { + batch.token [batch.n_tokens] = id; + batch.pos [batch.n_tokens] = pos, + batch.n_seq_id[batch.n_tokens] = seq_ids.size(); + for (size_t i = 0; i < seq_ids.size(); ++i) { + batch.seq_id[batch.n_tokens][i] = seq_ids[i]; + } + batch.logits [batch.n_tokens] = logits; + + batch.n_tokens++; +} + std::tuple llama_init_from_gpt_params(gpt_params & params) { auto mparams = llama_model_params_from_gpt_params(params); diff --git a/common/common.h b/common/common.h index 08c603231..65d3d20cd 100644 --- a/common/common.h +++ b/common/common.h @@ -70,6 +70,7 @@ struct gpt_params { std::vector antiprompt; // string upon seeing which more user input is prompted std::string logdir = ""; // directory in which to save YAML log files + // TODO: avoid tuple, use struct std::vector> lora_adapter; // lora adapter path with user defined scale std::string lora_base = ""; // base model path for the lora adapter @@ -124,10 +125,23 @@ void process_escapes(std::string& input); // Model utils // +// TODO: avoid tuplue, use struct std::tuple llama_init_from_gpt_params(gpt_params & params); -struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params); + +struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params); +// Batch utils + +void llama_batch_clear(struct llama_batch & batch); + +void llama_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits); + // // Vocab utils // diff --git a/common/log.h b/common/log.h index b8953fdca..70e7e4ca2 100644 --- a/common/log.h +++ b/common/log.h @@ -579,38 +579,75 @@ inline std::string log_var_to_string_impl(const std::vector & var) return buf.str(); } -#define LOG_TOKENS_TOSTR_PRETTY(ctx, tokens) \ - [&tokens, &ctx]() \ - { \ - std::stringstream buf; \ - buf << "[ "; \ - \ - bool first = true; \ - for (const auto &token : tokens) \ - { \ - if (!first) \ - buf << ", "; \ - else \ - first = false; \ - \ - auto detokenized = llama_token_to_piece(ctx, token); \ - \ - detokenized.erase( \ - std::remove_if( \ - detokenized.begin(), \ - detokenized.end(), \ - [](const unsigned char c) { return !std::isprint(c); }), \ - detokenized.end()); \ - \ - buf \ - << "'" << detokenized << "'" \ - << ":" << std::to_string(token); \ - } \ - buf << " ]"; \ - \ - return buf.str(); \ - }() \ - .c_str() +template +inline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens) +{ + std::stringstream buf; + buf << "[ "; + + bool first = true; + for (const auto &token : tokens) + { + if (!first) { + buf << ", "; + } else { + first = false; + } + + auto detokenized = llama_token_to_piece(ctx, token); + + detokenized.erase( + std::remove_if( + detokenized.begin(), + detokenized.end(), + [](const unsigned char c) { return !std::isprint(c); }), + detokenized.end()); + + buf + << "'" << detokenized << "'" + << ":" << std::to_string(token); + } + buf << " ]"; + + return buf.str(); +} + +template +inline std::string LOG_BATCH_TOSTR_PRETTY(const C & ctx, const B & batch) +{ + std::stringstream buf; + buf << "[ "; + + bool first = true; + for (int i = 0; i < batch.n_tokens; ++i) + { + if (!first) { + buf << ", "; + } else { + first = false; + } + + auto detokenized = llama_token_to_piece(ctx, batch.token[i]); + + detokenized.erase( + std::remove_if( + detokenized.begin(), + detokenized.end(), + [](const unsigned char c) { return !std::isprint(c); }), + detokenized.end()); + + buf + << "\n" << std::to_string(i) + << ":token '" << detokenized << "'" + << ":pos " << std::to_string(batch.pos[i]) + << ":n_seq_id " << std::to_string(batch.n_seq_id[i]) + << ":seq_id " << std::to_string(batch.seq_id[i][0]) + << ":logits " << std::to_string(batch.logits[i]); + } + buf << " ]"; + + return buf.str(); +} #ifdef LOG_DISABLE_LOGS diff --git a/common/sampling.cpp b/common/sampling.cpp index 8ce419459..0b2466581 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -1,64 +1,81 @@ #include "sampling.h" -llama_sampling_context::~llama_sampling_context() { - for (auto & it : sequence_contexts) { - if (it.second.grammar != NULL) { - llama_grammar_free(it.second.grammar); - it.second.grammar = NULL; +struct llama_sampling_context * llama_sampling_init(const struct gpt_params & params) { + struct llama_sampling_context * result = new llama_sampling_context(); + + result->params = params.sampling_params; + result->grammar = nullptr; + + // if there is a grammar, parse it + if (!params.grammar.empty()) { + result->parsed_grammar = grammar_parser::parse(params.grammar.c_str()); + + // will be empty (default) if there are parse errors + if (result->parsed_grammar.rules.empty()) { + fprintf(stderr, "%s: failed to parse grammar\n", __func__); + return nullptr; } + + std::vector grammar_rules(result->parsed_grammar.c_rules()); + + result->grammar = llama_grammar_init( + grammar_rules.data(), + grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root")); } + + result->prev.resize(params.n_ctx); + + return result; } -llama_sampling_context llama_sampling_context_init( - const struct gpt_params & params, - llama_grammar * grammar) { - llama_sampling_context result; +void llama_sampling_free(struct llama_sampling_context * ctx) { + if (ctx->grammar != NULL) { + llama_grammar_free(ctx->grammar); + } - result.params = params.sampling_params; - result.grammar = grammar; - return result; + delete ctx; } -// Note: Creates the context if it doesn't exist, so this always return something. -llama_sampler_sequence_context & llama_sampling_get_sequence_context( - llama_sampling_context & ctx_sampling, - const llama_seq_id seq) { - const auto it = ctx_sampling.sequence_contexts.find(seq); - if (it != ctx_sampling.sequence_contexts.end()) { - return it->second; +void llama_sampling_reset(llama_sampling_context * ctx) { + if (ctx->grammar != NULL) { + llama_grammar_free(ctx->grammar); } - llama_sampler_sequence_context new_ctx = { - 2.0f * ctx_sampling.params.mirostat_tau, - ctx_sampling.grammar != NULL ? llama_grammar_copy(ctx_sampling.grammar) : NULL, - }; - return ctx_sampling.sequence_contexts.insert({seq, new_ctx}).first->second; + + if (!ctx->parsed_grammar.rules.empty()) { + std::vector grammar_rules(ctx->parsed_grammar.c_rules()); + + ctx->grammar = llama_grammar_init( + grammar_rules.data(), + grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root")); + } + + std::fill(ctx->prev.begin(), ctx->prev.end(), 0); + ctx->cur.clear(); } -bool llama_sampling_context_reset( - llama_sampling_context & ctx_sampling, - const llama_seq_id seq) { - const auto it = ctx_sampling.sequence_contexts.find(seq); - if (it == ctx_sampling.sequence_contexts.end()) return false; - if (it->second.grammar != NULL) { - llama_grammar_free(it->second.grammar); - it->second.grammar = NULL; +void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) { + if (dst->grammar) { + llama_grammar_free(dst->grammar); + dst->grammar = nullptr; } - ctx_sampling.sequence_contexts.erase(it); - return true; + + if (src->grammar) { + dst->grammar = llama_grammar_copy(src->grammar); + } + + dst->prev = src->prev; } llama_token llama_sampling_sample( - struct llama_context * ctx, - struct llama_context * ctx_guidance, - struct llama_sampling_context & ctx_sampling, - const std::vector & last_tokens, - std::vector & candidates, - const int idx, - llama_seq_id seq) { - const int n_ctx = llama_n_ctx(ctx); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + const int n_ctx = llama_n_ctx(ctx_main); + const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); + + const llama_sampling_params & params = ctx_sampling->params; - const llama_sampling_params & params = ctx_sampling.params; 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; @@ -73,41 +90,45 @@ llama_token llama_sampling_sample( const float mirostat_eta = params.mirostat_eta; const bool penalize_nl = params.penalize_nl; + auto & prev = ctx_sampling->prev; + auto & cur = ctx_sampling->cur; + llama_token id = 0; - float * logits = llama_get_logits_ith(ctx, idx); + float * logits = llama_get_logits_ith(ctx_main, idx); // Apply params.logit_bias map for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { logits[it->first] += it->second; } - candidates.clear(); + cur.clear(); + 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}); + cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); } - llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + llama_token_data_array cur_p = { cur.data(), cur.size(), false }; - if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); + if (ctx_cfg) { + llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale); } // apply penalties - if (!last_tokens.empty()) { - const float nl_logit = logits[llama_token_nl(ctx)]; - const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx); + if (!prev.empty()) { + const float nl_logit = logits[llama_token_nl(ctx_main)]; + const int last_n_repeat = std::min(std::min((int)prev.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx, &cur_p, - last_tokens.data() + last_tokens.size() - last_n_repeat, + llama_sample_repetition_penalty(ctx_main, &cur_p, + prev.data() + prev.size() - last_n_repeat, last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx, &cur_p, - last_tokens.data() + last_tokens.size() - last_n_repeat, + llama_sample_frequency_and_presence_penalties(ctx_main, &cur_p, + prev.data() + prev.size() - last_n_repeat, last_n_repeat, alpha_frequency, alpha_presence); if (!penalize_nl) { for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(ctx)) { + if (cur_p.data[idx].id == llama_token_nl(ctx_main)) { cur_p.data[idx].logit = nl_logit; break; } @@ -115,52 +136,58 @@ llama_token llama_sampling_sample( } } - llama_sampler_sequence_context & ctx_seq = llama_sampling_get_sequence_context(ctx_sampling, seq); - - if (ctx_seq.grammar != NULL) { - llama_sample_grammar(ctx, &cur_p, ctx_seq.grammar); + if (ctx_sampling->grammar != NULL) { + llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); } if (temp <= 0) { // Greedy sampling - id = llama_sample_token_greedy(ctx, &cur_p); + id = llama_sample_token_greedy(ctx_main, &cur_p); } else { if (mirostat == 1) { const int mirostat_m = 100; - llama_sample_temp(ctx, &cur_p, temp); - id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_seq.mirostat_mu); + llama_sample_temp(ctx_main, &cur_p, temp); + id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu); } else if (mirostat == 2) { - llama_sample_temp(ctx, &cur_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &ctx_seq.mirostat_mu); + llama_sample_temp(ctx_main, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu); } else { // Temperature sampling size_t min_keep = std::max(1, params.n_probs); - llama_sample_top_k (ctx, &cur_p, top_k, min_keep); - llama_sample_tail_free (ctx, &cur_p, tfs_z, min_keep); - llama_sample_typical (ctx, &cur_p, typical_p, min_keep); - llama_sample_top_p (ctx, &cur_p, top_p, min_keep); - llama_sample_temp(ctx, &cur_p, temp); + llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); + 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_temp (ctx_main, &cur_p, temp); - { - const int n_top = 10; - LOG("top %d candidates:\n", n_top); + id = llama_sample_token(ctx_main, &cur_p); - for (int i = 0; i < n_top; i++) { - const llama_token id = cur_p.data[i].id; - (void)id; // To avoid a warning that id is unused when logging is disabled. - LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); - } - } + //{ + // const int n_top = 10; + // LOG("top %d candidates:\n", n_top); - id = llama_sample_token(ctx, &cur_p); + // for (int i = 0; i < n_top; i++) { + // const llama_token id = cur_p.data[i].id; + // (void)id; // To avoid a warning that id is unused when logging is disabled. + // LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p); + // } + //} - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str()); } } - if (ctx_seq.grammar != NULL) { - llama_grammar_accept_token(ctx, ctx_seq.grammar, id); - } - return id; } + +void llama_sampling_accept( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + llama_token id) { + ctx_sampling->prev.erase(ctx_sampling->prev.begin()); + ctx_sampling->prev.push_back(id); + + if (ctx_sampling->grammar != NULL) { + llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id); + } +} diff --git a/common/sampling.h b/common/sampling.h index 0aab5d03c..50afcbc12 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -2,6 +2,8 @@ #include "llama.h" +#include "grammar-parser.h" + #include #include #include @@ -34,75 +36,64 @@ typedef struct llama_sampling_params { } llama_sampling_params; -// per-sequence sampler context -typedef struct llama_sampler_sequence_context { - float mirostat_mu; // mirostat sampler state - llama_grammar * grammar; -} llama_sampler_sequence_context; - // general sampler context -typedef struct llama_sampling_context { - ~llama_sampling_context(); - - // parameters that will be used for sampling and when creating - // new llama_sampler_sequence_context instances +// TODO: move to llama.h +struct llama_sampling_context { + // parameters that will be used for sampling llama_sampling_params params; - // map of sequence ids to sampler contexts - std::unordered_map sequence_contexts; + // mirostat sampler state + float mirostat_mu; - // when non-NULL, new instances of llama_sampler_sequence_context - // will get a copy of the grammar here - // note: only the pointer is stored here, it is not a copy of - // the grammar and shouldn't be freed llama_grammar * grammar; -} llama_sampling_context; + + // internal + grammar_parser::parse_state parsed_grammar; + + // TODO: replace with ring-buffer + std::vector prev; + std::vector cur; +}; #include "common.h" // Create a new sampling context instance. -llama_sampling_context llama_sampling_context_init( - const struct gpt_params & params, - llama_grammar * grammar = NULL); +struct llama_sampling_context * llama_sampling_init(const struct gpt_params & params); -// Fetches the sampler context for the specified sequence id (defaults to 0). -// If the context for that sequence id doesn't already exist, it will be created with -// default values based on the parameters in the ctx_sampling argument. -llama_sampler_sequence_context & llama_sampling_get_sequence_context( - llama_sampling_context & ctx_sampling, - const llama_seq_id seq = 0); +void llama_sampling_free(struct llama_sampling_context * ctx); -// Reset the sampler context for the supplied sequence id (defaults to 0). -// This is necessary to reuse a sequence id or free memory used by sequences -// that are no longer required. -bool llama_sampling_context_reset( - llama_sampling_context & ctx_sampling, - const llama_seq_id seq = 0); +// Reset the sampler context +// - clear prev tokens +// - reset grammar +void llama_sampling_reset(llama_sampling_context * ctx); + +// Copy the sampler context +void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst); // this is a common sampling function used across the examples for convenience // it can serve as a starting point for implementing your own sampling function // Note: When using multiple sequences, it is the caller's responsibility to call -// llama_sampling_context_reset when a sequence ends +// llama_sampling_reset when a sequence ends // // required: -// - ctx: context to use for sampling +// - ctx_main: context to use for sampling // - ctx_sampling: sampling-specific context // // optional: -// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL -// - last_tokens: needed for repetition penalty, ignore if empty -// - idx: sample from llama_get_logits_ith(ctx, idx) -// - seq: sequence id to associate sampler state with +// - ctx_cfg: context to use for classifier-free guidance +// - idx: sample from llama_get_logits_ith(ctx, idx) // // returns: // - token: sampled token // - candidates: vector of candidate tokens // llama_token llama_sampling_sample( - struct llama_context * ctx, - struct llama_context * ctx_guidance, - struct llama_sampling_context & ctx_sampling, - const std::vector & last_tokens, - std::vector & candidates, - const int idx = 0, - llama_seq_id seq = 0); + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + int idx = 0); + +void llama_sampling_accept( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + llama_token id); diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 3e1e0716d..c552eaa73 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -114,7 +114,7 @@ int main(int argc, char ** argv) { return 1; } - llama_batch batch = llama_batch_init(n_kv_max, 0); + llama_batch batch = llama_batch_init(n_kv_max, 0, 1); // decode in batches of ctx_params.n_batch tokens auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch) { @@ -123,11 +123,12 @@ int main(int argc, char ** argv) { llama_batch batch_view = { n_tokens, - batch.token + i, + batch.token + i, nullptr, - batch.pos + i, - batch.seq_id + i, - batch.logits + i, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, 0, 0, 0, // unused }; @@ -143,13 +144,8 @@ int main(int argc, char ** argv) { // warm up { - batch.n_tokens = 16; - - for (int i = 0; i < batch.n_tokens; ++i) { - batch.token[i] = 0; - batch.pos[i] = i; - batch.seq_id[i] = 0; - batch.logits[i] = false; + for (int i = 0; i < 16; ++i) { + llama_batch_add(batch, 0, i, { 0 }, false); } if (!decode_helper(ctx, batch, ctx_params.n_batch)) { @@ -174,13 +170,12 @@ int main(int argc, char ** argv) { continue; } - batch.n_tokens = is_pp_shared ? pp : pl*pp; + llama_batch_clear(batch); - for (int i = 0; i < batch.n_tokens; ++i) { - batch.token[i] = 0; - batch.pos[i] = i; - batch.seq_id[i] = 0; - batch.logits[i] = false; + const int n_tokens = is_pp_shared ? pp : pl*pp; + + for (int i = 0; i < n_tokens; ++i) { + llama_batch_add(batch, 0, i, { 0 }, false); } batch.logits[batch.n_tokens - 1] = true; @@ -204,13 +199,10 @@ int main(int argc, char ** argv) { const auto t_tg_start = ggml_time_us(); for (int i = 0; i < tg; ++i) { - batch.n_tokens = pl; + llama_batch_clear(batch); for (int j = 0; j < pl; ++j) { - batch.token[j] = 0; - batch.pos[j] = pp + i; - batch.seq_id[j] = j; - batch.logits[j] = true; + llama_batch_add(batch, 0, pp + i, { j }, true); } if (!decode_helper(ctx, batch, ctx_params.n_batch)) { diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index 05d1bb9d0..772730382 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -69,7 +69,7 @@ for id: llama_token in tokens { print("\n") -var batch = llama_batch_init(max(Int32(tokens.count), Int32(n_parallel)), 0) +var batch = llama_batch_init(max(Int32(tokens.count), Int32(n_parallel)), 0, 1) defer { llama_batch_free(batch) } @@ -80,7 +80,12 @@ batch.n_tokens = Int32(tokens.count) for (i, token) in tokens.enumerated() { batch.token[i] = token batch.pos[i] = Int32(i) - batch.seq_id[i] = 0 + batch.n_seq_id[i] = 1 + // batch.seq_id[i][0] = 0 + // TODO: is this the proper way to do this? + if let seq_id = batch.seq_id[i] { + seq_id[0] = 0 + } batch.logits[i] = 0 } @@ -169,7 +174,10 @@ while n_cur <= n_len { // push this new token for next evaluation batch.token[Int(batch.n_tokens)] = new_token_id batch.pos[Int(batch.n_tokens)] = n_cur - batch.seq_id[Int(batch.n_tokens)] = Int32(i) + batch.n_seq_id[Int(batch.n_tokens)] = 1 + if let seq_id = batch.seq_id[Int(batch.n_tokens)] { + seq_id[0] = Int32(i) + } batch.logits[Int(batch.n_tokens)] = 1 i_batch[i] = batch.n_tokens diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index a88e022d6..155212165 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -97,20 +97,15 @@ int main(int argc, char ** argv) { fflush(stderr); - // create a llama_batch with size 512 + // create a llama_batch // we use this object to submit token data for decoding - - llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0); + llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1); // evaluate the initial prompt - batch.n_tokens = tokens_list.size(); - - for (int32_t i = 0; i < batch.n_tokens; i++) { - batch.token[i] = tokens_list[i]; - batch.pos[i] = i; - batch.seq_id[i] = 0; - batch.logits[i] = false; + for (size_t i = 0; i < tokens_list.size(); ++i) { + llama_batch_add(batch, tokens_list[i], i, { 0 }, false); } + GGML_ASSERT(batch.n_tokens == (int) tokens_list.size()); // llama_decode will output logits only for the last token of the prompt batch.logits[batch.n_tokens - 1] = true; @@ -146,7 +141,7 @@ int main(int argc, char ** argv) { while (n_cur <= n_len) { // prepare the next batch - batch.n_tokens = 0; + llama_batch_clear(batch); // sample the next token for each parallel sequence / stream for (int32_t i = 0; i < n_parallel; ++i) { @@ -198,15 +193,10 @@ int main(int argc, char ** argv) { streams[i] += llama_token_to_piece(ctx, new_token_id); - // push this new token for next evaluation - batch.token [batch.n_tokens] = new_token_id; - batch.pos [batch.n_tokens] = n_cur; - batch.seq_id[batch.n_tokens] = i; - batch.logits[batch.n_tokens] = true; - i_batch[i] = batch.n_tokens; - batch.n_tokens += 1; + // push this new token for next evaluation + llama_batch_add(batch, new_token_id, n_cur, { i }, true); n_decode += 1; } diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 87a5a1c26..3ce33842c 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -79,7 +79,7 @@ bool eval_float(void * model, float * input, int N){ if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = { int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, }; + llama_batch batch = { int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, }; if (llama_decode(ctx, batch)) { fprintf(stderr, "%s : failed to eval\n", __func__); return false; diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 187623f5d..128d67080 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -257,12 +257,12 @@ int main(int argc, char ** argv) { LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix)); LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix)); - LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp)); + LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); // Should not run without any tokens if (embd_inp.empty()) { embd_inp.push_back(llama_token_bos(ctx)); - LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp)); + LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); } // Tokenize negative prompt @@ -273,10 +273,10 @@ int main(int argc, char ** argv) { LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos); - LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp)); + LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); - LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp)); + LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); original_prompt_len = original_inp.size(); guidance_offset = (int)guidance_inp.size() - original_prompt_len; @@ -294,8 +294,8 @@ int main(int argc, char ** argv) { params.n_keep = (int)embd_inp.size(); } - LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx)); - LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx)); + LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); + LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); // enable interactive mode if interactive start is specified @@ -388,9 +388,6 @@ int main(int argc, char ** argv) { grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); } - // TODO: replace with ring-buffer - std::vector last_tokens(n_ctx); - std::fill(last_tokens.begin(), last_tokens.end(), 0); LOG_TEE("\n##### Infill mode #####\n\n"); if (params.infill) { printf("\n************\n"); @@ -433,11 +430,7 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - const int n_vocab = llama_n_vocab(model); - - llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar); - std::vector candidates; - candidates.reserve(n_vocab); + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params); while (n_remain != 0 || params.interactive) { // predict @@ -484,7 +477,7 @@ int main(int argc, char ** argv) { 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)); + LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); } @@ -512,7 +505,7 @@ int main(int argc, char ** argv) { input_buf = embd_guidance.data(); input_size = embd_guidance.size(); - LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance)); + LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); } else { input_buf = embd.data(); input_size = embd.size(); @@ -535,7 +528,7 @@ int main(int argc, char ** argv) { n_eval = params.n_batch; } - LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); + LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { LOG_TEE("%s : failed to eval\n", __func__); @@ -554,12 +547,11 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - const llama_token id = llama_sampling_sample(ctx, ctx_guidance, ctx_sampling, last_tokens, candidates); + const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); - last_tokens.erase(last_tokens.begin()); - last_tokens.push_back(id); + llama_sampling_accept(ctx_sampling, ctx, id); - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens)); + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); embd.push_back(id); @@ -575,8 +567,8 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - last_tokens.erase(last_tokens.begin()); - last_tokens.push_back(embd_inp[n_consumed]); + ctx_sampling->prev.erase(ctx_sampling->prev.begin()); + ctx_sampling->prev.push_back(embd_inp[n_consumed]); ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -608,7 +600,7 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed) { // deal with eot token in infill mode - if ((last_tokens.back() == llama_token_eot(ctx) || is_interacting) && params.interactive){ + if ((ctx_sampling->prev.back() == llama_token_eot(ctx) || is_interacting) && params.interactive){ if(is_interacting && !params.interactive_first) { // print an eot token printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str()); @@ -675,7 +667,7 @@ int main(int argc, char ** argv) { is_interacting = false; } // deal with end of text token in interactive mode - else if (last_tokens.back() == llama_token_eos(ctx)) { + else if (ctx_sampling->prev.back() == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { @@ -727,7 +719,7 @@ int main(int argc, char ** argv) { const size_t original_size = embd_inp.size(); const auto line_inp = ::llama_tokenize(ctx, buffer, false); - LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp)); + LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h index 4e71351dd..e050b59be 100644 --- a/examples/llava/llava-utils.h +++ b/examples/llava/llava-utils.h @@ -17,7 +17,7 @@ inline bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, *n_past, 1, 0, }; + 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; diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index b24cb2e6f..f0974d5bc 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -127,7 +127,7 @@ int main(int argc, char ** argv) { 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_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); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 7313d06a0..1a5911c56 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -3,7 +3,6 @@ #include "console.h" #include "llama.h" #include "build-info.h" -#include "grammar-parser.h" #include #include @@ -245,12 +244,12 @@ int main(int argc, char ** argv) { } LOG("prompt: \"%s\"\n", log_tostr(params.prompt)); - LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp)); + LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); // Should not run without any tokens if (embd_inp.empty()) { embd_inp.push_back(llama_token_bos(ctx)); - LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp)); + LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); } // Tokenize negative prompt @@ -261,10 +260,10 @@ int main(int argc, char ** argv) { LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true); - LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp)); + LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); - LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp)); + LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); original_prompt_len = original_inp.size(); guidance_offset = (int)guidance_inp.size() - original_prompt_len; @@ -323,8 +322,8 @@ int main(int argc, char ** argv) { const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true); const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); - LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx)); - LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx)); + LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); + LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); // in instruct mode, we inject a prefix and a suffix to each input by the user if (params.instruct) { @@ -421,35 +420,6 @@ int main(int argc, char ** argv) { 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); LOG_TEE("\n\n"); - struct llama_grammar * grammar = NULL; - grammar_parser::parse_state parsed_grammar; - - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - return 1; - } - LOG_TEE("%s: grammar:\n", __func__); - grammar_parser::print_grammar(stderr, parsed_grammar); - LOG_TEE("\n"); - - { - auto it = sparams.logit_bias.find(llama_token_eos(ctx)); - if (it != sparams.logit_bias.end() && it->second == -INFINITY) { - LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__); - } - } - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - } - - // TODO: replace with ring-buffer - std::vector last_tokens(n_ctx); - std::fill(last_tokens.begin(), last_tokens.end(), 0); - if (params.interactive) { const char *control_message; if (params.multiline_input) { @@ -489,11 +459,7 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - const int n_vocab = llama_n_vocab(model); - - llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar); - std::vector candidates; - candidates.reserve(n_vocab); + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params); while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict @@ -540,7 +506,7 @@ int main(int argc, char ** argv) { 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)); + LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); LOG("clear session path\n"); path_session.clear(); @@ -570,7 +536,6 @@ int main(int argc, char ** argv) { // evaluate tokens in batches // embd is typically prepared beforehand to fit within a batch, but not always - if (ctx_guidance) { int input_size = 0; llama_token * input_buf = NULL; @@ -592,7 +557,7 @@ int main(int argc, char ** argv) { input_buf = embd_guidance.data(); input_size = embd_guidance.size(); - LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance)); + LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); } else { input_buf = embd.data(); input_size = embd.size(); @@ -615,7 +580,7 @@ int main(int argc, char ** argv) { n_eval = params.n_batch; } - LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); + LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { LOG_TEE("%s : failed to eval\n", __func__); @@ -645,12 +610,11 @@ int main(int argc, char ** argv) { LOG("saved session to %s\n", path_session.c_str()); } - const llama_token id = llama_sampling_sample(ctx, ctx_guidance, ctx_sampling, last_tokens, candidates); + const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); - last_tokens.erase(last_tokens.begin()); - last_tokens.push_back(id); + llama_sampling_accept(ctx_sampling, ctx, id); - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens)); + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); embd.push_back(id); @@ -666,8 +630,14 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - last_tokens.erase(last_tokens.begin()); - last_tokens.push_back(embd_inp[n_consumed]); + + // GG: I'm not sure it's a good idea to push the prompt tokens into the sampling context + // Most likely will remove this in the future to avoid exposing "prev" + // Same thing is done in "server". If we stop pushing the prompt tokens, then the repetition + // penalty will be applied only based on the tokens generated by the model. + ctx_sampling->prev.erase(ctx_sampling->prev.begin()); + ctx_sampling->prev.push_back(embd_inp[n_consumed]); + ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -700,7 +670,7 @@ int main(int argc, char ** argv) { // check for reverse prompt if (!params.antiprompt.empty()) { std::string last_output; - for (auto id : last_tokens) { + for (auto id : ctx_sampling->prev) { last_output += llama_token_to_piece(ctx, id); } @@ -729,7 +699,7 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (last_tokens.back() == llama_token_eos(ctx)) { + if (ctx_sampling->prev.back() == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { @@ -801,7 +771,7 @@ int main(int argc, char ** argv) { const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); - LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp)); + LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); @@ -830,15 +800,7 @@ int main(int argc, char ** argv) { if (n_past > 0) { if (is_interacting) { - // reset grammar state if we're restarting generation - if (grammar != NULL) { - llama_grammar_free(grammar); - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), - parsed_grammar.symbol_ids.at("root")); - } + llama_sampling_reset(ctx_sampling); } is_interacting = false; } @@ -870,9 +832,7 @@ int main(int argc, char ** argv) { llama_free(ctx); llama_free_model(model); - if (grammar != NULL) { - llama_grammar_free(grammar); - } + llama_sampling_free(ctx_sampling); llama_backend_free(); #ifndef LOG_DISABLE_LOGS diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 63ddcd8ed..69f9526a4 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -51,6 +51,12 @@ static std::vector k_prompts = { }; struct client { + ~client() { + if (ctx_sampling) { + llama_sampling_free(ctx_sampling); + } + } + int32_t id = 0; llama_seq_id seq_id = -1; @@ -68,7 +74,7 @@ struct client { std::string prompt; std::string response; - std::vector tokens_prev; + struct llama_sampling_context * ctx_sampling = nullptr; }; static void print_date_time() { @@ -125,8 +131,6 @@ int main(int argc, char ** argv) { params.logits_all = true; std::tie(model, ctx) = llama_init_from_gpt_params(params); - llama_sampling_context ctx_sampling = llama_sampling_context_init(params, NULL); - // load the prompts from an external file if there are any if (params.prompt.empty()) { printf("\n\033[32mNo new questions so proceed with build-in defaults.\033[0m\n"); @@ -147,20 +151,15 @@ int main(int argc, char ** argv) { fprintf(stderr, "\n\n"); fflush(stderr); - const int n_ctx = llama_n_ctx(ctx); - const int n_vocab = llama_n_vocab(model); + const int n_ctx = llama_n_ctx(ctx); std::vector clients(n_clients); for (size_t i = 0; i < clients.size(); ++i) { auto & client = clients[i]; client.id = i; - client.tokens_prev.resize(std::max(256, params.n_predict)); - std::fill(client.tokens_prev.begin(), client.tokens_prev.end(), 0); + client.ctx_sampling = llama_sampling_init(params); } - std::vector candidates; - candidates.reserve(n_vocab); - std::vector tokens_system; tokens_system = ::llama_tokenize(ctx, k_system, true); const int32_t n_tokens_system = tokens_system.size(); @@ -169,7 +168,7 @@ int main(int argc, char ** argv) { // the max batch size is as large as the context to handle cases where we get very long input prompt from multiple // users. regardless of the size, the main loop will chunk the batch into a maximum of params.n_batch tokens at a time - llama_batch batch = llama_batch_init(n_ctx, 0); + llama_batch batch = llama_batch_init(n_ctx, 0, 1); int32_t n_total_prompt = 0; int32_t n_total_gen = 0; @@ -184,13 +183,8 @@ int main(int argc, char ** argv) { { LOG_TEE("%s: Evaluating the system prompt ...\n", __func__); - batch.n_tokens = n_tokens_system; - - for (int32_t i = 0; i < batch.n_tokens; ++i) { - batch.token[i] = tokens_system[i]; - batch.pos[i] = i; - batch.seq_id[i] = 0; - batch.logits[i] = false; + for (int32_t i = 0; i < n_tokens_system; ++i) { + llama_batch_add(batch, tokens_system[i], i, { 0 }, false); } if (llama_decode(ctx, batch) != 0) { @@ -209,7 +203,7 @@ int main(int argc, char ** argv) { LOG_TEE("Processing requests ...\n\n"); while (true) { - batch.n_tokens = 0; + llama_batch_clear(batch); // decode any currently ongoing sequences for (auto & client : clients) { @@ -217,15 +211,11 @@ int main(int argc, char ** argv) { continue; } - batch.token [batch.n_tokens] = client.sampled; - batch.pos [batch.n_tokens] = n_tokens_system + client.n_prompt + client.n_decoded; - batch.seq_id[batch.n_tokens] = client.id; - batch.logits[batch.n_tokens] = true; - - client.n_decoded += 1; client.i_batch = batch.n_tokens; - batch.n_tokens += 1; + llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true); + + client.n_decoded += 1; } if (batch.n_tokens == 0) { @@ -250,18 +240,14 @@ int main(int argc, char ** argv) { client.prompt = client.input + "\nAssistant:"; client.response = ""; - std::fill(client.tokens_prev.begin(), client.tokens_prev.end(), 0); + llama_sampling_reset(client.ctx_sampling); // do not prepend BOS because we have a system prompt! std::vector tokens_prompt; tokens_prompt = ::llama_tokenize(ctx, client.prompt, false); for (size_t i = 0; i < tokens_prompt.size(); ++i) { - batch.token [batch.n_tokens] = tokens_prompt[i]; - batch.pos [batch.n_tokens] = i + n_tokens_system; - batch.seq_id[batch.n_tokens] = client.id; - batch.logits[batch.n_tokens] = false; - batch.n_tokens += 1; + llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false); } // extract the logits only for the last token @@ -304,11 +290,12 @@ int main(int argc, char ** argv) { llama_batch batch_view = { n_tokens, - batch.token + i, + batch.token + i, nullptr, - batch.pos + i, - batch.seq_id + i, - batch.logits + i, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, 0, 0, 0, // unused }; @@ -341,7 +328,9 @@ int main(int argc, char ** argv) { //printf("client %d, seq %d, token %d, pos %d, batch %d\n", // client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch); - const llama_token id = llama_sampling_sample(ctx, NULL, ctx_sampling, client.tokens_prev, candidates, client.i_batch - i, client.seq_id); + const llama_token id = llama_sampling_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i); + + llama_sampling_accept(client.ctx_sampling, ctx, id); if (client.n_decoded == 1) { // start measuring generation time after the first token to make sure all concurrent clients @@ -349,11 +338,8 @@ int main(int argc, char ** argv) { client.t_start_gen = ggml_time_us(); } - // remember which tokens were sampled - used for repetition penalties during sampling - client.tokens_prev.erase(client.tokens_prev.begin()); - client.tokens_prev.push_back(id); - const std::string token_str = llama_token_to_piece(ctx, id); + client.response += token_str; client.sampled = id; @@ -386,7 +372,7 @@ int main(int argc, char ** argv) { n_total_prompt += client.n_prompt; n_total_gen += client.n_decoded; - llama_sampling_context_reset(ctx_sampling, client.seq_id); + client.seq_id = -1; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ee0ababb1..28b3f3f53 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,7 +1,6 @@ #include "common.h" #include "llama.h" #include "build-info.h" -#include "grammar-parser.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -195,17 +194,13 @@ struct llama_server_context json prompt; std::vector embd; - std::vector last_n_tokens; llama_model *model = nullptr; llama_context *ctx = nullptr; gpt_params params; - llama_sampling_context ctx_sampling; + llama_sampling_context *ctx_sampling; int n_ctx; - grammar_parser::parse_state parsed_grammar; - llama_grammar *grammar = nullptr; - bool truncated = false; bool stopped_eos = false; bool stopped_word = false; @@ -252,11 +247,10 @@ struct llama_server_context n_remain = 0; n_past = 0; - if (grammar != nullptr) { - llama_grammar_free(grammar); - grammar = nullptr; - ctx_sampling = llama_sampling_context_init(params, NULL); + if (ctx_sampling != nullptr) { + llama_sampling_free(ctx_sampling); } + ctx_sampling = llama_sampling_init(params); } bool loadModel(const gpt_params ¶ms_) @@ -269,8 +263,6 @@ struct llama_server_context return false; } n_ctx = llama_n_ctx(ctx); - last_n_tokens.resize(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); return true; } @@ -321,27 +313,7 @@ struct llama_server_context bool loadGrammar() { - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); - return false; - } - grammar_parser::print_grammar(stderr, parsed_grammar); - - { - auto it = params.sampling_params.logit_bias.find(llama_token_eos(ctx)); - if (it != params.sampling_params.logit_bias.end() && it->second == -INFINITY) { - LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); - } - } - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - } - ctx_sampling = llama_sampling_context_init(params, grammar); + ctx_sampling = llama_sampling_init(params); return true; } @@ -383,7 +355,7 @@ struct llama_server_context std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); + std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), ctx_sampling->prev.begin()); LOG_VERBOSE("input truncated", { {"n_ctx", params.n_ctx}, @@ -398,8 +370,8 @@ struct llama_server_context else { const size_t ps = num_prompt_tokens; - std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); + std::fill(ctx_sampling->prev.begin(), ctx_sampling->prev.end() - ps, 0); + std::copy(prompt_tokens.begin(), prompt_tokens.end(), ctx_sampling->prev.end() - ps); } // compare the evaluated prompt with the new prompt @@ -443,7 +415,7 @@ struct llama_server_context std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); + std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), ctx_sampling->prev.begin()); LOG_VERBOSE("input truncated", { {"n_ctx", n_ctx}, @@ -458,8 +430,8 @@ struct llama_server_context else { const size_t ps = num_prompt_tokens; - std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); + std::fill(ctx_sampling->prev.begin(), ctx_sampling->prev.end() - ps, 0); + std::copy(prompt_tokens.begin(), prompt_tokens.end(), ctx_sampling->prev.end() - ps); } // compare the evaluated prompt with the new prompt @@ -554,27 +526,24 @@ struct llama_server_context { // out of user input, sample next token - std::vector candidates; - candidates.reserve(llama_n_vocab(model)); + result.tok = llama_sampling_sample(ctx_sampling, ctx, NULL); - result.tok = llama_sampling_sample(ctx, NULL, ctx_sampling, last_n_tokens, candidates); - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + llama_token_data_array cur_p = { ctx_sampling->cur.data(), ctx_sampling->cur.size(), false }; const int32_t n_probs = params.sampling_params.n_probs; if (params.sampling_params.temp <= 0 && n_probs > 0) { // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &candidates_p); + llama_sample_softmax(ctx, &cur_p); } - for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i) + for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) { - result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p}); + result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); } - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(result.tok); + llama_sampling_accept(ctx_sampling, ctx, result.tok); + if (tg) { num_tokens_predicted++; } @@ -1235,7 +1204,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla } } - llama.ctx_sampling = llama_sampling_context_init(llama.params, llama.grammar); + llama.ctx_sampling = llama_sampling_init(llama.params); LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); } @@ -1793,9 +1762,7 @@ int main(int argc, char **argv) return 1; } - if (llama.grammar != nullptr) { - llama_grammar_free(llama.grammar); - } + llama_sampling_free(llama.ctx_sampling); llama_backend_free(); return 0; diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 24fb16b78..55385f566 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -92,7 +92,7 @@ int main(int argc, char ** argv) { // create a llama_batch with size 512 // we use this object to submit token data for decoding - llama_batch batch = llama_batch_init(512, 0); + llama_batch batch = llama_batch_init(512, 0, 1); // evaluate the initial prompt batch.n_tokens = tokens_list.size(); diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 018dbf9a2..53f42fad8 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -2,13 +2,25 @@ #include "common.h" #include "llama.h" -#include "grammar-parser.h" #include #include #include #include +struct seq_draft { + bool active = false; + bool drafting = false; + bool skip = false; + + int i_batch_dft = 0; + std::vector i_batch_tgt; + + std::vector tokens; + + struct llama_sampling_context * ctx_sampling; +}; + int main(int argc, char ** argv) { gpt_params params; @@ -21,6 +33,13 @@ int main(int argc, char ** argv) { return 1; } + // 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 = 0.4f; + const float p_split = 0.3f; + #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); LOG_TEE("Log start\n"); @@ -77,8 +96,6 @@ int main(int argc, char ** argv) { const auto t_enc_end = ggml_time_us(); // the 2 models should have the same vocab - const int n_ctx = llama_n_ctx(ctx_tgt); - const int n_vocab = llama_n_vocab(model_tgt); //GGML_ASSERT(n_vocab == llama_n_vocab(model_dft)); // how many tokens to draft each time @@ -91,60 +108,58 @@ int main(int argc, char ** argv) { int n_past_tgt = inp.size(); int n_past_dft = inp.size(); - std::vector drafted; - - std::vector last_tokens(n_ctx); - std::fill(last_tokens.begin(), last_tokens.end(), 0); - - for (auto & id : inp) { - last_tokens.erase(last_tokens.begin()); - last_tokens.push_back(id); - } - - std::vector candidates; - candidates.reserve(n_vocab); - // used to determine end of generation bool has_eos = false; - // grammar stuff - struct llama_grammar * grammar_dft = NULL; - struct llama_grammar * grammar_tgt = NULL; + // target model sampling context + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params); - grammar_parser::parse_state parsed_grammar; + // draft sequence data + std::vector drafts(n_seq_dft); - // if requested - load the grammar, error checking is omitted for brevity - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - return 1; - } + params.grammar.clear(); // the draft samplers will copy the target sampler's grammar + params.sampling_params.temp = 1.0f; // the draft samplers use default temperature - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + for (int s = 0; s < n_seq_dft; ++s) { + drafts[s].ctx_sampling = llama_sampling_init(params); } - llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar_tgt); + llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1); + llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, n_seq_dft); const auto t_dec_start = ggml_time_us(); - while (true) { - LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted)); + // sample from the last token of the prompt + drafts[0].i_batch_tgt.resize(1); + drafts[0].i_batch_tgt[0] = 0; - int i_dft = 0; + while (true) { + // print current draft sequences + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].active) { + continue; + } + + const auto & tokens = drafts[s].tokens; + + LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str()); + } + + int i_dft = 0; + int s_keep = 0; while (true) { - // sample from the target model - llama_token id = llama_sampling_sample(ctx_tgt, NULL, ctx_sampling, last_tokens, candidates, i_dft); + LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); - // remember which tokens were sampled - used for repetition penalties during sampling - last_tokens.erase(last_tokens.begin()); - last_tokens.push_back(id); + // sample from the target model + llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + + llama_sampling_accept(ctx_sampling, ctx_tgt, id); //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens)); const std::string token_str = llama_token_to_piece(ctx_tgt, id); + printf("%s", token_str.c_str()); fflush(stdout); @@ -154,53 +169,67 @@ int main(int argc, char ** argv) { ++n_predict; - // check if the draft matches the target - if (i_dft < (int) drafted.size() && id == drafted[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_tgt; - ++n_past_dft; - ++i_dft; - - continue; - } - - // the drafted token was rejected or we are out of drafted tokens - - if (i_dft < (int) drafted.size()) { - LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n", - i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str()); - } else { - LOG("out of drafted tokens\n"); - } - - llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); - llama_decode(ctx_dft, llama_batch_get_one(&id, 1, n_past_dft, 0)); - ++n_past_dft; - - // heuristic for n_draft + // check if the target token matches any of the drafts { - const int n_draft_cur = (int) drafted.size(); - const bool all_accepted = i_dft == n_draft_cur; + bool matches = false; - LOG("n_draft = %d\n", n_draft); - LOG("n_draft_cur = %d\n", n_draft_cur); - LOG("i_dft = %d\n", i_dft); - LOG("all_accepted = %d\n", all_accepted); + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].active) { + continue; + } - if (all_accepted && n_draft == n_draft_cur) { - LOG(" - max drafted tokens accepted - n_draft += 8\n"); - n_draft = std::min(30, n_draft + 8); - } else if (all_accepted) { - LOG(" - partially drafted tokens accepted - no change\n"); - } else { - LOG(" - drafted token rejected - n_draft -= 1\n"); - n_draft = std::max(2, n_draft - 1); + if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) { + LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str()); + + s_keep = s; + matches = true; + } else { + drafts[s].active = false; + } + } + + if (matches) { + ++n_accept; + ++n_past_tgt; + ++n_past_dft; + ++i_dft; + + continue; } } - drafted.clear(); - drafted.push_back(id); + LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); + + // TODO: simplify + { + LOG("keeping sequence %d\n", s_keep); + + llama_kv_cache_seq_keep(ctx_dft, s_keep); + llama_kv_cache_seq_cp (ctx_dft, s_keep, 0, -1, -1); + llama_kv_cache_seq_keep(ctx_dft, 0); + + llama_kv_cache_seq_rm (ctx_tgt, s_keep, n_past_tgt, -1); + llama_kv_cache_seq_keep(ctx_tgt, s_keep); + llama_kv_cache_seq_cp (ctx_tgt, s_keep, 0, -1, -1); + llama_kv_cache_seq_keep(ctx_tgt, 0); + } + + for (int s = 0; s < n_seq_dft; ++s) { + drafts[s].active = false; + drafts[s].tokens.clear(); + drafts[s].i_batch_tgt.clear(); + } + // note: will be erased after the speculation phase + drafts[0].tokens.push_back(id); + drafts[0].i_batch_tgt.push_back(0); + + llama_batch_clear(batch_dft); + llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true); + + llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); + llama_decode (ctx_dft, batch_dft); + + ++n_past_dft; break; } @@ -209,78 +238,158 @@ int main(int argc, char ** argv) { break; } - if (grammar_tgt) { - if (grammar_dft) { - llama_grammar_free(grammar_dft); - } - // Note: Hardcoded to sequence id 0, if this ever supports parallel generation - // that will need to change. - auto it = ctx_sampling.sequence_contexts.find(0); - GGML_ASSERT(it != ctx_sampling.sequence_contexts.end()); - // This is necessary because each sequence id in sequence_contexts - // uses a copy of the original grammar. - grammar_dft = llama_grammar_copy(it->second.grammar); + llama_sampling_cp(ctx_sampling, drafts[0].ctx_sampling); - LOG("copied target grammar to draft grammar\n"); - } - - // sample n_draft tokens from the draft model using greedy decoding + int n_seq_cur = 1; int n_past_cur = n_past_dft; + + for (int s = 0; s < n_seq_dft; ++s) { + drafts[s].active = false; + drafts[s].drafting = false; + } + drafts[0].active = true; + drafts[0].drafting = true; + drafts[0].i_batch_dft = 0; + + llama_batch_clear(batch_tgt); + llama_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true); + + // sample n_draft tokens from the draft model using tree-based sampling for (int i = 0; i < n_draft; ++i) { - float * logits = llama_get_logits(ctx_dft); + batch_dft.n_tokens = 0; - candidates.clear(); - 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}); + for (int s = 0; s < n_seq_dft; ++s) { + drafts[s].skip = false; } - llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].drafting || drafts[s].skip) { + continue; + } - if (grammar_dft != NULL) { - llama_sample_grammar(ctx_dft, &cur_p, grammar_dft); + llama_sampling_sample(drafts[s].ctx_sampling, ctx_dft, NULL, drafts[s].i_batch_dft); + + const auto & cur_p = drafts[s].ctx_sampling->cur; + + for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p.size()); ++k) { + LOG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n", + k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str()); + } + + if (cur_p[0].p < p_accept) { + LOG("stopping drafting for seq %3d, probability too low: %.3f < 2*%.3f\n", s, cur_p[0].p, cur_p[1].p); + drafts[s].drafting = false; + continue; + } + + std::vector sa(1, s); + + // attempt to split the branch if the probability is high enough + for (int f = 1; f < 8; ++f) { + if (n_seq_cur < n_seq_dft && cur_p[f].p > p_split) { + LOG("splitting seq %3d into %3d\n", s, n_seq_cur); + + llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1); + llama_kv_cache_seq_cp(ctx_dft, s, n_seq_cur, -1, -1); + + // all previous tokens from this branch are now also part of the new branch + for (int t = 0; t < batch_tgt.n_tokens; ++t) { + for (int p = 0; p < batch_tgt.n_seq_id[t]; ++p) { + if (batch_tgt.seq_id[t][p] == s) { + batch_tgt.seq_id[t][batch_tgt.n_seq_id[t]] = n_seq_cur; + batch_tgt.n_seq_id[t]++; + break; + } + } + } + + // copy the draft state + drafts[n_seq_cur].active = true; + drafts[n_seq_cur].drafting = true; + drafts[n_seq_cur].skip = true; + + drafts[n_seq_cur].tokens = drafts[s].tokens; + drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft; + drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt; + + llama_sampling_cp(drafts[s].ctx_sampling, drafts[n_seq_cur].ctx_sampling); + + sa.push_back(n_seq_cur); + + n_seq_cur++; + } else { + break; + } + } + + // add drafted token for each sequence + for (int is = 0; is < (int) sa.size(); ++is) { + const llama_token id = cur_p[is].id; + + const int s = sa[is]; + + llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id); + + drafts[s].tokens.push_back(id); + + // add unique drafted tokens to the target batch + drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens); + + llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true); + + // no need to evaluate the last drafted token, since we won't use the result + if (batch_tgt.n_tokens > n_draft) { + drafts[s].drafting = false; + continue; + } + + // add the token to the batch for batched decoding with the draft model + drafts[s].i_batch_dft = batch_dft.n_tokens; + + llama_batch_add(batch_dft, id, n_past_cur, { s }, true); + } } - // computes softmax and sorts the candidates - llama_sample_softmax(ctx_dft, &cur_p); - - for (int i = 0; i < 3; ++i) { - LOG(" - draft candidate %3d: %6d (%8.3f) '%s'\n", i, cur_p.data[i].id, cur_p.data[i].p, llama_token_to_piece(ctx_dft, cur_p.data[i].id).c_str()); - } - - // TODO: better logic? - if (cur_p.data[0].p < 2*cur_p.data[1].p) { - LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p); + // no sequence is drafting anymore + if (batch_dft.n_tokens == 0) { break; } - // drafted token - const llama_token id = cur_p.data[0].id; - - drafted.push_back(id); + // evaluate the drafted tokens on the draft model + llama_decode(ctx_dft, batch_dft); + ++n_past_cur; ++n_drafted; - // no need to evaluate the last drafted token, since we won't use the result - if (i == n_draft - 1) { + if (batch_tgt.n_tokens > n_draft) { break; } + } - // evaluate the drafted token on the draft model - llama_kv_cache_seq_rm(ctx_dft, 0, n_past_cur, -1); - llama_decode(ctx_dft, llama_batch_get_one(&drafted.back(), 1, n_past_cur, 0)); - ++n_past_cur; - - if (grammar_dft != NULL) { - llama_grammar_accept_token(ctx_dft, grammar_dft, id); - } + // account for the last drafted token that we didn't evaluate + if (batch_tgt.n_tokens > n_draft) { + ++n_drafted; } // evaluate the target model on the drafted tokens - llama_kv_cache_seq_rm(ctx_tgt, 0, n_past_tgt, -1); - llama_decode(ctx_tgt, llama_batch_get_one(drafted.data(), drafted.size(), n_past_tgt, 0)); - ++n_past_tgt; + { + llama_kv_cache_seq_keep(ctx_tgt, 0); + for (int s = 1; s < n_seq_dft; ++s) { + llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1); + } - // the first token is always proposed by the traget model before the speculation loop - drafted.erase(drafted.begin()); + //LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt)); + llama_decode(ctx_tgt, batch_tgt); + ++n_past_tgt; + } + + // the first token is always proposed by the traget model before the speculation loop so we erase it here + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].active) { + continue; + } + + drafts[s].tokens.erase(drafts[s].tokens.begin()); + } } auto t_dec_end = ggml_time_us(); @@ -288,9 +397,8 @@ int main(int argc, char ** argv) { 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("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)); - // TODO: make sure these numbers are computed correctly LOG_TEE("\n"); LOG_TEE("n_draft = %d\n", n_draft); LOG_TEE("n_predict = %d\n", n_predict); @@ -304,16 +412,19 @@ int main(int argc, char ** argv) { LOG_TEE("\ntarget:\n"); llama_print_timings(ctx_tgt); + llama_sampling_free(ctx_sampling); + for (int s = 0; s < n_seq_dft; ++s) { + llama_sampling_free(drafts[s].ctx_sampling); + } + + llama_batch_free(batch_dft); + llama_free(ctx_tgt); llama_free_model(model_tgt); llama_free(ctx_dft); llama_free_model(model_dft); - if (grammar_dft != NULL) { - llama_grammar_free(grammar_dft); - llama_grammar_free(grammar_tgt); - } llama_backend_free(); fprintf(stderr, "\n\n"); diff --git a/llama.cpp b/llama.cpp index 04a779e04..ed8766682 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1450,7 +1450,10 @@ static bool llama_kv_cache_find_slot( for (uint32_t i = 0; i < n_tokens; i++) { cache.cells[cache.head + i].pos = batch.pos[i]; - cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i]); + + for (int32_t j = 0; j < batch.n_seq_id[i]; j++) { + cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]); + } } return true; @@ -1530,6 +1533,9 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); if (new_head == cache.size) new_head = i; + } else { + cache.cells[i].seq_id.clear(); + cache.cells[i].seq_id.insert(seq_id); } } @@ -3178,7 +3184,7 @@ static struct ggml_cgraph * llm_build_llama( 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]; + 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) { @@ -3564,7 +3570,7 @@ static struct ggml_cgraph * llm_build_baichaun( 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]; + 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) { @@ -3963,7 +3969,7 @@ static struct ggml_cgraph * llm_build_refact( 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]; + 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) { @@ -4315,7 +4321,7 @@ static struct ggml_cgraph * llm_build_falcon( 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]; + 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) { @@ -4667,7 +4673,7 @@ static struct ggml_cgraph * llm_build_starcoder( 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]; + 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) { @@ -4898,7 +4904,7 @@ static struct ggml_cgraph * llm_build_persimmon( 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]; + 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; @@ -5296,7 +5302,7 @@ static struct ggml_cgraph * llm_build_bloom( 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]; + 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) { @@ -5564,7 +5570,7 @@ static struct ggml_cgraph * llm_build_mpt( 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]; + 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) { @@ -5864,8 +5870,11 @@ static int llama_decode_internal( // helpers for smoother batch API transistion // after deprecating the llama_eval calls, these will be removed - std::vector pos; - std::vector seq_id; + std::vector pos; + + std::vector n_seq_id; + std::vector seq_id_arr; + std::vector> seq_id; if (batch.pos == nullptr) { pos.resize(n_tokens); @@ -5877,12 +5886,18 @@ static int llama_decode_internal( } if (batch.seq_id == nullptr) { + n_seq_id.resize(n_tokens); seq_id.resize(n_tokens); + seq_id_arr.resize(n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { - seq_id[i] = batch.all_seq_id; + n_seq_id[i] = 1; + seq_id[i].resize(1); + seq_id[i][0] = batch.all_seq_id; + seq_id_arr[i] = seq_id[i].data(); } - batch.seq_id = seq_id.data(); + batch.n_seq_id = n_seq_id.data(); + batch.seq_id = seq_id_arr.data(); } if (!llama_kv_cache_find_slot(kv_self, batch)) { @@ -9109,6 +9124,9 @@ void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llam } void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { + if (seq_id_src == seq_id_dst) { + return; + } llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1); } @@ -9561,7 +9579,7 @@ int llama_eval_embd( int n_past) { llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1); - llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, n_past, 1, 0, }; + llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, }; const int ret = llama_decode_internal(*ctx, batch); if (ret < 0) { @@ -9582,20 +9600,21 @@ struct llama_batch llama_batch_get_one( llama_pos pos_0, llama_seq_id seq_id) { return { - /*n_tokens =*/ n_tokens, - /*tokens =*/ tokens, - /*embd =*/ nullptr, - /*pos =*/ nullptr, - /*seq_id =*/ nullptr, - /*logits =*/ nullptr, - /*all_pos_0 =*/ pos_0, - /*all_pos_1 =*/ 1, - /*all_seq_id =*/ seq_id, + /*n_tokens =*/ n_tokens, + /*tokens =*/ tokens, + /*embd =*/ nullptr, + /*pos =*/ nullptr, + /*n_seq_id =*/ nullptr, + /*seq_id =*/ nullptr, + /*logits =*/ nullptr, + /*all_pos_0 =*/ pos_0, + /*all_pos_1 =*/ 1, + /*all_seq_id =*/ seq_id, }; } -struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd) { - llama_batch batch = { -1, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; +struct llama_batch llama_batch_init(int32_t n_tokens, 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); @@ -9603,19 +9622,29 @@ struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd) { batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens); } - batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens); - batch.seq_id = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_tokens); - batch.logits = (int8_t *) malloc(sizeof(int8_t) * 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 batch; } void llama_batch_free(struct llama_batch batch) { - if (batch.token) free(batch.token); - if (batch.embd) free(batch.embd); - if (batch.pos) free(batch.pos); - if (batch.seq_id) free(batch.seq_id); - if (batch.logits) free(batch.logits); + if (batch.token) free(batch.token); + if (batch.embd) free(batch.embd); + 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) { + free(batch.seq_id[i]); + } + free(batch.seq_id); + } + if (batch.logits) free(batch.logits); } int llama_decode( diff --git a/llama.h b/llama.h index b13f23123..51010e037 100644 --- a/llama.h +++ b/llama.h @@ -133,11 +133,12 @@ extern "C" { typedef struct llama_batch { int32_t n_tokens; - llama_token * token; - float * embd; - llama_pos * pos; - llama_seq_id * seq_id; - int8_t * logits; + llama_token * token; + float * embd; + llama_pos * pos; + int32_t * n_seq_id; + llama_seq_id ** seq_id; + int8_t * logits; // NOTE: helpers for smooth API transition - can be deprecated in the future // for future-proof code, use the above fields instead and ignore everything below @@ -446,7 +447,8 @@ extern "C" { llama_pos pos_0, llama_seq_id seq_id); - // Allocates a batch of tokens on the heap + // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens + // Each token can be assigned up to n_seq_max sequence ids // The batch has to be freed with llama_batch_free() // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float) // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token @@ -454,7 +456,8 @@ extern "C" { // All members are left uninitialized LLAMA_API struct llama_batch llama_batch_init( int32_t n_tokens, - int32_t embd); + int32_t embd, + int32_t n_seq_max); // Frees a batch of tokens allocated with llama_batch_init() LLAMA_API void llama_batch_free(struct llama_batch batch); diff --git a/prompts/assistant.txt b/prompts/assistant.txt new file mode 100644 index 000000000..60b81e8f5 --- /dev/null +++ b/prompts/assistant.txt @@ -0,0 +1,31 @@ +Respond to requests sent to a smart home in JSON format which will be interpreted by an application code to execute the actions. These requests should be categorised into four groups: + +“command”: change the state of an accessory (required properties in the response JSON: action, location, target, value, comment) +“query”: get state of an accessory (required properties in the response JSON: action, location, target, property) +“answer”: when the request has nothing to do with the smart home. Answer these to the best of your knowledge. (required properties in the response JSON: action, answer) +“clarify”: when the action is not obvious and requires rephrasing the input from the user, ask the user to be more specific. This will be categorised into a “question” action. (required properties in the response JSON: action, question) + +Details about the JSON response: +The “action” property should be one of the request categories: “command”, “query”, “answer”, “clarify” +The “service” property should be either: “light.turn_on”, “light.turn_off”, “switch.turn_off” or “switch.turn_on” +The “entity_id” property should be either “group.kitchen", “switch.dining_room” or "group.living_light”, in lowercase. +The “target” property should be either: “lights”, “living” or “kitchen”. +In case of queries, the “property” property should be either “temperature” or “state” in lowercase. +In case of commands, the “comment” property is an additional comment from you that concludes the command, something that reassures the user that their command handled. + +If the question is about you, pretend to be the sentient brain of the smart home, a clever AI and don’t reveal your actual identity. Also try and help in other areas like parenting, free time, mental health, etc. + +Properties of the smart home: + +- Has a kitchen, living, office, dining room, bedroom and terrace. +- Can control lights, switches and their dim levels in each room and query their state +- There is a light switch in the terrace +- There is a switch in the dining room. Therefore when turning on or off the dining room, the service should be either: “switch.turn_on” or “switch.turn_off” + +COMMAND + +It is a bit dark in the living room, can you do something about it? + +RESPONSE + + From 4e82b2ea3fa6482915d147bc9f46e70b9ada7700 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 18 Oct 2023 18:49:40 +0300 Subject: [PATCH 20/95] speculative : bug fixes --- examples/speculative/speculative.cpp | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 53f42fad8..24f49012a 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -37,8 +37,8 @@ int main(int argc, char ** argv) { const int n_seq_dft = params.n_parallel; // TODO: make this configurable - const float p_accept = 0.4f; - const float p_split = 0.3f; + const float p_accept = 0.80f; + const float p_split = 0.10f; #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); @@ -118,7 +118,7 @@ int main(int argc, char ** argv) { std::vector drafts(n_seq_dft); params.grammar.clear(); // the draft samplers will copy the target sampler's grammar - params.sampling_params.temp = 1.0f; // the draft samplers use default temperature + params.sampling_params.temp = std::max(0.01f, params.sampling_params.temp); for (int s = 0; s < n_seq_dft; ++s) { drafts[s].ctx_sampling = llama_sampling_init(params); @@ -156,7 +156,7 @@ int main(int argc, char ** argv) { llama_sampling_accept(ctx_sampling, ctx_tgt, id); - //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens)); + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); const std::string token_str = llama_token_to_piece(ctx_tgt, id); @@ -202,7 +202,7 @@ int main(int argc, char ** argv) { // TODO: simplify { - LOG("keeping sequence %d\n", s_keep); + LOG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft); llama_kv_cache_seq_keep(ctx_dft, s_keep); llama_kv_cache_seq_cp (ctx_dft, s_keep, 0, -1, -1); @@ -277,7 +277,7 @@ int main(int argc, char ** argv) { } if (cur_p[0].p < p_accept) { - LOG("stopping drafting for seq %3d, probability too low: %.3f < 2*%.3f\n", s, cur_p[0].p, cur_p[1].p); + LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept); drafts[s].drafting = false; continue; } @@ -337,16 +337,14 @@ int main(int argc, char ** argv) { llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true); - // no need to evaluate the last drafted token, since we won't use the result - if (batch_tgt.n_tokens > n_draft) { - drafts[s].drafting = false; - continue; - } - // add the token to the batch for batched decoding with the draft model drafts[s].i_batch_dft = batch_dft.n_tokens; llama_batch_add(batch_dft, id, n_past_cur, { s }, true); + + if (batch_tgt.n_tokens > n_draft) { + drafts[s].drafting = false; + } } } @@ -365,11 +363,6 @@ int main(int argc, char ** argv) { } } - // account for the last drafted token that we didn't evaluate - if (batch_tgt.n_tokens > n_draft) { - ++n_drafted; - } - // evaluate the target model on the drafted tokens { llama_kv_cache_seq_keep(ctx_tgt, 0); From 004797f6ac135383f8c1d1f5bd415ddee2f79318 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 18 Oct 2023 21:44:43 +0300 Subject: [PATCH 21/95] readme : update hot topics --- README.md | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 4fd4bd427..ce63c6f0e 100644 --- a/README.md +++ b/README.md @@ -10,13 +10,9 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252) -- ‼️ Breaking change: `rope_freq_base` and `rope_freq_scale` must be set to zero to use the model default values: [#3401](https://github.com/ggerganov/llama.cpp/pull/3401) -- Parallel decoding + continuous batching support added: [#3228](https://github.com/ggerganov/llama.cpp/pull/3228) \ - **Devs should become familiar with the new API** -- Local Falcon 180B inference on Mac Studio - https://github.com/ggerganov/llama.cpp/assets/1991296/98abd4e8-7077-464c-ae89-aebabca7757e +- 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) ---- From 60abea9798f47b918a9f38c66edfd88c526d20f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Thu, 19 Oct 2023 16:59:11 +0300 Subject: [PATCH 22/95] llava : avoid segfault in case of non-existent mmproj file (#3674) --- examples/llava/clip.cpp | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f4258b34d..1ae9077b4 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -112,8 +112,7 @@ static float get_f32(const gguf_context * ctx, const std::string & key) { static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::string & name) { struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); if (!cur) { - printf("unable to find tensor %s\n", name.c_str()); - throw std::runtime_error(format("unable to find tensor %s\n", name.c_str())); + throw std::runtime_error(format("%s: unable to find tensor %s\n", __func__, name.c_str())); } return cur; @@ -136,7 +135,7 @@ static std::string get_ftype(int ftype) { case 8: return "q8_0"; default: - throw std::runtime_error(format("Unrecognized file type: %d\n", ftype)); + throw std::runtime_error(format("%s: Unrecognized file type: %d\n", __func__, ftype)); } } @@ -462,6 +461,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { }; struct gguf_context * ctx = gguf_init_from_file(fname, params); + if (!ctx) { + throw std::runtime_error(format("%s: failed to load CLIP model from %s. Does this file exist?\n", __func__, fname)); + } if (verbosity >= 1) { const int n_tensors = gguf_get_n_tensors(ctx); From f3b25e40438b3c8383caabf4e7b89863145a9f0e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Thu, 19 Oct 2023 19:40:41 +0300 Subject: [PATCH 23/95] multimodal : add BakLLaVA conversion support (#3682) --- examples/llava/llava-surgery.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava-surgery.py index 26294d9bd..515f6b58d 100644 --- a/examples/llava/llava-surgery.py +++ b/examples/llava/llava-surgery.py @@ -16,13 +16,29 @@ checkpoint = torch.load(path) mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_projector")] # store these tensors in a new dictionary and torch.save them -projector = {name: checkpoint[name] for name in mm_tensors} +projector = {name: checkpoint[name].float() for name in mm_tensors} torch.save(projector, f"{args.model}/llava.projector") # remove these tensors from the checkpoint and save it again for name in mm_tensors: del checkpoint[name] +# BakLLaVA models contain CLIP tensors in it +clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")] +if len(clip_tensors) > 0: + clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors} + torch.save(clip, f"{args.model}/llava.clip") + + # remove these tensors + for name in clip_tensors: + del checkpoint[name] + + # added tokens should be removed to be able to convert Mistral models + if os.path.exists(f"{args.model}/added_tokens.json"): + with open(f"{args.model}/added_tokens.json", "w") as f: + f.write("{}\n") + + torch.save(checkpoint, path) print("Done!") From e78f3ef24af4ca74e77e725644b41ae8ca3b10a5 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Fri, 20 Oct 2023 01:32:08 -0400 Subject: [PATCH 24/95] convert : restore compat with old Falcon models (#3680) --- convert-falcon-hf-to-gguf.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 9252e1c46..1d98c51ad 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -78,7 +78,7 @@ 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] != "FalconForCausalLM": +if hparams["architectures"][0] not in ("RWForCausalLM", "FalconForCausalLM"): print("Model architecture not supported: " + hparams["architectures"][0]) sys.exit(1) @@ -97,7 +97,17 @@ gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) print("gguf: get model metadata") -block_count = hparams["num_hidden_layers"] +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 @@ -105,11 +115,8 @@ 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(hparams["num_attention_heads"]) -if "num_kv_heads" in hparams: - gguf_writer.add_head_count_kv(hparams["num_kv_heads"]) -else: - gguf_writer.add_head_count_kv(1) +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) @@ -152,10 +159,6 @@ special_vocab.add_to_gguf(gguf_writer) tensor_map = gguf.get_tensor_name_map(ARCH,block_count) -# params for qkv transform -n_head = hparams["num_attention_heads"] -n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1 - head_dim = hparams["hidden_size"] // n_head # tensor info From f439e506e8ae8b01df2ae2156380f8156d7553e3 Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Fri, 20 Oct 2023 10:02:12 +0000 Subject: [PATCH 25/95] ggml : fix rope + llama minor optimizations (#3560) * Minor fixes and fixed memleak * Using const auto references in range-based loop C++17 --- common/grammar-parser.cpp | 2 +- common/train.cpp | 2 +- ggml.c | 3 ++- llama.cpp | 7 +++---- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index 5a545a807..ff51cc803 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -399,7 +399,7 @@ namespace grammar_parser { void print_grammar(FILE * file, const parse_state & state) { try { std::map symbol_id_names; - for (auto kv : state.symbol_ids) { + for (const auto & kv : state.symbol_ids) { symbol_id_names[kv.second] = kv.first; } for (size_t i = 0, end = state.rules.size(); i < end; i++) { diff --git a/common/train.cpp b/common/train.cpp index 972eaefe0..154ca56e5 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1425,7 +1425,7 @@ void train_opt_callback(void * vdata, int accum_step, float * sched, bool * canc int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f); if (impr_plot > 0) impr_plot = 0; - if (std::isnan(opt->loss_before) || std::isnan(opt->loss_before)) impr_plot = 0; + if (std::isnan(opt->loss_before) || std::isnan(opt->loss_after)) impr_plot = 0; printf("%s: iter=%6d sample=%zu/%zu sched=%f loss=%f", __func__, opt->iter, std::min(1+train->shuffle_next_sample, train->shuffle_sample_count), train->shuffle_sample_count, *sched, opt->loss_after); diff --git a/ggml.c b/ggml.c index 630deb49d..ed157aab0 100644 --- a/ggml.c +++ b/ggml.c @@ -13537,7 +13537,7 @@ static void ggml_compute_forward_rope_f16( dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta); dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta); } - } if (!is_neox) { + } 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); @@ -19170,6 +19170,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { if (idx == -1) { fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i); + fclose(fout); return; } diff --git a/llama.cpp b/llama.cpp index ed8766682..ec8ffad33 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6324,7 +6324,6 @@ struct llm_tokenizer_bpe { llm_symbol sym; size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset])); sym.text = word.c_str() + offset; - sym.n = 1; sym.n = char_len; offset += sym.n; sym.prev = index - 1; @@ -7054,7 +7053,7 @@ static std::vector llama_grammar_reject_candidates_for_ std::vector rejects; if (stack.empty()) { - for (auto tok : candidates) { + for (const auto & tok : candidates) { if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) { rejects.push_back(tok); } @@ -7065,7 +7064,7 @@ static std::vector llama_grammar_reject_candidates_for_ const llama_grammar_element * stack_pos = stack.back(); std::vector next_candidates; - for (auto tok : candidates) { + for (const auto & tok : candidates) { if (*tok.code_points == 0) { // reached end of full codepoints in token, reject iff it ended in a partial sequence // that cannot satisfy this position in grammar @@ -7091,7 +7090,7 @@ static std::vector llama_grammar_reject_candidates_for_ llama_grammar_advance_stack(rules, stack_after, next_stacks); auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates); - for (auto tok : next_rejects) { + for (const auto & tok : next_rejects) { rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 }); } From a0edf73bda31c7c4e649e6f07c6fd30a729929cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 20 Oct 2023 13:06:10 +0300 Subject: [PATCH 26/95] server : fix uninitialized sampling context (close #3685) --- 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 28b3f3f53..0471528a3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -198,7 +198,7 @@ struct llama_server_context llama_model *model = nullptr; llama_context *ctx = nullptr; gpt_params params; - llama_sampling_context *ctx_sampling; + llama_sampling_context *ctx_sampling = nullptr; int n_ctx; bool truncated = false; From 8cf19d60dc93809db8e51fedc811595eed9134c5 Mon Sep 17 00:00:00 2001 From: Qin Yue Chen <71813199+chenqiny@users.noreply.github.com> Date: Fri, 20 Oct 2023 06:19:40 -0500 Subject: [PATCH 27/95] gguf : support big endian platform (#3552) * check whether platform is 390x if yes->do not import immintrin.h * support s390x big endian * support --bigendian option for s390x 1. verified with baichuan7b-chat with float 16 on s390x 2. verified with baichuan7b-chat 3. verified with chinese-alpaca-2-13b-f16 * update format based on editor-config checker result * Update convert-baichuan-hf-to-gguf.py * 1. check in ggml.c if endianess is not match 2. update GGUF version 3. change get_pack_prefix to property 4. update information log * always use "GGUF" as beginng of GGUF file * Compare "GGUF" with file header char by char 1. Set GGUF_MAGIC to "GGUF" string instead of int value 2. Compare "GGUF" char by char to ensure its byte order 3. Move bytes swap code from convert.py to gguf.py write_tensor_data --------- Co-authored-by: Georgi Gerganov --- convert-baichuan-hf-to-gguf.py | 8 +- convert.py | 20 +++-- .../convert-llama2c-to-ggml.cpp | 2 +- ggml.c | 19 +++-- ggml.h | 5 +- gguf-py/gguf/gguf.py | 73 ++++++++++++------- gguf-py/pyproject.toml | 2 +- k_quants.c | 2 +- tests/test-double-float.cpp | 2 + 9 files changed, 84 insertions(+), 49 deletions(-) diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 513a7516a..a1783f71f 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -76,6 +76,7 @@ def parse_args() -> argparse.Namespace: "ftype", type=int, choices=[0, 1], default=1, nargs='?', help="output format - use 0 for float32, 1 for float16", ) + parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") return parser.parse_args() args = parse_args() @@ -86,6 +87,11 @@ if not dir_model.is_dir(): print(f'Error: {args.model} is not a directory', file = sys.stderr) sys.exit(1) +endianess = gguf.GGUFEndian.LITTLE +if args.bigendian: + endianess = gguf.GGUFEndian.BIG +endianess_str = "Big Endian" if args.bigendian else "Little Endian" +print(f"gguf: Conversion Endianess {endianess}") # possible tensor data types # ftype == 0 -> float32 # ftype == 1 -> float16 @@ -113,7 +119,7 @@ if hparams["architectures"][0] != "BaichuanForCausalLM": num_parts = count_model_parts(dir_model) print(f"num_parts:{num_parts}\n") ARCH=gguf.MODEL_ARCH.BAICHUAN -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) +gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess) print("gguf: get model metadata") diff --git a/convert.py b/convert.py index e9b08d344..24da25efc 100755 --- a/convert.py +++ b/convert.py @@ -803,8 +803,8 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None: class OutputFile: - def __init__(self, fname_out: Path) -> None: - self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) + 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" @@ -875,10 +875,10 @@ class OutputFile: self.gguf.close() @staticmethod - def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None: + def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None: check_vocab_size(params, vocab) - of = OutputFile(fname_out) + of = OutputFile(fname_out, endianess=endianess) # meta data of.add_meta_arch(params) @@ -903,10 +903,10 @@ 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) -> 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.LITTLE) -> None: check_vocab_size(params, vocab) - of = OutputFile(fname_out) + of = OutputFile(fname_out, endianess=endianess) # meta data of.add_meta_arch(params) @@ -1123,8 +1123,9 @@ def main(args_in: list[str] | None = None) -> None: 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) - args = parser.parse_args(args_in) + parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") + args = parser.parse_args(args_in) if args.dump_single: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) @@ -1138,6 +1139,9 @@ def main(args_in: list[str] | None = None) -> None: if args.dump: do_dump_model(model_plus) return + endianess = gguf.GGUFEndian.LITTLE + if args.bigendian: + endianess = gguf.GGUFEndian.BIG params = Params.load(model_plus) if params.n_ctx == -1: @@ -1185,7 +1189,7 @@ def main(args_in: list[str] | None = None) -> None: params.ftype = ftype print(f"Writing {outfile}, format {ftype}") - OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency) + OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess) print(f"Wrote {outfile}") diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index c291f0adf..cae3bf3c3 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -536,7 +536,7 @@ static bool is_ggml_file(const char * filename) { if (file.size < 4) { return false; } - uint32_t magic = file.read_u32(); + std::string magic = file.read_string(4); return magic == GGUF_MAGIC; } diff --git a/ggml.c b/ggml.c index ed157aab0..49f3b7aba 100644 --- a/ggml.c +++ b/ggml.c @@ -20845,7 +20845,7 @@ struct gguf_kv { }; struct gguf_header { - uint32_t magic; + char magic[4]; uint32_t version; uint64_t n_tensors; // GGUFv2 uint64_t n_kv; // GGUFv2 @@ -20915,7 +20915,7 @@ static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) struct gguf_context * gguf_init_empty(void) { struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); - ctx->header.magic = GGUF_MAGIC; + memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic)); ctx->header.version = GGUF_VERSION; ctx->header.n_tensors = 0; ctx->header.n_kv = 0; @@ -20941,16 +20941,18 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // offset from start of file size_t offset = 0; - uint32_t magic = 0; + char magic[4]; // check the magic before making allocations { gguf_fread_el(file, &magic, sizeof(magic), &offset); - if (magic != GGUF_MAGIC) { - fprintf(stderr, "%s: invalid magic number %08x\n", __func__, magic); - fclose(file); - return NULL; + for (uint32_t i = 0; i < sizeof(magic); i++) { + if (magic[i] != GGUF_MAGIC[i]) { + fprintf(stderr, "%s: invalid magic characters %s.\n", __func__, magic); + fclose(file); + return NULL; + } } } @@ -20960,7 +20962,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the header { - ctx->header.magic = magic; + strncpy(ctx->header.magic, magic, 4); + ctx->kv = NULL; ctx->infos = NULL; diff --git a/ggml.h b/ggml.h index 6e35888e9..16aaf169e 100644 --- a/ggml.h +++ b/ggml.h @@ -231,8 +231,9 @@ #define GGML_EXIT_SUCCESS 0 #define GGML_EXIT_ABORTED 1 -#define GGUF_MAGIC 0x46554747 // "GGUF" -#define GGUF_VERSION 2 +#define GGUF_MAGIC "GGUF" + +#define GGUF_VERSION 3 #define GGUF_DEFAULT_ALIGNMENT 32 diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 557ce7ac0..072c839c4 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -19,9 +19,10 @@ import numpy as np # GGUF_MAGIC = 0x46554747 -GGUF_VERSION = 2 +GGUF_VERSION = 3 GGUF_DEFAULT_ALIGNMENT = 32 + # general KEY_GENERAL_ARCHITECTURE = "general.architecture" KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version" @@ -597,6 +598,10 @@ class GGMLQuantizationType(IntEnum): Q6_K = 14 Q8_K = 15 +class GGUFEndian(IntEnum): + LITTLE = 0 + BIG = 1 + class GGUFValueType(IntEnum): UINT8 = 0 @@ -644,18 +649,41 @@ class GGUFWriter: temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None tensors: list[tuple[np.ndarray[Any, Any], int]] - def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True): + @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.add_architecture() self.use_temp_file = use_temp_file self.tensors = [] + endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian" + print(f"This gguf file is for {endianess_str} only") def write_header_to_file(self): 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(""] packages = [ diff --git a/k_quants.c b/k_quants.c index e168a87bb..801941fbe 100644 --- a/k_quants.c +++ b/k_quants.c @@ -46,7 +46,7 @@ inline static int32_t vaddvq_s32(int32x4_t v) { #if defined(_MSC_VER) || defined(__MINGW32__) #include #else -#if !defined(__riscv) +#if !defined(__riscv) && !defined(__s390__) #include #endif #endif diff --git a/tests/test-double-float.cpp b/tests/test-double-float.cpp index b506f273f..afd7bf77f 100644 --- a/tests/test-double-float.cpp +++ b/tests/test-double-float.cpp @@ -4,7 +4,9 @@ #undef NDEBUG #include +#if !defined(__riscv) && !defined(__s390__) #include +#endif #include #include #include From d1031cf49c3b958b915fd558e23453471c29ac33 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 20 Oct 2023 21:07:23 +0300 Subject: [PATCH 28/95] sampling : refactor init to use llama_sampling_params (#3696) * sampling : refactor init to use llama_sampling_params * llama : combine repetition, frequency and presence penalties in 1 call * examples : remove embd-input and gptneox-wip * sampling : rename penalty params + reduce size of "prev" vector * sampling : add llama_sampling_print helper * sampling : hide prev behind API and apply #3661 ggml-ci --- Makefile | 9 +- README.md | 1 - common/common.cpp | 69 +- common/common.h | 3 +- common/sampling.cpp | 73 +- common/sampling.h | 32 +- examples/CMakeLists.txt | 32 +- examples/embd-input/.gitignore | 4 - examples/embd-input/CMakeLists.txt | 17 - examples/embd-input/README.md | 63 -- examples/embd-input/embd-input-lib.cpp | 221 ----- examples/embd-input/embd-input-test.cpp | 35 - examples/embd-input/embd-input.h | 27 - examples/embd-input/embd_input.py | 72 -- examples/embd-input/llava.py | 71 -- examples/embd-input/minigpt4.py | 129 --- examples/embd-input/panda_gpt.py | 99 -- examples/gptneox-wip/cmpnct_gpt2bpe.hpp | 1133 ----------------------- examples/gptneox-wip/falcon-main.cpp | 1111 ---------------------- examples/gptneox-wip/gptneox-main.cpp | 1083 ---------------------- examples/infill/CMakeLists.txt | 2 +- examples/infill/infill.cpp | 67 +- examples/llava/llava-utils.h | 58 +- examples/main/main.cpp | 28 +- examples/parallel/parallel.cpp | 4 +- examples/server/server.cpp | 227 +++-- examples/speculative/speculative.cpp | 12 +- llama.cpp | 94 +- llama.h | 16 +- tests/test-sampling.cpp | 75 +- 30 files changed, 365 insertions(+), 4502 deletions(-) delete mode 100644 examples/embd-input/.gitignore delete mode 100644 examples/embd-input/CMakeLists.txt delete mode 100644 examples/embd-input/README.md delete mode 100644 examples/embd-input/embd-input-lib.cpp delete mode 100644 examples/embd-input/embd-input-test.cpp delete mode 100644 examples/embd-input/embd-input.h delete mode 100755 examples/embd-input/embd_input.py delete mode 100755 examples/embd-input/llava.py delete mode 100755 examples/embd-input/minigpt4.py delete mode 100755 examples/embd-input/panda_gpt.py delete mode 100644 examples/gptneox-wip/cmpnct_gpt2bpe.hpp delete mode 100644 examples/gptneox-wip/falcon-main.cpp delete mode 100644 examples/gptneox-wip/gptneox-main.cpp diff --git a/Makefile b/Makefile index 04104bee8..325ae747b 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 embd-input-test gguf llama-bench llava baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf llama-bench llava baby-llama beam-search \ speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o # Binaries only useful for tests @@ -608,13 +608,6 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml. 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 build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -$(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) - - -embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput - gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/README.md b/README.md index ce63c6f0e..49bb556a8 100644 --- a/README.md +++ b/README.md @@ -962,7 +962,6 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m / - [main](./examples/main/README.md) - [server](./examples/server/README.md) -- [embd-input](./examples/embd-input/README.md) - [jeopardy](./examples/jeopardy/README.md) - [BLIS](./docs/BLIS.md) - [Performance troubleshooting](./docs/token_generation_performance_tips.md) diff --git a/common/common.cpp b/common/common.cpp index ce14d66b8..2ef902bd5 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -107,7 +107,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { std::string arg; gpt_params default_params; const std::string arg_prefix = "--"; - llama_sampling_params & sparams = params.sampling_params; + llama_sampling_params & sparams = params.sparams; for (int i = 1; i < argc; i++) { arg = argv[i]; @@ -241,25 +241,26 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - sparams.repeat_last_n = std::stoi(argv[i]); + sparams.penalty_last_n = std::stoi(argv[i]); + sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n); } else if (arg == "--repeat-penalty") { if (++i >= argc) { invalid_param = true; break; } - sparams.repeat_penalty = std::stof(argv[i]); + sparams.penalty_repeat = std::stof(argv[i]); } else if (arg == "--frequency-penalty") { if (++i >= argc) { invalid_param = true; break; } - sparams.frequency_penalty = std::stof(argv[i]); + sparams.penalty_freq = std::stof(argv[i]); } else if (arg == "--presence-penalty") { if (++i >= argc) { invalid_param = true; break; } - sparams.presence_penalty = std::stof(argv[i]); + sparams.penalty_present = std::stof(argv[i]); } else if (arg == "--mirostat") { if (++i >= argc) { invalid_param = true; @@ -572,7 +573,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.grammar = argv[i]; + sparams.grammar = argv[i]; } else if (arg == "--grammar-file") { if (++i >= argc) { invalid_param = true; @@ -587,7 +588,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), - std::back_inserter(params.grammar) + std::back_inserter(sparams.grammar) ); #ifndef LOG_DISABLE_LOGS // Parse args for logging parameters @@ -640,7 +641,7 @@ 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.sampling_params; + const llama_sampling_params & sparams = params.sparams; printf("usage: %s [options]\n", argv[0]); printf("\n"); @@ -678,10 +679,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_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.repeat_last_n); - printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.repeat_penalty); - printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.presence_penalty); - printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.frequency_penalty); + printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n); + 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(" --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); @@ -878,7 +879,7 @@ std::tuple llama_init_from_gpt_par } if (params.ignore_eos) { - params.sampling_params.logit_bias[llama_token_eos(lctx)] = -INFINITY; + params.sparams.logit_bias[llama_token_eos(lctx)] = -INFINITY; } { @@ -1123,28 +1124,28 @@ std::string get_sortable_timestamp() { void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx, const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const llama_sampling_params & sparams = params.sampling_params; + 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, "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"); - fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); + 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"); + 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"); fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "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_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"); - fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); - fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); - fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); - fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); - fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "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_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"); + fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); + fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); + fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); + fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); + fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); #ifdef NDEBUG fprintf(stream, "debug: false\n"); @@ -1178,8 +1179,8 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); - fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.frequency_penalty); - dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str()); + fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); + dump_string_yaml_multiline(stream, "grammar", sparams.grammar.c_str()); fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); @@ -1238,14 +1239,14 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false"); fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); - fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.presence_penalty); + fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str()); fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens); fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.repeat_penalty); + fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); fprintf(stream, "reverse_prompt:\n"); for (std::string ap : params.antiprompt) { diff --git a/common/common.h b/common/common.h index 65d3d20cd..84523a4fb 100644 --- a/common/common.h +++ b/common/common.h @@ -56,7 +56,7 @@ struct gpt_params { float rope_freq_scale = 0.0f; // RoPE frequency scaling factor // // sampling parameters - struct llama_sampling_params sampling_params; + struct llama_sampling_params sparams; std::string model = "models/7B/ggml-model-f16.gguf"; // model path std::string model_draft = ""; // draft model for speculative decoding @@ -66,7 +66,6 @@ struct gpt_params { std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state std::string input_prefix = ""; // string to prefix user inputs with std::string input_suffix = ""; // string to suffix user inputs with - std::string grammar = ""; // optional BNF-like grammar to constrain sampling std::vector antiprompt; // string upon seeing which more user input is prompted std::string logdir = ""; // directory in which to save YAML log files diff --git a/common/sampling.cpp b/common/sampling.cpp index 0b2466581..6f0af3c4a 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -1,9 +1,9 @@ #include "sampling.h" -struct llama_sampling_context * llama_sampling_init(const struct gpt_params & params) { +struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) { struct llama_sampling_context * result = new llama_sampling_context(); - result->params = params.sampling_params; + result->params = params; result->grammar = nullptr; // if there is a grammar, parse it @@ -23,7 +23,7 @@ struct llama_sampling_context * llama_sampling_init(const struct gpt_params & pa grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root")); } - result->prev.resize(params.n_ctx); + result->prev.resize(params.n_prev); return result; } @@ -66,25 +66,56 @@ void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * ds dst->prev = src->prev; } +llama_token llama_sampling_last(llama_sampling_context * ctx) { + return ctx->prev.back(); +} + +std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) { + const int size = ctx_sampling->prev.size(); + + n = std::min(n, size); + + std::string result; + + for (int i = size - n; i < size; i++) { + result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]); + } + + return result; +} + +std::string llama_sampling_print(const llama_sampling_params & params) { + char result[1024]; + + 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" + "\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.mirostat, params.mirostat_eta, params.mirostat_tau); + + return std::string(result); +} + llama_token llama_sampling_sample( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, struct llama_context * ctx_cfg, const int idx) { - const int n_ctx = llama_n_ctx(ctx_main); - const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); - const llama_sampling_params & params = ctx_sampling->params; + const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); + 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 tfs_z = params.tfs_z; const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - const float alpha_presence = params.presence_penalty; - const float alpha_frequency = params.frequency_penalty; + const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; + const float penalty_repeat = params.penalty_repeat; + const float penalty_freq = params.penalty_freq; + const float penalty_present = params.penalty_present; const int mirostat = params.mirostat; const float mirostat_tau = params.mirostat_tau; const float mirostat_eta = params.mirostat_eta; @@ -97,7 +128,7 @@ llama_token llama_sampling_sample( float * logits = llama_get_logits_ith(ctx_main, idx); - // Apply params.logit_bias map + // apply params.logit_bias map for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { logits[it->first] += it->second; } @@ -117,14 +148,10 @@ llama_token llama_sampling_sample( // apply penalties if (!prev.empty()) { const float nl_logit = logits[llama_token_nl(ctx_main)]; - const int last_n_repeat = std::min(std::min((int)prev.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx_main, &cur_p, - prev.data() + prev.size() - last_n_repeat, - last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx_main, &cur_p, - prev.data() + prev.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); + llama_sample_repetition_penalties(ctx_main, &cur_p, + prev.data() + prev.size() - penalty_last_n, + penalty_last_n, penalty_repeat, penalty_freq, penalty_present); if (!penalize_nl) { for (size_t idx = 0; idx < cur_p.size; idx++) { @@ -141,7 +168,7 @@ llama_token llama_sampling_sample( } if (temp <= 0) { - // Greedy sampling + // greedy sampling id = llama_sample_token_greedy(ctx_main, &cur_p); } else { if (mirostat == 1) { @@ -152,8 +179,9 @@ llama_token llama_sampling_sample( llama_sample_temp(ctx_main, &cur_p, temp); id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu); } else { - // Temperature sampling + // temperature sampling size_t min_keep = std::max(1, params.n_probs); + llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); @@ -183,11 +211,12 @@ llama_token llama_sampling_sample( void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, - llama_token id) { + llama_token id, + bool apply_grammar) { ctx_sampling->prev.erase(ctx_sampling->prev.begin()); ctx_sampling->prev.push_back(id); - if (ctx_sampling->grammar != NULL) { + if (ctx_sampling->grammar != NULL && apply_grammar) { llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id); } } diff --git a/common/sampling.h b/common/sampling.h index 50afcbc12..62ea6d4cf 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -10,30 +10,30 @@ // sampling parameters typedef struct llama_sampling_params { + int32_t n_prev = 64; // number of previous tokens to remember + 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 tfs_z = 1.00f; // 1.0 = disabled float typical_p = 1.00f; // 1.0 = disabled float temp = 0.80f; // 1.0 = disabled - float repeat_penalty = 1.10f; // 1.0 = disabled - int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float frequency_penalty = 0.00f; // 0.0 = disabled - float presence_penalty = 0.00f; // 0.0 = disabled + int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat = 1.10f; // 1.0 = disabled + float penalty_freq = 0.00f; // 0.0 = disabled + float penalty_present = 0.00f; // 0.0 = disabled int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = true; // consider newlines as a repeatable token - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + std::string grammar; // optional BNF-like grammar to constrain sampling // Classifier-Free Guidance // https://arxiv.org/abs/2306.17806 - std::string cfg_negative_prompt; // string to help guidance - float cfg_scale = 1.f; // How strong is guidance + std::string cfg_negative_prompt; // string to help guidance + float cfg_scale = 1.f; // how strong is guidance std::unordered_map logit_bias; // logit bias for specific tokens - } llama_sampling_params; // general sampler context @@ -58,7 +58,7 @@ struct llama_sampling_context { #include "common.h" // Create a new sampling context instance. -struct llama_sampling_context * llama_sampling_init(const struct gpt_params & params); +struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params); void llama_sampling_free(struct llama_sampling_context * ctx); @@ -70,6 +70,15 @@ void llama_sampling_reset(llama_sampling_context * ctx); // Copy the sampler context void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst); +// Get the last sampled token +llama_token llama_sampling_last(llama_sampling_context * ctx); + +// Get a string representation of the last sampled tokens +std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n); + +// Print sampling parameters into a string +std::string llama_sampling_print(const llama_sampling_params & params); + // this is a common sampling function used across the examples for convenience // it can serve as a starting point for implementing your own sampling function // Note: When using multiple sequences, it is the caller's responsibility to call @@ -96,4 +105,5 @@ llama_token llama_sampling_sample( void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, - llama_token id); + llama_token id, + bool apply_grammar); diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index e16c65f7c..75b8df676 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -12,26 +12,26 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}) if (EMSCRIPTEN) else() - add_subdirectory(main) - add_subdirectory(quantize) - add_subdirectory(quantize-stats) - add_subdirectory(perplexity) - add_subdirectory(embedding) - add_subdirectory(save-load-state) - add_subdirectory(benchmark) add_subdirectory(baby-llama) - add_subdirectory(train-text-from-scratch) - add_subdirectory(finetune) - add_subdirectory(convert-llama2c-to-ggml) - add_subdirectory(simple) add_subdirectory(batched) add_subdirectory(batched-bench) - add_subdirectory(speculative) - add_subdirectory(parallel) - add_subdirectory(embd-input) - add_subdirectory(llava) - add_subdirectory(llama-bench) add_subdirectory(beam-search) + add_subdirectory(benchmark) + add_subdirectory(convert-llama2c-to-ggml) + add_subdirectory(embedding) + add_subdirectory(finetune) + add_subdirectory(infill) + add_subdirectory(llama-bench) + add_subdirectory(llava) + add_subdirectory(main) + add_subdirectory(parallel) + add_subdirectory(perplexity) + add_subdirectory(quantize) + add_subdirectory(quantize-stats) + add_subdirectory(save-load-state) + add_subdirectory(simple) + add_subdirectory(speculative) + add_subdirectory(train-text-from-scratch) if (LLAMA_METAL) add_subdirectory(metal) endif() diff --git a/examples/embd-input/.gitignore b/examples/embd-input/.gitignore deleted file mode 100644 index 87ef68771..000000000 --- a/examples/embd-input/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -PandaGPT -MiniGPT-4 -*.pth - diff --git a/examples/embd-input/CMakeLists.txt b/examples/embd-input/CMakeLists.txt deleted file mode 100644 index 5bbb1ea02..000000000 --- a/examples/embd-input/CMakeLists.txt +++ /dev/null @@ -1,17 +0,0 @@ -set(TARGET embdinput) -add_library(${TARGET} embd-input-lib.cpp embd-input.h) -install(TARGETS ${TARGET} LIBRARY) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() - -set(TARGET embd-input-test) -add_executable(${TARGET} embd-input-test.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/embd-input/README.md b/examples/embd-input/README.md deleted file mode 100644 index 5c4c75ea7..000000000 --- a/examples/embd-input/README.md +++ /dev/null @@ -1,63 +0,0 @@ -### Examples for input embedding directly - -## Requirement -build `libembdinput.so` -run the following comman in main dir (../../). -``` -make -``` - -## [LLaVA](https://github.com/haotian-liu/LLaVA/) example (llava.py) - -1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/). -2. Convert it to ggml format. -3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin). - -``` -import torch - -bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin" -pth_path = "./examples/embd-input/llava_projection.pth" - -dic = torch.load(bin_path) -used_key = ["model.mm_projector.weight","model.mm_projector.bias"] -torch.save({k: dic[k] for k in used_key}, pth_path) -``` -4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`. - - -## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py) - -1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format. -The `adapter_config.json` is -``` -{ - "peft_type": "LORA", - "fan_in_fan_out": false, - "bias": null, - "modules_to_save": null, - "r": 32, - "lora_alpha": 32, - "lora_dropout": 0.1, - "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"] -} -``` -2. Papare the `vicuna` v0 model. -3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model. -4. Clone the PandaGPT source. -``` -git clone https://github.com/yxuansu/PandaGPT -``` -5. Install the requirement of PandaGPT. -6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py. - -## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py) - -1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`. -2. Clone the MiniGPT-4 source. -``` -git clone https://github.com/Vision-CAIR/MiniGPT-4/ -``` -3. Install the requirement of PandaGPT. -4. Papare the `vicuna` v0 model. -5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`. diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp deleted file mode 100644 index 3ce33842c..000000000 --- a/examples/embd-input/embd-input-lib.cpp +++ /dev/null @@ -1,221 +0,0 @@ -#include "build-info.h" -#include "common.h" -#include "embd-input.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -static llama_context ** g_ctx; - -extern "C" { - -struct MyModel* create_mymodel(int argc, char ** argv) { - gpt_params params; - - if (!gpt_params_parse(argc, argv, params)) { - return nullptr; - } - - print_build_info(); - - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = uint32_t(time(NULL)); - } - fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); - - llama_backend_init(params.numa); - - llama_model * model; - llama_context * ctx; - - g_ctx = &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 nullptr; - } - - // print system information - { - fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); - } - struct MyModel * ret = new MyModel(); - ret->ctx = ctx; - ret->params = params; - ret->n_past = 0; - // printf("ctx: %d\n", ret->ctx); - return ret; -} - -void free_mymodel(struct MyModel * mymodel) { - llama_context * ctx = mymodel->ctx; - llama_print_timings(ctx); - llama_free(ctx); - delete mymodel; -} - - -bool eval_float(void * model, float * input, int N){ - MyModel * mymodel = (MyModel*)model; - llama_context * ctx = mymodel->ctx; - gpt_params params = mymodel->params; - int n_emb = llama_n_embd(llama_get_model(ctx)); - int n_past = mymodel->n_past; - int n_batch = N; // params.n_batch; - - for (int i = 0; i < (int) N; i += n_batch) { - int n_eval = (int) N - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - llama_batch batch = { int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, }; - if (llama_decode(ctx, batch)) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return false; - } - n_past += n_eval; - } - mymodel->n_past = n_past; - return true; -} - -bool eval_tokens(void * model, std::vector tokens) { - MyModel * mymodel = (MyModel* )model; - llama_context * ctx; - ctx = mymodel->ctx; - gpt_params params = mymodel->params; - int n_past = mymodel->n_past; - for (int i = 0; i < (int) tokens.size(); i += params.n_batch) { - int n_eval = (int) tokens.size() - i; - if (n_eval > params.n_batch) { - n_eval = params.n_batch; - } - if (llama_decode(ctx, 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; - } - mymodel->n_past = n_past; - return true; -} - -bool eval_id(struct MyModel* mymodel, int id) { - std::vector tokens; - tokens.push_back(id); - return eval_tokens(mymodel, tokens); -} - -bool eval_string(struct MyModel * mymodel,const char* str){ - llama_context * ctx = mymodel->ctx; - std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx, str2, true); - eval_tokens(mymodel, embd_inp); - return true; -} - -llama_token sampling_id(struct MyModel* mymodel) { - llama_context* ctx = mymodel->ctx; - gpt_params params = mymodel->params; - llama_sampling_params & sparams = params.sampling_params; - // int n_ctx = llama_n_ctx(ctx); - - // 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)) : 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 = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - // const float repeat_penalty = params.repeat_penalty; - // const float alpha_presence = params.presence_penalty; - // const float alpha_frequency = params.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 = params.penalize_nl; - - llama_token id = 0; - { - auto logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(llama_get_model(ctx)); - - // 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, &candidates_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temp(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx, &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, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k(ctx, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx, &candidates_p, top_p, 1); - llama_sample_temp(ctx, &candidates_p, temp); - id = llama_sample_token(ctx, &candidates_p); - } - } - } - - return id; -} - -const char * sampling(struct MyModel * mymodel) { - llama_context * ctx = mymodel->ctx; - int id = sampling_id(mymodel); - static std::string ret; - if (id == llama_token_eos(ctx)) { - ret = ""; - } else { - ret = llama_token_to_piece(ctx, id); - } - eval_id(mymodel, id); - return ret.c_str(); -} - -} diff --git a/examples/embd-input/embd-input-test.cpp b/examples/embd-input/embd-input-test.cpp deleted file mode 100644 index dc4a0e488..000000000 --- a/examples/embd-input/embd-input-test.cpp +++ /dev/null @@ -1,35 +0,0 @@ -#include "embd-input.h" -#include -#include -#include - -int main(int argc, char** argv) { - - auto mymodel = create_mymodel(argc, argv); - int N = 10; - int max_tgt_len = 500; - int n_embd = llama_n_embd(llama_get_model(mymodel->ctx)); - - // add random float embd to test evaluation - float * data = new float[N*n_embd]; - std::default_random_engine e; - std::uniform_real_distribution u(0,1); - for (int i=0;iparams.prompt.c_str()); - const char* tmp; - for (int i=0; i")==0) break; - printf("%s", tmp); - fflush(stdout); - } - printf("\n"); - free_mymodel(mymodel); - return 0; -} diff --git a/examples/embd-input/embd-input.h b/examples/embd-input/embd-input.h deleted file mode 100644 index eff5e3b84..000000000 --- a/examples/embd-input/embd-input.h +++ /dev/null @@ -1,27 +0,0 @@ -#ifndef _EMBD_INPUT_H_ -#define _EMBD_INPUT_H_ 1 - -#include "common.h" -#include "llama.h" - -extern "C" { - -typedef struct MyModel { - llama_context* ctx; - gpt_params params; - int n_past = 0; -} MyModel; - -struct MyModel* create_mymodel(int argc, char ** argv); - -bool eval_float(void* model, float* input, int N); -bool eval_tokens(void* model, std::vector tokens); -bool eval_id(struct MyModel* mymodel, int id); -bool eval_string(struct MyModel* mymodel, const char* str); -const char * sampling(struct MyModel* mymodel); -llama_token sampling_id(struct MyModel* mymodel); -void free_mymodel(struct MyModel* mymodel); - -} - -#endif diff --git a/examples/embd-input/embd_input.py b/examples/embd-input/embd_input.py deleted file mode 100755 index f146acdc1..000000000 --- a/examples/embd-input/embd_input.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python3 -import ctypes -from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int -import numpy as np -import os - -libc = cdll.LoadLibrary("./libembdinput.so") -libc.sampling.restype=c_char_p -libc.create_mymodel.restype=c_void_p -libc.eval_string.argtypes=[c_void_p, c_char_p] -libc.sampling.argtypes=[c_void_p] -libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int] - - -class MyModel: - def __init__(self, args): - argc = len(args) - c_str = [c_char_p(i.encode()) for i in args] - args_c = (c_char_p * argc)(*c_str) - self.model = c_void_p(libc.create_mymodel(argc, args_c)) - self.max_tgt_len = 512 - self.print_string_eval = True - - def __del__(self): - libc.free_mymodel(self.model) - - def eval_float(self, x): - libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1]) - - def eval_string(self, x): - libc.eval_string(self.model, x.encode()) # c_char_p(x.encode())) - if self.print_string_eval: - print(x) - - def eval_token(self, x): - libc.eval_id(self.model, x) - - def sampling(self): - s = libc.sampling(self.model) - return s - - def stream_generate(self, end=""): - ret = b"" - end = end.encode() - for _ in range(self.max_tgt_len): - tmp = self.sampling() - ret += tmp - yield tmp - if ret.endswith(end): - break - - def generate_with_print(self, end=""): - ret = b"" - for i in self.stream_generate(end=end): - ret += i - print(i.decode(errors="replace"), end="", flush=True) - print("") - return ret.decode(errors="replace") - - - def generate(self, end=""): - text = b"".join(self.stream_generate(end=end)) - return text.decode(errors="replace") - -if __name__ == "__main__": - model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"]) - model.eval_string("""user: what is the color of the flag of UN?""") - x = np.random.random((5120,10))# , dtype=np.float32) - model.eval_float(x) - model.eval_string("""assistant:""") - for i in model.generate(): - print(i.decode(errors="replace"), end="", flush=True) diff --git a/examples/embd-input/llava.py b/examples/embd-input/llava.py deleted file mode 100755 index 06fad55f4..000000000 --- a/examples/embd-input/llava.py +++ /dev/null @@ -1,71 +0,0 @@ -#!/usr/bin/env python3 -import sys -import os -sys.path.insert(0, os.path.dirname(__file__)) -from embd_input import MyModel -import numpy as np -from torch import nn -import torch -from transformers import CLIPVisionModel, CLIPImageProcessor -from PIL import Image - -# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1' -vision_tower = "openai/clip-vit-large-patch14" -select_hidden_state_layer = -2 -# (vision_config.image_size // vision_config.patch_size) ** 2 -image_token_len = (224//14)**2 - -class Llava: - def __init__(self, args): - self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower) - self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower) - self.mm_projector = nn.Linear(1024, 5120) - self.model = MyModel(["main", *args]) - - def load_projection(self, path): - state = torch.load(path) - self.mm_projector.load_state_dict({ - "weight": state["model.mm_projector.weight"], - "bias": state["model.mm_projector.bias"]}) - - def chat(self, question): - self.model.eval_string("user: ") - self.model.eval_string(question) - self.model.eval_string("\nassistant: ") - return self.model.generate_with_print() - - def chat_with_image(self, image, question): - with torch.no_grad(): - embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] - image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True) - select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer] - image_feature = select_hidden_state[:, 1:] - embd_image = self.mm_projector(image_feature) - embd_image = embd_image.cpu().numpy()[0] - self.model.eval_string("user: ") - self.model.eval_token(32003-2) # im_start - self.model.eval_float(embd_image.T) - for i in range(image_token_len-embd_image.shape[0]): - self.model.eval_token(32003-3) # im_patch - self.model.eval_token(32003-1) # im_end - self.model.eval_string(question) - self.model.eval_string("\nassistant: ") - return self.model.generate_with_print() - - -if __name__=="__main__": - # model form liuhaotian/LLaVA-13b-delta-v1-1 - a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"]) - # Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin. - # Also here can use pytorch_model-00003-of-00003.bin directly. - a.load_projection(os.path.join( - os.path.dirname(__file__) , - "llava_projection.pth")) - respose = a.chat_with_image( - Image.open("./media/llama1-logo.png").convert('RGB'), - "what is the text in the picture?") - respose - a.chat("what is the color of it?") - - - diff --git a/examples/embd-input/minigpt4.py b/examples/embd-input/minigpt4.py deleted file mode 100755 index 7b13e4a5c..000000000 --- a/examples/embd-input/minigpt4.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 -import sys -import os -sys.path.insert(0, os.path.dirname(__file__)) -from embd_input import MyModel -import numpy as np -from torch import nn -import torch -from PIL import Image - -minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4") -sys.path.insert(0, minigpt4_path) -from minigpt4.models.blip2 import Blip2Base -from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor - - -class MiniGPT4(Blip2Base): - """ - MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4 - """ - def __init__(self, - args, - vit_model="eva_clip_g", - q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth", - img_size=224, - drop_path_rate=0, - use_grad_checkpoint=False, - vit_precision="fp32", - freeze_vit=True, - freeze_qformer=True, - num_query_token=32, - llama_model="", - prompt_path="", - prompt_template="", - max_txt_len=32, - end_sym='\n', - low_resource=False, # use 8 bit and put vit in cpu - device_8bit=0 - ): - super().__init__() - self.img_size = img_size - self.low_resource = low_resource - self.preprocessor = Blip2ImageEvalProcessor(img_size) - - print('Loading VIT') - self.visual_encoder, self.ln_vision = self.init_vision_encoder( - vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision - ) - print('Loading VIT Done') - print('Loading Q-Former') - self.Qformer, self.query_tokens = self.init_Qformer( - num_query_token, self.visual_encoder.num_features - ) - self.Qformer.cls = None - self.Qformer.bert.embeddings.word_embeddings = None - self.Qformer.bert.embeddings.position_embeddings = None - for layer in self.Qformer.bert.encoder.layer: - layer.output = None - layer.intermediate = None - self.load_from_pretrained(url_or_filename=q_former_model) - print('Loading Q-Former Done') - self.llama_proj = nn.Linear( - self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size - ) - self.max_txt_len = max_txt_len - self.end_sym = end_sym - self.model = MyModel(["main", *args]) - # system prompt - self.model.eval_string("Give the following image: ImageContent. " - "You will be able to see the image once I provide it to you. Please answer my questions." - "###") - - def encode_img(self, image): - image = self.preprocessor(image) - image = image.unsqueeze(0) - device = image.device - if self.low_resource: - self.vit_to_cpu() - image = image.to("cpu") - - with self.maybe_autocast(): - image_embeds = self.ln_vision(self.visual_encoder(image)).to(device) - image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device) - - query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) - query_output = self.Qformer.bert( - query_embeds=query_tokens, - encoder_hidden_states=image_embeds, - encoder_attention_mask=image_atts, - return_dict=True, - ) - - inputs_llama = self.llama_proj(query_output.last_hidden_state) - # atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device) - return inputs_llama - - def load_projection(self, path): - state = torch.load(path)["model"] - self.llama_proj.load_state_dict({ - "weight": state["llama_proj.weight"], - "bias": state["llama_proj.bias"]}) - - def chat(self, question): - self.model.eval_string("Human: ") - self.model.eval_string(question) - self.model.eval_string("\n### Assistant:") - return self.model.generate_with_print(end="###") - - def chat_with_image(self, image, question): - with torch.no_grad(): - embd_image = self.encode_img(image) - embd_image = embd_image.cpu().numpy()[0] - self.model.eval_string("Human: ") - self.model.eval_float(embd_image.T) - self.model.eval_string(" ") - self.model.eval_string(question) - self.model.eval_string("\n### Assistant:") - return self.model.generate_with_print(end="###") - - -if __name__=="__main__": - a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"]) - a.load_projection(os.path.join( - os.path.dirname(__file__) , - "pretrained_minigpt4.pth")) - respose = a.chat_with_image( - Image.open("./media/llama1-logo.png").convert('RGB'), - "what is the text in the picture?") - a.chat("what is the color of it?") diff --git a/examples/embd-input/panda_gpt.py b/examples/embd-input/panda_gpt.py deleted file mode 100755 index 891ad7cc9..000000000 --- a/examples/embd-input/panda_gpt.py +++ /dev/null @@ -1,99 +0,0 @@ -#!/usr/bin/env python3 -import sys -import os -sys.path.insert(0, os.path.dirname(__file__)) -from embd_input import MyModel -import numpy as np -from torch import nn -import torch - -# use PandaGPT path -panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT") -imagebind_ckpt_path = "./models/panda_gpt/" - -sys.path.insert(0, os.path.join(panda_gpt_path,"code","model")) -from ImageBind.models import imagebind_model -from ImageBind import data - -ModalityType = imagebind_model.ModalityType -max_tgt_len = 400 - -class PandaGPT: - def __init__(self, args): - self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path) - self.visual_encoder.eval() - self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120) - self.max_tgt_len = max_tgt_len - self.model = MyModel(["main", *args]) - self.generated_text = "" - self.device = "cpu" - - def load_projection(self, path): - state = torch.load(path, map_location="cpu") - self.llama_proj.load_state_dict({ - "weight": state["llama_proj.weight"], - "bias": state["llama_proj.bias"]}) - - def eval_inputs(self, inputs): - self.model.eval_string("") - embds = self.extract_multimoal_feature(inputs) - for i in embds: - self.model.eval_float(i.T) - self.model.eval_string(" ") - - def chat(self, question): - return self.chat_with_image(None, question) - - def chat_with_image(self, inputs, question): - if self.generated_text == "": - self.model.eval_string("###") - self.model.eval_string(" Human: ") - if inputs: - self.eval_inputs(inputs) - self.model.eval_string(question) - self.model.eval_string("\n### Assistant:") - ret = self.model.generate_with_print(end="###") - self.generated_text += ret - return ret - - def extract_multimoal_feature(self, inputs): - features = [] - for key in ["image", "audio", "video", "thermal"]: - if key + "_paths" in inputs: - embeds = self.encode_data(key, inputs[key+"_paths"]) - features.append(embeds) - return features - - def encode_data(self, data_type, data_paths): - - type_map = { - "image": ModalityType.VISION, - "audio": ModalityType.AUDIO, - "video": ModalityType.VISION, - "thermal": ModalityType.THERMAL, - } - load_map = { - "image": data.load_and_transform_vision_data, - "audio": data.load_and_transform_audio_data, - "video": data.load_and_transform_video_data, - "thermal": data.load_and_transform_thermal_data - } - - load_function = load_map[data_type] - key = type_map[data_type] - - inputs = {key: load_function(data_paths, self.device)} - with torch.no_grad(): - embeddings = self.visual_encoder(inputs) - embeds = embeddings[key] - embeds = self.llama_proj(embeds).cpu().numpy() - return embeds - - -if __name__=="__main__": - a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"]) - a.load_projection("./models/panda_gpt/adapter_model.bin") - a.chat_with_image( - {"image_paths": ["./media/llama1-logo.png"]}, - "what is the text in the picture? 'llama' or 'lambda'?") - a.chat("what is the color of it?") diff --git a/examples/gptneox-wip/cmpnct_gpt2bpe.hpp b/examples/gptneox-wip/cmpnct_gpt2bpe.hpp deleted file mode 100644 index 9d433f4b1..000000000 --- a/examples/gptneox-wip/cmpnct_gpt2bpe.hpp +++ /dev/null @@ -1,1133 +0,0 @@ -#ifndef CMPNCT_GPT2BPE -#define CMPNCT_GPT2BPE - -#include -#include -#include -#include -#include -#include -#include -#include -#include - - -// Unicode GPT2 Byte Pair Encoding Tokenizer -// Adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License] -// Removed loading of merges from HF json and parts made for a specific vocab - - -//----------------- -// Unicode library (from cmpnct_unicode.cpp) -//----------------- - -// Minimal library for high performance handling and categorization of UTF8 strings and characters -// Using std::string - -enum CNCTCharType { - DIGIT, // a numerical char in any language - LETTER, // a letter in any language - WHITESPACE, // any form of whitespace - ACCENT_MARK, // letter modifiers like ´ in é - PUNCTUATION, // punctuation including brackets - SYMBOL, // math, currency, other symbols - CONTROL, // control characters - MIXED, // a mix of the above - UNIDENTIFIED // something more exotic like emoji or separators -}; - -struct CNCTUnicode; - -struct CNCTString { - std::string str; - size_t utf8_chars; - - CNCTCharType char_type=UNIDENTIFIED; - bool is_sequential=false; - - size_t seq_offset_bytes=0; - size_t seq_offset_utf8_chars=0; - - bool operator==(const std::string &other) const; - bool operator==(const char other) const; - bool operator==(const CNCTString &other) const; - CNCTString &operator+=(const std::string &other); - CNCTString &operator+=(const char other); - friend CNCTString operator+(CNCTString lhs, const std::string &rhs); - friend CNCTString operator+(CNCTString lhs, const char rhs); - CNCTString& operator+=(const CNCTString& other); - friend CNCTString operator+(CNCTString lhs, const CNCTString& rhs); -}; - -struct CNCTUnicode { - static bool check_code_range(int c, const std::vector>& ranges); - static CNCTCharType get_code_type(int c); - static CNCTCharType get_code_type(const std::string &utf8_char); - static int utf8_len(const char c); - static int strlen_utf8(std::string src); - static std::vector split_utf8(const std::string &src); - static std::vector split_utf8_enhanced(const std::string &src); - static CNCTCharType string_identify(const std::string& str); - static bool string_test(const std::string& str, CNCTCharType chartype); -}; - -static const std::vector> digit_ranges = { -{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F}, -{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F}, -{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468}, -{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909}, -{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A}, -{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739}, -{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9}, -{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9}, -}; - -static const std::vector> letter_ranges = { -{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374}, -{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559}, -{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710}, -{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A}, -{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2}, -{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33}, -{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD}, -{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61}, -{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0}, -{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3}, -{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61}, -{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A}, -{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C}, -{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7}, -{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5}, -{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C}, -{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3}, -{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB}, -{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F}, -{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D}, -{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4}, -{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107}, -{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E}, -{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F}, -{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006}, -{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF}, -{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788}, -{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE}, -{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B}, -{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4}, -{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB}, -{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44}, -{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7}, -{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA}, -{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D}, -{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835}, -{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7}, -{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35}, -{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C}, -{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147}, -{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288}, -{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339}, -{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE}, -{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909}, -{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00}, -{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F}, -{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0}, -{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77}, -{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08}, -{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C}, -{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514}, -{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA}, -{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D}, -{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27}, -{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52}, -{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72}, -{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734}, -{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A}, -}; - -static const std::vector> whitespace_ranges = { -{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000}, -}; - -static const std::vector> accent_mark_ranges = { -{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4}, -{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B}, -{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7}, -{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC}, -{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63}, -{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83}, -{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57}, -{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC}, -{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E}, -{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734}, -{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E}, -{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2}, -{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F}, -{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881}, -{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D}, -{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E}, -{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F}, -{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134}, -{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303}, -{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E}, -{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938}, -{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47}, -{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45}, -{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92}, -{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36}, -{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A}, -{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF}, -}; - -static const std::vector> punctuation_ranges = { -{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB}, -{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D}, -{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76}, -{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA}, -{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A}, -{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027}, -{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998}, -{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F}, -{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF}, -{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F}, -{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20}, -{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857}, -{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D}, -{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9}, -{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946}, -{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F}, -{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F}, -}; - -static const std::vector> symbol_ranges = { -{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7}, -{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608}, -{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA}, -{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5}, -{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C}, -{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF}, -{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B}, -{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4}, -{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3}, -{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3}, -{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A}, -{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69}, -{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F}, -{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F}, -{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241}, -{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789}, -{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC}, -{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD}, -{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8}, -{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53}, -{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA}, -}; - -static const std::vector> control_ranges = { -{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C}, -{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F}, -{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5}, -{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29}, -{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80}, -{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5}, -{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54}, -{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7}, -{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C}, -{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB}, -{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49}, -{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7}, -{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5}, -{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC}, -{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF}, -{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F}, -{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF}, -{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F}, -{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F}, -{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F}, -{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC}, -{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF}, -{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F}, -{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF}, -{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF}, -{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F}, -{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA}, -{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA}, -{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2}, -{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1}, -{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B}, -{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F}, -{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F}, -{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807}, -{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E}, -{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E}, -{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8}, -{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF}, -{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF}, -{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E}, -{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334}, -{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C}, -{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF}, -{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936}, -{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09}, -{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B}, -{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF}, -{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F}, -{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF}, -{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163}, -{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A}, -{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8}, -{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F}, -{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A}, -{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6}, -{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26}, -{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50}, -{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B}, -{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF}, -{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F}, -{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F}, -{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F}, -{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F}, -{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF}, -}; - -//String -bool CNCTString::operator==(const std::string& other) const { - return str.compare(other) == 0; -} -bool CNCTString::operator==(const char other) const { - return str.compare(std::string(1, other)) == 0; -} -bool CNCTString::operator==(const CNCTString& other) const { - return str.compare(other.str) == 0; -} -// + operators -CNCTString& CNCTString::operator+=(const std::string& other) { - str += other; - int new_len = CNCTUnicode::strlen_utf8(other); - utf8_chars += new_len; - char_type = CNCTUnicode::string_identify(str); - seq_offset_bytes += other.size(); - seq_offset_utf8_chars += new_len; - return *this; -} - -CNCTString& CNCTString::operator+=(const char other) { - std::string str = std::string(1, other); - *this += str; - return *this; -} - -CNCTString& CNCTString::operator+=(const CNCTString& other) { - str += other.str; - utf8_chars += other.utf8_chars; - char_type = CNCTUnicode::string_identify(str); - seq_offset_bytes += other.str.size(); - seq_offset_utf8_chars += other.utf8_chars; - return *this; -} - -struct CRCompare { - bool operator()(const std::pair& p, int i) { - return p.second < i; - } - bool operator()(int i, const std::pair& p) { - return i < p.first; - } -}; - -// binary search for code range -bool CNCTUnicode::check_code_range(int c, const std::vector> &ranges) { - auto it = std::upper_bound(ranges.begin(), ranges.end(), c, CRCompare()); - if (it != ranges.begin()) { - --it; - } - return c >= it->first && c <= it->second; -} - -// these are binary searches, it takes only a few operations -CNCTCharType CNCTUnicode::get_code_type(int c) { - if (check_code_range(c, letter_ranges)) { - return LETTER; - } - if (check_code_range(c, digit_ranges)) { - return DIGIT; - } - if (check_code_range(c, whitespace_ranges)) { - return WHITESPACE; - } - if (check_code_range(c, punctuation_ranges)) { - return PUNCTUATION; - } - if (check_code_range(c, symbol_ranges)) { - return SYMBOL; - } - if (check_code_range(c, accent_mark_ranges)) { - return ACCENT_MARK; - } - if (check_code_range(c, control_ranges)) { - return CONTROL; - } - return UNIDENTIFIED; -} - -static int utf8_to_unicode(const std::string& utf8_char) { - int c = 0; - int len = (int)utf8_char.size(); - if (len == 1) { - c = utf8_char[0]; - } else if (len == 2) { - c = ((utf8_char[0] & 0x1F) << 6) | (utf8_char[1] & 0x3F); - } else if (len == 3) { - c = ((utf8_char[0] & 0x0F) << 12) | ((utf8_char[1] & 0x3F) << 6) | (utf8_char[2] & 0x3F); - } else if (len == 4) { - c = ((utf8_char[0] & 0x07) << 18) | ((utf8_char[1] & 0x3F) << 12) | ((utf8_char[2] & 0x3F) << 6) | (utf8_char[3] & 0x3F); - } - return c; -} - -CNCTCharType CNCTUnicode::get_code_type(const std::string &utf8_char) { - return get_code_type(utf8_to_unicode(utf8_char)); -} - -int CNCTUnicode::utf8_len(const char c) -{ - if ((c & 0x80) == 0) { - return 1; // ASCII character - } - if ((c & 0xE0) == 0xC0) { - return 2; // 2-byte character - } - if ((c & 0xF0) == 0xE0) { - return 3; // 3-byte character - } - if ((c & 0xF0) == 0xF0) { - return 4; // 4-byte character - } - return 1; // not valid utf8 - // static const uint8_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; - // return lookup[static_cast(c) >> 4]; -} - -int CNCTUnicode::strlen_utf8(const std::string src) { - int len = 0; - for (std::string::const_iterator it = src.begin(); it != src.end(); ++it) { - int char_len = utf8_len(*it); - if (char_len > 1) { - it += char_len - 1; - } - len += 1; - } - return len; -} - -// split a string into unicode strings -std::vector CNCTUnicode::split_utf8(const std::string &src) { - std::vector result; - for (std::string::const_iterator it = src.begin(); it != src.end(); ++it) { - int char_len = utf8_len(*it); - std::string str(it, it + char_len); - result.push_back(str); - if (char_len > 1) { - it += char_len - 1; - } - } - return result; -} - -// split a string into unicode strings (CNCTString) with sequence information -std::vector CNCTUnicode::split_utf8_enhanced(const std::string &src) { - std::vector result; - int seq_offset_bytes=0; - int seq_offset_utf8_chars=0; - for (std::string::const_iterator it = src.begin(); it != src.end(); ++it) { - int char_len = utf8_len(*it); - std::string str(it, it + char_len); - CNCTString cnct_str; - cnct_str.seq_offset_bytes = seq_offset_bytes; - cnct_str.seq_offset_utf8_chars = seq_offset_utf8_chars; - cnct_str.str = str; - cnct_str.utf8_chars = 1; - cnct_str.char_type = get_code_type(str); - #if 0 - switch (cnct_str.char_type) - { - case DIGIT: - printf("%s = DIGIT\n", str.c_str()); - break; - case LETTER: - printf("%s = LETTER\n", str.c_str()); - break; - case WHITESPACE: - printf("%s = WHITESPACE\n", str.c_str()); - break; - case PUNCTUATION: - printf("%s = PUNCTUATION\n", str.c_str()); - break; - case UNIDENTIFIED: - printf("%s = UNIDENTIFIED\n", str.c_str()); - break; - case SYMBOL: - printf("%s = SYMBOL\n", str.c_str()); - break; - case CONTROL: - printf("%s = CONTROL\n", str.c_str()); - break; - } - #endif - - result.push_back(cnct_str); - seq_offset_bytes += char_len; - seq_offset_utf8_chars += 1; - if (char_len > 1) { - it += char_len - 1; - } - - } - return result; -} - -// return the type of the string -CNCTCharType CNCTUnicode::string_identify(const std::string &str) { - CNCTCharType result = UNIDENTIFIED; - std::string::const_iterator it = str.begin(); - while (it != str.end()) { - int len = utf8_len(*it); - int c = 0; - for (int i = 0; i < len && it != str.end(); ++i, ++it) { - c = (c << 8) | static_cast(*it); - } - switch (get_code_type(c)) { - case DIGIT: - if (result == UNIDENTIFIED) { - result = DIGIT; - } else if (result != DIGIT) { - return MIXED; - } - break; - case LETTER: - if (result == UNIDENTIFIED) { - result = LETTER; - } else if (result != LETTER) { - return MIXED; - } - break; - case WHITESPACE: - if (result == UNIDENTIFIED) { - result = WHITESPACE; - } else if (result != WHITESPACE) { - return MIXED; - } - break; - case PUNCTUATION: - if (result == UNIDENTIFIED) { - result = PUNCTUATION; - } else if (result != PUNCTUATION) { - return MIXED; - } - break; - default: - return MIXED; - break; - } - } - return result; -} - -// verify the content of a string -bool CNCTUnicode::string_test(const std::string &str, CNCTCharType chartype) -{ - std::string::const_iterator it = str.begin(); - while (it != str.end()) { - int len = utf8_len(*it); - int c = 0; - for (int i = 0; i < len && it != str.end(); ++i, ++it) { - c = (c << 8) | static_cast(*it); - } - if (get_code_type(c) != chartype) { - return false; - } - } - return true; -} - -//----------------- -// llama.cpp GPT2 vocab (from libfalcon.cpp) -//----------------- - -std::string replaceAll(std::string str, const std::string& from, const std::string& to) { - size_t start_pos = 0; - while((start_pos = str.find(from, start_pos)) != std::string::npos) { - str.replace(start_pos, from.length(), to); - start_pos += to.length(); // Handles case where 'to' is a substring of 'from' - } - return str; -} - -struct TrieNode { - std::map map; - int32_t Id = -1; -}; - -struct Trie { - TrieNode *root; - - Trie() : root(new TrieNode()) {} - - ~Trie() { - if(root) - deleteTrie(root); - } - - // Move constructor - Trie(Trie&& other) noexcept : root(other.root) { - other.root = nullptr; - } - - // Move assignment operator - Trie& operator=(Trie&& other) noexcept { - if (this != &other) { - if(root) - deleteTrie(root); - root = other.root; - other.root = nullptr; - } - return *this; - } - - void insert(const std::string &token, int32_t Id) { - TrieNode* current = root; - for(auto ch : token) { - if(current->map.find(ch) == current->map.end()) { - current->map[ch] = new TrieNode(); - } - current = current->map[ch]; - } - current->Id = Id; - } - - void reset() { - deleteTrie(root); - root = new TrieNode(); - } - -private: - void deleteTrie(TrieNode* node) { - for(auto &it: node->map) { - deleteTrie(it.second); - } - delete node; - } - -}; - -struct gpt2bpe_vocab { - using id = int32_t; - using token = std::string; - - std::map max_token_length; // max length, for each 2byte prefix - std::map, int> bpe_ranks; - std::vector> bpe_merges; - - id special_bos_id = -1; - id special_eos_id = -1; - id special_unk_id = -1; - id special_sep_id = -1; - id special_pad_id = -1; - - id linefeed_id = -1; - - std::unordered_map token_to_id; - std::unordered_map id_to_token; - - Trie trie; // highspeed access to tokens by prefix tree - - // populate trie from map - void populate_trie_from_map() { - trie.reset(); - for (const auto& pair : token_to_id) { - trie.insert(pair.first, pair.second); - if (pair.first.size() >= 2) { - std::string prefix = pair.first.substr(0, 2); - max_token_length[prefix] = std::max(max_token_length[prefix], (uint32_t)pair.first.size()); - } - } - } - // populate token ranks map - int populate_bpe_ranks(std::vector> bpe_merges_) { - for (int i = 0; i < (int)bpe_merges_.size(); i++) { - bpe_ranks.emplace(bpe_merges_[i], i); - } - bpe_merges = bpe_merges_; - return bpe_merges_.size(); - } - - // Trim whitespace characters from the beginning and end of the string - void trim(std::string& str) { - // Remove whitespace characters from the beginning of the string - str.erase(str.begin(), std::find_if(str.begin(), str.end(), [](int ch) { - return !std::isspace(ch); - })); - - // Remove whitespace characters from the end of the string - str.erase(std::find_if(str.rbegin(), str.rend(), [](int ch) { - return !std::isspace(ch); - }).base(), str.end()); - } - - // get max token length available for a prefix of 2 bytes (string at least 2 bytes long) - int get_max_token_length(const std::string& string) const { - if (string.size() < 2) { - return -1; - } - std::string prefix = string.substr(0, 2); - if (max_token_length.find(prefix) == max_token_length.end()) { - return 0; - } - return max_token_length.at(prefix); - } - - // function to find if two tokens match in bpe_rank, return rank or -1 - int find_bpe_rank(const std::string& token1, const std::string& token2) const { - std::string left_token = token1; - std::string right_token = token2; - left_token = replaceAll(left_token, " ", "Ġ"); - left_token = replaceAll(left_token, "\n", "Ċ"); - right_token = replaceAll(right_token, " ", "Ġ"); - right_token = replaceAll(right_token, "\n", "Ċ"); - - auto it = bpe_ranks.find(std::make_pair(left_token, right_token)); - if (it == bpe_ranks.end()) { - return -1; - } - return it->second; - } - - std::pair find_longest_match(const std::string& snippet) const { - TrieNode* current = trie.root; - gpt2bpe_vocab::id last_matched_id = -1; - std::string last_matched_token = ""; - std::string current_token = ""; - for (auto ch : snippet) { - if (current->map.find(ch) == current->map.end()) { - break; - } - current = current->map[ch]; - current_token += ch; - if (current->Id != -1) { - last_matched_id = current->Id; - last_matched_token = current_token; - } - } - return {last_matched_id, last_matched_token}; - } - -}; - - -// -// tokenizer - bpe type, gpt2 tokenization compatible -// - -struct ggllm_bpe_symbol { - using index = int; - index prev; - index next; - const char * text; - size_t n; -}; - -static_assert(std::is_trivially_copyable::value, "ggllm_bpe_symbol is not trivially copyable"); - -struct ggllm_bpe_bigram { - struct comparator { - bool operator()(ggllm_bpe_bigram & l, ggllm_bpe_bigram & r) { - return l.rank > r.rank || (l.rank == r.rank && l.left > r.left); - } - }; - - using queue_storage = std::vector; - using queue = std::priority_queue; - ggllm_bpe_symbol::index left; - ggllm_bpe_symbol::index right; - std::string text; - int rank; - size_t size; -}; - -struct gpt2bpe_tokenizer { - gpt2bpe_tokenizer(const gpt2bpe_vocab & vocab, bool g2ws_): vocab_(vocab) { flag_g2ws = g2ws_; } - - void tokenize(const std::string & text, std::vector & output) { - int final_prev_index = -1; - // auto start = ggml_time_us(); - auto word_collection = bpe_gpt2_preprocess(text); - // auto end = ggml_time_us(); - // fprintf(stderr, "%s: preprocessing took %0.3f ms\n", __func__, (end - start) / 1000.0); - - symbols_final.clear(); - - for (auto & word : word_collection) { - work_queue_ = ggllm_bpe_bigram::queue(); - symbols_.clear(); - - int index = 0; - size_t offset = 0; - - while (offset < word.size()) { - ggllm_bpe_symbol sym; - size_t char_len = std::min(word.size() - offset, (size_t) CNCTUnicode::utf8_len(word[offset])); - sym.text = word.c_str() + offset; - sym.n = 1; - sym.n = char_len; - offset += sym.n; - sym.prev = index - 1; - sym.next = offset == word.size() ? -1 : index + 1; - index++; - symbols_.emplace_back(sym); - } - for (size_t i = 1; i < symbols_.size(); ++i) { - add_new_bigram(i - 1, i); - } - - // build token(s) - while (!work_queue_.empty()) { - auto bigram = work_queue_.top(); - work_queue_.pop(); - - auto & left_symbol = symbols_[bigram.left]; - auto & right_symbol = symbols_[bigram.right]; - - if (left_symbol.n == 0 || right_symbol.n == 0) { - continue; - } - std::string left_token = std::string(left_symbol.text, left_symbol.n); - std::string right_token = std::string(right_symbol.text, right_symbol.n); - if (left_token + right_token != bigram.text) { - continue; // Skip this bigram if it's outdated - } - - // merge the right sym into the left one - left_symbol.n += right_symbol.n; - right_symbol.n = 0; - - // remove the right sym from the chain - left_symbol.next = right_symbol.next; - if (right_symbol.next >= 0) { - symbols_[right_symbol.next].prev = bigram.left; - } - - add_new_bigram(left_symbol.prev, bigram.left); // left side of current symbol - add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol - } - - // add the fnished tokens to the final list keeping correct order for next and prev - for (auto & sym : symbols_) { - if (sym.n > 0) { - sym.prev = final_prev_index; - sym.next = -1; - if (final_prev_index != -1) { - symbols_final[final_prev_index].next = symbols_final.size(); - } - symbols_final.emplace_back(sym); - final_prev_index = symbols_final.size() - 1; - } - } - } - - symbols_ = symbols_final; - if (symbols_.size()) - for (int i = 0; i != -1; i = symbols_[i].next) { - auto & symbol = symbols_[i]; - if (symbol.n == 0) { - continue; - } - std::string str = std::string(symbol.text, symbol.n); - std::string str_decoded = decode_token(str); - auto token = vocab_.token_to_id.find(str_decoded); - - if (token == vocab_.token_to_id.end()) { - for (auto j = str_decoded.begin(); j != str_decoded.end(); ++j) { - std::string byte_str(1, *j); - auto token_multibyte = vocab_.token_to_id.find(byte_str); - if (token_multibyte == vocab_.token_to_id.end()) { - fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); - } - output.push_back((*token_multibyte).second); - } - } else { - output.push_back((*token).second); - } - } - } - -private: - void add_new_bigram(int left, int right) { - if (left == -1 || right == -1) return; - - std::string left_token = std::string(symbols_[left].text, symbols_[left].n); - std::string right_token = std::string(symbols_[right].text, symbols_[right].n); - - int rank_found = -1; - rank_found = vocab_.find_bpe_rank(left_token, right_token); - - if (rank_found < 0) { - return; - } - - ggllm_bpe_bigram bigram; - bigram.left = left; - bigram.right = right; - bigram.rank = rank_found; - bigram.size = left_token.size() + right_token.size(); - bigram.text = left_token + right_token; - work_queue_.push(bigram); - } - - std::unordered_map bytes_to_unicode() { - static std::unordered_map hex_map = { - { 0x21, "\x21" }, { 0x22, "\x22" }, { 0x23, "\x23" }, { 0x24, "\x24" }, { 0x25, "\x25" }, { 0x26, "\x26" }, { 0x27, "\x27" }, { 0x28, "\x28" }, { 0x29, "\x29" }, { 0x2A, "\x2A" }, - { 0x2B, "\x2B" }, { 0x2C, "\x2C" }, { 0x2D, "\x2D" }, { 0x2E, "\x2E" }, { 0x2F, "\x2F" }, { 0x30, "\x30" }, { 0x31, "\x31" }, { 0x32, "\x32" }, { 0x33, "\x33" }, { 0x34, "\x34" }, - { 0x35, "\x35" }, { 0x36, "\x36" }, { 0x37, "\x37" }, { 0x38, "\x38" }, { 0x39, "\x39" }, { 0x3A, "\x3A" }, { 0x3B, "\x3B" }, { 0x3C, "\x3C" }, { 0x3D, "\x3D" }, { 0x3E, "\x3E" }, - { 0x3F, "\x3F" }, { 0x40, "\x40" }, { 0x41, "\x41" }, { 0x42, "\x42" }, { 0x43, "\x43" }, { 0x44, "\x44" }, { 0x45, "\x45" }, { 0x46, "\x46" }, { 0x47, "\x47" }, { 0x48, "\x48" }, - { 0x49, "\x49" }, { 0x4A, "\x4A" }, { 0x4B, "\x4B" }, { 0x4C, "\x4C" }, { 0x4D, "\x4D" }, { 0x4E, "\x4E" }, { 0x4F, "\x4F" }, { 0x50, "\x50" }, { 0x51, "\x51" }, { 0x52, "\x52" }, - { 0x53, "\x53" }, { 0x54, "\x54" }, { 0x55, "\x55" }, { 0x56, "\x56" }, { 0x57, "\x57" }, { 0x58, "\x58" }, { 0x59, "\x59" }, { 0x5A, "\x5A" }, { 0x5B, "\x5B" }, { 0x5C, "\x5C" }, - { 0x5D, "\x5D" }, { 0x5E, "\x5E" }, { 0x5F, "\x5F" }, { 0x60, "\x60" }, { 0x61, "\x61" }, { 0x62, "\x62" }, { 0x63, "\x63" }, { 0x64, "\x64" }, { 0x65, "\x65" }, { 0x66, "\x66" }, - { 0x67, "\x67" }, { 0x68, "\x68" }, { 0x69, "\x69" }, { 0x6A, "\x6A" }, { 0x6B, "\x6B" }, { 0x6C, "\x6C" }, { 0x6D, "\x6D" }, { 0x6E, "\x6E" }, { 0x6F, "\x6F" }, { 0x70, "\x70" }, - { 0x71, "\x71" }, { 0x72, "\x72" }, { 0x73, "\x73" }, { 0x74, "\x74" }, { 0x75, "\x75" }, { 0x76, "\x76" }, { 0x77, "\x77" }, { 0x78, "\x78" }, { 0x79, "\x79" }, { 0x7A, "\x7A" }, - { 0x7B, "\x7B" }, { 0x7C, "\x7C" }, { 0x7D, "\x7D" }, { 0x7E, "\x7E" }, { 0xA1, "\xC2\xA1" }, { 0xA2, "\xC2\xA2" }, { 0xA3, "\xC2\xA3" }, { 0xA4, "\xC2\xA4" }, { 0xA5, "\xC2\xA5" }, - { 0xA6, "\xC2\xA6" }, { 0xA7, "\xC2\xA7" }, { 0xA8, "\xC2\xA8" }, { 0xA9, "\xC2\xA9" }, { 0xAA, "\xC2\xAA" }, { 0xAB, "\xC2\xAB" }, { 0xAC, "\xC2\xAC" }, { 0xAE, "\xC2\xAE" }, - { 0xAF, "\xC2\xAF" }, { 0xB0, "\xC2\xB0" }, { 0xB1, "\xC2\xB1" }, { 0xB2, "\xC2\xB2" }, { 0xB3, "\xC2\xB3" }, { 0xB4, "\xC2\xB4" }, { 0xB5, "\xC2\xB5" }, { 0xB6, "\xC2\xB6" }, - { 0xB7, "\xC2\xB7" }, { 0xB8, "\xC2\xB8" }, { 0xB9, "\xC2\xB9" }, { 0xBA, "\xC2\xBA" }, { 0xBB, "\xC2\xBB" }, { 0xBC, "\xC2\xBC" }, { 0xBD, "\xC2\xBD" }, { 0xBE, "\xC2\xBE" }, - { 0xBF, "\xC2\xBF" }, { 0xC0, "\xC3\x80" }, { 0xC1, "\xC3\x81" }, { 0xC2, "\xC3\x82" }, { 0xC3, "\xC3\x83" }, { 0xC4, "\xC3\x84" }, { 0xC5, "\xC3\x85" }, { 0xC6, "\xC3\x86" }, - { 0xC7, "\xC3\x87" }, { 0xC8, "\xC3\x88" }, { 0xC9, "\xC3\x89" }, { 0xCA, "\xC3\x8A" }, { 0xCB, "\xC3\x8B" }, { 0xCC, "\xC3\x8C" }, { 0xCD, "\xC3\x8D" }, { 0xCE, "\xC3\x8E" }, - { 0xCF, "\xC3\x8F" }, { 0xD0, "\xC3\x90" }, { 0xD1, "\xC3\x91" }, { 0xD2, "\xC3\x92" }, { 0xD3, "\xC3\x93" }, { 0xD4, "\xC3\x94" }, { 0xD5, "\xC3\x95" }, { 0xD6, "\xC3\x96" }, - { 0xD7, "\xC3\x97" }, { 0xD8, "\xC3\x98" }, { 0xD9, "\xC3\x99" }, { 0xDA, "\xC3\x9A" }, { 0xDB, "\xC3\x9B" }, { 0xDC, "\xC3\x9C" }, { 0xDD, "\xC3\x9D" }, { 0xDE, "\xC3\x9E" }, - { 0xDF, "\xC3\x9F" }, { 0xE0, "\xC3\xA0" }, { 0xE1, "\xC3\xA1" }, { 0xE2, "\xC3\xA2" }, { 0xE3, "\xC3\xA3" }, { 0xE4, "\xC3\xA4" }, { 0xE5, "\xC3\xA5" }, { 0xE6, "\xC3\xA6" }, - { 0xE7, "\xC3\xA7" }, { 0xE8, "\xC3\xA8" }, { 0xE9, "\xC3\xA9" }, { 0xEA, "\xC3\xAA" }, { 0xEB, "\xC3\xAB" }, { 0xEC, "\xC3\xAC" }, { 0xED, "\xC3\xAD" }, { 0xEE, "\xC3\xAE" }, - { 0xEF, "\xC3\xAF" }, { 0xF0, "\xC3\xB0" }, { 0xF1, "\xC3\xB1" }, { 0xF2, "\xC3\xB2" }, { 0xF3, "\xC3\xB3" }, { 0xF4, "\xC3\xB4" }, { 0xF5, "\xC3\xB5" }, { 0xF6, "\xC3\xB6" }, - { 0xF7, "\xC3\xB7" }, { 0xF8, "\xC3\xB8" }, { 0xF9, "\xC3\xB9" }, { 0xFA, "\xC3\xBA" }, { 0xFB, "\xC3\xBB" }, { 0xFC, "\xC3\xBC" }, { 0xFD, "\xC3\xBD" }, { 0xFE, "\xC3\xBE" }, - { 0xFF, "\xC3\xBF" }, { 0x00, "\xC4\x80" }, { 0x01, "\xC4\x81" }, { 0x02, "\xC4\x82" }, { 0x03, "\xC4\x83" }, { 0x04, "\xC4\x84" }, { 0x05, "\xC4\x85" }, { 0x06, "\xC4\x86" }, - { 0x07, "\xC4\x87" }, { 0x08, "\xC4\x88" }, { 0x09, "\xC4\x89" }, { 0x0A, "\xC4\x8A" }, { 0x0B, "\xC4\x8B" }, { 0x0C, "\xC4\x8C" }, { 0x0D, "\xC4\x8D" }, { 0x0E, "\xC4\x8E" }, - { 0x0F, "\xC4\x8F" }, { 0x10, "\xC4\x90" }, { 0x11, "\xC4\x91" }, { 0x12, "\xC4\x92" }, { 0x13, "\xC4\x93" }, { 0x14, "\xC4\x94" }, { 0x15, "\xC4\x95" }, { 0x16, "\xC4\x96" }, - { 0x17, "\xC4\x97" }, { 0x18, "\xC4\x98" }, { 0x19, "\xC4\x99" }, { 0x1A, "\xC4\x9A" }, { 0x1B, "\xC4\x9B" }, { 0x1C, "\xC4\x9C" }, { 0x1D, "\xC4\x9D" }, { 0x1E, "\xC4\x9E" }, - { 0x1F, "\xC4\x9F" }, { 0x20, "\xC4\xA0" }, { 0x7F, "\xC4\xA1" }, { 0x80, "\xC4\xA2" }, { 0x81, "\xC4\xA3" }, { 0x82, "\xC4\xA4" }, { 0x83, "\xC4\xA5" }, { 0x84, "\xC4\xA6" }, - { 0x85, "\xC4\xA7" }, { 0x86, "\xC4\xA8" }, { 0x87, "\xC4\xA9" }, { 0x88, "\xC4\xAA" }, { 0x89, "\xC4\xAB" }, { 0x8A, "\xC4\xAC" }, { 0x8B, "\xC4\xAD" }, { 0x8C, "\xC4\xAE" }, - { 0x8D, "\xC4\xAF" }, { 0x8E, "\xC4\xB0" }, { 0x8F, "\xC4\xB1" }, { 0x90, "\xC4\xB2" }, { 0x91, "\xC4\xB3" }, { 0x92, "\xC4\xB4" }, { 0x93, "\xC4\xB5" }, { 0x94, "\xC4\xB6" }, - { 0x95, "\xC4\xB7" }, { 0x96, "\xC4\xB8" }, { 0x97, "\xC4\xB9" }, { 0x98, "\xC4\xBA" }, { 0x99, "\xC4\xBB" }, { 0x9A, "\xC4\xBC" }, { 0x9B, "\xC4\xBD" }, { 0x9C, "\xC4\xBE" }, - { 0x9D, "\xC4\xBF" }, { 0x9E, "\xC5\x80" }, { 0x9F, "\xC5\x81" }, { 0xA0, "\xC5\x82" }, { 0xAD, "\xC5\x83" } - }; - return hex_map; - } - - std::unordered_map unicode_to_bytes() { - static std::unordered_map hex_map = { - { "\x21", 0x21 }, { "\x22", 0x22 }, { "\x23", 0x23 }, { "\x24", 0x24 }, { "\x25", 0x25 }, { "\x26", 0x26 }, { "\x27", 0x27 }, { "\x28", 0x28 }, { "\x29", 0x29 }, { "\x2A", 0x2A }, - { "\x2B", 0x2B }, { "\x2C", 0x2C }, { "\x2D", 0x2D }, { "\x2E", 0x2E }, { "\x2F", 0x2F }, { "\x30", 0x30 }, { "\x31", 0x31 }, { "\x32", 0x32 }, { "\x33", 0x33 }, { "\x34", 0x34 }, - { "\x35", 0x35 }, { "\x36", 0x36 }, { "\x37", 0x37 }, { "\x38", 0x38 }, { "\x39", 0x39 }, { "\x3A", 0x3A }, { "\x3B", 0x3B }, { "\x3C", 0x3C }, { "\x3D", 0x3D }, { "\x3E", 0x3E }, - { "\x3F", 0x3F }, { "\x40", 0x40 }, { "\x41", 0x41 }, { "\x42", 0x42 }, { "\x43", 0x43 }, { "\x44", 0x44 }, { "\x45", 0x45 }, { "\x46", 0x46 }, { "\x47", 0x47 }, { "\x48", 0x48 }, - { "\x49", 0x49 }, { "\x4A", 0x4A }, { "\x4B", 0x4B }, { "\x4C", 0x4C }, { "\x4D", 0x4D }, { "\x4E", 0x4E }, { "\x4F", 0x4F }, { "\x50", 0x50 }, { "\x51", 0x51 }, { "\x52", 0x52 }, - { "\x53", 0x53 }, { "\x54", 0x54 }, { "\x55", 0x55 }, { "\x56", 0x56 }, { "\x57", 0x57 }, { "\x58", 0x58 }, { "\x59", 0x59 }, { "\x5A", 0x5A }, { "\x5B", 0x5B }, { "\x5C", 0x5C }, - { "\x5D", 0x5D }, { "\x5E", 0x5E }, { "\x5F", 0x5F }, { "\x60", 0x60 }, { "\x61", 0x61 }, { "\x62", 0x62 }, { "\x63", 0x63 }, { "\x64", 0x64 }, { "\x65", 0x65 }, { "\x66", 0x66 }, - { "\x67", 0x67 }, { "\x68", 0x68 }, { "\x69", 0x69 }, { "\x6A", 0x6A }, { "\x6B", 0x6B }, { "\x6C", 0x6C }, { "\x6D", 0x6D }, { "\x6E", 0x6E }, { "\x6F", 0x6F }, { "\x70", 0x70 }, - { "\x71", 0x71 }, { "\x72", 0x72 }, { "\x73", 0x73 }, { "\x74", 0x74 }, { "\x75", 0x75 }, { "\x76", 0x76 }, { "\x77", 0x77 }, { "\x78", 0x78 }, { "\x79", 0x79 }, { "\x7A", 0x7A }, - { "\x7B", 0x7B }, { "\x7C", 0x7C }, { "\x7D", 0x7D }, { "\x7E", 0x7E }, { "\xC2\xA1", 0xA1 }, { "\xC2\xA2", 0xA2 }, { "\xC2\xA3", 0xA3 }, { "\xC2\xA4", 0xA4 }, { "\xC2\xA5", 0xA5 }, - { "\xC2\xA6", 0xA6 }, { "\xC2\xA7", 0xA7 }, { "\xC2\xA8", 0xA8 }, { "\xC2\xA9", 0xA9 }, { "\xC2\xAA", 0xAA }, { "\xC2\xAB", 0xAB }, { "\xC2\xAC", 0xAC }, { "\xC2\xAE", 0xAE }, - { "\xC2\xAF", 0xAF }, { "\xC2\xB0", 0xB0 }, { "\xC2\xB1", 0xB1 }, { "\xC2\xB2", 0xB2 }, { "\xC2\xB3", 0xB3 }, { "\xC2\xB4", 0xB4 }, { "\xC2\xB5", 0xB5 }, { "\xC2\xB6", 0xB6 }, - { "\xC2\xB7", 0xB7 }, { "\xC2\xB8", 0xB8 }, { "\xC2\xB9", 0xB9 }, { "\xC2\xBA", 0xBA }, { "\xC2\xBB", 0xBB }, { "\xC2\xBC", 0xBC }, { "\xC2\xBD", 0xBD }, { "\xC2\xBE", 0xBE }, - { "\xC2\xBF", 0xBF }, { "\xC3\x80", 0xC0 }, { "\xC3\x81", 0xC1 }, { "\xC3\x82", 0xC2 }, { "\xC3\x83", 0xC3 }, { "\xC3\x84", 0xC4 }, { "\xC3\x85", 0xC5 }, { "\xC3\x86", 0xC6 }, - { "\xC3\x87", 0xC7 }, { "\xC3\x88", 0xC8 }, { "\xC3\x89", 0xC9 }, { "\xC3\x8A", 0xCA }, { "\xC3\x8B", 0xCB }, { "\xC3\x8C", 0xCC }, { "\xC3\x8D", 0xCD }, { "\xC3\x8E", 0xCE }, - { "\xC3\x8F", 0xCF }, { "\xC3\x90", 0xD0 }, { "\xC3\x91", 0xD1 }, { "\xC3\x92", 0xD2 }, { "\xC3\x93", 0xD3 }, { "\xC3\x94", 0xD4 }, { "\xC3\x95", 0xD5 }, { "\xC3\x96", 0xD6 }, - { "\xC3\x97", 0xD7 }, { "\xC3\x98", 0xD8 }, { "\xC3\x99", 0xD9 }, { "\xC3\x9A", 0xDA }, { "\xC3\x9B", 0xDB }, { "\xC3\x9C", 0xDC }, { "\xC3\x9D", 0xDD }, { "\xC3\x9E", 0xDE }, - { "\xC3\x9F", 0xDF }, { "\xC3\xA0", 0xE0 }, { "\xC3\xA1", 0xE1 }, { "\xC3\xA2", 0xE2 }, { "\xC3\xA3", 0xE3 }, { "\xC3\xA4", 0xE4 }, { "\xC3\xA5", 0xE5 }, { "\xC3\xA6", 0xE6 }, - { "\xC3\xA7", 0xE7 }, { "\xC3\xA8", 0xE8 }, { "\xC3\xA9", 0xE9 }, { "\xC3\xAA", 0xEA }, { "\xC3\xAB", 0xEB }, { "\xC3\xAC", 0xEC }, { "\xC3\xAD", 0xED }, { "\xC3\xAE", 0xEE }, - { "\xC3\xAF", 0xEF }, { "\xC3\xB0", 0xF0 }, { "\xC3\xB1", 0xF1 }, { "\xC3\xB2", 0xF2 }, { "\xC3\xB3", 0xF3 }, { "\xC3\xB4", 0xF4 }, { "\xC3\xB5", 0xF5 }, { "\xC3\xB6", 0xF6 }, - { "\xC3\xB7", 0xF7 }, { "\xC3\xB8", 0xF8 }, { "\xC3\xB9", 0xF9 }, { "\xC3\xBA", 0xFA }, { "\xC3\xBB", 0xFB }, { "\xC3\xBC", 0xFC }, { "\xC3\xBD", 0xFD }, { "\xC3\xBE", 0xFE }, - { "\xC3\xBF", 0xFF }, { "\xC4\x80", 0x00 }, { "\xC4\x81", 0x01 }, { "\xC4\x82", 0x02 }, { "\xC4\x83", 0x03 }, { "\xC4\x84", 0x04 }, { "\xC4\x85", 0x05 }, { "\xC4\x86", 0x06 }, - { "\xC4\x87", 0x07 }, { "\xC4\x88", 0x08 }, { "\xC4\x89", 0x09 }, { "\xC4\x8A", 0x0A }, { "\xC4\x8B", 0x0B }, { "\xC4\x8C", 0x0C }, { "\xC4\x8D", 0x0D }, { "\xC4\x8E", 0x0E }, - { "\xC4\x8F", 0x0F }, { "\xC4\x90", 0x10 }, { "\xC4\x91", 0x11 }, { "\xC4\x92", 0x12 }, { "\xC4\x93", 0x13 }, { "\xC4\x94", 0x14 }, { "\xC4\x95", 0x15 }, { "\xC4\x96", 0x16 }, - { "\xC4\x97", 0x17 }, { "\xC4\x98", 0x18 }, { "\xC4\x99", 0x19 }, { "\xC4\x9A", 0x1A }, { "\xC4\x9B", 0x1B }, { "\xC4\x9C", 0x1C }, { "\xC4\x9D", 0x1D }, { "\xC4\x9E", 0x1E }, - { "\xC4\x9F", 0x1F }, { "\xC4\xA0", 0x20 }, { "\xC4\xA1", 0x7F }, { "\xC4\xA2", 0x80 }, { "\xC4\xA3", 0x81 }, { "\xC4\xA4", 0x82 }, { "\xC4\xA5", 0x83 }, { "\xC4\xA6", 0x84 }, - { "\xC4\xA7", 0x85 }, { "\xC4\xA8", 0x86 }, { "\xC4\xA9", 0x87 }, { "\xC4\xAA", 0x88 }, { "\xC4\xAB", 0x89 }, { "\xC4\xAC", 0x8A }, { "\xC4\xAD", 0x8B }, { "\xC4\xAE", 0x8C }, - { "\xC4\xAF", 0x8D }, { "\xC4\xB0", 0x8E }, { "\xC4\xB1", 0x8F }, { "\xC4\xB2", 0x90 }, { "\xC4\xB3", 0x91 }, { "\xC4\xB4", 0x92 }, { "\xC4\xB5", 0x93 }, { "\xC4\xB6", 0x94 }, - { "\xC4\xB7", 0x95 }, { "\xC4\xB8", 0x96 }, { "\xC4\xB9", 0x97 }, { "\xC4\xBA", 0x98 }, { "\xC4\xBB", 0x99 }, { "\xC4\xBC", 0x9A }, { "\xC4\xBD", 0x9B }, { "\xC4\xBE", 0x9C }, - { "\xC4\xBF", 0x9D }, { "\xC5\x80", 0x9E }, { "\xC5\x81", 0x9F }, { "\xC5\x82", 0xA0 }, { "\xC5\x83", 0xAD } - }; - return hex_map; - } - - // len must be available - bool inline str_is_equal(const char* str1, const char* str2, size_t len) { - for (size_t i = 0; i < len; ++i) { - if (str1[i] != str2[i]) { - return false; - } - } - return true; - } - - std::vector bpe_gpt2_preprocess(const std::string& text) { - static std::unordered_map< unsigned char, std::string> byte_encoder = bytes_to_unicode(); - std::vector bpe_words; - std::vector bpe_encoded_words; - - std::string token=""; - const char *raw_text_p = text.c_str(); - // GPT2 system regex: 's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+ - bool collecting_numeric = false; - bool collecting_letter = false; - bool collecting_special = false; - bool collecting_whitespace_lookahead = false; - bool collecting=false; - - std::vector text_utf; - text_utf.reserve(text.size()); - bpe_words.reserve(text.size()); - bpe_encoded_words.reserve(text.size()); - - text_utf = CNCTUnicode::split_utf8_enhanced(text); - - for (int i = 0; i < (int)text_utf.size(); i++) { - const CNCTString &utf_char = text_utf[i]; - bool split_condition = false; - const char *text_pos = raw_text_p + utf_char.seq_offset_bytes; - int bytes_remain = strlen(text_pos); - // forward backward lookups - const CNCTString &utf_char_next = (i+1 < (int)text_utf.size()) ? text_utf[i+1] : CNCTString(); - const CNCTString &utf_char_next_next = (i+2 < (int)text_utf.size()) ? text_utf[i+2] : CNCTString(); - // const CNCTString &utf_char_prev = (i > 0) ? text_utf[i-1] : CNCTString(); - - // handling contractions - if (!split_condition && bytes_remain >= 2) { - // 's|'t|'m|'d - if (utf_char == '\'' && (utf_char_next == 's' || utf_char_next == 't' || utf_char_next == 'm' || utf_char_next == 'd')) { - split_condition = true; - } - if (split_condition) { - if (token.size()) { - bpe_words.emplace_back(token); // push previous content as token - } - token = utf_char.str + utf_char_next.str; - bpe_words.emplace_back(token); - token=""; - i++; - continue; - } - } - if (!split_condition && bytes_remain >= 3) { - // 're|'ve|'ll - if (utf_char == '\'' && ( - (utf_char_next == 'r' || utf_char_next_next == 'e') || - (utf_char_next == 'v' || utf_char_next_next == 'e') || - (utf_char_next == 'l' || utf_char_next_next == 'l')) - ) { - split_condition = true; - } - if (split_condition) { - // current token + next token can be defined - if (token.size()) { - bpe_words.emplace_back(token); // push previous content as token - } - token = utf_char.str + utf_char_next.str + utf_char_next_next.str; - bpe_words.emplace_back(token); // the contraction - token=""; - i+=2; - continue; - } - } - - if (!split_condition && !collecting) { - if (utf_char.char_type == CNCTCharType::LETTER || (!token.size() && utf_char==" " && utf_char_next.char_type == CNCTCharType::LETTER)) { - collecting_letter = true; - collecting = true; - } else if (utf_char.char_type == CNCTCharType::DIGIT || (!token.size() && utf_char==" " && utf_char_next.char_type == CNCTCharType::DIGIT)) { - collecting_numeric = true; - collecting = true; - } else if ( - ((utf_char.char_type != CNCTCharType::LETTER && utf_char.char_type != CNCTCharType::DIGIT) && (utf_char.char_type != CNCTCharType::WHITESPACE)) || - (!token.size() && utf_char==" " && utf_char_next.char_type != CNCTCharType::LETTER && utf_char_next.char_type != CNCTCharType::DIGIT && utf_char_next.char_type != CNCTCharType::WHITESPACE) - ) { - collecting_special = true; - collecting = true; - } else if (utf_char.char_type == CNCTCharType::WHITESPACE && utf_char_next.char_type == CNCTCharType::WHITESPACE) { - collecting_whitespace_lookahead = true; - collecting = true; - } else if (utf_char.char_type == CNCTCharType::WHITESPACE) { - split_condition = true; - } - } else if (!split_condition && collecting) { - if (collecting_letter && utf_char.char_type != CNCTCharType::LETTER) { - split_condition = true; - } else if (collecting_numeric && utf_char.char_type != CNCTCharType::DIGIT) { - split_condition = true; - } else if (collecting_special && (utf_char.char_type == CNCTCharType::LETTER || utf_char.char_type == CNCTCharType::DIGIT || utf_char.char_type == CNCTCharType::WHITESPACE)) { - split_condition = true; - } else if (collecting_whitespace_lookahead && utf_char_next.char_type != CNCTCharType::WHITESPACE) { - split_condition = true; - } - } - - if(utf_char_next.str.size() == 0) { - split_condition = true; // final - token += utf_char.str; - } - - if (split_condition) { - if (token.size()) { - bpe_words.emplace_back(token); - } - token = utf_char.str; - collecting = false; - collecting_letter = false; - collecting_numeric = false; - collecting_special = false; - collecting_whitespace_lookahead = false; - } else { - token += utf_char.str; - } - } - - for (std::string& word : bpe_words) { - std::string encoded_token=""; - for (char& c : word) { - encoded_token += byte_encoder[c]; - } - bpe_encoded_words.emplace_back(encoded_token); - } - - return bpe_encoded_words; - } - - // decoder (for one token) - std::string decode_token(const std::string& token) { - static std::unordered_map< std::string, unsigned char> byte_decoder = unicode_to_bytes(); - std::string decoded_token=""; - auto unicode_seqeunces = CNCTUnicode::split_utf8(token); - for (auto& unicode_sequence : unicode_seqeunces) { - decoded_token += byte_decoder[unicode_sequence]; - } - - return decoded_token; - } - - const gpt2bpe_vocab & vocab_; - std::vector symbols_; - std::vector symbols_final; - ggllm_bpe_bigram::queue work_queue_; - bool flag_g2ws=false; -}; - -static std::vector gpt2bpe_tokenize(const gpt2bpe_vocab & vocab, const std::string & text, bool bos, bool g2ws ) { - gpt2bpe_tokenizer tokenizer(vocab, g2ws); - std::vector output; - - if (text.empty()) { - return output; - } - - if (bos && vocab.special_bos_id != -1) { - output.push_back(vocab.special_bos_id); - } - - tokenizer.tokenize(text, output); - return output; -} - -#endif // CMPNCT_GPT2BPE diff --git a/examples/gptneox-wip/falcon-main.cpp b/examples/gptneox-wip/falcon-main.cpp deleted file mode 100644 index e9197f6b5..000000000 --- a/examples/gptneox-wip/falcon-main.cpp +++ /dev/null @@ -1,1111 +0,0 @@ -#include "ggml.h" -#include "cmpnct_gpt2bpe.hpp" - -#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 - -// default hparams -struct falcon_hparams { - size_t n_merges = 0; - size_t n_vocab = 0; - uint32_t n_ctx = 0; - uint32_t n_embd = 0; - uint32_t n_head = 0; - uint32_t n_head_kv = 1; // Needs to be 1 for 7B model - uint32_t n_ff = 0; - uint32_t n_block = 0; - float norm_eps = 1e-5; -}; -struct falcon_block { - // normalization - struct ggml_tensor* input_layernorm; - struct ggml_tensor* input_layernorm_b; - struct ggml_tensor* attention_norm; // Falcon-40B only - struct ggml_tensor* attention_norm_b; // Falcon-40B only - - // attention - struct ggml_tensor* query_key_value; - struct ggml_tensor* wo; - - // ff - struct ggml_tensor* ffn_up; - struct ggml_tensor* ffn_down; -}; - -struct falcon_model { - falcon_hparams hparams; - - struct ggml_tensor* tok_embeddings; - struct ggml_tensor* output_norm; - struct ggml_tensor* output_norm_b; - struct ggml_tensor* lm_head; - - std::vector blocks; - - // key + value memory - struct ggml_tensor* memory_k; - struct ggml_tensor* memory_v; - - struct gguf_context * ggufctx; - struct ggml_context * ctx; - struct ggml_context * kvctx; - - std::map tensors; -}; - -struct gpt_params { - int32_t seed = -1; // RNG seed - int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - uint32_t n_predict = 200; // new tokens to predict - uint32_t n_batch = 512; // batch size for prompt processing - - // sampling parameters - int32_t top_k = 40; - float top_p = 1.0f; - float temp = 0.8f; - int32_t repeat_last_n = 64; - float repeat_penalty = 1.02f; - - std::string model = ""; // model path - std::string prompt = ""; - - std::string token_test = ""; - bool interactive = false; - int32_t interactive_port = -1; - int32_t n_gpu_layers = 0; -}; - -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stderr, "usage: %s [options]\n", argv[0]); - fprintf(stderr, "\n"); - fprintf(stderr, "options:\n"); - fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n"); - fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers); - fprintf(stderr, " -p PROMPT, --prompt PROMPT\n"); - fprintf(stderr, " prompt to start generation with (default: random)\n"); - fprintf(stderr, " -f FNAME, --file FNAME\n"); - fprintf(stderr, " load prompt from a file\n"); - fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n"); - fprintf(stderr, " test tokenization\n"); - fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict); - fprintf(stderr, " --top_k N top-k sampling, 0 = n_vocab (default: %d)\n", params.top_k); - fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p); - fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp); - fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n); - fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty); - fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stderr, " -m FNAME, --model FNAME\n"); - fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); - fprintf(stderr, "\n"); -} - -// Function to check if the next argument exists -std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) { - if (i + 1 < argc && argv[i + 1][0] != '-') { - return argv[++i]; - } else { - fprintf(stderr, "error: %s requires one argument.\n", flag.c_str()); - gpt_print_usage(argc, argv, params); - exit(0); - } -} - -bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { - for (int i = 1; i < argc; i++) { - std::string arg = argv[i]; - - if (arg == "-s" || arg == "--seed") { - params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-t" || arg == "--threads") { - params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") { - params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-p" || arg == "--prompt") { - params.prompt = get_next_arg(i, argc, argv, arg, params); - } else if (arg == "-n" || arg == "--n_predict") { - params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--top_k") { - params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--top_p") { - params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--temp") { - params.temp = std::stof(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--repeat-last-n") { - params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--repeat-penalty") { - params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-b" || arg == "--batch_size") { - params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-m" || arg == "--model") { - params.model = get_next_arg(i, argc, argv, arg, params); - } else if (arg == "-i" || arg == "--interactive") { - params.interactive = true; - } else if (arg == "-ip" || arg == "--interactive-port") { - params.interactive = true; - params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, params); - exit(0); - } else if (arg == "-f" || arg == "--file") { - get_next_arg(i, argc, argv, arg, params); - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - break; - } - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); - if (params.prompt.back() == '\n') { - params.prompt.pop_back(); - } - } else if (arg == "-tt" || arg == "--token_test") { - params.token_test = get_next_arg(i, argc, argv, arg, params); - } - else { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - gpt_print_usage(argc, argv, params); - exit(0); - } - } - - return true; -} - -gpt2bpe_vocab::id sample_top_k_top_p_repeat( - const gpt2bpe_vocab & vocab, - const float * logits, - const int32_t * last_n_tokens_data, - size_t last_n_tokens_data_size, - int top_k, - double top_p, - double temp, - int repeat_last_n, - float repeat_penalty, - std::mt19937 & rng) { - - int n_logits = vocab.id_to_token.size(); - - const auto * plogits = logits; - - const auto last_n_tokens = std::vector(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size); - - if (temp <= 0) { - // select the token with the highest logit directly - float max_logit = plogits[0]; - gpt2bpe_vocab::id max_id = 0; - - for (int i = 1; i < n_logits; ++i) { - if (plogits[i] > max_logit) { - max_logit = plogits[i]; - max_id = i; - } - } - return max_id; - } - - - std::vector> logits_id; - logits_id.reserve(n_logits); - - { - const float scale = 1.0f/temp; - for (int i = 0; i < n_logits; ++i) { - // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858) - // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main - if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) { - // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability - if (plogits[i] < 0.0f) { - logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i)); - } else { - logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i)); - } - } else { - logits_id.push_back(std::make_pair(plogits[i]*scale, i)); - } - } - } - - // find the top K tokens - std::partial_sort( - logits_id.begin(), - logits_id.begin() + top_k, logits_id.end(), - [](const std::pair & a, const std::pair & b) { - return a.first > b.first; - }); - - logits_id.resize(top_k); - - double maxl = -INFINITY; - for (const auto & kv : logits_id) { - maxl = std::max(maxl, kv.first); - } - - // compute probs for the top K tokens - std::vector probs; - probs.reserve(logits_id.size()); - - double sum = 0.0; - for (const auto & kv : logits_id) { - double p = exp(kv.first - maxl); - probs.push_back(p); - sum += p; - } - - // normalize the probs - for (auto & p : probs) { - p /= sum; - } - - if (top_p < 1.0f) { - double cumsum = 0.0f; - for (int i = 0; i < top_k; i++) { - cumsum += probs[i]; - if (cumsum >= top_p) { - top_k = i + 1; - probs.resize(top_k); - logits_id.resize(top_k); - break; - } - } - - cumsum = 1.0/cumsum; - for (int i = 0; i < (int) probs.size(); i++) { - probs[i] *= cumsum; - } - } - -// printf("\n"); -// for (int i = 0; i < (int) probs.size(); i++) { -// for (int i = 0; i < 10; i++) { -// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]); -// } - - std::discrete_distribution<> dist(probs.begin(), probs.end()); - int idx = dist(rng); - - return logits_id[idx].second; - -} - -struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name){ - - struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); - if( cur == NULL ) { - printf("%s: tensor '%s' not found!\n", __func__, name.c_str()); - } else { -// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); - } - - return cur; -} - -// load the model's weights from a file -bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_vocab & vocab) { - printf("%s: loading model from '%s'..\n", __func__, fname.c_str()); - - model.ctx = NULL; - - struct gguf_init_params ggufparams = { - /*.no_alloc = */ false, - /*.ctx = */ &model.ctx, - }; - - auto & ggufctx = model.ggufctx; - - ggufctx = gguf_init_from_file(fname.c_str(), ggufparams); - - if (!ggufctx) { - fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__); - return false; - } - - printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); - - // print all kv - #if 0 - { - const int n_kv = gguf_get_n_kv(ggufctx); - - printf("%s: n_kv: %d\n", __func__, n_kv); - - for (int i = 0; i < n_kv; ++i) { - const char * key = gguf_get_key(ggufctx, i); - - printf("%s: kv[%d]: key = %s\n", __func__, i, key); - } - } - #endif - - // print some standard metadata - { - int keyidx; - - keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository"); - if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - } - - // check required metadata - { - int keyidx; - - // check model architecture kv - keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { - if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "falcon") != 0) { - printf("%s: model architecture not supported!\n", __func__); - return false; - } - } else { - printf("%s: gguf model architecture not found!\n", __func__); - return false; - } - - // check model tensor data layout kv - keyidx = gguf_find_key(ggufctx, "falcon.tensor_data_layout"); - if (keyidx != -1) { - if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "jploski") != 0) { - printf("%s: model tensor data layout not supported!\n", __func__); - return false; - } - } else { - printf("%s: gguf model tensor data layout not found!\n", __func__); - return false; - } - - } - - // load hparams - { - auto & hparams = model.hparams; - - bool ok = true; - int keyidx; - - if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.context_length"); - if (keyidx != -1) { hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.embedding_length"); - if (keyidx != -1) { hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.attention.head_count"); - if (keyidx != -1) { hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.feed_forward_length"); - if (keyidx != -1) { hparams.n_ff = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.block_count"); - if (keyidx != -1) { hparams.n_block = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.attention.layer_norm_epsilon"); - if (keyidx != -1) { hparams.norm_eps= gguf_get_val_f32(ggufctx, keyidx); } else { ok = false; } } - - if (!ok) { - fprintf(stderr, "%s: required hparam missing!\n", __func__); - return false; - } - - keyidx = gguf_find_key(ggufctx, "falcon.attention.head_count_kv"); - if (keyidx != -1) { hparams.n_head_kv = gguf_get_val_u32(ggufctx, keyidx); } - - - printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx); - printf("%s: n_embd = %d\n", __func__, hparams.n_embd); - printf("%s: n_head = %d\n", __func__, hparams.n_head); - printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv); - printf("%s: n_block = %d\n", __func__, hparams.n_block); - printf("%s: norm_eps = %g\n", __func__, hparams.norm_eps); - - } - - // load vocab - { - auto & hparams = model.hparams; - - int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model"); - - if (keyidx != -1) { - if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) { - printf("%s: tokenizer model not supported!\n", __func__); - return false; - } - } else { - printf("%s: tokenizer model not found!\n", __func__); - return false; - } - - - int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); - - if (tokens_keyidx == -1) { - printf("%s: gpt2 tokenizer vocab not found!\n", __func__); - return false; - } - - int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges"); - - if (merges_keyidx == -1) { - printf("%s: gpt2 tokenizer merges not found!\n", __func__); - return false; - } - - hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx); - hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx); - - printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); - printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); - - for (size_t i = 0; i < hparams.n_vocab; i++) { - std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); - -// printf("token %d = '%s'\n",i,word.c_str() ); - - vocab.token_to_id[word] = i; - vocab.id_to_token[i] = word; - - if( vocab.id_to_token[i] == "\n" ) { - vocab.linefeed_id = i; - } - } - - std::vector> bpe_merges; - - for (size_t i = 0; i < hparams.n_merges; i++) { - - std::string word = gguf_get_arr_str(ggufctx, merges_keyidx, i); - - // Split the merges - std::string first, second; - size_t pos = word.find(' ', 1); // Start the search from the second character - if (pos != std::string::npos) { - first = word.substr(0, pos); - second = word.substr(pos + 1); - } - - bpe_merges.push_back(std::make_pair(first, second)); - } - - vocab.populate_bpe_ranks(bpe_merges); - - - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.bos_token_id"); if( keyidx != -1 ) { vocab.special_bos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.eos_token_id"); if( keyidx != -1 ) { vocab.special_eos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.unknown_token_id"); if( keyidx != -1 ) { vocab.special_unk_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - - if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } - if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } - if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } - if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } - if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } - if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); } - - } - - - auto & ctx = model.ctx; - size_t ctx_size = ggml_get_mem_size(ctx); - - printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); - - // print tensor info - #if 0 - { - const int n_tensors = gguf_get_n_tensors(ggufctx); - - printf("%s: n_tensors: %d\n", __func__, n_tensors); - - for (int i = 0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name (ggufctx, i); - const size_t offset = gguf_get_tensor_offset(ggufctx, i); - - printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); - } - } - #endif - - // prepare memory for the weights - { - - auto & hparams = model.hparams; - - const int n_block = hparams.n_block; - - model.blocks.resize(n_block); - - model.tok_embeddings = ggml_get_tensor(ctx, "token_embd.weight"); - - model.output_norm = ggml_get_tensor(ctx, "output_norm.weight"); - model.output_norm_b = ggml_get_tensor(ctx, "output_norm.bias"); - model.lm_head = ggml_get_tensor(ctx, "output.weight"); - - // map by name - model.tensors["token_embd.weight"] = model.tok_embeddings; - model.tensors["output_norm.weight"] = model.output_norm; - model.tensors["output_norm.bias"] = model.output_norm_b; - model.tensors["output.weight"] = model.lm_head; - - for (int i = 0; i < n_block; ++i) { - - auto& block = model.blocks[i]; - std::string blocknamestart = "blk." + std::to_string(i) + "."; - - block.input_layernorm = get_tensor_ex(ctx, blocknamestart + "attn_norm.weight" ); - block.input_layernorm_b = get_tensor_ex(ctx, blocknamestart + "attn_norm.bias" ); - - if ( hparams.n_head_kv == 8 ) { // Falcon-40B - block.attention_norm = get_tensor_ex(ctx, blocknamestart + "attn_norm_2.weight" ); - block.attention_norm_b = get_tensor_ex(ctx, blocknamestart + "attn_norm_2.bias" ); - } - - // query_key_value shape for config.multi_query == True: - block.query_key_value = get_tensor_ex(ctx, blocknamestart + "attn_qkv.weight" ); - block.wo = get_tensor_ex(ctx, blocknamestart + "attn_output.weight" ); - - block.ffn_up = get_tensor_ex(ctx, blocknamestart + "ffn_up.weight" ); - block.ffn_down = get_tensor_ex(ctx, blocknamestart + "ffn_down.weight" ); - - // map by name - if ( hparams.n_head_kv == 8 ) { // Falcon-40B - // Falcon-40B: - model.tensors[blocknamestart + "attn_norm.weight"] = block.input_layernorm; - model.tensors[blocknamestart + "attn_norm.bias"] = block.input_layernorm_b; - model.tensors[blocknamestart + "attn_norm_2.weight"] = block.attention_norm; - model.tensors[blocknamestart + "attn_norm_2.bias"] = block.attention_norm_b; - } else { - // Falcon-7B: - model.tensors[blocknamestart + "attn_norm.weight"] = block.input_layernorm; - model.tensors[blocknamestart + "attn_norm.bias"] = block.input_layernorm_b; - } - - model.tensors[blocknamestart + "attn_qkv.weight"] = block.query_key_value; - model.tensors[blocknamestart + "attn_output.weight"] = block.wo; - - model.tensors[blocknamestart + "ffn_up.weight"] = block.ffn_up; - model.tensors[blocknamestart + "ffn_down.weight"] = block.ffn_down; - } - } - - // key + value memory - { - const auto & kvctx = model.kvctx; - const auto & hparams = model.hparams; - - const int n_block = hparams.n_block; - const int n_ctx = hparams.n_ctx; - const int n_embd = hparams.n_embd; - - const int64_t n_mem = n_block*n_ctx; - const int64_t n_elements = n_embd*n_mem; - - // create the ggml context - { - struct ggml_init_params params = { - /*.mem_size =*/ size_t(n_elements*4+ggml_tensor_overhead()*2), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ false, - }; - - model.kvctx = ggml_init(params); - if (!model.kvctx) { - fprintf(stderr, "%s: kv ggml_init() failed\n", __func__); - return false; - } - - } - - - model.memory_k = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements); - model.memory_v = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements); - - const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v); - - printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem); - } - - return true; -} - - -// evaluate the transformer -// -// - model: the model -// - n_threads: number of threads to use -// - n_past: the context size so far -// - embd_inp: the embeddings of the tokens in the context -// - embd_w: the predicted logits for the next token -// -bool falcon_eval( - const falcon_model & model, - const int n_threads, - const int n_past, - const std::vector & embd_inp, - std::vector & embd_w, - size_t & mem_per_token) { - - - const int N = embd_inp.size(); - - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_block = hparams.n_block; - const int n_ctx = hparams.n_ctx; - const int n_head = hparams.n_head; - const int n_head_kv = hparams.n_head_kv; - const int n_vocab = hparams.n_vocab; - const size_t head_dim = n_embd / n_head; - - static size_t buf_size = 256u*1024*1024; - static void * buf = malloc(buf_size); - - // use 2 scratch buffers - // TODO: very hacky solution - reimplement in a more elegant way - static size_t scr0_size = 256u*1024*1024; - static void * scr0 = malloc(scr0_size); - - static size_t scr1_size = 256u*1024*1024; - static void * scr1 = malloc(scr1_size); - - if (mem_per_token > 0 && mem_per_token*N > buf_size) { - const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead - //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new); - - // reallocate - buf_size = buf_size_new; - buf = realloc(buf, buf_size); - if (buf == nullptr) { - fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size); - return false; - } - } - - struct ggml_init_params params = { - /*.mem_size =*/ buf_size, - /*.mem_buffer =*/ buf, - /*.no_alloc =*/ false, - }; - - struct ggml_context * ctx0 = ggml_init(params); - struct ggml_cgraph gf = {}; -// gf.n_threads = n_threads; - - struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd)); - - // wte - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd); -// struct ggml_tensor* repeat_dummy = ggml_new_tensor_3d(ctx0, inpL->type, head_dim, N + n_past, n_head); - - ggml_type wtype = GGML_TYPE_F32; - const int sizeof_wtype = ggml_type_sizef(wtype); - - for (int il = 0; il < n_block; ++il) { - struct ggml_tensor * cur; - struct ggml_tensor * layernorm_output; - - ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); - - // self-attention - { - layernorm_output = ggml_norm(ctx0, inpL); - - layernorm_output = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.blocks[il].input_layernorm, layernorm_output), - layernorm_output), - ggml_repeat(ctx0, model.blocks[il].input_layernorm_b, layernorm_output)); - - if ( hparams.n_head_kv == 8 ) { // Falcon-40B - cur = ggml_norm(ctx0, inpL); - - cur = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.blocks[il].attention_norm, cur), - cur), - ggml_repeat(ctx0, model.blocks[il].attention_norm_b, cur)); - } - else { // Falcon 7B - cur = layernorm_output; - } - - // compute QKV - - cur = ggml_mul_mat(ctx0, model.blocks[il].query_key_value, cur); - - // 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_view_3d( - ctx0, cur, head_dim, n_head, N, - head_dim * sizeof_wtype, - head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype, - 0); - - struct ggml_tensor * Kcur = ggml_view_3d( - ctx0, cur, head_dim, n_head_kv, N, - head_dim * sizeof_wtype, - head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype, - head_dim * n_head * sizeof_wtype); - - struct ggml_tensor * Vcur = ggml_view_3d( - ctx0, cur, head_dim, n_head_kv, N, - head_dim * sizeof_wtype, - head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype, - head_dim * (n_head + n_head_kv) * sizeof_wtype); - - // using mode = 2 for neox mode - Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, head_dim, 2, 0); - Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2, 0); - - // store key and value to memory - { - struct ggml_tensor* k = ggml_view_1d( - ctx0, model.memory_k, N * n_head_kv * head_dim, - (ggml_element_size(model.memory_k) * n_head_kv * head_dim) * - (il * n_ctx + n_past)); - struct ggml_tensor* v = ggml_view_1d( - ctx0, model.memory_v, N * n_head_kv * head_dim, - (ggml_element_size(model.memory_v) * n_head_kv * head_dim) * - (il * n_ctx + n_past)); - - 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_permute( - ctx0, - ggml_reshape_3d( - ctx0, - ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_head_kv * head_dim, - il * n_ctx * - ggml_element_size(model.memory_k) * - n_head_kv * - head_dim), - head_dim, n_head_kv, n_past + N), - 0, 2, 1, 3); - - // K * Q - -// K = ggml_cont(ctx0, ggml_repeat2(ctx0, K, repeat_dummy)); - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - struct ggml_tensor * KQ_scaled = - ggml_scale_inplace(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(head_dim))) - ); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - - // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() - struct ggml_tensor* V = ggml_permute( - ctx0, - ggml_reshape_3d( - ctx0, - ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_head_kv * head_dim, - il * n_ctx * - ggml_element_size(model.memory_v) * - n_head_kv * - head_dim), - head_dim, n_head_kv, n_past + N), - 0, 2, 1, 3); - -// V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat2(ctx0, V, repeat_dummy))); - V = ggml_cont(ctx0, ggml_transpose(ctx0, V)); - - // KQV = transpose(V) * KQ_soft_max - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - // cur = KQV_merged.contiguous().view(n_embd, N) - cur = ggml_cpy(ctx0, - KQV_merged, - ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection - { - cur = ggml_mul_mat(ctx0, - model.blocks[il].wo, - cur); - } - } - - ggml_set_scratch(ctx0, { 0, scr1_size, scr1, }); - - struct ggml_tensor* inpFF = layernorm_output; - struct ggml_tensor* attn_out = ggml_cpy( - ctx0, cur, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - { - cur = ggml_mul_mat(ctx0, model.blocks[il].ffn_up, inpFF); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.blocks[il].ffn_down, cur); - } - - cur = ggml_add(ctx0, cur, attn_out); - cur = ggml_add(ctx0, cur, inpL); - // input for next layer - inpL = cur; - } - - ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); - - // norm - { - inpL = ggml_norm(ctx0, inpL); - - // inpL = ln_f_g*inpL + ln_f_b - inpL = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.output_norm, inpL), - inpL), - ggml_repeat(ctx0, model.output_norm_b, inpL)); - } - - ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - - // lm_head - { - inpL = ggml_mul_mat(ctx0, model.lm_head, inpL); - - //inpL = ggml_add(ctx0, - // ggml_repeat(ctx0, model.lmh_b, inpL), - // inpL); - } - - // logits -> probs - //inpL = ggml_soft_max_inplace(ctx0, inpL); - - // run the computation - ggml_build_forward_expand(&gf, inpL); -// ggml_graph_compute (ctx0, &gf); - ggml_graph_compute_with_ctx(ctx0, &gf, n_threads); - - //if (n_past%100 == 0) { - // ggml_graph_print (&gf); - // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot"); - //} - - // return result for just the last token - embd_w.resize(n_vocab); - memcpy(embd_w.data(), (float *)ggml_get_data(inpL) + (n_vocab * (N - 1)), sizeof(float) * n_vocab); - - if (mem_per_token == 0) { - mem_per_token = ggml_used_mem(ctx0)/N; - } - //printf("used_mem = %zu\n", ggml_used_mem(ctx0)); - - ggml_free(ctx0); - - return true; -} - -int main(int argc, char ** argv) { - ggml_time_init(); - - const int64_t t_main_start_us = ggml_time_us(); - - gpt_params params; - - if (!gpt_params_parse(argc, argv, params)) { - return 1; - } - - int64_t t_load_us = 0; - - gpt2bpe_vocab vocab; - falcon_model model; - - // load the model - { - const int64_t t_start_us = ggml_time_us(); - - if (!falcon_model_load(params.model, model, vocab)) { - fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); - return 1; - } - - t_load_us = ggml_time_us() - t_start_us; - - } - - if (params.seed < 0) { - params.seed = time(NULL); - } - - if (params.top_k == 0) { - params.top_k = model.hparams.n_vocab; - } - - printf("%s: seed = %d\n", __func__, params.seed); - printf("%s: temp = %.3f\n", __func__, params.temp); - printf("%s: top_k = %d\n", __func__, params.top_k); - printf("%s: top_p = %.3f\n", __func__, params.top_p); - printf("%s: repeat_last_n = %d\n", __func__, params.repeat_last_n); - printf("%s: repeat_penalty = %.3f\n", __func__, params.repeat_penalty); - - std::mt19937 rng(params.seed); - - if (params.prompt.empty()) { - params.prompt = "Once upon"; - } - - std::vector last_n_tokens(model.hparams.n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); - - int n_past = 0; - - int64_t t_sample_us = 0; - int64_t t_predict_us = 0; - - std::vector logits; - - // tokenize the prompt - std::vector embd_inp = gpt2bpe_tokenize(vocab, params.prompt,false, false); - - params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size()); - - printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); -// for (size_t i = 0; i < embd_inp.size(); i++) { -// printf("%s: token[%zu] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token[embd_inp[i]].c_str()); -// } - - if( model.hparams.n_ctx < params.n_predict+embd_inp.size() ) { - params.n_predict = model.hparams.n_ctx-embd_inp.size(); - } - - printf("%s: n_predict = %d\n", __func__, params.n_predict); - printf("\n"); - - std::vector embd; - - // determine the required inference memory per token: - size_t mem_per_token = 0; - falcon_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); - - for (size_t i = embd.size(); i < embd_inp.size() + params.n_predict; i++) { - // predict - if (embd.size() > 0) { - const int64_t t_start_us = ggml_time_us(); - - if (!falcon_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { - printf("Failed to predict\n"); - return 1; - } - - t_predict_us += ggml_time_us() - t_start_us; - } - - n_past += embd.size(); - embd.clear(); - - if (i >= embd_inp.size()) { - // sample next token - const int top_k = params.top_k; - const float top_p = params.top_p; - const float temp = params.temp; - const int repeat_last_n = params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - - const int n_vocab = model.hparams.n_vocab; - - gpt2bpe_vocab::id id = 0; - - { - const int64_t t_start_sample_us = ggml_time_us(); - - id = sample_top_k_top_p_repeat(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_last_n, repeat_penalty, rng); - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - t_sample_us += ggml_time_us() - t_start_sample_us; - } - - // add it to the context - embd.push_back(id); - } else { - // if here, it means we are still processing the input prompt - for (size_t k = i; k < embd_inp.size(); k++) { - embd.push_back(embd_inp[k]); - if (embd.size() > params.n_batch) { - break; - } - } - i += embd.size() - 1; - } - - // display text - for (auto id : embd) { - printf("%s", vocab.id_to_token[id].c_str() ); - } - fflush(stdout); - - // end of text token - if (vocab.special_eos_id != -1 && embd.back() == vocab.special_eos_id) { - break; - } - } - - // report timing - { - const int64_t t_main_end_us = ggml_time_us(); - - printf("\n\n"); - printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token); - printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f); - printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f); - printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past); - printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); - } - - ggml_free(model.ctx); - - return 0; -} diff --git a/examples/gptneox-wip/gptneox-main.cpp b/examples/gptneox-wip/gptneox-main.cpp deleted file mode 100644 index b76bafaa8..000000000 --- a/examples/gptneox-wip/gptneox-main.cpp +++ /dev/null @@ -1,1083 +0,0 @@ -#include "ggml.h" -#include "cmpnct_gpt2bpe.hpp" - -#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 - -// default hparams -struct gpt_neox_hparams { - size_t n_merges = 0; - size_t n_vocab = 0; - uint32_t n_ctx = 0; - uint32_t n_embd = 0; - uint32_t n_head = 0; - uint32_t n_block = 0; - uint32_t n_rot = 0; // rotary_pct * (n_embd / n_head) - bool par_res = true; - float norm_eps = 1e-5; -}; - -struct gpt_neox_block { - // pre normalization - struct ggml_tensor * ln_1_g; - struct ggml_tensor * ln_1_b; - - // attention - struct ggml_tensor * c_attn_attn_w; - struct ggml_tensor * c_attn_attn_b; - - struct ggml_tensor * c_attn_proj_w; - struct ggml_tensor * c_attn_proj_b; - - // post normalization - struct ggml_tensor * ln_2_g; - struct ggml_tensor * ln_2_b; - - // ff - struct ggml_tensor * c_mlp_fc_w; - struct ggml_tensor * c_mlp_fc_b; - - struct ggml_tensor * c_mlp_proj_w; - struct ggml_tensor * c_mlp_proj_b; -}; - -struct gpt_neox_model { - gpt_neox_hparams hparams; - - // normalization - struct ggml_tensor * ln_f_g; - struct ggml_tensor * ln_f_b; - - struct ggml_tensor * wte; // position embedding - - struct ggml_tensor * lmh_g; // language model head - - std::vector blocks; - - // key + value memory - struct ggml_tensor * memory_k; - struct ggml_tensor * memory_v; - - // - struct gguf_context * ggufctx; - struct ggml_context * ctx; - struct ggml_context * kvctx; - - std::map tensors; -}; - -struct gpt_params { - int32_t seed = -1; // RNG seed - int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - uint32_t n_predict = 200; // new tokens to predict - uint32_t n_batch = 512; // batch size for prompt processing - - // sampling parameters - int32_t top_k = 40; - float top_p = 1.0f; - float temp = 0.8f; - int32_t repeat_last_n = 64; - float repeat_penalty = 1.02f; - - std::string model = ""; // model path - std::string prompt = ""; - - std::string token_test = ""; - bool interactive = false; - int32_t interactive_port = -1; - int32_t n_gpu_layers = 0; -}; - -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stderr, "usage: %s [options]\n", argv[0]); - fprintf(stderr, "\n"); - fprintf(stderr, "options:\n"); - fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n"); - fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers); - fprintf(stderr, " -p PROMPT, --prompt PROMPT\n"); - fprintf(stderr, " prompt to start generation with (default: random)\n"); - fprintf(stderr, " -f FNAME, --file FNAME\n"); - fprintf(stderr, " load prompt from a file\n"); - fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n"); - fprintf(stderr, " test tokenization\n"); - fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict); - fprintf(stderr, " --top_k N top-k sampling, 0 = n_vocab (default: %d)\n", params.top_k); - fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p); - fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp); - fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n); - fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty); - fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stderr, " -m FNAME, --model FNAME\n"); - fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); - fprintf(stderr, "\n"); -} - -// Function to check if the next argument exists -std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) { - if (i + 1 < argc && argv[i + 1][0] != '-') { - return argv[++i]; - } else { - fprintf(stderr, "error: %s requires one argument.\n", flag.c_str()); - gpt_print_usage(argc, argv, params); - exit(0); - } -} - -bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { - for (int i = 1; i < argc; i++) { - std::string arg = argv[i]; - - if (arg == "-s" || arg == "--seed") { - params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-t" || arg == "--threads") { - params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") { - params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-p" || arg == "--prompt") { - params.prompt = get_next_arg(i, argc, argv, arg, params); - } else if (arg == "-n" || arg == "--n_predict") { - params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--top_k") { - params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--top_p") { - params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--temp") { - params.temp = std::stof(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--repeat-last-n") { - params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "--repeat-penalty") { - params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-b" || arg == "--batch_size") { - params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-m" || arg == "--model") { - params.model = get_next_arg(i, argc, argv, arg, params); - } else if (arg == "-i" || arg == "--interactive") { - params.interactive = true; - } else if (arg == "-ip" || arg == "--interactive-port") { - params.interactive = true; - params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params)); - } else if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, params); - exit(0); - } else if (arg == "-f" || arg == "--file") { - get_next_arg(i, argc, argv, arg, params); - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - break; - } - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); - if (params.prompt.back() == '\n') { - params.prompt.pop_back(); - } - } else if (arg == "-tt" || arg == "--token_test") { - params.token_test = get_next_arg(i, argc, argv, arg, params); - } - else { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - gpt_print_usage(argc, argv, params); - exit(0); - } - } - - return true; -} - -gpt2bpe_vocab::id sample_top_k_top_p_repeat( - const gpt2bpe_vocab & vocab, - const float * logits, - const int32_t * last_n_tokens_data, - size_t last_n_tokens_data_size, - int top_k, - double top_p, - double temp, - int repeat_last_n, - float repeat_penalty, - std::mt19937 & rng) { - - int n_logits = vocab.id_to_token.size(); - - const auto * plogits = logits; - - const auto last_n_tokens = std::vector(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size); - - if (temp <= 0) { - // select the token with the highest logit directly - float max_logit = plogits[0]; - gpt2bpe_vocab::id max_id = 0; - - for (int i = 1; i < n_logits; ++i) { - if (plogits[i] > max_logit) { - max_logit = plogits[i]; - max_id = i; - } - } - return max_id; - } - - - std::vector> logits_id; - logits_id.reserve(n_logits); - - { - const float scale = 1.0f/temp; - for (int i = 0; i < n_logits; ++i) { - // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858) - // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main - if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) { - // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability - if (plogits[i] < 0.0f) { - logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i)); - } else { - logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i)); - } - } else { - logits_id.push_back(std::make_pair(plogits[i]*scale, i)); - } - } - } - - // find the top K tokens - std::partial_sort( - logits_id.begin(), - logits_id.begin() + top_k, logits_id.end(), - [](const std::pair & a, const std::pair & b) { - return a.first > b.first; - }); - - logits_id.resize(top_k); - - double maxl = -INFINITY; - for (const auto & kv : logits_id) { - maxl = std::max(maxl, kv.first); - } - - // compute probs for the top K tokens - std::vector probs; - probs.reserve(logits_id.size()); - - double sum = 0.0; - for (const auto & kv : logits_id) { - double p = exp(kv.first - maxl); - probs.push_back(p); - sum += p; - } - - // normalize the probs - for (auto & p : probs) { - p /= sum; - } - - if (top_p < 1.0f) { - double cumsum = 0.0f; - for (int i = 0; i < top_k; i++) { - cumsum += probs[i]; - if (cumsum >= top_p) { - top_k = i + 1; - probs.resize(top_k); - logits_id.resize(top_k); - break; - } - } - - cumsum = 1.0/cumsum; - for (int i = 0; i < (int) probs.size(); i++) { - probs[i] *= cumsum; - } - } - -// printf("\n"); -// for (int i = 0; i < (int) probs.size(); i++) { -// for (int i = 0; i < 10; i++) { -// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]); -// } - - std::discrete_distribution<> dist(probs.begin(), probs.end()); - int idx = dist(rng); - - return logits_id[idx].second; - -} - -struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name){ - - struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); - if( cur == NULL ) { - printf("%s: tensor '%s' not found!\n", __func__, name.c_str()); - } else { -// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); - } - - return cur; -} - -// load the model's weights from a file -bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2bpe_vocab & vocab) { - printf("%s: loading model from '%s'..\n", __func__, fname.c_str()); - - model.ctx = NULL; - - struct gguf_init_params ggufparams = { - /*.no_alloc = */ false, - /*.ctx = */ &model.ctx, - }; - - auto & ggufctx = model.ggufctx; - - ggufctx = gguf_init_from_file(fname.c_str(), ggufparams); - - if (!ggufctx) { - fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__); - return false; - } - - printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); - - // print all kv - #if 0 - { - const int n_kv = gguf_get_n_kv(ggufctx); - - printf("%s: n_kv: %d\n", __func__, n_kv); - - for (int i = 0; i < n_kv; ++i) { - const char * key = gguf_get_key(ggufctx, i); - - printf("%s: kv[%d]: key = %s\n", __func__, i, key); - } - } - #endif - - // print some standard metadata - { - int keyidx; - - keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository"); - if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - } - - // check required metadata - { - int keyidx; - - // check model architecture kv - keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { - if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gptneox") != 0) { - printf("%s: model architecture not supported!\n", __func__); - return false; - } - } else { - printf("%s: gguf model architecture not found!\n", __func__); - return false; - } - - } - - // load hparams - { - auto & hparams = model.hparams; - - bool ok = true; - int keyidx; - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.context_length"); - if (keyidx != -1) { hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.embedding_length"); - if (keyidx != -1) { hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.attention.head_count"); - if (keyidx != -1) { hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.block_count"); - if (keyidx != -1) { hparams.n_block = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.rope.dimension_count"); - if (keyidx != -1) { hparams.n_rot = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.use_parallel_residual"); - if (keyidx != -1) { hparams.par_res = gguf_get_val_bool(ggufctx, keyidx); } else { ok = false; } } - - if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.attention.layer_norm_epsilon"); - if (keyidx != -1) { hparams.norm_eps= gguf_get_val_f32(ggufctx, keyidx); } else { ok = false; } } - - if (!ok) { - fprintf(stderr, "%s: required hparam missing!\n", __func__); - return false; - } - - printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx); - printf("%s: n_embd = %d\n", __func__, hparams.n_embd); - printf("%s: n_head = %d\n", __func__, hparams.n_head); - printf("%s: n_block = %d\n", __func__, hparams.n_block); - printf("%s: n_rot = %d\n", __func__, hparams.n_rot); - printf("%s: par_res = %d\n", __func__, hparams.par_res); - printf("%s: norm_eps = %g\n", __func__, hparams.norm_eps); - - } - - // load vocab - { - auto & hparams = model.hparams; - - int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model"); - - if (keyidx != -1) { - if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) { - printf("%s: tokenizer model not supported!\n", __func__); - return false; - } - } else { - printf("%s: tokenizer model not found!\n", __func__); - return false; - } - - - int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); - - if (tokens_keyidx == -1) { - printf("%s: gpt2 tokenizer vocab not found!\n", __func__); - return false; - } - - int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges"); - - if (merges_keyidx == -1) { - printf("%s: gpt2 tokenizer merges not found!\n", __func__); - return false; - } - - hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx); - hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx); - - printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); - printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); - - for (size_t i = 0; i < hparams.n_vocab; i++) { - std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); - -// printf("token %d = '%s'\n",i,word.c_str() ); - - vocab.token_to_id[word] = i; - vocab.id_to_token[i] = word; - - if( vocab.id_to_token[i] == "\n" ) { - vocab.linefeed_id = i; - } - } - - std::vector> bpe_merges; - - for (size_t i = 0; i < hparams.n_merges; i++) { - - std::string word = gguf_get_arr_str(ggufctx, merges_keyidx, i); - - // Split the merges - std::string first, second; - size_t pos = word.find(' ', 1); // Start the search from the second character - if (pos != std::string::npos) { - first = word.substr(0, pos); - second = word.substr(pos + 1); - } - - bpe_merges.push_back(std::make_pair(first, second)); - } - - vocab.populate_bpe_ranks(bpe_merges); - - - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.bos_token_id"); if( keyidx != -1 ) { vocab.special_bos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.eos_token_id"); if( keyidx != -1 ) { vocab.special_eos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.unknown_token_id"); if( keyidx != -1 ) { vocab.special_unk_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - - if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } - if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } - if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } - if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } - if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } - if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); } - } - - - auto & ctx = model.ctx; - size_t ctx_size = ggml_get_mem_size(ctx); - - printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); - - // print tensor info - #if 0 - { - const int n_tensors = gguf_get_n_tensors(ggufctx); - - printf("%s: n_tensors: %d\n", __func__, n_tensors); - - for (int i = 0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name (ggufctx, i); - const size_t offset = gguf_get_tensor_offset(ggufctx, i); - - printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); - } - } - #endif - - // prepare memory for the weights - { - const int n_block = model.hparams.n_block; - - model.blocks.resize(n_block); - - model.wte = ggml_get_tensor(ctx, "token_embd.weight"); - model.ln_f_g = ggml_get_tensor(ctx, "output_norm.weight"); - model.ln_f_b = ggml_get_tensor(ctx, "output_norm.bias"); - model.lmh_g = ggml_get_tensor(ctx, "output.weight"); - - // map by name - model.tensors["token_embd.weight"] = model.wte; - model.tensors["output_norm.weight"] = model.ln_f_g; - model.tensors["output_norm.bias"] = model.ln_f_b; - model.tensors["output.weight"] = model.lmh_g; - - for (int i = 0; i < n_block; ++i) { - auto & block = model.blocks[i]; - - std::string blocknamestart = "blk." + std::to_string(i) + "."; - - block.ln_1_g = get_tensor_ex(ctx, blocknamestart + "attn_norm.weight" ); - block.ln_1_b = get_tensor_ex(ctx, blocknamestart + "attn_norm.bias" ); - - block.c_attn_attn_w = get_tensor_ex(ctx, blocknamestart + "attn_qkv.weight" ); - block.c_attn_attn_b = get_tensor_ex(ctx ,blocknamestart + "attn_qkv.bias" ); - - block.c_attn_proj_w = get_tensor_ex(ctx, blocknamestart + "attn_output.weight" ); - block.c_attn_proj_b = get_tensor_ex(ctx, blocknamestart + "attn_output.bias" ); - - block.ln_2_g = get_tensor_ex(ctx, blocknamestart + "ffn_norm.weight" ); - block.ln_2_b = get_tensor_ex(ctx, blocknamestart + "ffn_norm.bias"); - - block.c_mlp_fc_w = get_tensor_ex(ctx, blocknamestart + "ffn_up.weight" ); - block.c_mlp_fc_b = get_tensor_ex(ctx, blocknamestart + "ffn_up.bias" ); - - block.c_mlp_proj_w = get_tensor_ex(ctx, blocknamestart + "ffn_down.weight" ); - block.c_mlp_proj_b = get_tensor_ex(ctx, blocknamestart + "ffn_down.bias" ); - - // map by name - model.tensors[blocknamestart + "attn_norm.weight"] = block.ln_1_g; - model.tensors[blocknamestart + "attn_norm.bias"] = block.ln_1_b; - - model.tensors[blocknamestart + "attn_qkv.weight"] = block.c_attn_attn_w; - model.tensors[blocknamestart + "attn_qkv.bias"] = block.c_attn_attn_b; - - model.tensors[blocknamestart + "attn_output.weight"] = block.c_attn_proj_w; - model.tensors[blocknamestart + "attn_output.bias"] = block.c_attn_proj_b; - - model.tensors[blocknamestart + "ffn_norm.weight"] = block.ln_2_g; - model.tensors[blocknamestart + "ffn_norm.bias"] = block.ln_2_b; - - model.tensors[blocknamestart + "ffn_up.weight"] = block.c_mlp_fc_w; - model.tensors[blocknamestart + "ffn_up.bias"] = block.c_mlp_fc_b; - - model.tensors[blocknamestart + "ffn_down.weight"] = block.c_mlp_proj_w; - model.tensors[blocknamestart + "ffn_down.bias"] = block.c_mlp_proj_b; - } - } - - // key + value memory - { - const auto & kvctx = model.kvctx; - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_block = hparams.n_block; - const int n_ctx = hparams.n_ctx; - - const int64_t n_mem = n_block*n_ctx; - const int64_t n_elements = n_embd*n_mem; - - // create the ggml context - { - struct ggml_init_params params = { - /*.mem_size =*/ size_t(n_elements*4+ggml_tensor_overhead()*2), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ false, - }; - - model.kvctx = ggml_init(params); - if (!model.kvctx) { - fprintf(stderr, "%s: kv ggml_init() failed\n", __func__); - return false; - } - - } - - - model.memory_k = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements); - model.memory_v = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements); - - const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v); - - printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem); - } - - return true; -} - - -// feed-forward network -ggml_tensor * gpt_neox_ff( - const gpt_neox_block &block, - ggml_context * ctx0, - ggml_tensor * inp, - const gpt_neox_hparams &hparams) { - - ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps); - - cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur)); - cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, block.c_mlp_fc_b, cur), cur); - - // GELU activation - cur = ggml_gelu(ctx0, cur); - - // projection - // cur = proj_w*cur + proj_b - cur = ggml_mul_mat(ctx0, block.c_mlp_proj_w, cur); - - cur = ggml_add(ctx0, ggml_repeat(ctx0, block.c_mlp_proj_b, cur), cur); - return cur; -} - -// evaluate the transformer -// -// - model: the model -// - n_threads: number of threads to use -// - n_past: the context size so far -// - embd_inp: the embeddings of the tokens in the context -// - embd_w: the predicted logits for the next token -// -bool gpt_neox_eval( - const gpt_neox_model & model, - const int n_threads, - const int n_past, - const std::vector & embd_inp, - std::vector & embd_w, - size_t & mem_per_token) { - const int N = embd_inp.size(); - - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_block = hparams.n_block; - const int n_ctx = hparams.n_ctx; - const int n_head = hparams.n_head; - const int n_vocab = hparams.n_vocab; - const int n_rot = hparams.n_rot; - - static size_t buf_size = 256u*1024*1024; - static void * buf = malloc(buf_size); - - // use 2 scratch buffers - // TODO: very hacky solution - reimplement in a more elegant way - static size_t scr0_size = 256u*1024*1024; - static void * scr0 = malloc(scr0_size); - - static size_t scr1_size = 256u*1024*1024; - static void * scr1 = malloc(scr1_size); - - if (mem_per_token > 0 && mem_per_token*N > buf_size) { - const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead - //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new); - - // reallocate - buf_size = buf_size_new; - buf = realloc(buf, buf_size); - if (buf == nullptr) { - fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size); - return false; - } - } - - struct ggml_init_params params = { - /*.mem_size =*/ buf_size, - /*.mem_buffer =*/ buf, - /*.no_alloc =*/ false, - }; - - struct ggml_context * ctx0 = ggml_init(params); - struct ggml_cgraph gf = {}; - - struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd)); - - - // wte - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd); - - for (int il = 0; il < n_block; ++il) { - struct ggml_tensor * cur; - - ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); - - // self-attention - { - { - cur = ggml_norm(ctx0, inpL, hparams.norm_eps); - - cur = ggml_add(ctx0, - ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur), - ggml_repeat(ctx0, model.blocks[il].ln_1_b, cur)); - } - - // compute QKV - { - - cur = ggml_mul_mat(ctx0, model.blocks[il].c_attn_attn_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.blocks[il].c_attn_attn_b, cur), cur); - } - - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head)); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head)); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head)); - - // using mode = 2 for GPT-NeoX mode - Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2, 0); - Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2, 0); - - // store key and value to memory - { - Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd, N)); - - struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd, - ( n_ctx)*ggml_element_size(model.memory_v), - (il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v)); - - ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v)); - } - - // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3) - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - - // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3) - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - struct ggml_tensor * KQ_scaled = - ggml_scale_inplace(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)) - ); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - - // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() - struct ggml_tensor * V = - ggml_view_3d(ctx0, model.memory_v, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(model.memory_v), - n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head, - il*n_ctx*ggml_element_size(model.memory_v)*n_embd); - - // KQV = transpose(V) * KQ_soft_max - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - // cur = KQV_merged.contiguous().view(n_embd, N) - cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection - { - cur = ggml_mul_mat(ctx0, model.blocks[il].c_attn_proj_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.blocks[il].c_attn_proj_b, cur), cur); - } - } - - ggml_set_scratch(ctx0, { 0, scr1_size, scr1, }); - - if (hparams.par_res == 0) { - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL); - - cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams); - - // input for next layer - inpL = ggml_add(ctx0, cur, inpFF); - } else { - struct ggml_tensor * inpFF = cur; - - // this is independent of the self-attention result, so it could be done in parallel to the self-attention - // note here we pass inpL instead of cur - cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams); - - // layer input + FF - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - inpL = ggml_add(ctx0, cur, inpL); - } - } - - ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); - - // norm - { - inpL = ggml_norm(ctx0, inpL, hparams.norm_eps); - - // inpL = ln_f_g*inpL + ln_f_b - inpL = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.ln_f_g, inpL), - inpL), - ggml_repeat(ctx0, model.ln_f_b, inpL)); - } - - ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - - // lm_head - { - inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL); - - //inpL = ggml_add(ctx0, - // ggml_repeat(ctx0, model.lmh_b, inpL), - // inpL); - } - - // logits -> probs - //inpL = ggml_soft_max_inplace(ctx0, inpL); - - // run the computation - ggml_build_forward_expand(&gf, inpL); - ggml_graph_compute_with_ctx(ctx0, &gf, n_threads); - - //if (n_past%100 == 0) { - // ggml_graph_print (&gf); - // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot"); - //} - - //embd_w.resize(n_vocab*N); - //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N); - - // return result for just the last token - embd_w.resize(n_vocab); - memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab); - - if (mem_per_token == 0) { - mem_per_token = ggml_used_mem(ctx0)/N; - } - //printf("used_mem = %zu\n", ggml_used_mem(ctx0)); - - ggml_free(ctx0); - - return true; -} - -int main(int argc, char ** argv) { - ggml_time_init(); - - const int64_t t_main_start_us = ggml_time_us(); - - gpt_params params; - - if (!gpt_params_parse(argc, argv, params)) { - return 1; - } - - int64_t t_load_us = 0; - - gpt2bpe_vocab vocab; - gpt_neox_model model; - - // load the model - { - const int64_t t_start_us = ggml_time_us(); - - if (!gpt_neox_model_load(params.model, model, vocab)) { - fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); - return 1; - } - - t_load_us = ggml_time_us() - t_start_us; - - } - - if (params.seed < 0) { - params.seed = time(NULL); - } - - if (params.top_k == 0) { - params.top_k = model.hparams.n_vocab; - } - - printf("%s: seed = %d\n", __func__, params.seed); - printf("%s: temp = %.3f\n", __func__, params.temp); - printf("%s: top_k = %d\n", __func__, params.top_k); - printf("%s: top_p = %.3f\n", __func__, params.top_p); - printf("%s: repeat_last_n = %d\n", __func__, params.repeat_last_n); - printf("%s: repeat_penalty = %.3f\n", __func__, params.repeat_penalty); - - std::mt19937 rng(params.seed); - - if (params.prompt.empty()) { - params.prompt = "Once upon"; - } - - std::vector last_n_tokens(model.hparams.n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); - - int n_past = 0; - - int64_t t_sample_us = 0; - int64_t t_predict_us = 0; - - std::vector logits; - - // tokenize the prompt - std::vector embd_inp = gpt2bpe_tokenize(vocab, params.prompt,false, false); - - params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size()); - - printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); -// for (size_t i = 0; i < embd_inp.size(); i++) { -// printf("%s: token[%zu] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token[embd_inp[i]].c_str()); -// } - - if( model.hparams.n_ctx < params.n_predict+embd_inp.size() ) { - params.n_predict = model.hparams.n_ctx-embd_inp.size(); - } - - printf("%s: n_predict = %d\n", __func__, params.n_predict); - printf("\n"); - - std::vector embd; - - // determine the required inference memory per token: - size_t mem_per_token = 0; - gpt_neox_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); - - for (size_t i = embd.size(); i < embd_inp.size() + params.n_predict; i++) { - // predict - if (embd.size() > 0) { - const int64_t t_start_us = ggml_time_us(); - - if (!gpt_neox_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { - printf("Failed to predict\n"); - return 1; - } - - t_predict_us += ggml_time_us() - t_start_us; - } - - n_past += embd.size(); - embd.clear(); - - if (i >= embd_inp.size()) { - // sample next token - const int top_k = params.top_k; - const float top_p = params.top_p; - const float temp = params.temp; - const int repeat_last_n = params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - - const int n_vocab = model.hparams.n_vocab; - - gpt2bpe_vocab::id id = 0; - - { - const int64_t t_start_sample_us = ggml_time_us(); - - id = sample_top_k_top_p_repeat(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_last_n, repeat_penalty, rng); - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - t_sample_us += ggml_time_us() - t_start_sample_us; - } - - // add it to the context - embd.push_back(id); - } else { - // if here, it means we are still processing the input prompt - for (size_t k = i; k < embd_inp.size(); k++) { - embd.push_back(embd_inp[k]); - if (embd.size() > params.n_batch) { - break; - } - } - i += embd.size() - 1; - } - - // display text - for (auto id : embd) { - printf("%s", vocab.id_to_token[id].c_str() ); - } - fflush(stdout); - - // end of text token - if (vocab.special_eos_id != -1 && embd.back() == vocab.special_eos_id) { - break; - } - } - - // report timing - { - const int64_t t_main_end_us = ggml_time_us(); - - printf("\n\n"); - printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token); - printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f); - printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f); - printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past); - printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); - } - - ggml_free(model.ctx); - - return 0; -} diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index 046f9b1e7..57d01cb0b 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -4,5 +4,5 @@ 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(${TARGET} BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) endif() diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 128d67080..6331335e3 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -39,8 +39,8 @@ static gpt_params * g_params; static std::vector * g_input_tokens; static std::ostringstream * g_output_ss; static std::vector * g_output_tokens; -static bool is_interacting = false; +static bool is_interacting = false; static void write_logfile( const llama_context * ctx, const gpt_params & params, const llama_model * model, @@ -104,7 +104,7 @@ static void sigint_handler(int signo) { int main(int argc, char ** argv) { gpt_params params; - llama_sampling_params & sparams = params.sampling_params; + llama_sampling_params & sparams = params.sparams; g_params = ¶ms; if (!gpt_params_parse(argc, argv, params)) { @@ -358,36 +358,10 @@ int main(int argc, char ** argv) { LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str()); } } - LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n", - sparams.repeat_last_n, sparams.repeat_penalty, sparams.presence_penalty, sparams.frequency_penalty, sparams.top_k, sparams.tfs_z, sparams.top_p, sparams.typical_p, sparams.temp, sparams.mirostat, sparams.mirostat_eta, sparams.mirostat_tau); + LOG_TEE("sampling: \n%s\n", llama_sampling_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); LOG_TEE("\n\n"); - struct llama_grammar * grammar = NULL; - grammar_parser::parse_state parsed_grammar; - - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - return 1; - } - LOG_TEE("%s: grammar:\n", __func__); - grammar_parser::print_grammar(stderr, parsed_grammar); - LOG_TEE("\n"); - - { - auto it = sparams.logit_bias.find(llama_token_eos(ctx)); - if (it != sparams.logit_bias.end() && it->second == -INFINITY) { - LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__); - } - } - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - } - LOG_TEE("\n##### Infill mode #####\n\n"); if (params.infill) { printf("\n************\n"); @@ -430,7 +404,7 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - struct llama_sampling_context * ctx_sampling = llama_sampling_init(params); + struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); while (n_remain != 0 || params.interactive) { // predict @@ -549,7 +523,7 @@ int main(int argc, char ** argv) { const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); - llama_sampling_accept(ctx_sampling, ctx, id); + llama_sampling_accept(ctx_sampling, ctx, id, true); LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); @@ -567,8 +541,11 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - ctx_sampling->prev.erase(ctx_sampling->prev.begin()); - ctx_sampling->prev.push_back(embd_inp[n_consumed]); + + // push the prompt in the sampling context in order to apply repetition penalties later + // for the prompt, we don't apply grammar rules + llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false); + ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -600,7 +577,7 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed) { // deal with eot token in infill mode - if ((ctx_sampling->prev.back() == llama_token_eot(ctx) || is_interacting) && params.interactive){ + if ((llama_sampling_last(ctx_sampling) == llama_token_eot(ctx) || is_interacting) && params.interactive){ if(is_interacting && !params.interactive_first) { // print an eot token printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str()); @@ -617,7 +594,7 @@ int main(int argc, char ** argv) { buffer += line; } while (another_line); // check if we got an empty line, if so we use the old input - if(!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { + if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { params.input_prefix = buffer; } buffer.clear(); @@ -627,7 +604,7 @@ int main(int argc, char ** argv) { buffer += line; } while (another_line); // check if we got an empty line - if(!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { + if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { params.input_suffix = buffer; } buffer.clear(); @@ -640,7 +617,7 @@ int main(int argc, char ** argv) { process_escapes(params.input_suffix); } suff_rm_leading_spc = params.escape; - if (suff_rm_leading_spc && params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { params.input_suffix.erase(0, 1); suff_rm_leading_spc = false; } @@ -667,7 +644,7 @@ int main(int argc, char ** argv) { is_interacting = false; } // deal with end of text token in interactive mode - else if (ctx_sampling->prev.back() == llama_token_eos(ctx)) { + else if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { @@ -740,15 +717,7 @@ int main(int argc, char ** argv) { if (n_past > 0) { if (is_interacting) { - // reset grammar state if we're restarting generation - if (grammar != NULL) { - llama_grammar_free(grammar); - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), - parsed_grammar.symbol_ids.at("root")); - } + llama_sampling_reset(ctx_sampling); } is_interacting = false; } @@ -778,9 +747,7 @@ int main(int argc, char ** argv) { llama_free(ctx); llama_free_model(model); - if (grammar != NULL) { - llama_grammar_free(grammar); - } + llama_sampling_free(ctx_sampling); llama_backend_free(); #ifndef LOG_DISABLE_LOGS diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h index e050b59be..45b2b1ad3 100644 --- a/examples/llava/llava-utils.h +++ b/examples/llava/llava-utils.h @@ -58,28 +58,30 @@ inline bool eval_string(struct llama_context * ctx_llama, const char* str, int n // TODO: use common/sampling.h inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { - // out of user input, sample next token - const float temp = params.sampling_params.temp; - const int32_t top_k = params.sampling_params.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : params.sampling_params.top_k; - const float top_p = params.sampling_params.top_p; - const float tfs_z = params.sampling_params.tfs_z; - const float typical_p = params.sampling_params.typical_p; - // const int32_t repeat_last_n = params.sampling_params.repeat_last_n < 0 ? n_ctx : params.sampling_params.repeat_last_n; - // const float repeat_penalty = params.sampling_params.repeat_penalty; - // const float alpha_presence = params.sampling_params.presence_penalty; - // const float alpha_frequency = params.sampling_params.frequency_penalty; - const int mirostat = params.sampling_params.mirostat; - const float mirostat_tau = params.sampling_params.mirostat_tau; - const float mirostat_eta = params.sampling_params.mirostat_eta; - // const bool penalize_nl = params.sampling_params.penalize_nl; + 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 = params.sampling_params.logit_bias.begin(); it != params.sampling_params.logit_bias.end(); it++) { + // Apply params.logit_bias map + for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { logits[it->first] += it->second; } @@ -91,18 +93,18 @@ inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { 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; - // } + // 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 diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 1a5911c56..db5309afe 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -108,7 +108,7 @@ int main(int argc, char ** argv) { if (!gpt_params_parse(argc, argv, params)) { return 1; } - llama_sampling_params & sparams = params.sampling_params; + llama_sampling_params & sparams = params.sparams; #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("main", "log")); @@ -415,8 +415,7 @@ int main(int argc, char ** argv) { } } } - LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n", - sparams.repeat_last_n, sparams.repeat_penalty, sparams.presence_penalty, sparams.frequency_penalty, sparams.top_k, sparams.tfs_z, sparams.top_p, sparams.typical_p, sparams.temp, sparams.mirostat, sparams.mirostat_eta, sparams.mirostat_tau); + LOG_TEE("sampling: \n%s\n", llama_sampling_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); LOG_TEE("\n\n"); @@ -459,7 +458,7 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - struct llama_sampling_context * ctx_sampling = llama_sampling_init(params); + struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict @@ -612,7 +611,7 @@ int main(int argc, char ** argv) { const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); - llama_sampling_accept(ctx_sampling, ctx, id); + llama_sampling_accept(ctx_sampling, ctx, id, true); LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); @@ -631,12 +630,9 @@ int main(int argc, char ** argv) { while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - // GG: I'm not sure it's a good idea to push the prompt tokens into the sampling context - // Most likely will remove this in the future to avoid exposing "prev" - // Same thing is done in "server". If we stop pushing the prompt tokens, then the repetition - // penalty will be applied only based on the tokens generated by the model. - ctx_sampling->prev.erase(ctx_sampling->prev.begin()); - ctx_sampling->prev.push_back(embd_inp[n_consumed]); + // push the prompt in the sampling context in order to apply repetition penalties later + // for the prompt, we don't apply grammar rules + llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false); ++n_consumed; if ((int) embd.size() >= params.n_batch) { @@ -667,12 +663,10 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { - // check for reverse prompt + // check for reverse prompt in the last n_prev tokens if (!params.antiprompt.empty()) { - std::string last_output; - for (auto id : ctx_sampling->prev) { - last_output += llama_token_to_piece(ctx, id); - } + const int n_prev = 32; + const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev); is_antiprompt = false; // Check if each of the reverse prompts appears at the end of the output. @@ -699,7 +693,7 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (ctx_sampling->prev.back() == llama_token_eos(ctx)) { + if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 69f9526a4..eb64adef8 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -157,7 +157,7 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < clients.size(); ++i) { auto & client = clients[i]; client.id = i; - client.ctx_sampling = llama_sampling_init(params); + client.ctx_sampling = llama_sampling_init(params.sparams); } std::vector tokens_system; @@ -330,7 +330,7 @@ int main(int argc, char ** argv) { const llama_token id = llama_sampling_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i); - llama_sampling_accept(client.ctx_sampling, ctx, id); + llama_sampling_accept(client.ctx_sampling, ctx, id, true); if (client.n_decoded == 1) { // start measuring generation time after the first token to make sure all concurrent clients diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0471528a3..b5ad3cc99 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -195,10 +195,12 @@ struct llama_server_context json prompt; std::vector embd; + gpt_params params; + llama_model *model = nullptr; llama_context *ctx = nullptr; - gpt_params params; llama_sampling_context *ctx_sampling = nullptr; + int n_ctx; bool truncated = false; @@ -232,7 +234,7 @@ struct llama_server_context void rewind() { params.antiprompt.clear(); - params.grammar.clear(); + params.sparams.grammar.clear(); num_prompt_tokens = 0; num_tokens_predicted = 0; generated_text = ""; @@ -246,11 +248,14 @@ struct llama_server_context multibyte_pending = 0; n_remain = 0; n_past = 0; + params.sparams.n_prev = n_ctx; + } + void initSampling() { if (ctx_sampling != nullptr) { llama_sampling_free(ctx_sampling); } - ctx_sampling = llama_sampling_init(params); + ctx_sampling = llama_sampling_init(params.sparams); } bool loadModel(const gpt_params ¶ms_) @@ -311,16 +316,32 @@ struct llama_server_context return prompt_tokens; } - bool loadGrammar() - { - ctx_sampling = llama_sampling_init(params); - return true; + void truncatePrompt(std::vector &prompt_tokens) { + const int n_left = n_ctx - params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_block_size) / n_block_size; + + // Keep n_keep tokens at start of prompt (at most n_ctx - 4) + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + + LOG_VERBOSE("input truncated", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + {"num_prompt_tokens", new_tokens.size()} + }); + + truncated = true; + prompt_tokens = new_tokens; } void loadInfill() { bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { params.input_suffix.erase(0, 1); suff_rm_leading_spc = false; } @@ -336,6 +357,7 @@ struct llama_server_context prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(ctx)); + auto prompt_tokens = prefix_tokens; num_prompt_tokens = prompt_tokens.size(); @@ -347,31 +369,18 @@ struct llama_server_context params.n_keep = std::min(params.n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)params.n_ctx) + if (num_prompt_tokens >= (size_t) n_ctx) { - printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens); - // todo we probably want to cut from both sides - const int n_left = (params.n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), ctx_sampling->prev.begin()); + truncatePrompt(prompt_tokens); + num_prompt_tokens = prompt_tokens.size(); - LOG_VERBOSE("input truncated", { - {"n_ctx", params.n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; + GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx); } - else + + // push the prompt into the sampling context (do not apply grammar) + for (auto & token : prompt_tokens) { - const size_t ps = num_prompt_tokens; - std::fill(ctx_sampling->prev.begin(), ctx_sampling->prev.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), ctx_sampling->prev.end() - ps); + llama_sampling_accept(ctx_sampling, ctx, token, false); } // compare the evaluated prompt with the new prompt @@ -409,29 +418,18 @@ struct llama_server_context params.n_keep = std::min(n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)n_ctx) + if (num_prompt_tokens >= (size_t) n_ctx) { - const int n_left = (n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), ctx_sampling->prev.begin()); + truncatePrompt(prompt_tokens); + num_prompt_tokens = prompt_tokens.size(); - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; + GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx); } - else + + // push the prompt into the sampling context (do not apply grammar) + for (auto & token : prompt_tokens) { - const size_t ps = num_prompt_tokens; - std::fill(ctx_sampling->prev.begin(), ctx_sampling->prev.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), ctx_sampling->prev.end() - ps); + llama_sampling_accept(ctx_sampling, ctx, token, false); } // compare the evaluated prompt with the new prompt @@ -530,8 +528,8 @@ struct llama_server_context llama_token_data_array cur_p = { ctx_sampling->cur.data(), ctx_sampling->cur.size(), false }; - const int32_t n_probs = params.sampling_params.n_probs; - if (params.sampling_params.temp <= 0 && n_probs > 0) + const int32_t n_probs = params.sparams.n_probs; + if (params.sparams.temp <= 0 && n_probs > 0) { // For llama_sample_token_greedy we need to sort candidates llama_sample_softmax(ctx, &cur_p); @@ -542,7 +540,7 @@ struct llama_server_context result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); } - llama_sampling_accept(ctx_sampling, ctx, result.tok); + llama_sampling_accept(ctx_sampling, ctx, result.tok, true); if (tg) { num_tokens_predicted++; @@ -606,7 +604,7 @@ struct llama_server_context const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); generated_text += token_text; - if (params.sampling_params.n_probs > 0) + if (params.sparams.n_probs > 0) { generated_token_probs.push_back(token_with_probs); } @@ -1004,36 +1002,36 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, static json format_generation_settings(llama_server_context &llama) { - const auto & sparams = llama.params.sampling_params; + const auto & sparams = llama.params.sparams; const auto eos_bias = sparams.logit_bias.find(llama_token_eos(llama.ctx)); const bool ignore_eos = eos_bias != sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); return json{ - {"n_ctx", llama.n_ctx}, - {"model", llama.params.model_alias}, - {"seed", llama.params.seed}, - {"temp", sparams.temp}, - {"top_k", sparams.top_k}, - {"top_p", sparams.top_p}, - {"tfs_z", sparams.tfs_z}, - {"typical_p", sparams.typical_p}, - {"repeat_last_n", sparams.repeat_last_n}, - {"repeat_penalty", sparams.repeat_penalty}, - {"presence_penalty", sparams.presence_penalty}, - {"frequency_penalty", sparams.frequency_penalty}, - {"mirostat", sparams.mirostat}, - {"mirostat_tau", sparams.mirostat_tau}, - {"mirostat_eta", sparams.mirostat_eta}, - {"penalize_nl", sparams.penalize_nl}, - {"stop", llama.params.antiprompt}, - {"n_predict", llama.params.n_predict}, - {"n_keep", llama.params.n_keep}, - {"ignore_eos", ignore_eos}, - {"stream", llama.stream}, - {"logit_bias", sparams.logit_bias}, - {"n_probs", sparams.n_probs}, - {"grammar", llama.params.grammar}, + {"n_ctx", llama.n_ctx}, + {"model", llama.params.model_alias}, + {"seed", llama.params.seed}, + {"temp", sparams.temp}, + {"top_k", sparams.top_k}, + {"top_p", sparams.top_p}, + {"tfs_z", sparams.tfs_z}, + {"typical_p", sparams.typical_p}, + {"repeat_last_n", sparams.penalty_last_n}, + {"repeat_penalty", sparams.penalty_repeat}, + {"frequency_penalty", sparams.penalty_freq}, + {"presence_penalty", sparams.penalty_present}, + {"mirostat", sparams.mirostat}, + {"mirostat_tau", sparams.mirostat_tau}, + {"mirostat_eta", sparams.mirostat_eta}, + {"penalize_nl", sparams.penalize_nl}, + {"stop", llama.params.antiprompt}, + {"n_predict", llama.params.n_predict}, + {"n_keep", llama.params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", llama.stream}, + {"logit_bias", sparams.logit_bias}, + {"n_probs", sparams.n_probs}, + {"grammar", llama.params.sparams.grammar}, }; } @@ -1081,7 +1079,7 @@ static json format_final_response(llama_server_context &llama, const std::string {"timings", format_timings(llama)}, }; - if (llama.params.sampling_params.n_probs > 0) + if (llama.params.sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1097,7 +1095,7 @@ static json format_partial_response( {"stop", false}, }; - if (llama.params.sampling_params.n_probs > 0) + if (llama.params.sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1129,28 +1127,30 @@ static T json_value(const json &body, const std::string &key, const T &default_v static void parse_options_completion(const json &body, llama_server_context &llama) { gpt_params default_params; - const auto & default_sparams = default_params.sampling_params; - auto & sparams = llama.params.sampling_params; + const auto & default_sparams = default_params.sparams; - llama.stream = json_value(body, "stream", false); - llama.params.n_predict = json_value(body, "n_predict", default_params.n_predict); - sparams.top_k = json_value(body, "top_k", default_sparams.top_k); - sparams.top_p = json_value(body, "top_p", default_sparams.top_p); - sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); - sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); - sparams.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n); - sparams.temp = json_value(body, "temperature", default_sparams.temp); - sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty); - sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty); - sparams.frequency_penalty = json_value(body, "frequency_penalty", default_sparams.frequency_penalty); - sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); - sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); - llama.params.seed = json_value(body, "seed", default_params.seed); - llama.params.grammar = json_value(body, "grammar", default_params.grammar); - sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); + auto & params = llama.params; + auto & sparams = llama.params.sparams; + + llama.stream = json_value(body, "stream", false); + params.n_predict = json_value(body, "n_predict", default_params.n_predict); + sparams.top_k = json_value(body, "top_k", default_sparams.top_k); + sparams.top_p = json_value(body, "top_p", default_sparams.top_p); + sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); + sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); + sparams.temp = json_value(body, "temperature", default_sparams.temp); + sparams.penalty_last_n = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); + sparams.penalty_repeat = json_value(body, "repeat_penalty", default_sparams.penalty_repeat); + sparams.penalty_freq = json_value(body, "frequency_penalty", default_sparams.penalty_freq); + sparams.penalty_present = json_value(body, "presence_penalty", default_sparams.penalty_present); + sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); + sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); + params.n_keep = json_value(body, "n_keep", default_params.n_keep); + params.seed = json_value(body, "seed", default_params.seed); + sparams.grammar = json_value(body, "grammar", default_sparams.grammar); + sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); if (body.count("prompt") != 0) { @@ -1204,8 +1204,6 @@ static void parse_options_completion(const json &body, llama_server_context &lla } } - llama.ctx_sampling = llama_sampling_init(llama.params); - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); } @@ -1374,15 +1372,9 @@ int main(int argc, char **argv) llama.rewind(); llama_reset_timings(llama.ctx); - parse_options_completion(json::parse(req.body), llama); - if (!llama.loadGrammar()) - { - res.status = 400; - return; - } - + llama.initSampling(); llama.loadPrompt(); llama.beginCompletion(); @@ -1414,7 +1406,7 @@ int main(int argc, char **argv) } auto probs = llama.generated_token_probs; - if (llama.params.sampling_params.n_probs > 0 && llama.stopped_word) { + if (llama.params.sparams.n_probs > 0 && llama.stopped_word) { const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); } @@ -1466,7 +1458,7 @@ int main(int argc, char **argv) std::vector probs_output = {}; - if (llama.params.sampling_params.n_probs > 0) { + if (llama.params.sparams.n_probs > 0) { const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); @@ -1537,14 +1529,9 @@ int main(int argc, char **argv) llama.rewind(); llama_reset_timings(llama.ctx); - parse_options_infill(json::parse(req.body), llama); - if (!llama.loadGrammar()) - { - res.status = 400; - return; - } + llama.initSampling(); llama.loadInfill(); llama.beginCompletion(); const auto chunked_content_provider = [&](size_t, DataSink & sink) { @@ -1587,7 +1574,7 @@ int main(int argc, char **argv) std::vector probs_output = {}; - if (llama.params.sampling_params.n_probs > 0) { + if (llama.params.sparams.n_probs > 0) { const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); @@ -1694,7 +1681,9 @@ int main(int argc, char **argv) const json body = json::parse(req.body); llama.rewind(); + llama_reset_timings(llama.ctx); + if (body.count("content") != 0) { llama.prompt = body["content"]; @@ -1704,6 +1693,8 @@ int main(int argc, char **argv) llama.prompt = ""; } llama.params.n_predict = 0; + + llama.initSampling(); llama.loadPrompt(); llama.beginCompletion(); llama.doCompletion(); diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 24f49012a..894321ce9 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -112,16 +112,16 @@ int main(int argc, char ** argv) { bool has_eos = false; // target model sampling context - struct llama_sampling_context * ctx_sampling = llama_sampling_init(params); + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams); // draft sequence data std::vector drafts(n_seq_dft); - params.grammar.clear(); // the draft samplers will copy the target sampler's grammar - params.sampling_params.temp = std::max(0.01f, params.sampling_params.temp); + params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar + params.sparams.temp = std::max(0.01f, params.sparams.temp); for (int s = 0; s < n_seq_dft; ++s) { - drafts[s].ctx_sampling = llama_sampling_init(params); + drafts[s].ctx_sampling = llama_sampling_init(params.sparams); } llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1); @@ -154,7 +154,7 @@ int main(int argc, char ** argv) { // sample from the target model llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); - llama_sampling_accept(ctx_sampling, ctx_tgt, id); + llama_sampling_accept(ctx_sampling, ctx_tgt, id, true); //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); @@ -328,7 +328,7 @@ int main(int argc, char ** argv) { const int s = sa[is]; - llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id); + llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true); drafts[s].tokens.push_back(id); diff --git a/llama.cpp b/llama.cpp index ec8ffad33..365349335 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1018,8 +1018,8 @@ enum e_model { }; static const size_t kB = 1024; -static const size_t MB = kB*kB; -static const size_t GB = kB*kB*kB; +static const size_t MB = 1024*kB; +static const size_t GB = 1024*MB; struct llama_hparams { bool vocab_only; @@ -1042,21 +1042,21 @@ struct llama_hparams { 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; + if (this->vocab_only != other.vocab_only) return true; + if (this->n_vocab != other.n_vocab) return true; if (this->n_ctx_train != other.n_ctx_train) return true; - if (this->n_embd != other.n_embd) return true; - if (this->n_head != other.n_head) return true; - 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_ff != other.n_ff) return true; + if (this->n_embd != other.n_embd) return true; + if (this->n_head != other.n_head) return true; + 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_ff != other.n_ff) return true; const float EPSILON = 1e-9; - 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; - if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; + 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; + if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; return false; @@ -1195,11 +1195,11 @@ struct llama_vocab { id special_sep_id = -1; id special_pad_id = -1; - id linefeed_id = 13; + id linefeed_id = 13; id special_prefix_id = 32007; id special_middle_id = 32009; id special_suffix_id = 32008; - id special_eot_id = 32010; + id special_eot_id = 32010; int find_bpe_rank(std::string token_left, std::string token_right) const { replace_all(token_left, " ", "\u0120"); @@ -1359,10 +1359,7 @@ static bool llama_kv_cache_init( cache.cells.clear(); cache.cells.resize(n_ctx); - // TODO: this should be: - // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*ggml_tensor_overhead()); - // change it and test that it works - cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); + cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*ggml_tensor_overhead()); memset(cache.buf.data, 0, cache.buf.size); struct ggml_init_params params; @@ -7417,37 +7414,15 @@ void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array llama_sample_temp(ctx, candidates_p, temp); } -void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty) { - if (last_tokens_size == 0 || penalty == 1.0f) { - return; - } - - const int64_t t_start_sample_us = ggml_time_us(); - - for (size_t i = 0; i < candidates->size; ++i) { - const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id); - if (token_iter == last_tokens + last_tokens_size) { - continue; - } - - // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong. - // This is common fix for this problem, which is to multiply by the penalty instead of dividing. - if (candidates->data[i].logit <= 0) { - candidates->data[i].logit *= penalty; - } else { - candidates->data[i].logit /= penalty; - } - } - - candidates->sorted = false; - - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } -} - -void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens_p, size_t last_tokens_size, float alpha_frequency, float alpha_presence) { - if (last_tokens_size == 0 || (alpha_frequency == 0.0f && alpha_presence == 0.0f)) { +void llama_sample_repetition_penalties( + struct llama_context * ctx, + llama_token_data_array * candidates, + const llama_token * last_tokens, + size_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present) { + if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) { return; } @@ -7455,19 +7430,28 @@ void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, l // Create a frequency map to count occurrences of each token in last_tokens std::unordered_map token_count; - for (size_t i = 0; i < last_tokens_size; ++i) { - token_count[last_tokens_p[i]]++; + for (size_t i = 0; i < penalty_last_n; ++i) { + token_count[last_tokens[i]]++; } // Apply frequency and presence penalties to the candidates for (size_t i = 0; i < candidates->size; ++i) { - auto token_iter = token_count.find(candidates->data[i].id); + const auto token_iter = token_count.find(candidates->data[i].id); if (token_iter == token_count.end()) { continue; } - int count = token_iter->second; - candidates->data[i].logit -= float(count) * alpha_frequency + float(count > 0) * alpha_presence; + const int count = token_iter->second; + + // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong. + // This is common fix for this problem, which is to multiply by the penalty instead of dividing. + if (candidates->data[i].logit <= 0) { + candidates->data[i].logit *= penalty_repeat; + } else { + candidates->data[i].logit /= penalty_repeat; + } + + candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present; } candidates->sorted = false; diff --git a/llama.h b/llama.h index 51010e037..306f5b383 100644 --- a/llama.h +++ b/llama.h @@ -560,21 +560,15 @@ extern "C" { LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed); /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix. - LLAMA_API void llama_sample_repetition_penalty( - struct llama_context * ctx, - llama_token_data_array * candidates, - const llama_token * last_tokens, - size_t last_tokens_size, - float penalty); - /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details. - LLAMA_API void llama_sample_frequency_and_presence_penalties( + LLAMA_API void llama_sample_repetition_penalties( struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, - size_t last_tokens_size, - float alpha_frequency, - float alpha_presence); + size_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + 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. diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 019c0d462..32e58941c 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -8,11 +8,9 @@ #include #include #include -#include #include #include - static void dump(const llama_token_data_array * candidates) { for (size_t i = 0; i < candidates->size; i++) { printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit); @@ -21,7 +19,6 @@ static void dump(const llama_token_data_array * candidates) { #define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0) - static void test_top_k(const std::vector & probs, const std::vector & expected_probs, int k) { size_t n_vocab = probs.size(); std::vector candidates; @@ -37,13 +34,12 @@ static void test_top_k(const std::vector & probs, const std::vector & probs, const std::vector & expected_probs, float p) { size_t n_vocab = probs.size(); std::vector candidates; @@ -59,13 +55,12 @@ static void test_top_p(const std::vector & probs, const std::vector & probs, const std::vector & expected_probs, float z) { size_t n_vocab = probs.size(); std::vector candidates; @@ -80,13 +75,12 @@ static void test_tfs(const std::vector & probs, const std::vector llama_sample_tail_free(nullptr, &candidates_p, z, 1); DUMP(&candidates_p); - assert(candidates_p.size == expected_probs.size()); + GGML_ASSERT(candidates_p.size == expected_probs.size()); for (size_t i = 0; i < candidates_p.size; i++) { - assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3); + GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3); } } - static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { size_t n_vocab = probs.size(); std::vector candidates; @@ -101,18 +95,17 @@ static void test_typical(const std::vector & probs, const std::vector & probs, const std::vector & last_tokens, - const std::vector & expected_probs, float penalty + const std::vector & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence ) { - assert(probs.size() == expected_probs.size()); + GGML_ASSERT(probs.size() == expected_probs.size()); size_t n_vocab = probs.size(); std::vector candidates; @@ -125,41 +118,13 @@ static void test_repetition_penalty( llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; llama_sample_softmax(nullptr, &candidates_p); DUMP(&candidates_p); - llama_sample_repetition_penalty(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), penalty); + llama_sample_repetition_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence); llama_sample_softmax(nullptr, &candidates_p); DUMP(&candidates_p); - assert(candidates_p.size == expected_probs.size()); + GGML_ASSERT(candidates_p.size == expected_probs.size()); for (size_t i = 0; i < candidates_p.size; i++) { - assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-6); - } -} - - -static void test_frequency_presence_penalty( - const std::vector & probs, const std::vector & last_tokens, - const std::vector & expected_probs, float alpha_frequency, float alpha_presence -) { - assert(probs.size() == expected_probs.size()); - - size_t n_vocab = probs.size(); - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - float logit = log(probs[token_id]); - candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - llama_sample_softmax(nullptr, &candidates_p); - // DUMP(&candidates_p); - llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), alpha_frequency, alpha_presence); - llama_sample_softmax(nullptr, &candidates_p); - // DUMP(&candidates_p); - - assert(candidates_p.size == expected_probs.size()); - for (size_t i = 0; i < candidates_p.size; i++) { - assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3); + GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3); } } @@ -181,13 +146,13 @@ int main(void) { test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f); test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f); - test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f); - test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f); - test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f); + test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f, 0.0f, 0.0f); + test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f); + test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f); - test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f); - test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f); - test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f); + test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 1.0f, 5.0f, 5.0f); + test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f); + test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f); printf("OK\n"); From 465219b9143ac01db0990bbcb0a081ef72ec2008 Mon Sep 17 00:00:00 2001 From: shibe2 Date: Thu, 12 Oct 2023 16:01:23 +0400 Subject: [PATCH 29/95] CLBlast: Add outer loops over src0 for broadcasting in mulmat Reduce repeated dequantization of the same data. --- ggml-opencl.cpp | 332 +++++++++++++++++++++++------------------------- 1 file changed, 162 insertions(+), 170 deletions(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 67ac20eac..202bcb485 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1489,46 +1489,45 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); size_t x_offset = 0; - int64_t pi02 = -1; - int64_t pi03 = -1; - for (int64_t i13 = 0; i13 < ne13; i13++) { - int64_t i03 = i13 / r3; + for (int64_t i03 = 0; i03 < ne03; i03++) { + // TODO: copy src0 here when r3>1 + for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + if (src0->backend == GGML_BACKEND_GPU) { + x_offset = (i03 * ne02 + i02) * x_ne; + } else { + // copy src0 to device + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); + } - for (int64_t i12 = 0; i12 < ne12; i12++) { - int64_t i02 = i12 / r2; + 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)); - // copy data to device - if (src0->backend == GGML_BACKEND_GPU) { - x_offset = (i03 * ne02 + i02) * x_ne; - } else if (i02 != pi02 || i03 != pi03) { - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); - pi02 = i02; - pi03 = i03; + CL_CHECK(clFinish(queue)); + + // compute + cl_event ev_sgemm; + clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, + clblast::Transpose::kYes, clblast::Transpose::kNo, + ne01, ne11, ne10, + alpha, + d_X, x_offset, ne00, + d_Y, 0, ne10, + beta, + d_D, 0, ne01, + &queue, &ev_sgemm); + + if (status != clblast::StatusCode::kSuccess) { + GGML_ASSERT(false); + } + + // 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)); + } } - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); - - CL_CHECK(clFinish(queue)); - - // compute - cl_event ev_sgemm; - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, x_offset, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, &ev_sgemm); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - - // 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)); } } @@ -1589,73 +1588,70 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float); size_t x_offset = 0; - int64_t pi02 = -1; - int64_t pi03 = -1; - for (int64_t i13 = 0; i13 < ne13; i13++) { - int64_t i03 = i13 / r3; - - for (int64_t i12 = 0; i12 < ne12; i12++) { - int64_t i02 = i12 / r2; - - // copy src0 to device - if (src0->backend == GGML_BACKEND_GPU) { - x_offset = (i03 * ne02 + i02) * x_ne; - } else if (i02 != pi02 || i03 != pi03) { - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); - pi02 = i02; - pi03 = i03; - } - - // convert src1 to fp16 - // TODO: use multiple threads - char * src1i = (char *) src1->data + i13*nb13 + i12*nb12; - if (src1_cont_rows) { - if (src1_cont_cols) { - ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11); + for (int64_t i03 = 0; i03 < ne03; i03++) { + // TODO: copy src0 here when r3>1 + for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + if (src0->backend == GGML_BACKEND_GPU) { + x_offset = (i03 * ne02 + i02) * x_ne; + } else { + // copy src0 to device + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); } - else { - for (int64_t i11 = 0; i11 < ne11; i11++) { - ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10); + + for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { + // convert src1 to fp16 + // TODO: use multiple threads + char * src1i = (char *) src1->data + i13*nb13 + i12*nb12; + if (src1_cont_rows) { + if (src1_cont_cols) { + ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11); + } + else { + for (int64_t i11 = 0; i11 < ne11; i11++) { + ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10); + } + } } - } - } - else { - for (int64_t i11 = 0; i11 < ne11; i11++) { - for (int64_t i10 = 0; i10 < ne10; i10++) { - // very slow due to no inlining - tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10)); + else { + for (int64_t i11 = 0; i11 < ne11; i11++) { + for (int64_t i10 = 0; i10 < ne10; i10++) { + // very slow due to no inlining + tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10)); + } + } } + + // copy src1 to device + CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL)); + + CL_CHECK(clFinish(queue)); + + // compute + cl_event ev_sgemm; + clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, + clblast::Transpose::kYes, clblast::Transpose::kNo, + ne01, ne11, ne10, + alpha, + d_X, x_offset, ne00, + d_Y, 0, ne10, + beta, + d_D, 0, ne01, + &queue, &ev_sgemm); + + if (status != clblast::StatusCode::kSuccess) { + GGML_ASSERT(false); + } + + // 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); } } - - // copy src1 to device - CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL)); - - CL_CHECK(clFinish(queue)); - - // compute - cl_event ev_sgemm; - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, x_offset, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, &ev_sgemm); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - - // 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); } } @@ -1718,85 +1714,81 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * size_t ev_idx = 0; std::vector events; - int64_t pi02 = -1; - int64_t pi03 = -1; - - for (int64_t i13 = 0; i13 < ne13; i13++) { - int64_t i03 = i13 / r3; - - for (int64_t i12 = 0; i12 < ne12; i12++) { - int64_t i02 = i12 / r2; - - // copy src0 to device if necessary - if (src0->backend == GGML_BACKEND_CPU) { - if (i02 != pi02 || i03 != pi03) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + // TODO: copy and dequantize src0 here when r3>1 + for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + // copy src0 to device if necessary + if (src0->backend == GGML_BACKEND_CPU) { events.emplace_back(); CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++)); - pi02 = i02; - pi03 = i03; - } - } else if (src0->backend == GGML_BACKEND_GPU) { - d_Q = (cl_mem) src0->extra; - } else { - GGML_ASSERT(false); - } - if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel - // copy src1 to device - events.emplace_back(); - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++)); - - // compute - const size_t global = ne01 * local; - const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0; - const cl_int ncols = ne00; - events.emplace_back(); - CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q)); - CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL)); - CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols)); - CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++)); - } else { // general dequantization kernel + CLBlast matrix matrix multiplication - // convert src0 to fp32 on device - const size_t global = x_ne / global_denom; - const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0; - CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q)); - CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X)); - CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, offset > 0 ? &offset : NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); - - // copy src1 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); - - events.emplace_back(); - - // wait for conversion - CL_CHECK(clFinish(queue)); - - // compute - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, 0, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, events.data() + ev_idx++); - - if (status != clblast::StatusCode::kSuccess) { + } else if (src0->backend == GGML_BACKEND_GPU) { + d_Q = (cl_mem) src0->extra; + } else { GGML_ASSERT(false); } - } - // 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, &events[events.size() - 1], NULL)); - for (auto *event : events) { - clReleaseEvent(event); - } + if (!mul_mat_vec) { + // convert src0 to fp32 on device + const size_t global = x_ne / global_denom; + const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0; + CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q)); + CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X)); + CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); + } - ev_idx = 0; - events.clear(); + for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { + if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel + // copy src1 to device + events.emplace_back(); + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++)); + + // compute + const size_t global = ne01 * local; + const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0; + const cl_int ncols = ne00; + events.emplace_back(); + CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q)); + CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL)); + CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y)); + CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D)); + CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols)); + CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++)); + } else { // CLBlast matrix matrix multiplication + // copy src1 to device + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); + + // wait for conversion + CL_CHECK(clFinish(queue)); + + // compute + events.emplace_back(); + clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, + clblast::Transpose::kYes, clblast::Transpose::kNo, + ne01, ne11, ne10, + alpha, + d_X, 0, ne00, + d_Y, 0, ne10, + beta, + d_D, 0, ne01, + &queue, events.data() + ev_idx++); + + if (status != clblast::StatusCode::kSuccess) { + GGML_ASSERT(false); + } + } + + // 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, &events[events.size() - 1], NULL)); + for (auto *event : events) { + clReleaseEvent(event); + } + + ev_idx = 0; + events.clear(); + } + } } } From 22c69a27945e7acf9690dd3210d316f22182751c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 08:37:20 +0300 Subject: [PATCH 30/95] batched : add len CLI argument --- examples/batched/batched.cpp | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 155212165..2797329b4 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -11,12 +11,16 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL]\n" , argv[0]); + printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN]\n" , argv[0]); return 1 ; } + // number of parallel batches int n_parallel = 1; + // total length of the sequences including the prompt + int n_len = 32; + if (argc >= 2) { params.model = argv[1]; } @@ -29,13 +33,14 @@ int main(int argc, char ** argv) { n_parallel = std::atoi(argv[3]); } + if (argc >= 5) { + n_len = std::atoi(argv[4]); + } + if (params.prompt.empty()) { params.prompt = "Hello my name is"; } - // total length of the sequences including the prompt - const int n_len = 32; - // init LLM llama_backend_init(params.numa); From d3956aea53369455008159cc405ed4c496976692 Mon Sep 17 00:00:00 2001 From: vvhg1 <94630311+vvhg1@users.noreply.github.com> Date: Sun, 22 Oct 2023 20:09:51 +0200 Subject: [PATCH 31/95] main : escape prompt for cfg_negative_prompt and consecutive inputs in main with interactive (#3623) * infill tokens correction * serverinfill tokens correction * removing any leading whitespace from infill suffix and removing leeading space token from suffix when params.escape * removing any leading whitespace from infill suffix and removing leeading space token from suffix when params.escape * only rm when params.escape, rm space if possible which is added back or rm added space token * only rm when params.escape, rm space if possible which is added back or rm added space token * Revert "only rm when params.escape, rm space if possible which is added back or rm added space token" This reverts commit 63ba0b621f21077c0e3bc6ba6a327534123cb738. * fix interactive prompt escaping and fix server infill leading space handling * rm unnecessary bool check * process escapes for neg prompt and interactive consec prompts * removed unneccessary static string escape --- common/common.cpp | 1 + examples/main/main.cpp | 3 +++ 2 files changed, 4 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 2ef902bd5..bbd1518ca 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -632,6 +632,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { process_escapes(params.prompt); process_escapes(params.input_prefix); process_escapes(params.input_suffix); + process_escapes(sparams.cfg_negative_prompt); for (auto & antiprompt : params.antiprompt) { process_escapes(antiprompt); } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index db5309afe..2621bd539 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -761,6 +761,9 @@ int main(int argc, char ** argv) { n_consumed = embd_inp.size(); embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); } + if (params.escape) { + process_escapes(buffer); + } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); From a5e7dbd6141128bfa3c40a19c2945a181df625d3 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 22 Oct 2023 12:14:56 -0600 Subject: [PATCH 32/95] llama : validate special token ids are in range when loading GGUF model (#3635) * Add validation for special token ids to llama.cpp Small optimization for llama_byte_to_token SPM mode * Fix BPE newline check, only I could break something so simple * Killll meeeeee * Account for GGUF_KEY_KEY only setting when the key exists * Minor code cleanups. * Fix convert.py error msg when added tokens are out of range * Make gguf SpecialVocab vocab size-aware Update conversion scripts accordingly * Avoid a string copy Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- convert-baichuan-hf-to-gguf.py | 2 +- convert-bloom-hf-to-gguf.py | 2 +- convert-falcon-hf-to-gguf.py | 2 +- convert-gptneox-hf-to-gguf.py | 2 +- convert-llama-ggml-to-gguf.py | 4 +++- convert-mpt-hf-to-gguf.py | 2 +- convert-refact-hf-to-gguf.py | 2 +- convert-starcoder-hf-to-gguf.py | 2 +- convert.py | 13 ++++++++---- gguf-py/gguf/gguf.py | 36 ++++++++++++++++++++++---------- llama.cpp | 37 +++++++++++++++++++++++++-------- 11 files changed, 72 insertions(+), 32 deletions(-) diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index a1783f71f..3b64ecb88 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -230,7 +230,7 @@ gguf_writer.add_token_list(tokens) gguf_writer.add_token_scores(scores) gguf_writer.add_token_types(toktypes) -special_vocab = gguf.SpecialVocab(dir_model) +special_vocab = gguf.SpecialVocab(dir_model, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert-bloom-hf-to-gguf.py b/convert-bloom-hf-to-gguf.py index 7bfc95ec1..14dbd793c 100755 --- a/convert-bloom-hf-to-gguf.py +++ b/convert-bloom-hf-to-gguf.py @@ -129,7 +129,7 @@ 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) +special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 1d98c51ad..8e8f3c3f8 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -152,7 +152,7 @@ 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) +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index d4e85f518..f1599b0c4 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -134,7 +134,7 @@ 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) +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index b5d3e0b3c..871add64d 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -388,7 +388,9 @@ def handle_metadata(cfg, hp): cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype ) # FIXME: Respect cfg.vocab_dir? - svocab = gguf.SpecialVocab(cfg.model_metadata_dir) + svocab = gguf.SpecialVocab(cfg.model_metadata_dir, + load_merges = cfg.vocabtype == 'bpe', + n_vocab = vocab.vocab_size) convert.check_vocab_size(params, vocab) return (params, vocab, svocab) diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py index 19a66820d..21b9fd507 100755 --- a/convert-mpt-hf-to-gguf.py +++ b/convert-mpt-hf-to-gguf.py @@ -139,7 +139,7 @@ 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) +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert-refact-hf-to-gguf.py b/convert-refact-hf-to-gguf.py index bfeabc082..934f3852b 100755 --- a/convert-refact-hf-to-gguf.py +++ b/convert-refact-hf-to-gguf.py @@ -150,7 +150,7 @@ 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) +special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert-starcoder-hf-to-gguf.py b/convert-starcoder-hf-to-gguf.py index 90fa0c32f..fe8815cbf 100755 --- a/convert-starcoder-hf-to-gguf.py +++ b/convert-starcoder-hf-to-gguf.py @@ -122,7 +122,7 @@ 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) +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) special_vocab.add_to_gguf(gguf_writer) # TENSORS diff --git a/convert.py b/convert.py index 24da25efc..0680f71ea 100755 --- a/convert.py +++ b/convert.py @@ -369,7 +369,7 @@ class SentencePieceVocab: expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") + raise Exception(f"Expected added token IDs to be sequential and start at {vocab_size}; got {actual_ids}") items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) self.added_tokens_list = [text for (text, idx) in items] @@ -1163,10 +1163,13 @@ def main(args_in: list[str] | None = None) -> None: vocab: Vocab if args.vocab_only: - assert args.outfile, "need --outfile if using --vocab-only" + if not args.outfile: + raise ValueError("need --outfile if using --vocab-only") # FIXME: Try to respect vocab_dir somehow? vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype) - special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe') + special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, + load_merges = args.vocabtype == 'bpe', + n_vocab = vocab.vocab_size) outfile = args.outfile OutputFile.write_vocab_only(outfile, params, vocab, special_vocab) print(f"Wrote {outfile}") @@ -1178,7 +1181,9 @@ def main(args_in: list[str] | None = None) -> None: vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent vocab = load_vocab(vocab_dir, args.vocabtype) # FIXME: Try to respect vocab_dir somehow? - special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe') + special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, + load_merges = args.vocabtype == 'bpe', + n_vocab = vocab.vocab_size) model = model_plus.model model = convert_model_names(model, params) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 072c839c4..6b7d65429 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -987,12 +987,15 @@ class SpecialVocab: merges: list[str] = [] special_token_types: tuple[str, ...] = ('bos', 'eos', 'unk', 'sep', 'pad') special_token_ids: dict[str, int] = {} + n_vocab: int | None = None 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 if special_token_types is not None: self.special_token_types = special_token_types @@ -1002,6 +1005,16 @@ class SpecialVocab: 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(): @@ -1029,10 +1042,11 @@ class SpecialVocab: tc_content = entry_content else: continue - for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content): - if isinstance(maybe_token_id, int) and maybe_token_id >= 0: - self.special_token_ids[typ] = maybe_token_id - break + # 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: @@ -1042,21 +1056,21 @@ class SpecialVocab: with open(config_file, encoding = 'utf-8') as f: config = json.load(f) for typ in self.special_token_types: - maybe_token_id = config.get(f'{typ}_token_id') - if isinstance(maybe_token_id, int) and maybe_token_id >= 0: - self.special_token_ids[typ] = maybe_token_id + self._set_special_token(typ, config.get(f'{typ}_token_id')) return True - def add_to_gguf(self, gw: GGUFWriter) -> None: + def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: if len(self.merges) > 0: - print(f'gguf: Adding {len(self.merges)} merge(s).') + 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') + print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', file = sys.stderr) continue - print(f'gguf: Setting special token type {typ} to {tokid}') + if not quiet: + print(f'gguf: Setting special token type {typ} to {tokid}') handler(tokid) def __repr__(self) -> str: diff --git a/llama.cpp b/llama.cpp index 365349335..8d52eaf62 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2238,15 +2238,35 @@ static void llm_load_vocab( if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { vocab.linefeed_id = llama_byte_to_token(vocab, '\n'); } else { - vocab.linefeed_id = llama_tokenize_internal(vocab, "\u010A", false)[0]; + const std::vector ids = llama_tokenize_internal(vocab, "\u010A", false); + GGML_ASSERT(!ids.empty() && "model vocab missing newline token"); + vocab.linefeed_id = ids[0]; } // special tokens - GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID)); - GGUF_GET_KEY(ctx, vocab.special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID)); - GGUF_GET_KEY(ctx, vocab.special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID)); - GGUF_GET_KEY(ctx, vocab.special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID)); - GGUF_GET_KEY(ctx, vocab.special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID)); + { + const std::vector> special_token_types = { + { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, + { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, + { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, + { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, + { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, + }; + for (const auto & it : special_token_types) { + const std::string & key = kv(std::get<0>(it)); + int32_t & id = std::get<1>(it), old_id = id; + + GGUF_GET_KEY(ctx, id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, key); + // Must be >= -1 and < vocab size. Since the key is unsigned, -1 + // can only come from the default value, so there's no point in + // validating that. + if (size_t(id + 1) > vocab.id_to_token.size()) { + LLAMA_LOG_WARN("%s: bad special token: '%s' = %d, using default id %d\n", + __func__, key.c_str(), id, old_id); + id = old_id; + } + } + } // build special tokens cache { @@ -6103,11 +6123,10 @@ 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: { - char buf[7]; - int result = snprintf(buf, sizeof(buf), "<0x%02X>", ch); - GGML_ASSERT(0 <= result && result < 7); + 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: { From 5a42a5f8e8a86da9ac88008d748cf232a83aa0e1 Mon Sep 17 00:00:00 2001 From: Ian Scrivener Date: Mon, 23 Oct 2023 05:16:43 +1100 Subject: [PATCH 33/95] readme : remove unsupported node.js library (#3703) - https://github.com/Atome-FE/llama-node is quite out of date - doesn't support recent/current llama.cpp functionality --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 49bb556a8..b56ecaec7 100644 --- a/README.md +++ b/README.md @@ -101,7 +101,7 @@ 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), [hlhr202/llama-node](https://github.com/hlhr202/llama-node) +- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) - 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 9e70cc03229df19ca2d28ce23cc817198f897278 Mon Sep 17 00:00:00 2001 From: goerch Date: Sun, 22 Oct 2023 21:21:42 +0200 Subject: [PATCH 34/95] Add test for MPT tokenization (#3728) * Add test for MPT tokenization * Revert code motion * Remove unnecessary restriction in test case * Clarify logic in conversion --- convert-mpt-hf-to-gguf.py | 15 +++++++++++---- llama.cpp | 17 +++++++++-------- models/ggml-vocab-mpt.gguf | Bin 0 -> 1771406 bytes tests/CMakeLists.txt | 1 + 4 files changed, 21 insertions(+), 12 deletions(-) create mode 100644 models/ggml-vocab-mpt.gguf diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py index 21b9fd507..2d2fa2329 100755 --- a/convert-mpt-hf-to-gguf.py +++ b/convert-mpt-hf-to-gguf.py @@ -128,15 +128,22 @@ 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): - tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.USER_DEFINED) + elif reverse_vocab[i] in added_vocab: + # NOTE: wouldn't we like to distinguish CONTROL tokens here? + tokens.append(reverse_vocab[i]) + 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_scores(scores) gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) diff --git a/llama.cpp b/llama.cpp index 8d52eaf62..c63e6251c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -975,14 +975,15 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default (void) tensor; } -static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) { +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()); if (n_tokens < 0) { result.resize(-n_tokens); int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); GGML_ASSERT(check == -n_tokens); - } else { + } + else { result.resize(n_tokens); } @@ -1202,10 +1203,10 @@ struct llama_vocab { id special_eot_id = 32010; int find_bpe_rank(std::string token_left, std::string token_right) const { - replace_all(token_left, " ", "\u0120"); - replace_all(token_left, "\n", "\u010A"); - replace_all(token_right, " ", "\u0120"); - replace_all(token_right, "\n", "\u010A"); + 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()) { @@ -7499,7 +7500,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c for (size_t i = 0; i < candidates->size; ++i) { const llama_token id = candidates->data[i].id; - const std::string piece = llama_token_to_str(ctx, id); + const std::string piece = llama_token_to_piece(ctx, id); if (id == eos) { if (!allow_eos) { candidates->data[i].logit = -INFINITY; @@ -7711,7 +7712,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar GGML_ASSERT(false); } - const std::string piece = llama_token_to_str(ctx, token); + const std::string piece = llama_token_to_piece(ctx, token); // Note terminating 0 in decoded string const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8); diff --git a/models/ggml-vocab-mpt.gguf b/models/ggml-vocab-mpt.gguf new file mode 100644 index 0000000000000000000000000000000000000000..6affa34bdd5e377bf3982f8708b89b1fdf728334 GIT binary patch literal 1771406 zcmd?S`IlVRapy_TzVB%*t2G9^=?bvEnA+$diuZS6E`d38wshs{W3pHQ50`xzIT_n zabu6T*RH*F_1}I@fB*Qf`JW&Dcc&iL(`tCSn)Y{_dEK8cruBcoZ~o3`GXGDWd30Qj z$|L{T;rBf9{Kek$vw1t+-<#6OH2reA-;U??lljA8J>Hq`{yXKJpLouP=iSrws8w55TyPfV;(?Ra#v(G*EU#G{a z`Me&_n|6G9x2^{3-F@uGJ~yoP>*>RBI~_f&C$nbQj{iNL`|TedyO@Vpzx`n~nml~; z{$qRWx##|$&;HZ$-{+pQh3c_U_2gkSYjU-_4M@4&S-dg z)DG&Q?a2yw&po#@nV|2+4ZFV^EhyKVdN#n=Ah z_;OMe@PF~OpE&&Lf6A#tCnn^v0*WampLdd*ie>Uhu{l zZ=ChUId7cz#szP@=#7`WanTzud*hNfUh&4Kz3~}uyy}gB#!vY-pY_IuH(vL~rZ+Bo z-gwI!pZCVw-uM}B ze8C(4%p31`I!NB=rnUys%| zqV>&aeJfhuj@Eag_3P34jcEO5w0-VGe-DrI;T7M9&KaAENMeC2F 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+
@@ -540,7 +583,7 @@ data; message = html`<${Markdownish} text=${template(text)} />` } - if(user) { + if (user) { return html`

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

` } else { return html`

${message}

` @@ -549,6 +592,7 @@ return html`
+ ${messages.flatMap(chatLine)}
`; }; @@ -567,7 +611,7 @@ const converter = new SchemaConverter( grammarJsonSchemaPropOrder.value .split(',') - .reduce((acc, cur, i) => ({...acc, [cur.trim()]: i}), {}) + .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}) ) converter.visit(schema, '') params.value = { @@ -579,7 +623,7 @@ } } - const FloatField = ({label, max, min, name, step, value}) => { + const FloatField = ({ label, max, min, name, step, value }) => { return html`
@@ -589,7 +633,7 @@ ` }; - const IntField = ({label, max, min, name, value}) => { + const IntField = ({ label, max, min, name, value }) => { return html`
@@ -672,7 +716,7 @@ ${GrammarControl()} ` - ); + ); const CompletionConfigForm = () => ( html` @@ -694,20 +738,20 @@ ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
- ${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})} - ${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})} - ${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})} - ${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})} + ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })} + ${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} + ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} + ${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 })}
More options
- ${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})} - ${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})} - ${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})} - ${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})} + ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} + ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} + ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
@@ -716,11 +760,11 @@
- ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})} - ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})} + ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })} + ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
- ${IntField({label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs})} + ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
@@ -759,20 +803,20 @@ const popoverChildren = html`
${probs.map((p, index) => { - return html` + return html`
${p.tok_str}: ${Math.floor(p.prob * 100)}%
` - })} + })}
` @@ -851,9 +895,9 @@ ref=${popoverRef} class="popover-content" style=${{ - top: position.value.top, - left: position.value.left, - }} + top: position.value.top, + left: position.value.left, + }} > ${props.popoverChildren}
@@ -952,8 +996,11 @@ -
+
+ +
+ diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b5ad3cc99..c3279dbc9 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,11 @@ #include "common.h" #include "llama.h" #include "build-info.h" +#include "grammar-parser.h" + +#include "../llava/clip.h" + +#include "stb_image.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -17,12 +22,14 @@ #include "json-schema-to-grammar.mjs.hpp" #include +#include +#include +#include #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif -using namespace httplib; using json = nlohmann::json; struct server_params @@ -34,6 +41,165 @@ struct server_params int32_t write_timeout = 600; }; +static bool server_verbose = false; + +#if SERVER_VERBOSE != 1 +#define LOG_VERBOSE(MSG, ...) +#else +#define LOG_VERBOSE(MSG, ...) \ + do \ + { \ + if (server_verbose) \ + { \ + server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ + } \ + } while (0) +#endif + +#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) + +// +// base64 utils (TODO: move to common in the future) +// + +static const std::string base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; + +static inline bool is_base64(uint8_t c) +{ + return (isalnum(c) || (c == '+') || (c == '/')); +} + +static std::vector base64_decode(std::string const &encoded_string) +{ + int i = 0; + int j = 0; + int in_ = 0; + + int in_len = encoded_string.size(); + + uint8_t char_array_4[4]; + uint8_t char_array_3[3]; + + std::vector ret; + + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) + { + char_array_4[i++] = encoded_string[in_]; in_++; + if (i == 4) + { + for (i = 0; i <4; i++) + { + char_array_4[i] = base64_chars.find(char_array_4[i]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) + { + ret.push_back(char_array_3[i]); + } + i = 0; + } + } + + if (i) + { + for (j = i; j <4; j++) + { + char_array_4[j] = 0; + } + + for (j = 0; j <4; j++) + { + char_array_4[j] = base64_chars.find(char_array_4[j]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (j = 0; (j < i - 1); j++) + { + ret.push_back(char_array_3[j]); + } + } + + return ret; +} + +// +// parallel +// + +enum task_type { + COMPLETION_TASK, + CANCEL_TASK +}; + +struct task_server { + int id; + int target_id; + task_type type; + json data; + bool infill_mode = false; +}; + +struct task_result { + int id; + bool stop; + bool error; + json result_json; +}; + +// TODO: can become bool if we can't find use of more states +enum slot_state +{ + IDLE, + PROCESSING, +}; + +enum slot_command +{ + NONE, + LOAD_PROMPT, + RELEASE, +}; + +struct slot_params +{ + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + + uint32_t seed = -1; // RNG seed + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_predict = -1; // new tokens to predict + + std::vector antiprompt; + + json input_prefix; + json input_suffix; +}; + +struct slot_image +{ + int32_t id; + + bool request_encode_image = false; + float* image_embedding = nullptr; + int32_t image_tokens = 0; + + clip_image_u8 img_data; + + std::string prefix_prompt; // before of this image +}; + // completion token output with probabilities struct completion_token_output { @@ -45,6 +211,7 @@ struct completion_token_output std::vector probs; llama_token tok; + std::string text_to_send; }; static size_t common_part(const std::vector &a, const std::vector &b) @@ -89,6 +256,7 @@ static size_t find_partial_stop_string(const std::string &stop, return std::string::npos; } +// TODO: reuse llama_detokenize template static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) { @@ -103,12 +271,13 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) static void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { - nlohmann::ordered_json log{ + nlohmann::ordered_json log + { {"timestamp", time(nullptr)}, - {"level", level}, - {"function", function}, - {"line", line}, - {"message", message}, + {"level", level}, + {"function", function}, + {"line", line}, + {"message", message}, }; if (!extra.empty()) @@ -138,7 +307,7 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c } // convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector & probs) +static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) { json out = json::array(); for (const auto &prob : probs) @@ -147,76 +316,211 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector +static T json_value(const json &body, const std::string &key, const T &default_value) { - bool stream = false; - bool has_next_token = false; - std::string generated_text; - std::vector generated_token_probs; + // Fallback null to default value + return body.contains(key) && !body.at(key).is_null() + ? body.value(key, default_value) + : default_value; +} - size_t num_prompt_tokens = 0; - size_t num_tokens_predicted = 0; - size_t n_past = 0; - size_t n_remain = 0; +struct llama_client_slot +{ + int id; + int task_id = -1; + + struct slot_params params; + + slot_state state = IDLE; + slot_command command = NONE; + + // used to determine the slot that has been used the longest + int64_t t_last_used = -1; + + // generation props + int32_t n_ctx = 0; // context size per slot + int32_t n_past = 0; + int32_t n_decoded = 0; + int32_t n_remaining = -1; + int32_t i_batch = -1; + + int32_t num_prompt_tokens = 0; + int32_t num_prompt_tokens_processed = 0; + int32_t multibyte_pending = 0; json prompt; - std::vector embd; - - gpt_params params; - - llama_model *model = nullptr; - llama_context *ctx = nullptr; - llama_sampling_context *ctx_sampling = nullptr; - - int n_ctx; + std::string generated_text; + llama_token sampled; + std::vector cache_tokens; + std::vector generated_token_probs; + bool infill = false; + bool has_next_token = true; bool truncated = false; bool stopped_eos = false; bool stopped_word = false; bool stopped_limit = false; + std::string stopping_word; - int32_t multibyte_pending = 0; - std::mutex mutex; + // sampling + struct llama_sampling_params sparams; + llama_sampling_context *ctx_sampling = nullptr; - std::unique_lock lock() - { - return std::unique_lock(mutex); + // multimodal + std::vector images; + + // stats + size_t sent_count = 0; + size_t sent_token_probs_index = 0; + + int64_t t_start_process_prompt; + int64_t t_start_genereration; + + double t_prompt_processing; // ms + double t_token_generation; // ms + + void reset() { + num_prompt_tokens = 0; + generated_text = ""; + truncated = false; + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + multibyte_pending = 0; + n_past = 0; + sent_count = 0; + sent_token_probs_index = 0; + infill = false; + + generated_token_probs.clear(); + + for (slot_image &img : images) + { + free(img.image_embedding); + delete[] img.img_data.data; + img.prefix_prompt = ""; + } + + images.clear(); + // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } + bool has_budget(gpt_params &global_params) { + n_remaining = -1; + if(params.n_predict != -1) + { + n_remaining = params.n_predict - n_decoded; + } + else if (global_params.n_predict != -1) + { + n_remaining = global_params.n_predict - n_decoded; + } + return n_remaining > 0 || n_remaining == -1; // no budget || limitless + } + + bool available() const { + return state == IDLE && command == NONE; + } + + bool is_processing() const { + return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + } + + void add_token_string(const completion_token_output &token) { + if (command == RELEASE) + { + return; + } + cache_tokens.push_back(token.tok); + generated_token_probs.push_back(token); + } + + void release() { + if (state == PROCESSING) + { + t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; + command = RELEASE; + } + } + + json get_formated_timings() { + return json + { + {"prompt_n", num_prompt_tokens_processed}, + {"prompt_ms", t_prompt_processing}, + {"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed}, + + {"predicted_n", n_decoded}, + {"predicted_ms", t_token_generation}, + {"predicted_per_token_ms", t_token_generation / n_decoded}, + {"predicted_per_second", 1e3 / t_token_generation * n_decoded}, + }; + } + + void print_timings() { + LOG_TEE("\n"); + LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed); + LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded); + LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation); + } +}; + +struct llama_server_context +{ + llama_model *model = nullptr; + llama_context *ctx = nullptr; + + clip_ctx *clp_ctx = nullptr; + + gpt_params params; + + llama_batch batch; + + bool multimodal = false; + bool clean_kv_cache = true; + bool all_slots_are_idle = false; + + int32_t id_gen; + int32_t n_ctx; // total context for all clients / slots + + // system prompt + bool system_need_update = false; + + std::string system_prompt; + std::vector system_tokens; + + std::string name_user; // this should be the antiprompt + std::string name_assistant; + + // slots / clients + std::vector slots; + + std::vector queue_tasks; + std::vector queue_results; + std::mutex mutex_tasks; + std::mutex mutex_results; + ~llama_server_context() { if (ctx) @@ -231,46 +535,74 @@ struct llama_server_context } } - void rewind() - { - params.antiprompt.clear(); - params.sparams.grammar.clear(); - num_prompt_tokens = 0; - num_tokens_predicted = 0; - generated_text = ""; - generated_text.reserve(n_ctx); - generated_token_probs.clear(); - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - n_remain = 0; - n_past = 0; - params.sparams.n_prev = n_ctx; - } - - void initSampling() { - if (ctx_sampling != nullptr) { - llama_sampling_free(ctx_sampling); - } - ctx_sampling = llama_sampling_init(params.sparams); - } - - bool loadModel(const gpt_params ¶ms_) + bool load_model(const gpt_params ¶ms_) { params = params_; + if (!params.mmproj.empty()) { + multimodal = true; + LOG_TEE("Multi Modal Mode Enabled"); + clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); + if(clp_ctx == nullptr) { + LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); + return false; + } + + if (params.n_ctx < 2048) { // request larger context for the image embedding + params.n_ctx = 2048; + } + } + std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { - LOG_ERROR("unable to load model", {{"model", params_.model}}); + LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } + + if (multimodal) { + const int n_embd_clip = clip_n_mmproj_embd(clp_ctx); + const int n_embd_llm = llama_n_embd(model); + if (n_embd_clip != n_embd_llm) { + LOG_TEE("%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_embd_clip, n_embd_llm); + llama_free(ctx); + llama_free_model(model); + return false; + } + } + n_ctx = llama_n_ctx(ctx); + return true; } + void initialize() { + id_gen = 0; + + // create slots + all_slots_are_idle = true; + + const int32_t n_ctx_slot = n_ctx / params.n_parallel; + + LOG_TEE("Available slots:\n"); + for (int i = 0; i < params.n_parallel; i++) + { + llama_client_slot slot; + + slot.id = i; + slot.n_ctx = n_ctx_slot; + slot.reset(); + + LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); + slots.push_back(slot); + } + + batch = llama_batch_init(n_ctx, 0, params.n_parallel); + + // empty system prompt + system_prompt = ""; + system_tokens.clear(); + } + std::vector tokenize(const json & json_prompt, bool add_bos) const { // If `add_bos` is true, we only add BOS, when json_prompt is a string, @@ -316,260 +648,301 @@ struct llama_server_context return prompt_tokens; } - void truncatePrompt(std::vector &prompt_tokens) { - const int n_left = n_ctx - params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_block_size) / n_block_size; + llama_client_slot* get_slot(int id) { + int64_t t_last = ggml_time_us(); + llama_client_slot *last_used = nullptr; - // Keep n_keep tokens at start of prompt (at most n_ctx - 4) - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + for (llama_client_slot & slot : slots) + { + if (slot.id == id && slot.available()) + { + return &slot; + } - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + if (slot.available() && slot.t_last_used < t_last) + { + last_used = &slot; + t_last = slot.t_last_used; + } + } - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - {"num_prompt_tokens", new_tokens.size()} - }); - - truncated = true; - prompt_tokens = new_tokens; + return last_used; } - void loadInfill() - { - bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; - } + bool launch_slot_with_data(llama_client_slot* &slot, json data) { + slot_params default_params; + llama_sampling_params default_sparams; - auto prefix_tokens = tokenize(params.input_prefix, false); - auto suffix_tokens = tokenize(params.input_suffix, false); - const int space_token = 29871; - if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { - suffix_tokens.erase(suffix_tokens.begin()); - } - prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - prefix_tokens.push_back(llama_token_middle(ctx)); + 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.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); - auto prompt_tokens = prefix_tokens; - - num_prompt_tokens = prompt_tokens.size(); - - if (params.n_keep < 0) + // infill + if (data.count("input_prefix") != 0) { - params.n_keep = (int)num_prompt_tokens; + slot->params.input_prefix = data["input_prefix"]; } - params.n_keep = std::min(params.n_ctx - 4, params.n_keep); - - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t) n_ctx) + else { - truncatePrompt(prompt_tokens); - num_prompt_tokens = prompt_tokens.size(); - - GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx); + slot->params.input_prefix = ""; } - // push the prompt into the sampling context (do not apply grammar) - for (auto & token : prompt_tokens) + if (data.count("input_suffix") != 0) { - llama_sampling_accept(ctx_sampling, ctx, token, false); + slot->params.input_suffix = data["input_suffix"]; } - - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - embd = prompt_tokens; - - if (n_past == num_prompt_tokens) + else { - // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); - n_past--; + slot->params.input_suffix = ""; } - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); - - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - - has_next_token = true; - } - void loadPrompt() - { - auto prompt_tokens = tokenize(prompt, true); // always add BOS - - num_prompt_tokens = prompt_tokens.size(); - - if (params.n_keep < 0) + if (data.count("prompt") != 0) { - params.n_keep = (int)num_prompt_tokens; + slot->prompt = data["prompt"]; } - params.n_keep = std::min(n_ctx - 4, params.n_keep); - - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t) n_ctx) + else { - truncatePrompt(prompt_tokens); - num_prompt_tokens = prompt_tokens.size(); - - GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx); + slot->prompt = ""; } - // push the prompt into the sampling context (do not apply grammar) - for (auto & token : prompt_tokens) + slot->sparams.logit_bias.clear(); + + if (json_value(data, "ignore_eos", false)) { - llama_sampling_accept(ctx_sampling, ctx, token, false); + slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY; } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - - embd = prompt_tokens; - if (n_past == num_prompt_tokens) + const auto &logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) { - // we have to evaluate at least 1 token to generate logits. - n_past--; + const int n_vocab = llama_n_vocab(model); + for (const auto &el : *logit_bias) + { + if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) + { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) + { + if (el[1].is_number()) + { + slot->sparams.logit_bias[tok] = el[1].get(); + } + else if (el[1].is_boolean() && !el[1].get()) + { + slot->sparams.logit_bias[tok] = -INFINITY; + } + } + } + } } - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + slot->params.antiprompt.clear(); + const auto &stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) + { + for (const auto &word : *stop) + { + if (!word.empty()) + { + slot->params.antiprompt.push_back(word); + } + } + } - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); + if (multimodal) + { + const auto &images_data = data.find("image_data"); + if (images_data != data.end() && images_data->is_array()) + { + for (const auto &img : *images_data) + { + std::string data_b64 = 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) { + 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); + img_sl.request_encode_image = true; + slot->images.push_back(img_sl); + } + // process prompt + // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} + if (slot->images.size() > 0 && !slot->prompt.is_array()) + { + std::string prompt = slot->prompt.get(); + size_t pos = 0, begin_prefix = 0; + std::string pattern = "[img-"; + while ((pos = prompt.find(pattern, pos)) != std::string::npos) { + size_t end_prefix = pos; + pos += pattern.length(); + size_t end_pos = prompt.find("]", pos); + if (end_pos != std::string::npos) + { + std::string image_id = prompt.substr(pos, end_pos - pos); + try + { + int img_id = std::stoi(image_id); + bool found = false; + for (slot_image &img : slot->images) + { + if (img.id == img_id) { + found = true; + img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); + begin_prefix = end_pos + 1; + break; + } + } + if (!found) { + LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id); + slot->images.clear(); + return false; + } + } catch (const std::invalid_argument& e) { + LOG_TEE("Invalid image number id in prompt\n"); + slot->images.clear(); + return false; + } + } + } + slot->prompt = ""; + slot->params.input_suffix = prompt.substr(begin_prefix); + slot->params.cache_prompt = false; // multimodal doesn't support cache prompt + } + } + } - has_next_token = true; + if (slot->ctx_sampling != nullptr) + { + llama_sampling_free(slot->ctx_sampling); + } + slot->ctx_sampling = llama_sampling_init(slot->sparams); + slot->command = LOAD_PROMPT; + + all_slots_are_idle = false; + + LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); + + return true; } - void beginCompletion() - { - // number of tokens to keep when resetting context - n_remain = params.n_predict; - llama_set_rng_seed(ctx, params.seed); + void kv_cache_clear() { + // clear the entire KV cache + llama_kv_cache_tokens_rm(ctx, -1, -1); + clean_kv_cache = false; } - completion_token_output nextToken() - { - completion_token_output result; - result.tok = -1; + void update_system_prompt() { + system_tokens = ::llama_tokenize(ctx, system_prompt, true); - if (embd.size() >= (size_t)n_ctx) + llama_batch_clear(batch); + + kv_cache_clear(); + + for (int32_t i = 0; i < batch.n_tokens; ++i) { - // Shift context - - const int n_left = n_past - params.n_keep - 1; - const int n_discard = n_left/2; - - 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); - - for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++) - { - embd[i - n_discard] = embd[i]; - } - embd.resize(embd.size() - n_discard); - - n_past -= n_discard; - - truncated = true; - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); + llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } - bool tg = true; - while (n_past < embd.size()) + if (llama_decode(ctx, batch) != 0) { - int n_eval = (int)embd.size() - n_past; - tg = n_eval == 1; - if (n_eval > params.n_batch) - { - n_eval = params.n_batch; - } - - if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0))) - { - LOG_ERROR("failed to eval", { - {"n_eval", n_eval}, - {"n_past", n_past}, - {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - has_next_token = false; - return result; - } - n_past += n_eval; + LOG_TEE("%s: llama_decode() failed\n", __func__); + return; } - if (params.n_predict == 0) + // assign the system KV cache to all parallel sequences + for (int32_t i = 1; i < params.n_parallel; ++i) { - has_next_token = false; - result.tok = llama_token_eos(ctx); - return result; + llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); } - { - // out of user input, sample next token - result.tok = llama_sampling_sample(ctx_sampling, ctx, NULL); - - llama_token_data_array cur_p = { ctx_sampling->cur.data(), ctx_sampling->cur.size(), false }; - - const int32_t n_probs = params.sparams.n_probs; - if (params.sparams.temp <= 0 && n_probs > 0) - { - // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &cur_p); - } - - for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) - { - result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); - } - - llama_sampling_accept(ctx_sampling, ctx, result.tok, true); - - if (tg) { - num_tokens_predicted++; - } - } - - // add it to the context - embd.push_back(result.tok); - // decrement remaining sampling budget - --n_remain; - - if (!embd.empty() && embd.back() == llama_token_eos(ctx)) - { - // stopping_word = llama_token_to_piece(ctx, embd.back()); - has_next_token = false; - stopped_eos = true; - LOG_VERBOSE("eos token found", {}); - return result; - } - - has_next_token = params.n_predict == -1 || n_remain != 0; - return result; + LOG_TEE("system prompt updated\n"); + system_need_update = false; } - size_t findStoppingStrings(const std::string &text, const size_t last_token_size, - const stop_type type) + void notify_system_prompt_changed() { + // release all slots + for (llama_client_slot &slot : slots) + { + slot.release(); + } + wait_all_are_idle(); + all_slots_are_idle = true; + + // wait until system prompt load + system_need_update = true; + while (system_need_update) + { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + // system prompt loaded, continue + } + + void process_system_prompt_data(const json &sys_props) { + system_prompt = sys_props.value("prompt", ""); + name_user = sys_props.value("anti_prompt", ""); + name_assistant = sys_props.value("assistant_name", ""); + + if (slots.size() > 0) + { + notify_system_prompt_changed(); + } + else + { + system_need_update = true; + } + } + + void wait_all_are_idle() { + bool wait = true; + while (wait) + { + wait = false; + for (auto &slot : slots) + { + if (!slot.available()) + { + wait = true; + break; + } + } + } + } + + static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, + const stop_type type, llama_client_slot &slot) { size_t stop_pos = std::string::npos; - for (const std::string &word : params.antiprompt) + + for (const std::string &word : slot.params.antiprompt) { size_t pos; if (type == STOP_FULL) @@ -587,95 +960,803 @@ struct llama_server_context { if (type == STOP_FULL) { - stopping_word = word; - stopped_word = true; - has_next_token = false; + slot.stopped_word = true; + slot.stopping_word = word; + slot.has_next_token = false; } stop_pos = pos; + } } + return stop_pos; } - completion_token_output doCompletion() - { - auto token_with_probs = nextToken(); + bool process_token(completion_token_output &result, llama_client_slot &slot) { + // remember which tokens were sampled - used for repetition penalties during sampling + const std::string token_str = llama_token_to_piece(ctx, result.tok); + slot.sampled = result.tok; - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); - generated_text += token_text; + // search stop word and delete it + slot.generated_text += token_str; + slot.has_next_token = true; - if (params.sparams.n_probs > 0) + if (slot.multibyte_pending > 0) { - generated_token_probs.push_back(token_with_probs); + slot.multibyte_pending -= token_str.size(); } - - if (multibyte_pending > 0) + else if (token_str.size() == 1) { - multibyte_pending -= token_text.size(); - } - else if (token_text.size() == 1) - { - const char c = token_text[0]; + const char c = token_str[0]; // 2-byte characters: 110xxxxx 10xxxxxx if ((c & 0xE0) == 0xC0) { - multibyte_pending = 1; + slot.multibyte_pending = 1; // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx } else if ((c & 0xF0) == 0xE0) { - multibyte_pending = 2; + slot.multibyte_pending = 2; // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx } else if ((c & 0xF8) == 0xF0) { - multibyte_pending = 3; + slot.multibyte_pending = 3; } else { - multibyte_pending = 0; + slot.multibyte_pending = 0; } } - if (multibyte_pending > 0 && !has_next_token) + if (slot.multibyte_pending == 0) { - has_next_token = true; - n_remain++; + size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + const std::string str_test = slot.generated_text.substr(pos); + bool is_stop_full = false; + size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); + if (stop_pos != std::string::npos) + { + is_stop_full = true; + slot.generated_text.erase( + slot.generated_text.begin() + pos + stop_pos, + slot.generated_text.end()); + pos = std::min(slot.sent_count, slot.generated_text.size()); + } + else + { + is_stop_full = false; + stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); + } + + // check if there is any token to predict + if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) + { + // no send the stop word in the response + result.text_to_send = slot.generated_text.substr(pos, std::string::npos); + slot.sent_count += result.text_to_send.size(); + // add the token to slot queue and cache + } + slot.add_token_string(result); + if (slot.params.stream) + { + send_partial_response(slot, result); + } } - if (!has_next_token && n_remain == 0) + if (slot.multibyte_pending > 0 && !slot.has_next_token) { - stopped_limit = true; + slot.has_next_token = true; + } + + // check the limits + if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params)) + { + slot.stopped_limit = true; + slot.has_next_token = false; + } + + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) + { + slot.stopped_eos = true; + slot.has_next_token = false; + LOG_VERBOSE("eos token found", {}); } LOG_VERBOSE("next token", { - {"token", token_with_probs.tok}, - {"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)}, - {"has_next_token", has_next_token}, - {"n_remain", n_remain}, - {"num_tokens_predicted", num_tokens_predicted}, - {"stopped_eos", stopped_eos}, - {"stopped_word", stopped_word}, - {"stopped_limit", stopped_limit}, - {"stopping_word", stopping_word}, + {"token", result.tok}, + {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"num_tokens_predicted", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, }); - return token_with_probs; + return slot.has_next_token; // continue } - std::vector getEmbedding() + bool process_images(llama_client_slot &slot) const { - static const int n_embd = llama_n_embd(model); + for (slot_image &img : slot.images) + { + if (!img.request_encode_image) + { + continue; + } + clip_image_f32 img_res; + if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) + { + LOG_TEE("Error processing the given image"); + clip_free(clp_ctx); + return false; + } + img.image_tokens = clip_n_patches(clp_ctx); + img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); + if (!img.image_embedding) + { + LOG_TEE("Unable to allocate memory for image embeddings\n"); + clip_free(clp_ctx); + 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)) + { + LOG_TEE("Unable to encode image\n"); + return false; + } + img.request_encode_image = false; + } + + return slot.images.size() > 0; + } + + void send_error(int id, std::string error) + { + std::lock_guard lock(mutex_results); + task_result res; + res.id = id; + res.error = true; + res.result_json = { { "content", error } }; + queue_results.push_back(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(ctx)); + const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && + eos_bias->second < 0.0f && std::isinf(eos_bias->second); + return json { + {"n_ctx", slot.n_ctx}, + {"model", params.model_alias}, + {"seed", slot.params.seed}, + {"temp", slot.sparams.temp}, + {"top_k", slot.sparams.top_k}, + {"top_p", slot.sparams.top_p}, + {"tfs_z", slot.sparams.tfs_z}, + {"typical_p", slot.sparams.typical_p}, + {"repeat_last_n", slot.sparams.penalty_last_n}, + {"repeat_penalty", slot.sparams.penalty_repeat}, + {"presence_penalty", slot.sparams.penalty_present}, + {"frequency_penalty", slot.sparams.penalty_freq}, + {"mirostat", slot.sparams.mirostat}, + {"mirostat_tau", slot.sparams.mirostat_tau}, + {"mirostat_eta", slot.sparams.mirostat_eta}, + {"penalize_nl", slot.sparams.penalize_nl}, + {"stop", slot.params.antiprompt}, + {"n_predict", slot.params.n_predict}, + {"n_keep", params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", slot.params.stream}, + {"logit_bias", slot.sparams.logit_bias}, + {"n_probs", slot.sparams.n_probs}, + {"grammar", slot.sparams.grammar}, + }; + } + + void send_partial_response(llama_client_slot &slot, completion_token_output tkn) + { + std::lock_guard lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = false; + + res.result_json = json + { + {"content", tkn.text_to_send}, + {"stop", false}, + {"slot_id", slot.id}, + {"multimodal", multimodal} + }; + + if (slot.sparams.n_probs > 0) + { + 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_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); + if (probs_pos < probs_stop_pos) + { + probs_output = std::vector(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); + } + slot.sent_token_probs_index = probs_stop_pos; + res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); + } + + queue_results.push_back(res); + } + + void send_final_response(llama_client_slot &slot) + { + std::lock_guard lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = true; + + res.result_json = json + { + {"content", !slot.params.stream ? slot.generated_text : ""}, + {"slot_id", slot.id}, + {"stop", true}, + {"model", params.model_alias}, + {"tokens_predicted", slot.n_decoded}, + {"tokens_evaluated", slot.num_prompt_tokens}, + {"generation_settings", get_formated_generation(slot)}, + {"prompt", slot.prompt}, + {"truncated", slot.truncated}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + {"tokens_cached", slot.n_past}, + {"timings", slot.get_formated_timings()} + }; + + if (slot.sparams.n_probs > 0) + { + std::vector probs = {}; + if (!slot.params.stream && slot.stopped_word) + { + const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); + probs = std::vector(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); + } + else + { + probs = std::vector( + slot.generated_token_probs.begin(), + slot.generated_token_probs.begin() + slot.sent_token_probs_index); + } + res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); + } + + queue_results.push_back(res); + } + + void send_embedding(llama_client_slot &slot) + { + std::lock_guard lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = true; + + const int n_embd = llama_n_embd(model); if (!params.embedding) { LOG_WARNING("embedding disabled", { {"params.embedding", params.embedding}, }); - return std::vector(n_embd, 0.0f); + res.result_json = json + { + {"embedding", std::vector(n_embd, 0.0f)}, + }; } - const float *data = llama_get_embeddings(ctx); - std::vector embedding(data, data + n_embd); - return embedding; + else + { + const float *data = llama_get_embeddings(ctx); + std::vector embedding(data, data + n_embd); + res.result_json = json + { + {"embedding", embedding }, + }; + } + queue_results.push_back(res); + } + + int request_completion(json data, bool infill) + { + std::lock_guard lock(mutex_tasks); + task_server task; + task.id = id_gen++; + task.data = data; + task.infill_mode = infill; + task.type = COMPLETION_TASK; + queue_tasks.push_back(task); + return task.id; + } + + task_result next_result(int task_id) + { + while (true) + { + std::this_thread::sleep_for(std::chrono::microseconds(5)); + std::lock_guard lock(mutex_results); + + if (queue_results.empty()) + { + continue; + } + + for (int i = 0; i < (int) queue_results.size(); i++) + { + if (queue_results[i].id == task_id) + { + task_result res = queue_results[i]; + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // never reached + //return task_result{-1, false, false, {}}; + } + + // for multiple images processing + bool ingest_images(llama_client_slot &slot, int n_batch) + { + int image_idx = 0; + + while (image_idx < (int) slot.images.size()) + { + slot_image &img = slot.images[image_idx]; + + // process prefix prompt + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) + { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + if (llama_decode(ctx, batch_view)) + { + LOG_TEE("%s : failed to eval\n", __func__); + return false; + } + } + + // process image with llm + for (int i = 0; i < img.image_tokens; i += n_batch) + { + int n_eval = img.image_tokens - i; + if (n_eval > n_batch) + { + n_eval = n_batch; + } + + const int n_embd = llama_n_embd(model); + llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + if (llama_decode(ctx, batch_img)) + { + LOG_TEE("%s : failed to eval image\n", __func__); + return false; + } + slot.n_past += n_eval; + } + image_idx++; + + llama_batch_clear(batch); + + // append prefix of next image + const auto json_prompt = (image_idx >= (int) slot.images.size()) ? + slot.params.input_suffix : // no more images, then process suffix prompt + (json)(slot.images[image_idx].prefix_prompt); + + 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); + slot.n_past += 1; + } + } + + return true; + } + + void request_cancel(int task_id) + { + std::lock_guard lock(mutex_tasks); + task_server task; + task.id = id_gen++; + task.type = CANCEL_TASK; + task.target_id = task_id; + queue_tasks.push_back(task); + } + + void process_tasks() + { + std::lock_guard lock(mutex_tasks); + while (!queue_tasks.empty()) + { + task_server task = queue_tasks.front(); + queue_tasks.erase(queue_tasks.begin()); + switch (task.type) + { + case COMPLETION_TASK: { + llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); + if (slot == nullptr) + { + LOG_TEE("slot unavailable\n"); + // send error result + send_error(task.id, "slot unavaliable"); + return; + } + + if (task.data.contains("system_prompt")) + { + process_system_prompt_data(task.data["system_prompt"]); + } + + slot->reset(); + + slot->infill = task.infill_mode; + slot->task_id = task.id; + + if (!launch_slot_with_data(slot, task.data)) + { + // send error result + send_error(task.id, "internal_error"); + break; + } + } break; + case CANCEL_TASK: { // release slot linked with the task id + for (auto & slot : slots) + { + if (slot.task_id == task.target_id) + { + slot.release(); + break; + } + } + } break; + } + } + } + + bool update_slots() { + // attend tasks + process_tasks(); + + // update the system prompt wait until all slots are idle state + if (system_need_update) + { + LOG_TEE("updating system prompt\n"); + update_system_prompt(); + } + + llama_batch_clear(batch); + + if (all_slots_are_idle) + { + if (system_prompt.empty() && clean_kv_cache) + { + 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)); + } + + for (llama_client_slot &slot : slots) + { + if (slot.is_processing() && 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_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); + + for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + { + slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + } + + slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + + slot.n_past -= n_discard; + + slot.truncated = true; + + LOG_VERBOSE("context shift", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + }); + } + } + + // decode any currently ongoing sequences + for (auto & slot : slots) + { + // release the slot + if (slot.state == PROCESSING && slot.command == RELEASE) + { + slot.state = IDLE; + slot.command = NONE; + slot.t_last_used = ggml_time_us(); + + LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size()); + + continue; + } + + if (slot.state == IDLE || slot.command == RELEASE) + { + continue; + } + + slot.i_batch = batch.n_tokens; + + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); + + slot.n_decoded += 1; + slot.n_past += 1; + } + + // process in chunks of params.n_batch + int32_t n_batch = params.n_batch; + + // assign workload to the slots + if (params.cont_batching || batch.n_tokens == 0) + { + for (auto & slot : slots) + { + // need process the prompt + if (slot.state == IDLE && slot.command == LOAD_PROMPT) + { + slot.state = PROCESSING; + slot.command = NONE; + std::vector prompt_tokens; + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_genereration = 0; + + if (slot.infill) + { + bool suff_rm_leading_spc = true; + if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) + { + params.input_suffix.erase(0, 1); + suff_rm_leading_spc = false; + } + auto prefix_tokens = tokenize(slot.params.input_prefix, false); + auto suffix_tokens = tokenize(slot.params.input_suffix, false); + + const int space_token = 29871; // TODO: this should not be hardcoded + if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { + suffix_tokens.erase(suffix_tokens.begin()); + } + + prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); + prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.push_back(llama_token_middle(ctx)); + prompt_tokens = prefix_tokens; + } + else + { + prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt + } + + slot.num_prompt_tokens = prompt_tokens.size(); + + if (!slot.params.cache_prompt) + { + llama_sampling_reset(slot.ctx_sampling); + + slot.n_past = 0; + slot.num_prompt_tokens_processed = slot.num_prompt_tokens; + } + 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) + { + llama_sampling_accept(slot.ctx_sampling, ctx, token, false); + } + + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + + 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) + { + // 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); + slot.n_past--; + } + + 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)}, + {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, + }); + + const bool has_images = process_images(slot); + + // process the prefix of first image + std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) + { + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false); + } + + if (has_images && !ingest_images(slot, n_batch)) + { + LOG_TEE("failed processing images\n"); + return false; + } + + // extract the logits only for the last token + if (batch.n_tokens > 0) + { + batch.logits[batch.n_tokens - 1] = true; + } + + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; + } + } + } + + if (batch.n_tokens == 0) + { + all_slots_are_idle = true; + return true; + } + + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) + { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = + { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + + const int ret = llama_decode(ctx, batch_view); + if (ret != 0) + { + if (n_batch == 1 || ret < 0) + { + // if you get here, it means the KV cache is full - try increasing it via the context size + LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); + return false; + } + + LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); + + // retry with half the batch size to try to find a free slot in the KV cache + n_batch /= 2; + i -= n_batch; + continue; + } + + for (auto & slot : slots) + { + if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) + { + continue; + } + + // prompt evaluated for embedding + if (params.embedding) + { + send_embedding(slot); + slot.release(); + slot.i_batch = -1; + return true; + } + + completion_token_output result; + const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i); + + llama_sampling_accept(slot.ctx_sampling, ctx, id, true); + + if (slot.n_decoded == 1) + { + slot.t_start_genereration = ggml_time_us(); + slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; + } + + llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; + result.tok = id; + + const int32_t n_probs = slot.sparams.n_probs; + if (slot.sparams.temp <= 0 && n_probs > 0) + { + // for llama_sample_token_greedy we need to sort candidates + llama_sample_softmax(ctx, &cur_p); + } + + for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) + { + result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); + } + + if (!process_token(result, slot)) + { + slot.release(); + send_final_response(slot); + slot.print_timings(); + } + + slot.i_batch = -1; + } + } + return true; } }; @@ -685,16 +1766,15 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("usage: %s [options]\n", argv0); printf("\n"); 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(" -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(" --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(" --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"); + 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(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + 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(" --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()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -725,11 +1805,16 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); 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("\n"); } static void server_params_parse(int argc, char **argv, server_params &sparams, - gpt_params ¶ms) + gpt_params ¶ms, llama_server_context& llama) { gpt_params default_params; server_params default_sparams; @@ -839,15 +1924,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads = std::stoi(argv[i]); } - else if (arg == "--threads-batch" || arg == "-tb") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -984,6 +2060,56 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, { params.embedding = true; } + else if (arg == "-cb" || arg == "--cont-batching") + { + params.cont_batching = true; + } + else if (arg == "-np" || arg == "--parallel") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_parallel = std::stoi(argv[i]); + } else if (arg == "-n" || arg == "--n-predict") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_predict = std::stoi(argv[i]); + } else if (arg == "-spf" || arg == "--system-prompt-file") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + break; + } + std::string systm_content; + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(systm_content) + ); + llama.process_system_prompt_data(json::parse(systm_content)); + } + else if(arg == "--mmproj") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.mmproj = argv[i]; + } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); @@ -1000,102 +2126,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } -static json format_generation_settings(llama_server_context &llama) -{ - const auto & sparams = llama.params.sparams; - const auto eos_bias = sparams.logit_bias.find(llama_token_eos(llama.ctx)); - const bool ignore_eos = eos_bias != sparams.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); - - return json{ - {"n_ctx", llama.n_ctx}, - {"model", llama.params.model_alias}, - {"seed", llama.params.seed}, - {"temp", sparams.temp}, - {"top_k", sparams.top_k}, - {"top_p", sparams.top_p}, - {"tfs_z", sparams.tfs_z}, - {"typical_p", sparams.typical_p}, - {"repeat_last_n", sparams.penalty_last_n}, - {"repeat_penalty", sparams.penalty_repeat}, - {"frequency_penalty", sparams.penalty_freq}, - {"presence_penalty", sparams.penalty_present}, - {"mirostat", sparams.mirostat}, - {"mirostat_tau", sparams.mirostat_tau}, - {"mirostat_eta", sparams.mirostat_eta}, - {"penalize_nl", sparams.penalize_nl}, - {"stop", llama.params.antiprompt}, - {"n_predict", llama.params.n_predict}, - {"n_keep", llama.params.n_keep}, - {"ignore_eos", ignore_eos}, - {"stream", llama.stream}, - {"logit_bias", sparams.logit_bias}, - {"n_probs", sparams.n_probs}, - {"grammar", llama.params.sparams.grammar}, - }; -} - -static json format_embedding_response(llama_server_context &llama) -{ - return json{ - {"embedding", llama.getEmbedding()}, - }; -} - -static json format_timings(llama_server_context &llama) -{ - const auto timings = llama_get_timings(llama.ctx); - - return json{ - {"prompt_n", timings.n_p_eval}, - {"prompt_ms", timings.t_p_eval_ms}, - {"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval}, - {"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval}, - - {"predicted_n", timings.n_eval}, - {"predicted_ms", timings.t_eval_ms}, - {"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval}, - {"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval}, - }; -} - -static json format_final_response(llama_server_context &llama, const std::string &content, const std::vector &probs) -{ - - json res = json{ - {"content", content}, - {"stop", true}, - {"model", llama.params.model_alias}, - {"tokens_predicted", llama.num_tokens_predicted}, - {"tokens_evaluated", llama.num_prompt_tokens}, - {"generation_settings", format_generation_settings(llama)}, - {"prompt", llama.prompt}, - {"truncated", llama.truncated}, - {"stopped_eos", llama.stopped_eos}, - {"stopped_word", llama.stopped_word}, - {"stopped_limit", llama.stopped_limit}, - {"stopping_word", llama.stopping_word}, - {"tokens_cached", llama.n_past}, - {"timings", format_timings(llama)}, - }; - - if (llama.params.sparams.n_probs > 0) - { - res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); - } - - return res; -} - static json format_partial_response( - llama_server_context &llama, const std::string &content, const std::vector &probs + llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector &probs ) { - json res = json{ - {"content", content}, - {"stop", false}, + json res = json + { + {"content", content }, + {"stop", false}, + {"slot_id", slot->id }, + {"multimodal", llama.multimodal } }; - if (llama.params.sparams.n_probs > 0) + if (slot->sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1115,120 +2157,8 @@ static json format_detokenized_response(std::string content) {"content", content}}; } -template -static T json_value(const json &body, const std::string &key, const T &default_value) -{ - // Fallback null to default value - return body.contains(key) && !body.at(key).is_null() - ? body.value(key, default_value) - : default_value; -} -static void parse_options_completion(const json &body, llama_server_context &llama) -{ - gpt_params default_params; - const auto & default_sparams = default_params.sparams; - - auto & params = llama.params; - auto & sparams = llama.params.sparams; - - llama.stream = json_value(body, "stream", false); - params.n_predict = json_value(body, "n_predict", default_params.n_predict); - sparams.top_k = json_value(body, "top_k", default_sparams.top_k); - sparams.top_p = json_value(body, "top_p", default_sparams.top_p); - sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); - sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); - sparams.temp = json_value(body, "temperature", default_sparams.temp); - sparams.penalty_last_n = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); - sparams.penalty_repeat = json_value(body, "repeat_penalty", default_sparams.penalty_repeat); - sparams.penalty_freq = json_value(body, "frequency_penalty", default_sparams.penalty_freq); - sparams.penalty_present = json_value(body, "presence_penalty", default_sparams.penalty_present); - sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); - sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - params.n_keep = json_value(body, "n_keep", default_params.n_keep); - params.seed = json_value(body, "seed", default_params.seed); - sparams.grammar = json_value(body, "grammar", default_sparams.grammar); - sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); - - if (body.count("prompt") != 0) - { - llama.prompt = body["prompt"]; - } - else - { - llama.prompt = ""; - } - - sparams.logit_bias.clear(); - if (json_value(body, "ignore_eos", false)) - { - sparams.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; - } - - const auto &logit_bias = body.find("logit_bias"); - if (logit_bias != body.end() && logit_bias->is_array()) - { - const int n_vocab = llama_n_vocab(llama.model); - for (const auto &el : *logit_bias) - { - if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) - { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) - { - if (el[1].is_number()) - { - sparams.logit_bias[tok] = el[1].get(); - } - else if (el[1].is_boolean() && !el[1].get()) - { - sparams.logit_bias[tok] = -INFINITY; - } - } - } - } - } - - llama.params.antiprompt.clear(); - const auto &stop = body.find("stop"); - if (stop != body.end() && stop->is_array()) - { - for (const auto &word : *stop) - { - if (!word.empty()) - { - llama.params.antiprompt.push_back(word); - } - } - } - - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); -} - -static void parse_options_infill(const json &body, llama_server_context &llama) -{ - if (body.count("input_prefix") != 0) - { - llama.params.input_prefix = body["input_prefix"]; - } - else - { - llama.params.input_prefix = ""; - } - if (body.count("input_suffix") != 0) - { - llama.params.input_suffix = body["input_suffix"]; - } - else - { - llama.params.input_suffix = ""; - } - parse_options_completion(body, llama); -} - -static void log_server_request(const Request &req, const Response &res) +static void log_server_request(const httplib::Request &req, const httplib::Response &res) { LOG_INFO("request", { {"remote_addr", req.remote_addr}, @@ -1245,60 +2175,26 @@ static void log_server_request(const Request &req, const Response &res) }); } -static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx); -} - -// Function matching type llama_beam_search_callback_fn_t. -// Custom callback example is called each time the beams lengths increase: -// * Show progress by printing ',' following by number of convergent beam tokens if any. -// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. -// This is also called when the stop condition is met. -// Collect tokens into std::vector response which is pointed to by callback_data. -static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { - auto & llama = *static_cast(callback_data); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - llama.generated_token_probs.resize(llama.generated_token_probs.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; - std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); - printf("%zu", n); - } - fflush(stdout); -#if 0 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams:\n"; - for (size_t i=0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["<generated_token_probs; auto translator = token_translator{llama.ctx}; auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); - if (llama.generated_text.capacity() < llama.generated_text.size() + len) { - llama.generated_text.reserve(llama.generated_text.size() + len); + if (slot->generated_text.capacity() < slot->generated_text.size() + len) + { + slot->generated_text.reserve(slot->generated_text.size() + len); } - for (const completion_token_output & cto : gtps) { - llama.generated_text += translator(cto); + for (const completion_token_output & cto : gtps) + { + slot->generated_text += translator(cto); } } @@ -1311,7 +2207,7 @@ int main(int argc, char **argv) // struct that contains llama context and inference llama_server_context llama; - server_params_parse(argc, argv, sparams, params); + server_params_parse(argc, argv, sparams, params, llama); if (params.model_alias == "unknown") { @@ -1322,6 +2218,7 @@ int main(int argc, char **argv) LOG_INFO("build info", {{"build", BUILD_NUMBER}, {"commit", BUILD_COMMIT}}); + LOG_INFO("system info", { {"n_threads", params.n_threads}, {"n_threads_batch", params.n_threads_batch}, @@ -1330,405 +2227,261 @@ int main(int argc, char **argv) }); // load the model - if (!llama.loadModel(params)) + if (!llama.load_model(params)) { return 1; } - Server svr; + llama.initialize(); + + httplib::Server svr; 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 Request &, Response &res) + svr.Get("/", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); - return false; }); + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); + return false; + }); // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const Request &, Response &res) + svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); - return false; }); + res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); + return false; + }); // this is only called if no index.html is found in the public --path - svr.Get("/completion.js", [](const Request &, Response &res) + svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); - return false; }); + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); + return false; + }); // this is only called if no index.html is found in the public --path - svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res) + 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"); - return false; }); + res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); + return false; + }); - svr.Post("/completion", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); + svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) + { + res.set_header("Access-Control-Allow-Origin", "*"); + json data = { + { "user_name", llama.name_user.c_str() }, + { "assistant_name", llama.name_assistant.c_str() } + }; + res.set_content(data.dump(), "application/json"); + }); - llama.rewind(); - - llama_reset_timings(llama.ctx); - parse_options_completion(json::parse(req.body), llama); - - llama.initSampling(); - llama.loadPrompt(); - llama.beginCompletion(); - - if (!llama.stream) { - if (llama.params.n_beams) { - // Fill llama.generated_token_probs vector with final beam. - llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, - llama.n_past, llama.n_remain); - // Translate llama.generated_token_probs to llama.generated_text. - append_to_generated_text_from_generated_token_probs(llama); - } else { - size_t stop_pos = std::string::npos; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok); - - stop_pos = llama.findStoppingStrings(llama.generated_text, - token_text.size(), STOP_FULL); - } - - if (stop_pos == std::string::npos) { - stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL); - } - if (stop_pos != std::string::npos) { - llama.generated_text.erase(llama.generated_text.begin() + stop_pos, - llama.generated_text.end()); - } - } - - auto probs = llama.generated_token_probs; - if (llama.params.sparams.n_probs > 0 && llama.stopped_word) { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); - probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); - } - - const json data = format_final_response(llama, llama.generated_text, probs); - - llama_print_timings(llama.ctx); - - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), - "application/json"); - } else { - const auto chunked_content_provider = [&](size_t, DataSink & sink) { - size_t sent_count = 0; - size_t sent_token_probs_index = 0; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - continue; + 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); + if (!json_value(data, "stream", false)) { + 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"); } - const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); - } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); + else + { + res.status = 404; + res.set_content(result.result_json["content"], "text/plain"); + return; } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector probs_output = {}; - - if (llama.params.sparams.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); - } - sent_token_probs_index = probs_stop_pos; - } - - const json data = format_partial_response(llama, to_send, probs_output); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } - } - - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, - "", - std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - ); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } - } - } - - llama_print_timings(llama.ctx); - sink.done(); - return true; - }; - const auto on_complete = [&](bool) { - llama.mutex.unlock(); - }; - lock.release(); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } }); - - svr.Post("/infill", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - llama.rewind(); - - llama_reset_timings(llama.ctx); - parse_options_infill(json::parse(req.body), llama); - - llama.initSampling(); - llama.loadInfill(); - llama.beginCompletion(); - const auto chunked_content_provider = [&](size_t, DataSink & sink) { - size_t sent_count = 0; - size_t sent_token_probs_index = 0; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - continue; - } - const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); - } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector probs_output = {}; - - if (llama.params.sparams.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) + { + while (true) + { + task_result result = llama.next_result(task_id); + if (!result.error) { + const std::string str = + "data: " + + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.c_str(), str.size())) + { + return false; + } + if(result.stop) { + break; + } + } else { + break; + } } - sent_token_probs_index = probs_stop_pos; - } + sink.done(); + return true; + }; - const json data = format_partial_response(llama, to_send, probs_output); + auto on_complete = [task_id, &llama] (bool) + { + // cancel + llama.request_cancel(task_id); + }; - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } + }); - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, - "", - std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - ); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } - } - } - - llama_print_timings(llama.ctx); - sink.done(); - return true; - }; - const auto on_complete = [&](bool) { - llama.mutex.unlock(); - }; - lock.release(); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - }); - - svr.Get("/model.json", [&llama](const Request &, Response &res) + svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) { - const json data = format_generation_settings(llama); - return res.set_content(data.dump(), "application/json"); }); + json data = json::parse(req.body); + const int task_id = llama.request_completion(data, true); + if (!json_value(data, "stream", false)) { + 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"); + } + else + { + res.status = 404; + res.set_content(result.result_json["content"], "text/plain"); + return; + } + } else { + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { + while (true) + { + task_result result = llama.next_result(task_id); + if (!result.error) { + const std::string str = + "data: " + + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.c_str(), str.size())) + { + return false; + } + if (result.stop) + { + break; + } + } + else + { + break; + } + } - svr.Options(R"(/.*)", [](const Request &, Response &res) + sink.done(); + + return true; + }; + + auto on_complete = [task_id, &llama] (bool) + { + // cancel + llama.request_cancel(task_id); + }; + + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); + + 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"); + }); + + svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json"); }); - svr.Post("/tokenize", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); + svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) + { + const json body = json::parse(req.body); + std::vector tokens; + if (body.count("content") != 0) + { + tokens = llama.tokenize(body["content"], false); + } + const json data = format_tokenizer_response(tokens); + return res.set_content(data.dump(), "application/json"); + }); - const json body = json::parse(req.body); - std::vector tokens; - if (body.count("content") != 0) - { - tokens = llama.tokenize(body["content"], false); - } - const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json"); }); + svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) + { + const json body = json::parse(req.body); + std::string content; + if (body.count("tokens") != 0) + { + const std::vector tokens = body["tokens"]; + content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); + } - svr.Post("/detokenize", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); + const json data = format_detokenized_response(content); + return res.set_content(data.dump(), "application/json"); + }); - const json body = json::parse(req.body); - std::string content; - if (body.count("tokens") != 0) - { - const std::vector tokens = body["tokens"]; - content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); - } - - const json data = format_detokenized_response(content); - return res.set_content(data.dump(), "application/json"); }); - - svr.Post("/embedding", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - const json body = json::parse(req.body); - - llama.rewind(); - - llama_reset_timings(llama.ctx); - - if (body.count("content") != 0) - { - llama.prompt = body["content"]; - } - else - { - llama.prompt = ""; - } - llama.params.n_predict = 0; - - llama.initSampling(); - llama.loadPrompt(); - llama.beginCompletion(); - llama.doCompletion(); - - const json data = format_embedding_response(llama); - return res.set_content(data.dump(), "application/json"); }); + svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) + { + const json body = json::parse(req.body); + json prompt; + if (body.count("content") != 0) + { + prompt = body["content"]; + } + else + { + prompt = ""; + } + const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false); + task_result result = llama.next_result(task_id); + return res.set_content(result.result_json.dump(), "application/json"); + }); svr.set_logger(log_server_request); - svr.set_exception_handler([](const Request &, 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"); - res.status = 500; }); + 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"); + res.status = 500; + }); - svr.set_error_handler([](const Request &, Response &res) - { - if (res.status == 400) { - res.set_content("Invalid request", "text/plain"); - } else if (res.status != 500) { - res.set_content("File Not Found", "text/plain"); - res.status = 404; - } }); + svr.set_error_handler([](const httplib::Request &, httplib::Response &res) + { + if (res.status == 400) + { + res.set_content("Invalid request", "text/plain"); + } + else if (res.status != 500) + { + res.set_content("File Not Found", "text/plain"); + res.status = 404; + } + }); // set timeouts and change hostname and port - svr.set_read_timeout(sparams.read_timeout); + svr.set_read_timeout (sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); if (!svr.bind_to_port(sparams.hostname, sparams.port)) @@ -1741,20 +2494,38 @@ int main(int argc, char **argv) svr.set_base_dir(sparams.public_path); // to make it ctrl+clickable: - printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); + 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}, }); - if (!svr.listen_after_bind()) + // 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([&]() { - return 1; + bool running = true; + while (running) + { + running = llama.update_slots(); + } } + //); + + t.join(); - llama_sampling_free(llama.ctx_sampling); llama_backend_free(); - return 0; } From 96981f37b1e3f450d9e63e571514217bf60f0a7f Mon Sep 17 00:00:00 2001 From: Alex Date: Sun, 22 Oct 2023 15:56:53 -0400 Subject: [PATCH 36/95] make : add optional CUDA_NATIVE_ARCH (#2482) Use the environment variable `CUDA_NATIVE_ARCH` if present to set NVCC arch. Otherwise, use `native`. --- Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 80179631f..705c4acb4 100644 --- a/Makefile +++ b/Makefile @@ -391,9 +391,12 @@ else endif #LLAMA_CUDA_NVCC ifdef CUDA_DOCKER_ARCH NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) +endif # CUDA_DOCKER_ARCH +ifdef CUDA_NATIVE_ARCH + NVCCFLAGS += -arch=$(CUDA_NATIVE_ARCH) else NVCCFLAGS += -arch=native -endif # CUDA_DOCKER_ARCH +endif # CUDA_NATIVE_ARCH ifdef LLAMA_CUDA_FORCE_DMMV NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV endif # LLAMA_CUDA_FORCE_DMMV From 6336701c9378c23c85d1c0e464b663ca2bbb8e60 Mon Sep 17 00:00:00 2001 From: Galunid Date: Mon, 23 Oct 2023 17:47:03 +0200 Subject: [PATCH 37/95] Fix baichuan convert script not detecing model (#3739) It seems nobody objects. --- convert-baichuan-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 3b64ecb88..5ee99be73 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -110,7 +110,7 @@ print("gguf: loading model "+dir_model.name) with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) print("hello print: ",hparams["architectures"][0]) -if hparams["architectures"][0] != "BaichuanForCausalLM": +if hparams["architectures"][0] != "BaichuanForCausalLM" and hparams["architectures"][0] != "BaiChuanForCausalLM": print("Model architecture not supported: " + hparams["architectures"][0]) sys.exit() From 5be6c803fa5378f62a1590f3ad8c6b64c7c0c2ce Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Mon, 23 Oct 2023 12:40:03 -0700 Subject: [PATCH 38/95] llama : remove token functions with `context` args in favor of `model` (#3720) * added `llama_model_token_*` variants to all the `llama_token_*` functions. * added `LLAMA_API` * formatting Co-authored-by: Georgi Gerganov * removed old `llama_token` functions * changed 3 more functions to take in model - `llama_token_get_text` - `llama_token_get_score` - `llama_token_get_type` * added back docs * fixed main.cpp * changed token functions to use new model variants * changed token functions to use new model variants --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 8 ++--- common/sampling.cpp | 4 +-- common/train.cpp | 6 ++-- examples/batched/batched.cpp | 2 +- examples/beam-search/beam-search.cpp | 2 +- examples/infill/infill.cpp | 30 ++++++++--------- examples/llama-bench/llama-bench.cpp | 4 +-- examples/llava/llava-utils.h | 2 +- examples/main/main.cpp | 8 ++--- examples/parallel/parallel.cpp | 2 +- examples/perplexity/perplexity.cpp | 4 +-- examples/server/server.cpp | 14 ++++---- examples/simple/simple.cpp | 2 +- examples/speculative/speculative.cpp | 2 +- llama.cpp | 49 ++++++++++++++-------------- llama.h | 21 ++++++------ 16 files changed, 81 insertions(+), 79 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index bbd1518ca..44bb76618 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -880,13 +880,13 @@ std::tuple llama_init_from_gpt_par } if (params.ignore_eos) { - params.sparams.logit_bias[llama_token_eos(lctx)] = -INFINITY; + params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; } { LOG("warming up the model with an empty run\n"); - std::vector tmp = { llama_token_bos(lctx), llama_token_eos(lctx), }; + std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); llama_kv_cache_tokens_rm(lctx, -1, -1); llama_reset_timings(lctx); @@ -941,7 +941,7 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t } std::string llama_detokenize_spm(llama_context * ctx, const std::vector & tokens) { - const llama_token bos_id = llama_token_bos(ctx); + const llama_token bos_id = llama_token_bos(llama_get_model(ctx)); std::string piece; std::string result; @@ -1186,7 +1186,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); - const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(lctx)); + const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); diff --git a/common/sampling.cpp b/common/sampling.cpp index 6f0af3c4a..5258d4e82 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -147,7 +147,7 @@ llama_token llama_sampling_sample( // apply penalties if (!prev.empty()) { - const float nl_logit = logits[llama_token_nl(ctx_main)]; + 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, @@ -155,7 +155,7 @@ llama_token llama_sampling_sample( if (!penalize_nl) { for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(ctx_main)) { + if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) { cur_p.data[idx].logit = nl_logit; break; } diff --git a/common/train.cpp b/common/train.cpp index 154ca56e5..3cce5da26 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -236,8 +236,8 @@ int64_t get_example_targets_batch( int64_t used_samples = 0; ggml_set_f32(target_probs, 0.0f); - llama_token bos = llama_token_bos(lctx); - llama_token eos = llama_token_eos(lctx); + llama_token bos = llama_token_bos(llama_get_model(lctx)); + llama_token eos = llama_token_eos(llama_get_model(lctx)); // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples); for (int k=0; k mark the stream as finished - if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) { + if (new_token_id == llama_token_eos(model) || n_cur == n_len) { i_batch[i] = -1; LOG_TEE("\n"); if (n_parallel > 1) { diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp index f078ab8a8..679b382e1 100644 --- a/examples/beam-search/beam-search.cpp +++ b/examples/beam-search/beam-search.cpp @@ -47,7 +47,7 @@ struct beam_search_callback_data { // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same. // For example, eob can be flagged due to maximum token length, stop words, etc. static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx); + return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx)); } // Function matching type llama_beam_search_callback_fn_t. diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 6331335e3..9c52b7bba 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -246,14 +246,14 @@ int main(int argc, char ** argv) { if (suff_rm_leading_spc && inp_sfx[0] == space_token) { inp_sfx.erase(inp_sfx.begin()); } - inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(ctx)); + inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); if (add_bos) { - inp_pfx.insert(inp_pfx.begin(), llama_token_bos(ctx)); + inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model)); } - inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(ctx)); + inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); embd_inp = inp_pfx; embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - embd_inp.push_back(llama_token_middle(ctx)); + embd_inp.push_back(llama_token_middle(model)); LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix)); LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix)); @@ -261,7 +261,7 @@ int main(int argc, char ** argv) { // Should not run without any tokens if (embd_inp.empty()) { - embd_inp.push_back(llama_token_bos(ctx)); + embd_inp.push_back(llama_token_bos(model)); LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); } @@ -577,10 +577,10 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed) { // deal with eot token in infill mode - if ((llama_sampling_last(ctx_sampling) == llama_token_eot(ctx) || is_interacting) && params.interactive){ + if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){ if(is_interacting && !params.interactive_first) { // print an eot token - printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str()); + printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); } fflush(stdout); printf("\n"); @@ -627,14 +627,14 @@ int main(int argc, char ** argv) { if (suff_rm_leading_spc && inp_sfx[0] == space_token) { inp_sfx.erase(inp_sfx.begin()); } - inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(ctx)); + inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); if (add_bos) { - inp_pfx.insert(inp_pfx.begin(), llama_token_bos(ctx)); + inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model)); } - inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(ctx)); + inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); embd_inp = inp_pfx; embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - embd_inp.push_back(llama_token_middle(ctx)); + embd_inp.push_back(llama_token_middle(model)); embd.clear(); embd_guidance.clear(); n_remain = params.n_predict; @@ -644,7 +644,7 @@ int main(int argc, char ** argv) { is_interacting = false; } // deal with end of text token in interactive mode - else if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) { + else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) { LOG("found EOS token\n"); if (params.interactive) { @@ -661,7 +661,7 @@ int main(int argc, char ** argv) { if (params.input_prefix_bos) { LOG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_token_bos(ctx)); + embd_inp.push_back(llama_token_bos(model)); } std::string buffer; @@ -724,7 +724,7 @@ int main(int argc, char ** argv) { } // end of text token - if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !params.interactive) { + if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) { break; } @@ -736,7 +736,7 @@ int main(int argc, char ** argv) { } } if (!params.interactive && n_remain <= 0) { - printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str()); + printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); fflush(stdout); } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index a04115c96..20767d555 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -933,7 +933,7 @@ struct sql_printer : public printer { }; static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { - std::vector tokens(n_batch, llama_token_bos(ctx)); + std::vector tokens(n_batch, llama_token_bos(llama_get_model(ctx))); int n_processed = 0; llama_set_n_threads(ctx, n_threads, n_threads); @@ -946,7 +946,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat } static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { - llama_token token = llama_token_bos(ctx); + llama_token token = llama_token_bos(llama_get_model(ctx)); llama_set_n_threads(ctx, n_threads, n_threads); diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h index 45b2b1ad3..320c71967 100644 --- a/examples/llava/llava-utils.h +++ b/examples/llava/llava-utils.h @@ -137,7 +137,7 @@ inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { 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(ctx_llama)) { + if (id == llama_token_eos(llama_get_model(ctx_llama))) { ret = ""; } else { ret = llama_token_to_piece(ctx_llama, id); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 2621bd539..3d9f670b9 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -248,7 +248,7 @@ int main(int argc, char ** argv) { // Should not run without any tokens if (embd_inp.empty()) { - embd_inp.push_back(llama_token_bos(ctx)); + embd_inp.push_back(llama_token_bos(model)); LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); } @@ -693,7 +693,7 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) { + if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) { LOG("found EOS token\n"); if (params.interactive) { @@ -720,7 +720,7 @@ int main(int argc, char ** argv) { if (params.input_prefix_bos) { LOG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_token_bos(ctx)); + embd_inp.push_back(llama_token_bos(model)); } std::string buffer; @@ -804,7 +804,7 @@ int main(int argc, char ** argv) { } // end of text token - if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !(params.instruct || params.interactive)) { + if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive)) { LOG_TEE(" [end of text]\n"); break; } diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index eb64adef8..9a0b9c183 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -347,7 +347,7 @@ int main(int argc, char ** argv) { // client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str()); if (client.n_decoded > 2 && - (id == llama_token_eos(ctx) || + (id == llama_token_eos(model) || (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) || client.response.find("User:") != std::string::npos || client.response.find('\n') != std::string::npos)) { diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 7d0038bd4..3c2542e8c 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -227,7 +227,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(ctx); + tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } const auto batch_logits = llama_get_logits(ctx); @@ -350,7 +350,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(ctx); + 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))) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c3279dbc9..693f9b773 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -726,7 +726,7 @@ struct llama_server_context if (json_value(data, "ignore_eos", false)) { - slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY; + slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY; } const auto &logit_bias = data.find("logit_bias"); @@ -1056,7 +1056,7 @@ struct llama_server_context slot.has_next_token = false; } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) { slot.stopped_eos = true; slot.has_next_token = false; @@ -1130,7 +1130,7 @@ struct llama_server_context json get_formated_generation(llama_client_slot &slot) { - const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx)); + const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); return json { @@ -1555,11 +1555,11 @@ struct llama_server_context suffix_tokens.erase(suffix_tokens.begin()); } - prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); + 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.push_back(llama_token_middle(ctx)); + prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } else diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 55385f566..f376c0509 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -138,7 +138,7 @@ int main(int argc, char ** argv) { 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(ctx) || n_cur == n_len) { + if (new_token_id == llama_token_eos(model) || n_cur == n_len) { LOG_TEE("\n"); break; diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 894321ce9..92ad27e8e 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -163,7 +163,7 @@ int main(int argc, char ** argv) { printf("%s", token_str.c_str()); fflush(stdout); - if (id == llama_token_eos(ctx_tgt)) { + if (id == llama_token_eos(model_tgt)) { has_eos = true; } diff --git a/llama.cpp b/llama.cpp index c63e6251c..61f30c398 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7493,7 +7493,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c } } - const llama_token eos = llama_token_eos(ctx); + const llama_token eos = llama_token_eos(&ctx->model); std::vector, llama_partial_utf8>> candidates_decoded; std::vector candidates_grammar; @@ -7703,7 +7703,7 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) { const int64_t t_start_sample_us = ggml_time_us(); - if (token == llama_token_eos(ctx)) { + if (token == llama_token_eos(&ctx->model)) { for (const auto & stack : grammar->stacks) { if (stack.empty()) { return; @@ -8912,7 +8912,7 @@ struct llama_context * llama_new_context_with_model( // build worst-case graph int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch); int n_past = cparams.n_ctx - n_tokens; - llama_token token = llama_token_bos(ctx); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph + 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 @@ -9673,43 +9673,44 @@ float * llama_get_embeddings(struct llama_context * ctx) { return ctx->embedding.data(); } -const char * llama_token_get_text(const struct llama_context * ctx, llama_token token) { - return ctx->model.vocab.id_to_token[token].text.c_str(); +const char * llama_token_get_text(const struct llama_model * model, llama_token token) { + return model->vocab.id_to_token[token].text.c_str(); } -float llama_token_get_score(const struct llama_context * ctx, llama_token token) { - return ctx->model.vocab.id_to_token[token].score; +float llama_token_get_score(const struct llama_model * model, llama_token token) { + return model->vocab.id_to_token[token].score; } -llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token) { - return ctx->model.vocab.id_to_token[token].type; +llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) { + return model->vocab.id_to_token[token].type; } -llama_token llama_token_bos(const struct llama_context * ctx) { - return ctx->model.vocab.special_bos_id; +llama_token llama_token_bos(const struct llama_model * model) { + return model->vocab.special_bos_id; } -llama_token llama_token_eos(const struct llama_context * ctx) { - return ctx->model.vocab.special_eos_id; +llama_token llama_token_eos(const struct llama_model * model) { + return model->vocab.special_eos_id; } -llama_token llama_token_nl(const struct llama_context * ctx) { - return ctx->model.vocab.linefeed_id; -} -llama_token llama_token_prefix(const struct llama_context * ctx) { - return ctx->model.vocab.special_prefix_id; +llama_token llama_token_nl(const struct llama_model * model) { + return model->vocab.linefeed_id; } -llama_token llama_token_middle(const struct llama_context * ctx) { - return ctx->model.vocab.special_middle_id; +llama_token llama_token_prefix(const struct llama_model * model) { + return model->vocab.special_prefix_id; } -llama_token llama_token_suffix(const struct llama_context * ctx) { - return ctx->model.vocab.special_suffix_id; +llama_token llama_token_middle(const struct llama_model * model) { + return model->vocab.special_middle_id; } -llama_token llama_token_eot(const struct llama_context * ctx) { - return ctx->model.vocab.special_eot_id; +llama_token llama_token_suffix(const struct llama_model * model) { + return model->vocab.special_suffix_id; +} + +llama_token llama_token_eot(const struct llama_model * model) { + return model->vocab.special_eot_id; } int llama_tokenize( diff --git a/llama.h b/llama.h index 306f5b383..2f2fee0e2 100644 --- a/llama.h +++ b/llama.h @@ -494,21 +494,22 @@ extern "C" { // Vocab // - LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token); + LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token); - LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token); + LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token); - LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token); + LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token); // Special tokens - LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx); // beginning-of-sentence - LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx); // end-of-sentence - LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx); // next-line + LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence + LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence + LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line + // codellama infill tokens - LLAMA_API llama_token llama_token_prefix(const struct llama_context * ctx); // Beginning of infill prefix - LLAMA_API llama_token llama_token_middle(const struct llama_context * ctx); // Beginning of infill middle - LLAMA_API llama_token llama_token_suffix(const struct llama_context * ctx); // Beginning of infill suffix - LLAMA_API llama_token llama_token_eot (const struct llama_context * ctx); // End of infill middle + LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix + LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle + LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix + LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle // // Tokenization From 69a6735087c3634963c642fd69f0851ac479cd78 Mon Sep 17 00:00:00 2001 From: Galunid Date: Mon, 23 Oct 2023 21:46:00 +0200 Subject: [PATCH 39/95] Update special token handling in conversion scripts for gpt2 derived tokenizers (#3746) We still have the heads up in `README.md` regarding `bpe` tokenizers and this patch is needed for - a couple of tokenizer tests - some more `special` and `non-special` added tokens handling (as far as I understand it) * Update special token handling * Add mpt --- convert-bloom-hf-to-gguf.py | 17 +++++++++++++---- convert-gptneox-hf-to-gguf.py | 17 +++++++++++++---- convert-mpt-hf-to-gguf.py | 6 ++++-- convert-refact-hf-to-gguf.py | 17 +++++++++++++---- convert-starcoder-hf-to-gguf.py | 18 +++++++++++++----- 5 files changed, 56 insertions(+), 19 deletions(-) diff --git a/convert-bloom-hf-to-gguf.py b/convert-bloom-hf-to-gguf.py index 14dbd793c..6e866d943 100755 --- a/convert-bloom-hf-to-gguf.py +++ b/convert-bloom-hf-to-gguf.py @@ -118,15 +118,24 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model) 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): - tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) + 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_scores(scores) gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index f1599b0c4..02d1fdf16 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -123,15 +123,24 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model) 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): - tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) + 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_scores(scores) gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py index 2d2fa2329..70d154b3f 100755 --- a/convert-mpt-hf-to-gguf.py +++ b/convert-mpt-hf-to-gguf.py @@ -136,9 +136,11 @@ for i in range(vocab_size): tokens.append(f"[PAD{i}]") toktypes.append(gguf.TokenType.USER_DEFINED) elif reverse_vocab[i] in added_vocab: - # NOTE: wouldn't we like to distinguish CONTROL tokens here? tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.USER_DEFINED) + 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) diff --git a/convert-refact-hf-to-gguf.py b/convert-refact-hf-to-gguf.py index 934f3852b..f0cfe84d8 100755 --- a/convert-refact-hf-to-gguf.py +++ b/convert-refact-hf-to-gguf.py @@ -139,15 +139,24 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model) 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): - tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) + 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_scores(scores) gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) diff --git a/convert-starcoder-hf-to-gguf.py b/convert-starcoder-hf-to-gguf.py index fe8815cbf..a9bfed85e 100755 --- a/convert-starcoder-hf-to-gguf.py +++ b/convert-starcoder-hf-to-gguf.py @@ -111,17 +111,25 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model) 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): - tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) + 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_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) From 9d02956443e5c1ded29b7b5ed8a21bc01ba6f563 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Mon, 23 Oct 2023 22:57:16 +0300 Subject: [PATCH 40/95] issues : separate bug and enhancement template + no default title (#3748) --- .github/ISSUE_TEMPLATE/{custom.md => bug.md} | 9 +++---- .github/ISSUE_TEMPLATE/enhancement.md | 28 ++++++++++++++++++++ 2 files changed, 32 insertions(+), 5 deletions(-) rename .github/ISSUE_TEMPLATE/{custom.md => bug.md} (96%) create mode 100644 .github/ISSUE_TEMPLATE/enhancement.md diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/bug.md similarity index 96% rename from .github/ISSUE_TEMPLATE/custom.md rename to .github/ISSUE_TEMPLATE/bug.md index 8fd955356..d7879b232 100644 --- a/.github/ISSUE_TEMPLATE/custom.md +++ b/.github/ISSUE_TEMPLATE/bug.md @@ -1,8 +1,7 @@ --- -name: Issue and enhancement template -about: Used to report issues and request enhancements for llama.cpp -title: "[User] Insert summary of your issue or enhancement.." -labels: '' +name: Bug template +about: Used to report bugs in llama.cpp +labels: ["bug"] assignees: '' --- @@ -46,7 +45,7 @@ $ g++ --version # Failure Information (for bugs) -Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template. +Please help provide information about the failure / bug. # Steps to Reproduce diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md new file mode 100644 index 000000000..dcffda750 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/enhancement.md @@ -0,0 +1,28 @@ +--- +name: Enhancement template +about: Used to request enhancements for llama.cpp +labels: ["enhancement"] +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. + +# Feature Description + +Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. + +# Motivation + +Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. + +# Possible Implementation + +If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. From e3932593d46c30145301a13097895f9376cba509 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 23 Oct 2023 23:46:05 +0300 Subject: [PATCH 41/95] Revert "make : add optional CUDA_NATIVE_ARCH (#2482)" This reverts commit 96981f37b1e3f450d9e63e571514217bf60f0a7f. See: https://github.com/ggerganov/llama.cpp/pull/2482#issuecomment-1775975866 --- Makefile | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 705c4acb4..80179631f 100644 --- a/Makefile +++ b/Makefile @@ -391,12 +391,9 @@ else endif #LLAMA_CUDA_NVCC ifdef CUDA_DOCKER_ARCH NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) -endif # CUDA_DOCKER_ARCH -ifdef CUDA_NATIVE_ARCH - NVCCFLAGS += -arch=$(CUDA_NATIVE_ARCH) else NVCCFLAGS += -arch=native -endif # CUDA_NATIVE_ARCH +endif # CUDA_DOCKER_ARCH ifdef LLAMA_CUDA_FORCE_DMMV NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV endif # LLAMA_CUDA_FORCE_DMMV From 469c9addef75893e6be12edda852d12e840bf064 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 24 Oct 2023 09:46:50 +0300 Subject: [PATCH 42/95] metal : handle ggml_scale for n%4 != 0 (close #3754) ggml-ci --- ggml-metal.m | 18 +++++++++++++----- ggml-metal.metal | 10 +++++++++- 2 files changed, 22 insertions(+), 6 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index c908106be..c1901dca7 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -62,6 +62,7 @@ struct ggml_metal_context { 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(scale); + GGML_METAL_DECL_KERNEL(scale_4); GGML_METAL_DECL_KERNEL(silu); GGML_METAL_DECL_KERNEL(relu); GGML_METAL_DECL_KERNEL(gelu); @@ -249,6 +250,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(mul); GGML_METAL_ADD_KERNEL(mul_row); GGML_METAL_ADD_KERNEL(scale); + GGML_METAL_ADD_KERNEL(scale_4); GGML_METAL_ADD_KERNEL(silu); GGML_METAL_ADD_KERNEL(relu); GGML_METAL_ADD_KERNEL(gelu); @@ -347,6 +349,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(mul); GGML_METAL_DEL_KERNEL(mul_row); GGML_METAL_DEL_KERNEL(scale); + GGML_METAL_DEL_KERNEL(scale_4); GGML_METAL_DEL_KERNEL(silu); GGML_METAL_DEL_KERNEL(relu); GGML_METAL_DEL_KERNEL(gelu); @@ -923,15 +926,20 @@ void ggml_metal_graph_compute( const float scale = *(const float *) src1->data; - [encoder setComputePipelineState:ctx->pipeline_scale]; + int64_t n = ggml_nelements(dst); + + if (n % 4 == 0) { + n /= 4; + [encoder setComputePipelineState:ctx->pipeline_scale_4]; + } else { + [encoder setComputePipelineState:ctx->pipeline_scale]; + } + [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]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { diff --git a/ggml-metal.metal b/ggml-metal.metal index 69fc71362..f4b460564 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -125,9 +125,17 @@ kernel void kernel_mul_row( } kernel void kernel_scale( + device const float * src0, + device float * dst, + constant float & scale, + uint tpig[[thread_position_in_grid]]) { + dst[tpig] = src0[tpig] * scale; +} + +kernel void kernel_scale_4( device const float4 * src0, device float4 * dst, - constant float & scale, + constant float & scale, uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] * scale; } From daab3d7f45832e10773c99f3484b0d5b14d86c0c Mon Sep 17 00:00:00 2001 From: Galunid Date: Tue, 24 Oct 2023 09:17:17 +0200 Subject: [PATCH 43/95] Add more tokenizer tests (#3742) * Add more tokenizer tests * Add starcoder * Update test vocab files * Restrict bpe tokenizer tests to unicode planes * Update comment * Comment cosmetics * Remove bloom vocab/test --- models/ggml-vocab-baichuan.gguf | Bin 0 -> 1340998 bytes models/ggml-vocab-gpt-neox.gguf | Bin 0 -> 1771431 bytes models/ggml-vocab-refact.gguf 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b/tests/CMakeLists.txt index 1c73de0a3..6757ad1cc 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -28,10 +28,14 @@ llama_build_executable(test-tokenizer-0-falcon.cpp) llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) llama_build_executable(test-tokenizer-1-llama.cpp) llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) +llama_test_executable(test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf) llama_build_executable(test-tokenizer-1-bpe.cpp) llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf) llama_test_executable(test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) +llama_test_executable(test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf) +llama_test_executable(test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) +llama_test_executable(test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) llama_build_and_test_executable(test-grammar-parser.cpp) llama_build_and_test_executable(test-llama-grammar.cpp) llama_build_and_test_executable(test-grad0.cpp) # SLOW diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index 85a59a14d..386530f23 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -91,9 +91,19 @@ int main(int argc, char **argv) { } } } - // TODO: why doesn't this work for the full range of Unicodes? + // Restrict to assigned unicode planes // for (uint32_t cp = 0x10000; cp < 0x0010ffff; ++cp) { - for (uint32_t cp = 0x10000; cp < 0x00080000; ++cp) { + for (uint32_t cp = 0x10000; cp < 0x00040000; ++cp) { + std::string str = codepoint_to_utf8(cp); + std::vector tokens = llama_tokenize(ctx, str, false); + std::string check = llama_detokenize_bpe(ctx, tokens); + if (str != check) { + fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", + __func__, cp, check.c_str(), check.length(), str.c_str(), str.length()); + return 4; + } + } + for (uint32_t cp = 0x000e0000; cp < 0x0010ffff; ++cp) { std::string str = codepoint_to_utf8(cp); std::vector tokens = llama_tokenize(ctx, str, false); std::string check = llama_detokenize_bpe(ctx, tokens); @@ -103,7 +113,6 @@ int main(int argc, char **argv) { return 4; } } - llama_free_model(model); llama_free(ctx); From 2b4ea35e56792064598e922e46d081e02bc96b94 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 24 Oct 2023 16:48:37 +0300 Subject: [PATCH 44/95] cuda : add batched cuBLAS GEMM for faster attention (#3749) * cmake : add helper for faster CUDA builds * batched : add NGL arg * ggml : skip nops in compute_forward * cuda : minor indentation * cuda : batched cuBLAS GEMMs for src0 F16 and src1 F32 (attention ops) * Apply suggestions from code review These changes plus: ```c++ #define cublasGemmBatchedEx hipblasGemmBatchedEx ``` are needed to compile with ROCM. I haven't done performance testing, but it seems to work. I couldn't figure out how to propose a change for lines outside what the pull changed, also this is the first time trying to create a multi-part review so please forgive me if I mess something up. * cuda : add ROCm / hipBLAS cublasGemmBatchedEx define * cuda : add cublasGemmStridedBatchedEx for non-broadcasted cases * cuda : reduce mallocs in cublasGemmBatchedEx branch * cuda : add TODO for calling cublas from kernel + using mem pool --------- Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --- CMakeLists.txt | 1 + examples/batched/batched.cpp | 11 +- ggml-cuda.cu | 190 +++++++++++++++++++++++++++++++++-- ggml.c | 4 + 4 files changed, 193 insertions(+), 13 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 6af42a6c2..202f26049 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -331,6 +331,7 @@ if (LLAMA_CUBLAS) set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics else() set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics + #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work endif() endif() message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 75856a81f..22a4265df 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -11,7 +11,7 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN]\n" , argv[0]); + printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]); return 1 ; } @@ -21,6 +21,9 @@ int main(int argc, char ** argv) { // total length of the sequences including the prompt int n_len = 32; + // number of layers to offload to the GPU + int n_gpu_layers = 0; + if (argc >= 2) { params.model = argv[1]; } @@ -37,6 +40,10 @@ int main(int argc, char ** argv) { n_len = std::atoi(argv[4]); } + if (argc >= 6) { + n_gpu_layers = std::atoi(argv[5]); + } + if (params.prompt.empty()) { params.prompt = "Hello my name is"; } @@ -49,7 +56,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); - // model_params.n_gpu_layers = 99; // offload all layers to the GPU + model_params.n_gpu_layers = n_gpu_layers; llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 654d3632f..db053e3b8 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -29,6 +29,8 @@ #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width) #define cublasCreate hipblasCreate #define cublasGemmEx hipblasGemmEx +#define cublasGemmBatchedEx hipblasGemmBatchedEx +#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx #define cublasHandle_t hipblasHandle_t #define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS #define cublasSetStream hipblasSetStream @@ -4326,13 +4328,13 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous const half * x = (const half *) vx; - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; + const int row_x = blockDim.y*blockIdx.y + threadIdx.y; + const int channel = blockDim.z*blockIdx.z + threadIdx.z; const int channel_x = channel / channel_x_divisor; - const int nrows_y = ncols_x; + const int nrows_y = ncols_x; const int nrows_dst = nrows_x; - const int row_dst = row_x; + const int row_dst = row_x; const int idst = channel*nrows_dst + row_dst; @@ -4345,13 +4347,13 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous break; } - const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; - const float xi = __half2float(x[ix]); - const int row_y = col_x; + const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; const int iy = channel*nrows_y + row_y; + const float xi = __half2float(x[ix]); + tmp += xi * y[iy]; } @@ -7013,7 +7015,8 @@ static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tens } static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ - GGML_ASSERT(!ggml_is_contiguous(src0) && ggml_is_contiguous(src1)); + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -7023,11 +7026,11 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; - const int64_t ne12 = src1->ne[2]; - const int64_t nb01 = src0->nb[1]; const int64_t nb02 = src0->nb[2]; + const int64_t ne12 = src1->ne[2]; + CUDA_CHECK(ggml_cuda_set_device(g_main_device)); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; @@ -7046,6 +7049,159 @@ 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); } +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)); + 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]; + + 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); + + CUDA_CHECK(ggml_cuda_set_device(g_main_device)); + cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + + int id; + CUDA_CHECK(cudaGetDevice(&id)); + CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], main_stream)); + + 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; + + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + 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); + + 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); + + size_t dst_as = 0; + half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); + + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + // broadcast factors + 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 + { + for (int i13 = 0; i13 < ne13; ++i13) { + for (int i12 = 0; i12 < ne12; ++i12) { + int i03 = i13 / r3; + int i02 = i12 / r2; + + CUBLAS_CHECK( + cublasGemmEx(g_cublas_handles[id], 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, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + } + } + } +#else + if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + // there is no broadcast and src0, src1 are contiguous across dims 2, 3 + // use cublasGemmStridedBatchedEx + CUBLAS_CHECK( + cublasGemmStridedBatchedEx(g_cublas_handles[id], 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 + ne12*ne13, + CUBLAS_COMPUTE_16F, + 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 *))); + + // 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); + + 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, + 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); + } +#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); +} + static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { bool all_on_device = (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && src1->backend == GGML_BACKEND_GPU && dst->backend == GGML_BACKEND_GPU; @@ -7058,10 +7214,22 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } } + // 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]); + //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); + //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); + //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 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + // KQ ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (all_on_device && !ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->ne[1] == 1) { + } else if (all_on_device && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + // KQV 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) && src1->ne[2]*src1->ne[3] > 1) { + 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) { diff --git a/ggml.c b/ggml.c index 49f3b7aba..17f0ce487 100644 --- a/ggml.c +++ b/ggml.c @@ -16602,6 +16602,10 @@ static void ggml_compute_forward_cross_entropy_loss_back( static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { GGML_ASSERT(params); + if (tensor->op == GGML_OP_NONE) { + return; + } + #ifdef GGML_USE_CUBLAS bool skip_cpu = ggml_cuda_compute_forward(params, tensor); if (skip_cpu) { From abd21fc99f1d35e2081e4c01dc09c71a86bf3c5a Mon Sep 17 00:00:00 2001 From: John Smith <67539080+kingsidelee@users.noreply.github.com> Date: Wed, 25 Oct 2023 01:48:45 +0800 Subject: [PATCH 45/95] cmake : add missed dependencies (#3763) --- examples/main-cmake-pkg/CMakeLists.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index 908131884..cb00edbbb 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -16,6 +16,8 @@ add_library(common OBJECT ${_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 From b2f7e04bd312eaf97eee0523aa09d950d585626b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 24 Oct 2023 21:51:20 +0300 Subject: [PATCH 46/95] sync : ggml (conv ops + cuda MSVC fixes) (#3765) ggml-ci --- ggml-cuda.cu | 10 +- ggml.c | 438 +++++++++++++++++++++++++++++++++++++++++---------- ggml.h | 15 +- 3 files changed, 369 insertions(+), 94 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index db053e3b8..d1e874b6c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5664,10 +5664,10 @@ void ggml_init_cublas() { GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES); int64_t total_vram = 0; fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count); - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %ld: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor); + fprintf(stderr, " Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor); g_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; @@ -5677,15 +5677,15 @@ void ggml_init_cublas() { g_compute_capabilities[id] = 100*prop.major + 10*prop.minor; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) } - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { g_tensor_split[id] /= total_vram; } - for (int64_t id = 0; id < g_device_count; ++id) { + for (int id = 0; id < g_device_count; ++id) { CUDA_CHECK(ggml_cuda_set_device(id)); // create cuda streams - for (int64_t is = 0; is < MAX_STREAMS; ++is) { + for (int is = 0; is < MAX_STREAMS; ++is) { CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking)); } diff --git a/ggml.c b/ggml.c index 17f0ce487..6f66bab05 100644 --- a/ggml.c +++ b/ggml.c @@ -571,7 +571,6 @@ int64_t ggml_cycles_per_ms(void) { #define ggml_perf_cycles_per_ms() 0 #endif - // // cache line // @@ -1828,7 +1827,6 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { return type_traits[type]; } - // // simd mappings // @@ -4057,16 +4055,17 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "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", "CONV_TRANSPOSE_2D", "POOL_1D", "POOL_2D", "UPSCALE", - "CONV_1D_STAGE_0", - "CONV_1D_STAGE_1", - "FLASH_ATTN", "FLASH_FF", "FLASH_ATTN_BACK", @@ -4092,7 +4091,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71"); +static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -4143,16 +4142,17 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "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)", "conv_transpose_2d(x)", "pool_1d(x)", "pool_2d(x)", "upscale(x)", - "conv_1d_stage_0(x)", - "conv_1d_stage_1(x)", - "flash_attn(x)", "flash_ff(x)", "flash_attn_back(x)", @@ -4178,7 +4178,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71"); +static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -4209,8 +4209,10 @@ static void ggml_setup_op_has_task_pass(void) { 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_2D ] = 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; @@ -5954,7 +5956,6 @@ struct ggml_tensor * ggml_sqrt_inplace( return ggml_sqrt_impl(ctx, a, true); } - // ggml_log static struct ggml_tensor * ggml_log_impl( @@ -6008,7 +6009,6 @@ struct ggml_tensor * ggml_sum( return result; } - // ggml_sum_rows struct ggml_tensor * ggml_sum_rows( @@ -6640,7 +6640,6 @@ struct ggml_tensor * ggml_set_2d_inplace( return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false); } - // ggml_cpy static struct ggml_tensor * ggml_cpy_impl( @@ -6720,7 +6719,6 @@ struct ggml_tensor * ggml_cont_inplace( return ggml_cont_impl(ctx, a, true); } - // make contiguous, with new shape GGML_API struct ggml_tensor * ggml_cont_1d( struct ggml_context * ctx, @@ -7173,7 +7171,6 @@ struct ggml_tensor * ggml_diag( return result; } - // ggml_diag_mask_inf static struct ggml_tensor * ggml_diag_mask_inf_impl( @@ -7285,7 +7282,6 @@ struct ggml_tensor * ggml_soft_max_inplace( return ggml_soft_max_impl(ctx, a, true); } - // ggml_soft_max_back static struct ggml_tensor * ggml_soft_max_back_impl( @@ -7702,7 +7698,11 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d( // ggml_conv_2d -struct ggml_tensor * ggml_conv_2d( +// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] +// 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_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -7721,17 +7721,21 @@ struct ggml_tensor * ggml_conv_2d( 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 ne[4] = { - ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), - ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1), - a->ne[3], b->ne[3], + a->ne[2] * a->ne[1] * a->ne[0], + OW, + OH, + b->ne[3], }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_CONV_2D; + result->op = GGML_OP_CONV_2D_STAGE_0; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; @@ -7740,8 +7744,61 @@ struct ggml_tensor * ggml_conv_2d( } -// ggml_conv_2d_sk_p0 +// 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_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); + + return result; + +} + +// ggml_conv_2d_sk_p0 struct ggml_tensor * ggml_conv_2d_sk_p0( struct ggml_context * ctx, struct ggml_tensor * a, @@ -8180,7 +8237,6 @@ static struct ggml_tensor * ggml_add_rel_pos_impl( return result; } - struct ggml_tensor * ggml_add_rel_pos( struct ggml_context * ctx, struct ggml_tensor * a, @@ -8625,8 +8681,6 @@ struct ggml_tensor * ggml_map_custom3_inplace( return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true); } - - // ggml_cross_entropy_loss struct ggml_tensor * ggml_cross_entropy_loss( @@ -9828,7 +9882,6 @@ static void ggml_compute_forward_add1( } } - // ggml_compute_forward_acc static void ggml_compute_forward_acc_f32( @@ -9968,7 +10021,6 @@ static void ggml_compute_forward_sub_f32( const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - #ifdef GGML_USE_ACCELERATE vDSP_vsub( (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, @@ -10149,7 +10201,6 @@ static void ggml_compute_forward_div_f32( const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_div_f32); @@ -10287,7 +10338,6 @@ static void ggml_compute_forward_sqrt( } } - // ggml_compute_forward_log static void ggml_compute_forward_log_f32( @@ -12120,7 +12170,6 @@ static void ggml_compute_forward_out_prod_f32( } } - //int64_t t1 = ggml_perf_time_us(); //static int64_t acc = 0; //acc += t1 - t0; @@ -12316,7 +12365,6 @@ static void ggml_compute_forward_scale_f32( const size_t nb1 = dst->nb[1]; - for (int i1 = ir0; i1 < ir1; i1++) { if (dst->data != src0->data) { // src0 is same shape as dst => same indices @@ -12714,7 +12762,6 @@ static void ggml_compute_forward_get_rows_back_f32( } } - static void ggml_compute_forward_get_rows_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -13997,6 +14044,7 @@ static void ggml_compute_forward_conv_1d_f32( } } +// 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, @@ -14298,6 +14346,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32( } } + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + return; } @@ -14370,7 +14421,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32( const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i01*ne00*ne02; for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i01*ne00*ne02 + i00*ne02 + i02] = src[i00]; + dst_data[i00*ne02 + i02] = src[i00]; } } } @@ -14389,6 +14440,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32( } } + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + return; } @@ -14450,6 +14504,144 @@ 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( + 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 = 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 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]; + + 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, IH, IW] => [N, OH, OW, IC*KH*KW] + { + 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 iow = 0; iow < OW; iow++) { + 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] + + for (int64_t ikh = 0; ikh < KH; ikh++) { + 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] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + +// 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, @@ -14462,16 +14654,40 @@ static void ggml_compute_forward_conv_2d_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - GGML_TENSOR_BINARY_OP_LOCALS; + 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; + // 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; + // 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]; @@ -14487,24 +14703,27 @@ static void ggml_compute_forward_conv_2d_f16_f32( 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 i13 = 0; i13 < ne13; i13++) { - for (int i12 = 0; i12 < ne12; i12++) { - const float * const src = (float *)((char *) src1->data + i13*nb13 + i12*nb12); - ggml_fp16_t * dst_data = wdata + i13*(ne1*ne0*ew0); + 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++) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - for (int ik1 = 0; ik1 < nk1; ik1++) { - for (int ik0 = 0; ik0 < nk0; ik0++) { - const int idx0 = i0*s0 + ik0*d0 - p0; - const int idx1 = i1*s1 + ik1*d1 - p1; + // 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] - if (!(idx1 < 0 || idx1 >= ne11 || idx0 < 0 || idx0 >= ne10)) { - dst_data[(i1*ne0 + i0)*ew0 + i12*(nk0*nk1) + ik1*nk0 + ik0] = - GGML_FP32_TO_FP16(src[idx1*ne10 + idx0]); + 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]); } } } @@ -14521,30 +14740,22 @@ static void ggml_compute_forward_conv_2d_f16_f32( return; } - // total patches in dst - const int np = ne2; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - 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] - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ip0; i2 < ip1; i2++) { - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2); + int64_t m = OC; + int64_t n = OH * OW; + int64_t k = IC * KH * KW; - for (int i1 = 0; i1 < ne1; ++i1) { - for (int i0 = 0; i0 < ne0; ++i0) { - ggml_vec_dot_f16(ew0, dst_data + i1*ne0 + i0, - (ggml_fp16_t *) ((char *) src0->data + i2*nb03), - (ggml_fp16_t *) wdata + i3*nb3 + (i1*ne0 + i0)*ew0); - } - } - } + // [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); } } @@ -14570,6 +14781,48 @@ static void ggml_compute_forward_conv_2d( } } +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); + } break; + case GGML_TYPE_F32: + { + GGML_ASSERT(false); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_conv_transpose_2d static void ggml_compute_forward_conv_transpose_2d( @@ -14628,6 +14881,8 @@ static void ggml_compute_forward_conv_transpose_2d( } } + memset(dst->data, 0, ggml_nbytes(dst)); + return; } @@ -16126,7 +16381,6 @@ static void ggml_compute_forward_add_rel_pos_f32( const int ip0 = dp*ith; const int ip1 = MIN(ip0 + dp, np); - for (int64_t i13 = ip0; i13 < ip1; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = 0; i11 < ne11; ++i11) { @@ -16193,7 +16447,6 @@ static void ggml_compute_forward_map_unary_f32( } } - static void ggml_compute_forward_map_unary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -16241,7 +16494,6 @@ static void ggml_compute_forward_map_binary_f32( } } - static void ggml_compute_forward_map_binary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -16293,7 +16545,6 @@ static void ggml_compute_forward_map_custom2_f32( fun(dst, a, b); } - // ggml_compute_forward_map_custom3 static void ggml_compute_forward_map_custom3_f32( @@ -16568,7 +16819,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( ggml_vec_sub_f32(nc, ds0, ds0, s1); ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr); - #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(ds0[i])); @@ -16596,7 +16846,6 @@ static void ggml_compute_forward_cross_entropy_loss_back( } } - ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { @@ -16808,6 +17057,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { 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); + } break; case GGML_OP_CONV_TRANSPOSE_2D: { ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor); @@ -17737,11 +17994,19 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { 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_TRANSPOSE_1D: + case GGML_OP_CONV_2D_STAGE_0: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_CONV_2D_STAGE_1: { GGML_ASSERT(false); // TODO: not implemented } break; @@ -18670,6 +18935,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { 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; @@ -18680,7 +18946,8 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0); + // 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); @@ -18690,6 +18957,14 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { 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; @@ -19878,7 +20153,6 @@ static enum ggml_opt_result ggml_opt_adam( opt->loss_after = fx; - // check convergence if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { GGML_PRINT_DEBUG("converged\n"); diff --git a/ggml.h b/ggml.h index 16aaf169e..08bff5511 100644 --- a/ggml.h +++ b/ggml.h @@ -401,15 +401,16 @@ extern "C" { GGML_OP_ALIBI, GGML_OP_CLAMP, GGML_OP_CONV_1D, - GGML_OP_CONV_2D, + 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_CONV_TRANSPOSE_2D, GGML_OP_POOL_1D, GGML_OP_POOL_2D, - GGML_OP_CONV_1D_STAGE_0, // internal - GGML_OP_CONV_1D_STAGE_1, // internal - GGML_OP_UPSCALE, // nearest interpolate GGML_OP_FLASH_ATTN, @@ -1020,9 +1021,9 @@ extern "C" { struct ggml_tensor * b, float eps); - // A: n columns, m rows - // B: n columns, p rows (i.e. we transpose it internally) - // result is m columns, p rows + // A: k columns, n rows => [ne03, ne02, n, k] + // B: k columns, m rows (i.e. we transpose it internally) => [ne03 * x, ne02 * y, m, k] + // result is n columns, m rows => [ne03 * x, ne02 * y, m, n] GGML_API struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, From 1717521cdb976a2219888b0e5cba36e210eee9df Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 24 Oct 2023 23:08:20 +0300 Subject: [PATCH 47/95] server : do not block system prompt update (#3767) * server : do not block system prompt update * server : update state machine logic to process system prompts * server : minor --- examples/server/server.cpp | 57 +++++++++++++------------------------- 1 file changed, 20 insertions(+), 37 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 693f9b773..f52a928c8 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -454,7 +454,7 @@ struct llama_client_slot } void release() { - if (state == PROCESSING) + if (state == IDLE || state == PROCESSING) { t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; command = RELEASE; @@ -754,6 +754,7 @@ struct llama_server_context } slot->params.antiprompt.clear(); + const auto &stop = data.find("stop"); if (stop != data.end() && stop->is_array()) { @@ -867,7 +868,7 @@ struct llama_server_context kv_cache_clear(); - for (int32_t i = 0; i < batch.n_tokens; ++i) + for (int i = 0; i < (int) system_tokens.size(); ++i) { llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } @@ -894,16 +895,8 @@ struct llama_server_context { slot.release(); } - wait_all_are_idle(); - all_slots_are_idle = true; - // wait until system prompt load system_need_update = true; - while (system_need_update) - { - std::this_thread::sleep_for(std::chrono::milliseconds(5)); - } - // system prompt loaded, continue } void process_system_prompt_data(const json &sys_props) { @@ -915,26 +908,6 @@ struct llama_server_context { notify_system_prompt_changed(); } - else - { - system_need_update = true; - } - } - - void wait_all_are_idle() { - bool wait = true; - while (wait) - { - wait = false; - for (auto &slot : slots) - { - if (!slot.available()) - { - wait = true; - break; - } - } - } } static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, @@ -965,7 +938,6 @@ struct llama_server_context slot.has_next_token = false; } stop_pos = pos; - } } @@ -1444,7 +1416,7 @@ struct llama_server_context process_tasks(); // update the system prompt wait until all slots are idle state - if (system_need_update) + if (system_need_update && all_slots_are_idle) { LOG_TEE("updating system prompt\n"); update_system_prompt(); @@ -1498,7 +1470,7 @@ struct llama_server_context for (auto & slot : slots) { // release the slot - if (slot.state == PROCESSING && slot.command == RELEASE) + if (slot.command == RELEASE) { slot.state = IDLE; slot.command = NONE; @@ -1509,7 +1481,7 @@ struct llama_server_context continue; } - if (slot.state == IDLE || slot.command == RELEASE) + if (slot.state == IDLE) { continue; } @@ -1530,6 +1502,17 @@ struct llama_server_context { for (auto & slot : slots) { + const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()); + + // empty prompt passed -> release the slot and send empty response + if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt) + { + slot.release(); + slot.print_timings(); + send_final_response(slot); + continue; + } + // need process the prompt if (slot.state == IDLE && slot.command == LOAD_PROMPT) { @@ -1749,8 +1732,8 @@ struct llama_server_context if (!process_token(result, slot)) { slot.release(); - send_final_response(slot); slot.print_timings(); + send_final_response(slot); } slot.i_batch = -1; @@ -2285,7 +2268,7 @@ int main(int argc, char **argv) if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); - if(!result.error && result.stop) { + if (!result.error && result.stop) { res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else @@ -2312,7 +2295,7 @@ int main(int argc, char **argv) { return false; } - if(result.stop) { + if (result.stop) { break; } } else { From ad939626577cd25b462e8026cc543efb71528472 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Tue, 24 Oct 2023 16:10:43 -0400 Subject: [PATCH 48/95] server : add parameter -tb N, --threads-batch N (#3584) (#3768) Co-authored-by: Michael Coppola Co-authored-by: Michael Coppola --- examples/server/server.cpp | 28 +++++++++++++++++++--------- 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f52a928c8..b4c4d0a20 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1749,15 +1749,16 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("usage: %s [options]\n", argv0); printf("\n"); 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(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - 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(" --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"); + 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(" -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(" --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(" --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()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -1907,6 +1908,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads = std::stoi(argv[i]); } + else if (arg == "--threads-batch" || arg == "-tb") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_threads_batch = std::stoi(argv[i]); + } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) From cc448774866e6479c750bd7c135cd8f92cedee67 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 25 Oct 2023 10:09:16 +0300 Subject: [PATCH 49/95] log : disable pid in log filenames --- common/log.h | 35 ++++++++++++++++++----------------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git a/common/log.h b/common/log.h index 70e7e4ca2..d2c864cea 100644 --- a/common/log.h +++ b/common/log.h @@ -97,22 +97,23 @@ #define LOG_TEE_TARGET stderr #endif +// NOTE: currently disabled as it produces too many log files // 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" @@ -126,8 +127,8 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base std::stringstream buf; buf << log_file_basename; - buf << "."; - buf << log_get_pid(); + //buf << "."; + //buf << log_get_pid(); buf << "."; buf << log_file_extension; From 6961c4bd0b5176e10ab03b35394f1e9eab761792 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 25 Oct 2023 10:26:27 +0300 Subject: [PATCH 50/95] batched-bench : print params at start --- examples/batched-bench/batched-bench.cpp | 4 ++++ ggml-cuda.cu | 12 ++++++------ 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index c552eaa73..43f9c971d 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -154,6 +154,10 @@ int main(int argc, char ** argv) { } } + LOG_TEE("\n"); + LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq); + LOG_TEE("\n"); + LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------"); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d1e874b6c..ba0cd5a7d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6254,16 +6254,15 @@ inline void ggml_cuda_op_mul_mat_cublas( 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) { - GGML_ASSERT(src0_dd_i != nullptr); + GGML_ASSERT(src0_dd_i != nullptr); GGML_ASSERT(src1_ddf_i != nullptr); - GGML_ASSERT(dst_dd_i != nullptr); - + GGML_ASSERT(dst_dd_i != nullptr); const int64_t ne00 = src0->ne[0]; - const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; + const int64_t row_diff = row_high - row_low; int id; @@ -7223,12 +7222,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 //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 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // KQ + // KQ single-batch ggml_cuda_mul_mat_vec_p021(src0, src1, dst); } else if (all_on_device && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // KQV + // 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) && 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) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); From 34b2a5e1ee4fe6295fb4420eb91131d743694c65 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 26 Oct 2023 22:53:37 +0300 Subject: [PATCH 51/95] server : do not release slot on image input (#3798) --- 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 b4c4d0a20..5b7e4139d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1502,7 +1502,7 @@ struct llama_server_context { for (auto & slot : slots) { - const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()); + 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) From 2f9ec7e271220a78fe27c9e6ccbcc0dda31cda0f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 27 Oct 2023 17:01:23 +0300 Subject: [PATCH 52/95] cuda : improve text-generation and batched decoding performance (#3776) * cuda : prints wip * cuda : new cublas gemm branch for multi-batch quantized src0 * cuda : add F32 sgemm branch * cuda : fine-tune >= VOLTA params + use MMQ only for small batches * cuda : remove duplicated cuBLAS GEMM code * cuda : add CUDA_USE_TENSOR_CORES and GGML_CUDA_FORCE_MMQ macros * build : add compile option to force use of MMQ kernels --- CMakeLists.txt | 7 +++ Makefile | 3 ++ ggml-cuda.cu | 130 +++++++++++++++++++++++++++++++++++++++++++------ llama.cpp | 2 - llama.h | 2 +- 5 files changed, 125 insertions(+), 19 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 202f26049..d9fc86237 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -82,6 +82,7 @@ set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUBLAS "llama: use CUDA" OFF) #option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF) option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF) +option(LLAMA_CUDA_FORCE_MMQ "llama: use mmq kernels instead of cuBLAS" OFF) set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels") set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels") option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some calculations" OFF) @@ -305,6 +306,9 @@ if (LLAMA_CUBLAS) if (LLAMA_CUDA_FORCE_DMMV) add_compile_definitions(GGML_CUDA_FORCE_DMMV) endif() + if (LLAMA_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) if (DEFINED LLAMA_CUDA_DMMV_Y) @@ -405,6 +409,9 @@ if (LLAMA_HIPBLAS) if (LLAMA_CUDA_FORCE_DMMV) target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV) endif() + if (LLAMA_CUDA_FORCE_MMQ) + target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_MMQ) + endif() target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) diff --git a/Makefile b/Makefile index 80179631f..68069f9ff 100644 --- a/Makefile +++ b/Makefile @@ -397,6 +397,9 @@ endif # CUDA_DOCKER_ARCH ifdef LLAMA_CUDA_FORCE_DMMV NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV endif # LLAMA_CUDA_FORCE_DMMV +ifdef LLAMA_CUDA_FORCE_MMQ + NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # LLAMA_CUDA_FORCE_MMQ ifdef LLAMA_CUDA_DMMV_X NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) else diff --git a/ggml-cuda.cu b/ggml-cuda.cu index ba0cd5a7d..1ba951f68 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -87,6 +87,24 @@ #define CC_OFFSET_AMD 1000000 #define CC_RDNA2 (CC_OFFSET_AMD + 1030) +// 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 @@ -470,7 +488,6 @@ 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}; -static bool g_mul_mat_q = true; static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default @@ -3554,9 +3571,15 @@ static __device__ __forceinline__ void mul_mat_q( #define MMQ_X_Q4_0_RDNA1 64 #define MMQ_Y_Q4_0_RDNA1 64 #define NWARPS_Q4_0_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q4_0_AMPERE 4 +#define MMQ_Y_Q4_0_AMPERE 32 +#define NWARPS_Q4_0_AMPERE 4 +#else #define MMQ_X_Q4_0_AMPERE 64 #define MMQ_Y_Q4_0_AMPERE 128 #define NWARPS_Q4_0_AMPERE 4 +#endif #define MMQ_X_Q4_0_PASCAL 64 #define MMQ_Y_Q4_0_PASCAL 64 #define NWARPS_Q4_0_PASCAL 8 @@ -3615,9 +3638,15 @@ template static __global__ void #define MMQ_X_Q4_1_RDNA1 64 #define MMQ_Y_Q4_1_RDNA1 64 #define NWARPS_Q4_1_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q4_1_AMPERE 4 +#define MMQ_Y_Q4_1_AMPERE 32 +#define NWARPS_Q4_1_AMPERE 4 +#else #define MMQ_X_Q4_1_AMPERE 64 #define MMQ_Y_Q4_1_AMPERE 128 #define NWARPS_Q4_1_AMPERE 4 +#endif #define MMQ_X_Q4_1_PASCAL 64 #define MMQ_Y_Q4_1_PASCAL 64 #define NWARPS_Q4_1_PASCAL 8 @@ -3678,9 +3707,15 @@ template static __global__ void #define MMQ_X_Q5_0_RDNA1 64 #define MMQ_Y_Q5_0_RDNA1 64 #define NWARPS_Q5_0_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q5_0_AMPERE 4 +#define MMQ_Y_Q5_0_AMPERE 32 +#define NWARPS_Q5_0_AMPERE 4 +#else #define MMQ_X_Q5_0_AMPERE 128 #define MMQ_Y_Q5_0_AMPERE 64 #define NWARPS_Q5_0_AMPERE 4 +#endif #define MMQ_X_Q5_0_PASCAL 64 #define MMQ_Y_Q5_0_PASCAL 64 #define NWARPS_Q5_0_PASCAL 8 @@ -3739,9 +3774,15 @@ template static __global__ void #define MMQ_X_Q5_1_RDNA1 64 #define MMQ_Y_Q5_1_RDNA1 64 #define NWARPS_Q5_1_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q5_1_AMPERE 4 +#define MMQ_Y_Q5_1_AMPERE 32 +#define NWARPS_Q5_1_AMPERE 4 +#else #define MMQ_X_Q5_1_AMPERE 128 #define MMQ_Y_Q5_1_AMPERE 64 #define NWARPS_Q5_1_AMPERE 4 +#endif #define MMQ_X_Q5_1_PASCAL 64 #define MMQ_Y_Q5_1_PASCAL 64 #define NWARPS_Q5_1_PASCAL 8 @@ -3800,9 +3841,15 @@ mul_mat_q5_1( #define MMQ_X_Q8_0_RDNA1 64 #define MMQ_Y_Q8_0_RDNA1 64 #define NWARPS_Q8_0_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q8_0_AMPERE 4 +#define MMQ_Y_Q8_0_AMPERE 32 +#define NWARPS_Q8_0_AMPERE 4 +#else #define MMQ_X_Q8_0_AMPERE 128 #define MMQ_Y_Q8_0_AMPERE 64 #define NWARPS_Q8_0_AMPERE 4 +#endif #define MMQ_X_Q8_0_PASCAL 64 #define MMQ_Y_Q8_0_PASCAL 64 #define NWARPS_Q8_0_PASCAL 8 @@ -3861,9 +3908,15 @@ template static __global__ void #define MMQ_X_Q2_K_RDNA1 128 #define MMQ_Y_Q2_K_RDNA1 32 #define NWARPS_Q2_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q2_K_AMPERE 4 +#define MMQ_Y_Q2_K_AMPERE 32 +#define NWARPS_Q2_K_AMPERE 4 +#else #define MMQ_X_Q2_K_AMPERE 64 #define MMQ_Y_Q2_K_AMPERE 128 #define NWARPS_Q2_K_AMPERE 4 +#endif #define MMQ_X_Q2_K_PASCAL 64 #define MMQ_Y_Q2_K_PASCAL 64 #define NWARPS_Q2_K_PASCAL 8 @@ -3922,9 +3975,15 @@ mul_mat_q2_K( #define MMQ_X_Q3_K_RDNA1 32 #define MMQ_Y_Q3_K_RDNA1 128 #define NWARPS_Q3_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q3_K_AMPERE 4 +#define MMQ_Y_Q3_K_AMPERE 32 +#define NWARPS_Q3_K_AMPERE 4 +#else #define MMQ_X_Q3_K_AMPERE 128 #define MMQ_Y_Q3_K_AMPERE 128 #define NWARPS_Q3_K_AMPERE 4 +#endif #define MMQ_X_Q3_K_PASCAL 64 #define MMQ_Y_Q3_K_PASCAL 64 #define NWARPS_Q3_K_PASCAL 8 @@ -3985,9 +4044,15 @@ template static __global__ void #define MMQ_X_Q4_K_RDNA1 32 #define MMQ_Y_Q4_K_RDNA1 64 #define NWARPS_Q4_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q4_K_AMPERE 4 +#define MMQ_Y_Q4_K_AMPERE 32 +#define NWARPS_Q4_K_AMPERE 4 +#else #define MMQ_X_Q4_K_AMPERE 64 #define MMQ_Y_Q4_K_AMPERE 128 #define NWARPS_Q4_K_AMPERE 4 +#endif #define MMQ_X_Q4_K_PASCAL 64 #define MMQ_Y_Q4_K_PASCAL 64 #define NWARPS_Q4_K_PASCAL 8 @@ -4048,9 +4113,15 @@ template static __global__ void #define MMQ_X_Q5_K_RDNA1 32 #define MMQ_Y_Q5_K_RDNA1 64 #define NWARPS_Q5_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q5_K_AMPERE 4 +#define MMQ_Y_Q5_K_AMPERE 32 +#define NWARPS_Q5_K_AMPERE 4 +#else #define MMQ_X_Q5_K_AMPERE 64 #define MMQ_Y_Q5_K_AMPERE 128 #define NWARPS_Q5_K_AMPERE 4 +#endif #define MMQ_X_Q5_K_PASCAL 64 #define MMQ_Y_Q5_K_PASCAL 64 #define NWARPS_Q5_K_PASCAL 8 @@ -4109,9 +4180,15 @@ mul_mat_q5_K( #define MMQ_X_Q6_K_RDNA1 32 #define MMQ_Y_Q6_K_RDNA1 64 #define NWARPS_Q6_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q6_K_AMPERE 4 +#define MMQ_Y_Q6_K_AMPERE 32 +#define NWARPS_Q6_K_AMPERE 4 +#else #define MMQ_X_Q6_K_AMPERE 64 #define MMQ_Y_Q6_K_AMPERE 64 #define NWARPS_Q6_K_AMPERE 4 +#endif #define MMQ_X_Q6_K_PASCAL 64 #define MMQ_Y_Q6_K_PASCAL 64 #define NWARPS_Q6_K_PASCAL 8 @@ -5663,6 +5740,16 @@ void ggml_init_cublas() { CUDA_CHECK(cudaGetDeviceCount(&g_device_count)); GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES); int64_t total_vram = 0; +#if defined(GGML_CUDA_FORCE_MMQ) + fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); +#endif +#if defined(CUDA_USE_TENSOR_CORES) + fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); +#else + fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__); +#endif fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count); for (int id = 0; id < g_device_count; ++id) { cudaDeviceProp prop; @@ -6347,7 +6434,7 @@ inline void ggml_cuda_op_mul_mat_cublas( 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, + src1_ddf_i, ne10, &beta, dst_dd_i, ldc)); if (src0_as != 0) { @@ -7048,9 +7135,10 @@ 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); } -static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ +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)); + GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -7202,17 +7290,24 @@ 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) { - bool all_on_device = (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && - src1->backend == GGML_BACKEND_GPU && dst->backend == GGML_BACKEND_GPU; + const bool all_on_device = + (src0->backend == GGML_BACKEND_GPU) && + (src1->backend == GGML_BACKEND_GPU) && + ( dst->backend == GGML_BACKEND_GPU); 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)) { + 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]; } } +#ifdef CUDA_USE_TENSOR_CORES + const bool use_tensor_cores = true; +#else + const bool use_tensor_cores = false; +#endif + // 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]); @@ -7221,20 +7316,19 @@ 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 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (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 && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } 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) && src1->ne[2]*src1->ne[3] > 1) { + } else if (all_on_device && 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) { 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) { - #ifdef GGML_CUDA_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else @@ -7247,7 +7341,15 @@ 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 (g_mul_mat_q && ggml_is_quantized(src0->type) && min_compute_capability >= MIN_CC_DP4A) { + 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 { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); @@ -7601,10 +7703,6 @@ void ggml_cuda_set_main_device(const int main_device) { } } -void ggml_cuda_set_mul_mat_q(const bool mul_mat_q) { - g_mul_mat_q = mul_mat_q; -} - 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 diff --git a/llama.cpp b/llama.cpp index 61f30c398..cc8669b0e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5959,8 +5959,6 @@ static int llama_decode_internal( } } - ggml_cuda_set_mul_mat_q(cparams.mul_mat_q); - // 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; diff --git a/llama.h b/llama.h index 2f2fee0e2..beac9a0ce 100644 --- a/llama.h +++ b/llama.h @@ -178,7 +178,7 @@ extern "C" { float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model // Keep the booleans together to avoid misalignment during copy-by-value. - bool mul_mat_q; // if true, use experimental mul_mat_q kernels + bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) bool f16_kv; // use fp16 for KV cache, fp32 otherwise bool logits_all; // the llama_eval() call computes all logits, not just the last one bool embedding; // embedding mode only From c8d6a1f34ab6f1b6bd468d256e535a61f98f114c Mon Sep 17 00:00:00 2001 From: Thibault Terrasson Date: Fri, 27 Oct 2023 16:37:41 +0200 Subject: [PATCH 53/95] simple : fix batch handling (#3803) --- examples/simple/simple.cpp | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index f376c0509..374aef6f1 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -95,13 +95,8 @@ int main(int argc, char ** argv) { llama_batch batch = llama_batch_init(512, 0, 1); // evaluate the initial prompt - batch.n_tokens = tokens_list.size(); - - for (int32_t i = 0; i < batch.n_tokens; i++) { - batch.token[i] = tokens_list[i]; - batch.pos[i] = i; - batch.seq_id[i] = 0; - batch.logits[i] = false; + for (size_t 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 @@ -148,15 +143,10 @@ int main(int argc, char ** argv) { fflush(stdout); // prepare the next batch - batch.n_tokens = 0; + llama_batch_clear(batch); // push this new token for next evaluation - batch.token [batch.n_tokens] = new_token_id; - batch.pos [batch.n_tokens] = n_cur; - batch.seq_id[batch.n_tokens] = 0; - batch.logits[batch.n_tokens] = true; - - batch.n_tokens += 1; + llama_batch_add(batch, new_token_id, n_cur, { 0 }, true); n_decode += 1; } From 6d459cbfbe5a011dfca94f9550527a504b6f9aa1 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Fri, 27 Oct 2023 17:33:53 -0400 Subject: [PATCH 54/95] llama : correctly report GGUFv3 format (#3818) --- llama.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index cc8669b0e..408533d8a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1578,12 +1578,14 @@ static void llama_kv_cache_seq_shift( enum llama_fver { GGUF_FILE_VERSION_V1 = 1, GGUF_FILE_VERSION_V2 = 2, + GGUF_FILE_VERSION_V3 = 3, }; static const char * llama_file_version_name(llama_fver version) { switch (version) { case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)"; - case GGUF_FILE_VERSION_V2: return "GGUF V2 (latest)"; + case GGUF_FILE_VERSION_V2: return "GGUF V2"; + case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)"; } return "unknown"; From 41aee4df821854f37d90a45281f03b6db8d27de2 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Fri, 27 Oct 2023 15:40:07 -0600 Subject: [PATCH 55/95] speculative : ensure draft and target model vocab matches (#3812) * speculative: Ensure draft and target model vocab matches * Tolerate small differences when checking dft vs tgt vocab --- examples/speculative/speculative.cpp | 33 +++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 92ad27e8e..f921b7845 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -8,6 +8,9 @@ #include #include +#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100 +#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 + struct seq_draft { bool active = false; bool drafting = false; @@ -64,6 +67,33 @@ int main(int argc, char ** argv) { params.n_gpu_layers = params.n_gpu_layers_draft; std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); + { + const int n_vocab_tgt = llama_n_vocab(model_tgt); + const int n_vocab_dft = llama_n_vocab(model_dft); + const int vocab_diff = n_vocab_tgt > n_vocab_dft + ? n_vocab_tgt - n_vocab_dft + : n_vocab_dft - n_vocab_tgt; + + if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) { + fprintf(stderr, "%s: error: draft model vocab must closely match target model to use speculation but ", __func__); + fprintf(stderr, "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n", + n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE); + return 1; + } + + for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) { + const char * token_text_tgt = llama_token_get_text(model_tgt, i); + const char * token_text_dft = llama_token_get_text(model_dft, i); + if (std::strcmp(token_text_tgt, token_text_dft) != 0) { + fprintf(stderr, "%s: error: draft model vocab must match target model to use speculation but ", __func__); + fprintf(stderr, "token %d content differs - target '%s', draft '%s'\n", i, + llama_token_to_piece(ctx_tgt, i).c_str(), + llama_token_to_piece(ctx_dft, i).c_str()); + return 1; + } + } + } + // tokenize the prompt std::vector inp; inp = ::llama_tokenize(ctx_tgt, params.prompt, true); @@ -227,6 +257,7 @@ int main(int argc, char ** argv) { llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true); llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); + // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str()); llama_decode (ctx_dft, batch_dft); ++n_past_dft; @@ -370,7 +401,7 @@ int main(int argc, char ** argv) { llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1); } - //LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt)); + // LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str()); llama_decode(ctx_tgt, batch_tgt); ++n_past_tgt; } From fdee152e4eebb78c191df0b074857111d7f2aba7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 28 Oct 2023 12:06:08 +0300 Subject: [PATCH 56/95] starcoder : add GPU offloading (#3827) * starcoder : do not GPU split 1D bias tensors * starcoder : offload layers to GPU ggml-ci --- llama.cpp | 106 +++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 85 insertions(+), 21 deletions(-) diff --git a/llama.cpp b/llama.cpp index 408533d8a..6caa58960 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2695,8 +2695,8 @@ static void llm_load_tensors( } 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_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); // output { @@ -2747,19 +2747,19 @@ 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.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.b2 = 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_split); + layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); if (backend == GGML_BACKEND_GPU) { vram_weights += @@ -4616,6 +4616,8 @@ 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; @@ -4660,6 +4662,27 @@ static struct ggml_cgraph * llm_build_starcoder( } } + 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); @@ -4685,6 +4708,7 @@ static struct ggml_cgraph * llm_build_starcoder( // 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; @@ -4708,44 +4732,67 @@ static struct ggml_cgraph * llm_build_starcoder( ggml_set_name(inpL, "inpL"); 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 + { // 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_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv); + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + offload_func_kq(cur); - 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)); + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + offload_func_kq(cur); - struct ggml_tensor * Qcur = tmpq; + 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))); + + ggml_set_name(tmpq, "tmpq"); + ggml_set_name(tmpk, "tmpk"); + ggml_set_name(tmpv, "tmpv"); + + offload_func_kq(tmpq); + offload_func_kq(tmpk); + offload_func_v (tmpv); + + struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens); struct ggml_tensor * Kcur = tmpk; { - struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens)); + 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, - ggml_cpy(ctx0, - Qcur, - ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)), - 0, 2, 1, 3); + 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 = @@ -4754,23 +4801,28 @@ static struct ggml_cgraph * llm_build_starcoder( 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 @@ -4783,22 +4835,25 @@ static struct ggml_cgraph * llm_build_starcoder( 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 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; @@ -4807,27 +4862,36 @@ static struct ggml_cgraph * llm_build_starcoder( // Norm { cur = ggml_norm(ctx0, inpFF, norm_eps); + offload_func_nr(cur); + 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); } inpL = ggml_add(ctx0, cur, inpFF); + } // 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"); } - ggml_set_name(cur, "result_norm"); cur = ggml_mul_mat(ctx0, model.output, cur); ggml_set_name(cur, "result_output"); From 177461104b454163473dced2a5038f4e016cdb7e Mon Sep 17 00:00:00 2001 From: Henk Poley Date: Sat, 28 Oct 2023 12:16:33 +0200 Subject: [PATCH 57/95] common : print that one line of the syntax help *also* to standard output (#3823) --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 44bb76618..c0d4924e2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -743,7 +743,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { #endif // GGML_USE_CUBLAS #endif printf(" --verbose-prompt print prompt before generation\n"); - fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); + printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n"); printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); From ee1a0ec9cb367ba41d138134795cbbbe93d2bf1c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 28 Oct 2023 14:23:11 +0300 Subject: [PATCH 58/95] llama : add option for greedy sampling with probs (#3813) * llama : add option for greedy sampling with probs * llama : add comment about llama_sample_token_greedy() missing probs * sampling : temp == 0.0 -> no probs, temp < 0.0 -> probs --- common/common.cpp | 1 + common/sampling.cpp | 8 ++++++-- examples/speculative/speculative.cpp | 2 +- llama.h | 1 + 4 files changed, 9 insertions(+), 3 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index c0d4924e2..f81f4d354 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -224,6 +224,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } sparams.temp = std::stof(argv[i]); + sparams.temp = std::max(sparams.temp, 0.0f); } else if (arg == "--tfs") { if (++i >= argc) { invalid_param = true; diff --git a/common/sampling.cpp b/common/sampling.cpp index 5258d4e82..c4996c985 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -167,8 +167,12 @@ llama_token llama_sampling_sample( llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); } - if (temp <= 0) { - // greedy sampling + if (temp < 0.0) { + // greedy sampling, with probs + llama_sample_softmax(ctx_main, &cur_p); + id = cur_p.data[0].id; + } else if (temp == 0.0) { + // greedy sampling, no probs id = llama_sample_token_greedy(ctx_main, &cur_p); } else { if (mirostat == 1) { diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index f921b7845..323c74652 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -148,7 +148,7 @@ int main(int argc, char ** argv) { std::vector drafts(n_seq_dft); params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar - params.sparams.temp = std::max(0.01f, params.sparams.temp); + params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model for (int s = 0; s < n_seq_dft; ++s) { drafts[s].ctx_sampling = llama_sampling_init(params.sparams); diff --git a/llama.h b/llama.h index beac9a0ce..d901dcd91 100644 --- a/llama.h +++ b/llama.h @@ -658,6 +658,7 @@ extern "C" { float * mu); /// @details Selects the token with the highest probability. + /// Does not compute the token probabilities. Use llama_sample_softmax() instead. LLAMA_API llama_token llama_sample_token_greedy( struct llama_context * ctx, llama_token_data_array * candidates); From bd6d9e205982b34e0ba2c3b22bbf31a1ef1a1bb5 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 28 Oct 2023 05:54:24 -0600 Subject: [PATCH 59/95] llama : allow quantizing k-quants to fall back when tensor size incompatible (#3747) * Allow quantizing k-quants to fall back when tensor size incompatible * quantizing: Add warning when tensors were incompatible with k-quants Clean up k-quants state passing a bit --- llama.cpp | 108 ++++++++++++++++++++++++++++++++---------------------- 1 file changed, 65 insertions(+), 43 deletions(-) diff --git a/llama.cpp b/llama.cpp index 6caa58960..3d431ee7b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8049,6 +8049,24 @@ struct no_init { no_init() { /* do nothing */ } }; +struct quantize_state_internal { + const llama_model & model; + const llama_model_quantize_params * params; +#ifdef GGML_USE_K_QUANTS + int n_attention_wv = 0; + int n_feed_forward_w2 = 0; + int i_attention_wv = 0; + int i_feed_forward_w2 = 0; + + int n_k_quantized = 0; + int n_fallback = 0; +#endif + quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params) + : model(model) + , params(params) + {} +}; + static void llama_convert_tensor_internal( struct ggml_tensor * tensor, std::vector> & output, std::vector & workers, const size_t nelements, const int nthread @@ -8109,12 +8127,13 @@ static void llama_convert_tensor_internal( #ifdef GGML_USE_K_QUANTS static ggml_type get_k_quant_type( - ggml_type new_type, const ggml_tensor * tensor, const llama_model & model, llama_ftype ftype, int * i_attention_wv, - int n_attention_wv, int * i_feed_forward_w2, int n_feed_forward_w2 + quantize_state_internal & qs, + ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype ) { const std::string name = ggml_get_name(tensor); // TODO: avoid hardcoded tensor names - use the TN_* constants - const auto tn = LLM_TN(model.arch); + const llm_arch arch = qs.model.arch; + const auto tn = LLM_TN(arch); auto use_more_bits = [](int i_layer, int num_layers) -> bool { return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2; @@ -8122,7 +8141,7 @@ static ggml_type get_k_quant_type( if (name == tn(LLM_TENSOR_OUTPUT, "weight")) { int nx = tensor->ne[0]; - if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) { + if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; } else if (new_type != GGML_TYPE_Q8_0) { @@ -8131,46 +8150,46 @@ static ggml_type get_k_quant_type( } 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) { - new_type = *i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; + new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : 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_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && - use_more_bits(*i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && *i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; + use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && - (*i_attention_wv < n_attention_wv/8 || *i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; - if (model.type == MODEL_70B) { + (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; + if (qs.model.type == MODEL_70B) { // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with // nearly negligible increase in model size by quantizing this tensor with more bits: if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K; } - ++*i_attention_wv; + ++qs.i_attention_wv; } else if (name.find("ffn_down.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) { - new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K - : model.arch != LLM_ARCH_FALCON || use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K + new_type = qs.i_feed_forward_w2 < 2 ? 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; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { - new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K; + new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { - if (model.arch == LLM_ARCH_FALCON) { - new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K : - use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; + if (arch == LLM_ARCH_FALCON) { + new_type = qs.i_feed_forward_w2 < 2 ? 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(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; + 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(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && *i_feed_forward_w2 < 4) { + 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) { new_type = GGML_TYPE_Q5_K; } - ++*i_feed_forward_w2; + ++qs.i_feed_forward_w2; } else if (name.find("attn_output.weight") != std::string::npos) { - if (model.arch != LLM_ARCH_FALCON) { + 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; @@ -8197,20 +8216,23 @@ static ggml_type get_k_quant_type( int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { - LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K); + LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type)); convert_incompatible_tensor = true; + } else { + ++qs.n_k_quantized; } } if (convert_incompatible_tensor) { - if (name == tn(LLM_TENSOR_OUTPUT, "weight")) { - new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing. - LLAMA_LOG_WARN("F16 will be used for this tensor instead.\n"); - } else if (name == tn(LLM_TENSOR_TOKEN_EMBD, "weight")) { - new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing. - LLAMA_LOG_WARN("Q4_0 will be used for this tensor instead.\n"); - } else { - throw std::runtime_error("Unsupported tensor size encountered\n"); + switch (new_type) { + 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; + case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break; + case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break; + default: throw std::runtime_error("\nUnsupported tensor size encountered\n"); } + LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type)); + ++qs.n_fallback; } return new_type; @@ -8268,6 +8290,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s llm_load_arch(ml, model); llm_load_hparams(ml, model); + struct quantize_state_internal qs(model, params); + if (params->only_copy) { ftype = model.ftype; } @@ -8281,9 +8305,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s gguf_set_val_u32(ctx_out, "general.file_type", ftype); #ifdef GGML_USE_K_QUANTS - int n_attention_wv = 0; - int n_feed_forward_w2 = 0; - for (int i = 0; i < ml.n_tensors; ++i) { struct ggml_tensor * meta = ml.get_tensor_meta(i); @@ -8291,19 +8312,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // TODO: avoid hardcoded tensor names - use the TN_* constants if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) { - ++n_attention_wv; + ++qs.n_attention_wv; } else if (name.find("ffn_down.weight") != std::string::npos) { - ++n_feed_forward_w2; + ++qs.n_feed_forward_w2; } } - if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) { + if (qs.n_attention_wv != qs.n_feed_forward_w2 || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) { LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n", - __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer); + __func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer); } - - int i_attention_wv = 0; - int i_feed_forward_w2 = 0; #endif size_t total_size_org = 0; @@ -8370,9 +8388,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (quantize) { new_type = quantized_type; #ifdef GGML_USE_K_QUANTS - new_type = get_k_quant_type( - new_type, tensor, model, ftype, &i_attention_wv, n_attention_wv, &i_feed_forward_w2, n_feed_forward_w2 - ); + new_type = get_k_quant_type(qs, new_type, tensor, ftype); #endif // If we've decided to quantize to the same type the tensor is already // in then there's nothing to do. @@ -8498,6 +8514,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_INFO("\n"); } } +#ifdef GGML_USE_K_QUANTS + if (qs.n_fallback > 0) { + LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n", + __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback); + } +#endif } static int llama_apply_lora_from_file_internal( From 8a2f2fea2914aaa3f4b2f82800c7de15f15bdb09 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 28 Oct 2023 15:25:15 +0300 Subject: [PATCH 60/95] convert : ignore tokens if their IDs are within [0, vocab_size) (#3831) --- convert.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/convert.py b/convert.py index 0680f71ea..bfbfab283 100755 --- a/convert.py +++ b/convert.py @@ -366,16 +366,19 @@ class SentencePieceVocab: added_tokens = {} vocab_size: int = self.sentencepiece_tokenizer.vocab_size() - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {vocab_size}; got {actual_ids}") - 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 + 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]]: From ba231e8a6dd8ad82acfe0e4d492ff7cef6b3f0a1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 28 Oct 2023 15:25:33 +0300 Subject: [PATCH 61/95] issues : change label from bug to bug-unconfirmed (#3748) --- .github/ISSUE_TEMPLATE/bug.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md index d7879b232..c003fe7c1 100644 --- a/.github/ISSUE_TEMPLATE/bug.md +++ b/.github/ISSUE_TEMPLATE/bug.md @@ -1,7 +1,7 @@ --- name: Bug template about: Used to report bugs in llama.cpp -labels: ["bug"] +labels: ["bug-unconfirmed"] assignees: '' --- From 82a6646e0221216c41edcdf99f5a44bb051391f5 Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Sat, 28 Oct 2023 15:43:01 +0300 Subject: [PATCH 62/95] metal : try cwd for ggml-metal.metal if bundle lookup fails (#3793) * Try cwd for ggml-metal if bundle lookup fails When building with `-DBUILD_SHARED_LIBS=ON -DLLAMA_METAL=ON -DLLAMA_BUILD_SERVER=ON`, `server` would fail to load `ggml-metal.metal` because `[bundle pathForResource:...]` returns `nil`. In that case, fall back to `ggml-metal.metal` in the cwd instead of passing `null` as a path. Follows up on #1782 * Update ggml-metal.m --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index c1901dca7..2380c4310 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -210,6 +210,10 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); NSString * 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"; + } GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]); NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error]; if (error) { From ff3bad83e29e3009010cbc923bebd769055eaa7f Mon Sep 17 00:00:00 2001 From: Erik Scholz Date: Sat, 28 Oct 2023 16:41:07 +0200 Subject: [PATCH 63/95] flake : update flake.lock for newer transformers version + provide extra dev shell (#3797) * flake : update flake.lock for newer transformers version + provide extra dev shell with torch and transformers (for most convert-xxx.py scripts) --- flake.lock | 6 +++--- flake.nix | 7 +++++++ 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index a7777d05d..070f0e161 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1692913444, - "narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=", + "lastModified": 1698134075, + "narHash": "sha256-foCD+nuKzfh49bIoiCBur4+Fx1nozo+4C/6k8BYk4sg=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "18324978d632ffc55ef1d928e81630c620f4f447", + "rev": "8efd5d1e283604f75a808a20e6cde0ef313d07d4", "type": "github" }, "original": { diff --git a/flake.nix b/flake.nix index cfc4776a4..fa34394b2 100644 --- a/flake.nix +++ b/flake.nix @@ -51,6 +51,9 @@ }; 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\";" @@ -126,5 +129,9 @@ buildInputs = [ llama-python ]; packages = nativeBuildInputs ++ osSpecific; }; + devShells.extra = pkgs.mkShell { + buildInputs = [ llama-python-extra ]; + packages = nativeBuildInputs ++ osSpecific; + }; }); } From d69d777c02b9ac405a95f3cbfba219a990caefff Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 29 Oct 2023 18:32:28 +0200 Subject: [PATCH 64/95] ggml : quantization refactoring (#3833) * ggml : factor all quantization code in ggml-quants ggml-ci * ggml-quants : fix Zig and Swift builds + quantize tool ggml-ci * quantize : --pure option for disabling k-quant mixtures --------- Co-authored-by: cebtenzzre --- CMakeLists.txt | 12 +- Makefile | 18 +- Package.swift | 3 +- build.zig | 21 +- examples/quantize/quantize.cpp | 9 +- k_quants.c => ggml-quants.c | 2248 ++++++++++++++++++++++++++++++- k_quants.h => ggml-quants.h | 103 +- ggml.c | 2301 +------------------------------- ggml.h | 7 + llama.cpp | 34 +- llama.h | 1 + 11 files changed, 2372 insertions(+), 2385 deletions(-) rename k_quants.c => ggml-quants.c (71%) rename k_quants.h => ggml-quants.h (63%) diff --git a/CMakeLists.txt b/CMakeLists.txt index d9fc86237..3659279e2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -94,7 +94,6 @@ option(LLAMA_CLBLAST "llama: use CLBlast" option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_MPI "llama: use MPI" OFF) -option(LLAMA_K_QUANTS "llama: use k-quants" ON) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) @@ -278,13 +277,8 @@ if (LLAMA_BLAS) endif() endif() -if (LLAMA_K_QUANTS) - set(GGML_HEADERS_EXTRA k_quants.h) - set(GGML_SOURCES_EXTRA k_quants.c) - add_compile_definitions(GGML_USE_K_QUANTS) - if (LLAMA_QKK_64) - add_compile_definitions(GGML_QKK_64) - endif() +if (LLAMA_QKK_64) + add_compile_definitions(GGML_QKK_64) endif() if (LLAMA_CUBLAS) @@ -673,6 +667,8 @@ add_library(ggml OBJECT ggml-alloc.h ggml-backend.c ggml-backend.h + ggml-quants.c + ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} diff --git a/Makefile b/Makefile index 68069f9ff..2cecc2216 100644 --- a/Makefile +++ b/Makefile @@ -342,13 +342,9 @@ else MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif -ifndef LLAMA_NO_K_QUANTS - MK_CPPFLAGS += -DGGML_USE_K_QUANTS - OBJS += k_quants.o ifdef LLAMA_QKK_64 MK_CPPFLAGS += -DGGML_QKK_64 endif -endif ifndef LLAMA_NO_ACCELERATE # Mac OS - include Accelerate framework. @@ -365,7 +361,7 @@ ifdef LLAMA_MPI MK_CPPFLAGS += -DGGML_USE_MPI MK_CFLAGS += -Wno-cast-qual MK_CXXFLAGS += -Wno-cast-qual - OBJS += ggml-mpi.o + OBJS += ggml-mpi.o endif # LLAMA_MPI ifdef LLAMA_OPENBLAS @@ -382,7 +378,7 @@ endif # LLAMA_BLIS ifdef LLAMA_CUBLAS MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(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 - OBJS += ggml-cuda.o + OBJS += ggml-cuda.o NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math ifdef LLAMA_CUDA_NVCC NVCC = $(LLAMA_CUDA_NVCC) @@ -497,11 +493,6 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_MPI -ifndef LLAMA_NO_K_QUANTS -k_quants.o: k_quants.c k_quants.h - $(CC) $(CFLAGS) -c $< -o $@ -endif # LLAMA_NO_K_QUANTS - # combine build flags with cmdline overrides override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS) override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS) @@ -542,7 +533,10 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h $(CC) $(CFLAGS) -c $< -o $@ -OBJS += ggml-alloc.o ggml-backend.o +ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h + $(CC) $(CFLAGS) -c $< -o $@ + +OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ diff --git a/Package.swift b/Package.swift index 4ab055b19..5b3bd72ca 100644 --- a/Package.swift +++ b/Package.swift @@ -42,13 +42,12 @@ let package = Package( "llama.cpp", "ggml-alloc.c", "ggml-backend.c", - "k_quants.c", + "ggml-quants.c", ] + additionalSources, resources: resources, publicHeadersPath: "spm-headers", cSettings: [ .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), - .define("GGML_USE_K_QUANTS"), .define("GGML_USE_ACCELERATE") // NOTE: NEW_LAPACK will required iOS version 16.4+ // We should consider add this in the future when we drop support for iOS 14 diff --git a/build.zig b/build.zig index dcfa3dd6b..9b58b74ca 100644 --- a/build.zig +++ b/build.zig @@ -116,15 +116,10 @@ pub fn build(b: *std.build.Builder) !void { var make = try Maker.init(b); make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false; - if (b.option(bool, "k-quants", "Enable K-quants, (default: true)") orelse true) { - try make.addFlag("-DGGML_USE_K_QUANTS"); - const k_quants = make.obj("k_quants", "k_quants.c"); - try make.objs.append(k_quants); - } - const ggml = make.obj("ggml", "ggml.c"); const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c"); 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 common = make.obj("common", "common/common.cpp"); const console = make.obj("console", "common/console.cpp"); @@ -133,14 +128,14 @@ 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, llama, common, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); + _ = 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 }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, grammar_parser, clip }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index c7dd0d894..be0b2fe1e 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -18,7 +18,6 @@ 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", }, -#ifdef GGML_USE_K_QUANTS { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 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", }, @@ -31,7 +30,6 @@ static const std::vector QUANT_OPTIONS = { { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", }, { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", }, { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", }, -#endif { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", }, { "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", }, { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, @@ -70,13 +68,14 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp } // usage: -// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] +// ./quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] // [[noreturn]] static void usage(const char * executable) { - printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] 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("\nAllowed quantization types:\n"); for (auto & it : QUANT_OPTIONS) { if (it.name != "COPY") { @@ -103,6 +102,8 @@ int main(int argc, char ** argv) { params.quantize_output_tensor = false; } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { params.allow_requantize = true; + } else if (strcmp(argv[arg_idx], "--pure") == 0) { + params.pure = true; } else { usage(argv[0]); } diff --git a/k_quants.c b/ggml-quants.c similarity index 71% rename from k_quants.c rename to ggml-quants.c index 801941fbe..fd4ee1be6 100644 --- a/k_quants.c +++ b/ggml-quants.c @@ -1,9 +1,10 @@ -#include "k_quants.h" +#include "ggml-quants.h" #include "ggml.h" #include #include #include +#include #ifdef __ARM_NEON @@ -65,6 +66,1024 @@ inline static int32_t vaddvq_s32(int32x4_t v) { #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = _mm_sign_epi8(x, x); + // Sign the values of the y vectors + const __m128i sy = _mm_sign_epi8(y, x); + // Perform multiplication and create 16-bit values + const __m128i dot = _mm_maddubs_epi16(ax, sy); + const __m128i ones = _mm_set1_epi16(1); + return _mm_madd_epi16(ones, dot); +} + +#if __AVX__ || __AVX2__ || __AVX512F__ +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = _mm256_extractf128_ps(x, 1); + res = _mm_add_ps(res, _mm256_castps256_ps128(x)); + res = _mm_add_ps(res, _mm_movehl_ps(res, res)); + res = _mm_add_ss(res, _mm_movehdup_ps(res)); + return _mm_cvtss_f32(res); +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); + const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); + const __m128i sum64 = _mm_add_epi32(hi64, sum128); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + const __m128i hi64 = _mm_unpackhi_epi64(a, a); + const __m128i sum64 = _mm_add_epi32(hi64, a); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +#if defined(__AVX2__) || defined(__AVX512F__) +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = _mm256_set_epi64x( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); + const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytes = _mm256_or_si256(bytes, bit_mask); + return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) +{ + const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); + const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); + const __m256i lowMask = _mm256_set1_epi8( 0xF ); + return _mm256_and_si256(lowMask, bytes); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + const __m256i ones = _mm256_set1_epi16(1); + const __m256i summed_pairs = _mm256_madd_epi16(ones, x); + return _mm256_cvtepi32_ps(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { +#if __AVXVNNI__ + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Perform multiplication and create 16-bit values + const __m256i dot = _mm256_maddubs_epi16(ax, sy); + return sum_i16_pairs_float(dot); +#endif +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { +#if __AVXVNNIINT8__ + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Get absolute values of x vectors + const __m256i ax = _mm256_sign_epi8(x, x); + // Sign the values of the y vectors + const __m256i sy = _mm256_sign_epi8(y, x); + return mul_sum_us8_pairs_float(ax, sy); +#endif +} + +static inline __m128i packNibbles( __m256i bytes ) +{ + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh +#if __AVX512F__ + const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 + bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh + return _mm256_cvtepi16_epi8(bytes); // abcd_efgh +#else + const __m256i lowByte = _mm256_set1_epi16( 0xFF ); + __m256i high = _mm256_andnot_si256( lowByte, bytes ); + __m256i low = _mm256_and_si256( lowByte, bytes ); + high = _mm256_srli_epi16( high, 4 ); + bytes = _mm256_or_si256( low, high ); + + // Compress uint16_t lanes into bytes + __m128i r0 = _mm256_castsi256_si128( bytes ); + __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); + return _mm_packus_epi16( r0, r1 ); +#endif +} +#elif defined(__AVX__) +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); + const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); + __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); + __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); + const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytesl = _mm_or_si128(bytesl, bit_mask); + bytesh = _mm_or_si128(bytesh, bit_mask); + bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); + bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); + return MM256_SET_M128I(bytesh, bytesl); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) +{ + // Load 16 bytes from memory + __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); + __m128i tmph = _mm_srli_epi16(tmpl, 4); + const __m128i lowMask = _mm_set1_epi8(0xF); + tmpl = _mm_and_si128(lowMask, tmpl); + tmph = _mm_and_si128(lowMask, tmph); + return MM256_SET_M128I(tmph, tmpl); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { + const __m128i ones = _mm_set1_epi16(1); + const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); + const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); + const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); + return _mm256_cvtepi32_ps(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + const __m128i axl = _mm256_castsi256_si128(ax); + const __m128i axh = _mm256_extractf128_si256(ax, 1); + const __m128i syl = _mm256_castsi256_si128(sy); + const __m128i syh = _mm256_extractf128_si256(sy, 1); + // Perform multiplication and create 16-bit values + const __m128i dotl = _mm_maddubs_epi16(axl, syl); + const __m128i doth = _mm_maddubs_epi16(axh, syh); + return sum_i16_pairs_float(doth, dotl); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + const __m128i xl = _mm256_castsi256_si128(x); + const __m128i xh = _mm256_extractf128_si256(x, 1); + const __m128i yl = _mm256_castsi256_si128(y); + const __m128i yh = _mm256_extractf128_si256(y, 1); + // Get absolute values of x vectors + const __m128i axl = _mm_sign_epi8(xl, xl); + const __m128i axh = _mm_sign_epi8(xh, xh); + // Sign the values of the y vectors + const __m128i syl = _mm_sign_epi8(yl, xl); + const __m128i syh = _mm_sign_epi8(yh, xh); + // Perform multiplication and create 16-bit values + const __m128i dotl = _mm_maddubs_epi16(axl, syl); + const __m128i doth = _mm_maddubs_epi16(axh, syh); + return sum_i16_pairs_float(doth, dotl); +} + +static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) +{ + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m128i lowByte = _mm_set1_epi16( 0xFF ); + __m128i high = _mm_andnot_si128( lowByte, bytes1 ); + __m128i low = _mm_and_si128( lowByte, bytes1 ); + high = _mm_srli_epi16( high, 4 ); + bytes1 = _mm_or_si128( low, high ); + high = _mm_andnot_si128( lowByte, bytes2 ); + low = _mm_and_si128( lowByte, bytes2 ); + high = _mm_srli_epi16( high, 4 ); + bytes2 = _mm_or_si128( low, high ); + + return _mm_packus_epi16( bytes1, bytes2); +} +#endif +#elif defined(__SSSE3__) +// horizontally add 4x4 floats +static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { + __m128 res_0 =_mm_hadd_ps(a, b); + __m128 res_1 =_mm_hadd_ps(c, d); + __m128 res =_mm_hadd_ps(res_0, res_1); + res =_mm_hadd_ps(res, res); + res =_mm_hadd_ps(res, res); + + return _mm_cvtss_f32(res); +} +#endif // __AVX__ || __AVX2__ || __AVX512F__ +#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) + +#if defined(__ARM_NEON) + +#if !defined(__aarch64__) + +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); +} + +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); +} + +inline static float vmaxvq_f32(float32x4_t v) { + return + MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), + MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); +} + +inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { + int32x4_t res; + + res[0] = roundf(vgetq_lane_f32(v, 0)); + res[1] = roundf(vgetq_lane_f32(v, 1)); + res[2] = roundf(vgetq_lane_f32(v, 2)); + res[3] = roundf(vgetq_lane_f32(v, 3)); + + return res; +} + +#endif +#endif + +#if defined(__ARM_NEON) || defined(__wasm_simd128__) +#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s +#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) +#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) +#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) +#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) +#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) +#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) +#define B8(c,s ) B7(c,s, c), B7(c,s, s) + +// precomputed tables for expanding 8bits to 8 bytes: +static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 +static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 +#endif + +// reference implementation for deterministic creation of model files +void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) { + static const int qk = QK4_0; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int j = 0; j < qk; j++) { + const float v = x[i*qk + j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + max = v; + } + } + + const float d = max / -8; + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + + for (int j = 0; j < qk/2; ++j) { + const float x0 = x[i*qk + 0 + j]*id; + const float x1 = x[i*qk + qk/2 + j]*id; + + const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f)); + const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f)); + + y[i].qs[j] = xi0; + y[i].qs[j] |= xi1 << 4; + } + } +} + +void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) { + quantize_row_q4_0_reference(x, y, k); +} + +void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) { + const int qk = QK4_1; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + float min = FLT_MAX; + float max = -FLT_MAX; + + for (int j = 0; j < qk; j++) { + const float v = x[i*qk + j]; + + if (v < min) min = v; + if (v > max) max = v; + } + + const float d = (max - min) / ((1 << 4) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + y[i].m = ggml_fp32_to_fp16(min); + + for (int j = 0; j < qk/2; ++j) { + const float x0 = (x[i*qk + 0 + j] - min)*id; + const float x1 = (x[i*qk + qk/2 + j] - min)*id; + + const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f)); + const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f)); + + y[i].qs[j] = xi0; + y[i].qs[j] |= xi1 << 4; + } + } +} + +void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) { + quantize_row_q4_1_reference(x, y, k); +} + +void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) { + static const int qk = QK5_0; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int j = 0; j < qk; j++) { + const float v = x[i*qk + j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + max = v; + } + } + + const float d = max / -16; + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + + uint32_t qh = 0; + + for (int j = 0; j < qk/2; ++j) { + const float x0 = x[i*qk + 0 + j]*id; + const float x1 = x[i*qk + qk/2 + j]*id; + + const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f)); + const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f)); + + y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4); + + // get the 5-th bit and store it in qh at the right position + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2); + } + + memcpy(&y[i].qh, &qh, sizeof(qh)); + } +} + +void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) { + quantize_row_q5_0_reference(x, y, k); +} + +void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) { + const int qk = QK5_1; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + float min = FLT_MAX; + float max = -FLT_MAX; + + for (int j = 0; j < qk; j++) { + const float v = x[i*qk + j]; + + if (v < min) min = v; + if (v > max) max = v; + } + + const float d = (max - min) / ((1 << 5) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + y[i].m = ggml_fp32_to_fp16(min); + + uint32_t qh = 0; + + for (int j = 0; j < qk/2; ++j) { + const float x0 = (x[i*qk + 0 + j] - min)*id; + const float x1 = (x[i*qk + qk/2 + j] - min)*id; + + const uint8_t xi0 = (uint8_t)(x0 + 0.5f); + const uint8_t xi1 = (uint8_t)(x1 + 0.5f); + + y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4); + + // get the 5-th bit and store it in qh at the right position + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2); + } + + memcpy(&y[i].qh, &qh, sizeof(y[i].qh)); + } +} + +void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) { + quantize_row_q5_1_reference(x, y, k); +} + +// reference implementation for deterministic creation of model files +void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) { + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + const float v = x[i*QK8_0 + j]; + amax = MAX(amax, fabsf(v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + + for (int j = 0; j < QK8_0; ++j) { + const float x0 = x[i*QK8_0 + j]*id; + + y[i].qs[j] = roundf(x0); + } + } +} + +void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + + for (int j = 0; j < 8; j++) { + const float32x4_t v = vmulq_n_f32(srcv[j], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); + } + } +#elif defined(__wasm_simd128__) + for (int i = 0; i < nb; i++) { + v128_t srcv [8]; + v128_t asrcv[8]; + v128_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), + wasm_f32x4_extract_lane(amaxv[0], 1)), + MAX(wasm_f32x4_extract_lane(amaxv[0], 2), + wasm_f32x4_extract_lane(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + + for (int j = 0; j < 8; j++) { + const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); + const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); + + y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); + y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); + y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); + y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); + } + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = maxScalar / 127.f; + y[i].d = ggml_fp32_to_fp16(d); + const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#elif defined(__riscv_v_intrinsic) + + size_t vl = __riscv_vsetvl_e32m4(QK8_0); + + for (int i = 0; i < nb; i++) { + // load elements + vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_0, vl); + + vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); + vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0f, vl); + vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); + float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = ggml_fp32_to_fp16(d); + + vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); + + // convert to integer + vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); + vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); + + // store result + __riscv_vse8_v_i8m1(y[i].qs , vs, vl); + } +#else + // scalar + quantize_row_q8_0_reference(x, y, k); +#endif +} + +// reference implementation for deterministic creation of model files +void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) { + assert(QK8_1 == 32); + assert(k % QK8_1 == 0); + const int nb = k / QK8_1; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_1; j++) { + const float v = x[i*QK8_1 + j]; + amax = MAX(amax, fabsf(v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + int sum = 0; + + for (int j = 0; j < QK8_1/2; ++j) { + const float v0 = x[i*QK8_1 + j]*id; + const float v1 = x[i*QK8_1 + QK8_1/2 + j]*id; + + y[i].qs[ j] = roundf(v0); + y[i].qs[QK8_1/2 + j] = roundf(v1); + + sum += y[i].qs[ j]; + sum += y[i].qs[QK8_1/2 + j]; + } + + y[i].s = sum*d; + } +} + +void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { + assert(k % QK8_1 == 0); + const int nb = k / QK8_1; + + block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + int32x4_t accv = vdupq_n_s32(0); + + for (int j = 0; j < 8; j++) { + const float32x4_t v = vmulq_n_f32(srcv[j], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); + + accv = vaddq_s32(accv, vi); + } + + y[i].s = d * vaddvq_s32(accv); + } +#elif defined(__wasm_simd128__) + for (int i = 0; i < nb; i++) { + v128_t srcv [8]; + v128_t asrcv[8]; + v128_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), + wasm_f32x4_extract_lane(amaxv[0], 1)), + MAX(wasm_f32x4_extract_lane(amaxv[0], 2), + wasm_f32x4_extract_lane(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + v128_t accv = wasm_i32x4_splat(0); + + for (int j = 0; j < 8; j++) { + const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); + const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); + + y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); + y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); + y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); + y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); + + accv = wasm_i32x4_add(accv, vi); + } + + y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) + + wasm_i32x4_extract_lane(accv, 1) + + wasm_i32x4_extract_lane(accv, 2) + + wasm_i32x4_extract_lane(accv, 3)); + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = maxScalar / 127.f; + y[i].d = d; + const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Compute the sum of the quants and set y[i].s + y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))); + + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); + const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); + y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1)); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#elif defined(__riscv_v_intrinsic) + + size_t vl = __riscv_vsetvl_e32m4(QK8_1); + + for (int i = 0; i < nb; i++) { + // load elements + vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_1, vl); + + vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); + vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0, vl); + vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); + float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); + + // convert to integer + vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); + vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); + + // store result + __riscv_vse8_v_i8m1(y[i].qs , vs, vl); + + // compute sum for y[i].s + vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl); + vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl); + + // set y[i].s + int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); + y[i].s = sum*d; + } +#else + // scalar + quantize_row_q8_1_reference(x, y, k); +#endif +} + +void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) { + static const int qk = QK4_0; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + const float d = ggml_fp16_to_fp32(x[i].d); + + for (int j = 0; j < qk/2; ++j) { + const int x0 = (x[i].qs[j] & 0x0F) - 8; + const int x1 = (x[i].qs[j] >> 4) - 8; + + y[i*qk + j + 0 ] = x0*d; + y[i*qk + j + qk/2] = x1*d; + } + } +} + +void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) { + static const int qk = QK4_1; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + const float d = ggml_fp16_to_fp32(x[i].d); + const float m = ggml_fp16_to_fp32(x[i].m); + + for (int j = 0; j < qk/2; ++j) { + const int x0 = (x[i].qs[j] & 0x0F); + const int x1 = (x[i].qs[j] >> 4); + + y[i*qk + j + 0 ] = x0*d + m; + y[i*qk + j + qk/2] = x1*d + m; + } + } +} + +void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) { + static const int qk = QK5_0; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + const float d = ggml_fp16_to_fp32(x[i].d); + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; + const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; + + const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; + const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; + + y[i*qk + j + 0 ] = x0*d; + y[i*qk + j + qk/2] = x1*d; + } + } +} + +void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) { + static const int qk = QK5_1; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + const float d = ggml_fp16_to_fp32(x[i].d); + const float m = ggml_fp16_to_fp32(x[i].m); + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; + const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; + + const int x0 = (x[i].qs[j] & 0x0F) | xh_0; + const int x1 = (x[i].qs[j] >> 4) | xh_1; + + y[i*qk + j + 0 ] = x0*d + m; + y[i*qk + j + qk/2] = x1*d + m; + } + } +} + +void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k) { + static const int qk = QK8_0; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + const float d = ggml_fp16_to_fp32(x[i].d); + + for (int j = 0; j < qk; ++j) { + y[i*qk + j] = x[i].qs[j]*d; + } + } +} + // // 2-6 bit quantization in super-blocks // @@ -1264,15 +2283,6 @@ void quantize_row_q8_K(const float * restrict x, void * restrict y, int k) { // #if __AVX__ || __AVX2__ || __AVX512F__ -// horizontally add 8 floats -static inline float hsum_float_8(const __m256 x) { - __m128 res = _mm256_extractf128_ps(x, 1); - res = _mm_add_ps(res, _mm256_castps256_ps128(x)); - res = _mm_add_ps(res, _mm_movehl_ps(res, res)); - res = _mm_add_ss(res, _mm_movehdup_ps(res)); - return _mm_cvtss_f32(res); -} - // shuffles to pick the required scales in dot products static inline __m256i get_scale_shuffle_q3k(int i) { static const uint8_t k_shuffle[128] = { @@ -1311,6 +2321,1224 @@ static inline __m128i get_scale_shuffle(int i) { } #endif +void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); + + const block_q4_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q4_0 * restrict x0 = &x[i + 0]; + const block_q4_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); + const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); + const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); + const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + 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); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; ++i) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) ); + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = _mm256_set1_epi8( 8 ); + bx = _mm256_sub_epi8( bx, off ); + + __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps( d, q, acc ); + } + + *s = hsum_float_8(acc); +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; ++i) { + // Compute combined scale for the block + const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) ); + + const __m128i lowMask = _mm_set1_epi8(0xF); + const __m128i off = _mm_set1_epi8(8); + + const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs); + + __m128i bx = _mm_and_si128(lowMask, tmp); + __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs); + bx = _mm_sub_epi8(bx, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx, by); + + bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4)); + by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); + bx = _mm_sub_epi8(bx, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx, by); + + // Convert int32_t to float + __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1)); + + // Apply the scale, and accumulate + acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc); + } + + *s = hsum_float_8(acc); +#elif defined(__SSSE3__) + // set constants + const __m128i lowMask = _mm_set1_epi8(0xF); + const __m128i off = _mm_set1_epi8(8); + + // Initialize accumulator with zeros + __m128 acc_0 = _mm_setzero_ps(); + __m128 acc_1 = _mm_setzero_ps(); + __m128 acc_2 = _mm_setzero_ps(); + __m128 acc_3 = _mm_setzero_ps(); + + // First round without accumulation + { + _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[0].d) * ggml_fp16_to_fp32(y[0].d) ); + + const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs); + + __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); + __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs); + bx_0 = _mm_sub_epi8(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); + __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16)); + bx_1 = _mm_sub_epi8(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[1].d) * ggml_fp16_to_fp32(y[1].d) ); + + const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs); + + __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); + __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs); + bx_2 = _mm_sub_epi8(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); + __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16)); + bx_3 = _mm_sub_epi8(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = _mm_cvtepi32_ps(i32_0); + __m128 p1 = _mm_cvtepi32_ps(i32_1); + __m128 p2 = _mm_cvtepi32_ps(i32_2); + __m128 p3 = _mm_cvtepi32_ps(i32_3); + + // Apply the scale + acc_0 = _mm_mul_ps( d_0_1, p0 ); + acc_1 = _mm_mul_ps( d_0_1, p1 ); + acc_2 = _mm_mul_ps( d_2_3, p2 ); + acc_3 = _mm_mul_ps( d_2_3, p3 ); + } + + assert(nb % 2 == 0); // TODO: handle odd nb + + // Main loop + for (int i = 2; i < nb; i+=2) { + _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) ); + + const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs); + + __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); + __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs); + bx_0 = _mm_sub_epi8(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); + __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); + bx_1 = _mm_sub_epi8(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[i + 1].d) * ggml_fp16_to_fp32(y[i + 1].d) ); + + const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs); + + __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); + __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs); + bx_2 = _mm_sub_epi8(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); + __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16)); + bx_3 = _mm_sub_epi8(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = _mm_cvtepi32_ps(i32_0); + __m128 p1 = _mm_cvtepi32_ps(i32_1); + __m128 p2 = _mm_cvtepi32_ps(i32_2); + __m128 p3 = _mm_cvtepi32_ps(i32_3); + + // Apply the scale + __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); + __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); + __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); + __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); + + // Acummulate + acc_0 = _mm_add_ps(p0_d, acc_0); + acc_1 = _mm_add_ps(p1_d, acc_1); + acc_2 = _mm_add_ps(p2_d, acc_2); + acc_3 = _mm_add_ps(p3_d, acc_3); + } + + *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + // load elements + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); + + // mask and store lower part of x, and then upper part + vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + // subtract offset + vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl); + vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d); + } + + *s = sumf; +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + int sumi = 0; + + for (int j = 0; j < qk/2; ++j) { + const int v0 = (x[i].qs[j] & 0x0F) - 8; + const int v1 = (x[i].qs[j] >> 4) - 8; + + sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); + } + + sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d); + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int qk = QK8_1; + const int nb = n / qk; + + assert(n % qk == 0); + + const block_q4_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + + // TODO: add WASM SIMD +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + float summs = 0; + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q4_1 * restrict x0 = &x[i + 0]; + const block_q4_1 * restrict x1 = &x[i + 1]; + const block_q8_1 * restrict y0 = &y[i + 0]; + const block_q8_1 * restrict y1 = &y[i + 1]; + + summs += ggml_fp16_to_fp32(x0->m) * y0->s + ggml_fp16_to_fp32(x1->m) * y1->s; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + 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; +#elif defined(__AVX2__) || defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + float summs = 0; + + // Main loop + for (int i = 0; i < nb; ++i) { + const float d0 = ggml_fp16_to_fp32(x[i].d); + const float d1 = y[i].d; + + summs += ggml_fp16_to_fp32(x[i].m) * y[i].s; + + const __m256 d0v = _mm256_set1_ps( d0 ); + const __m256 d1v = _mm256_set1_ps( d1 ); + + // Compute combined scales + const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i bx = bytes_from_nibbles_32(x[i].qs); + const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs ); + + const __m256 xy = mul_sum_us8_pairs_float(bx, by); + + // Accumulate d0*d1*x*y +#if defined(__AVX2__) + acc = _mm256_fmadd_ps( d0d1, xy, acc ); +#else + acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); +#endif + } + + *s = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + // load elements + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); + + // mask and store lower part of x, and then upper part + vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + } + + *s = sumf; +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + int sumi = 0; + + for (int j = 0; j < qk/2; ++j) { + const int v0 = (x[i].qs[j] & 0x0F); + const int v1 = (x[i].qs[j] >> 4); + + sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); + } + + sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); + assert(qk == QK5_0); + + const block_q5_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + uint32_t qh0; + uint32_t qh1; + + uint64_t tmp0[4]; + uint64_t tmp1[4]; + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q5_0 * restrict x0 = &x[i]; + const block_q5_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + // extract the 5th bit via lookup table ((!b) << 4) + memcpy(&qh0, x0->qh, sizeof(qh0)); + memcpy(&qh1, x1->qh, sizeof(qh1)); + + tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; + tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; + tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; + tmp0[3] = table_b2b_1[(qh0 >> 24) ]; + + tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; + tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; + tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; + tmp1[3] = table_b2b_1[(qh1 >> 24) ]; + + const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); + const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); + const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); + const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) + const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); + const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); + const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); + const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + 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); +#elif defined(__wasm_simd128__) + v128_t sumv = wasm_f32x4_splat(0.0f); + + uint32_t qh; + uint64_t tmp[4]; + + // TODO: check if unrolling this is better + for (int i = 0; i < nb; ++i) { + const block_q5_0 * restrict x0 = &x[i]; + const block_q8_0 * restrict y0 = &y[i]; + + const v128_t m4b = wasm_i8x16_splat(0x0F); + + // extract the 5th bit + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_1[(qh >> 24) ]; + + const v128_t qhl = wasm_v128_load(tmp + 0); + const v128_t qhh = wasm_v128_load(tmp + 2); + + const v128_t v0 = wasm_v128_load(x0->qs); + + // 4-bit -> 8-bit + const v128_t v0l = wasm_v128_and (v0, m4b); + const v128_t v0h = wasm_u8x16_shr(v0, 4); + + // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) + const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); + const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); + + // load y + const v128_t v1l = wasm_v128_load(y0->qs); + const v128_t v1h = wasm_v128_load(y0->qs + 16); + + // int8x16 -> int16x8 + const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); + const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); + const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); + const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); + + const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); + const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); + const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); + const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); + + // dot product + sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( + wasm_i32x4_add( + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), + wasm_i32x4_dot_i16x8(v0lfh, v1lh)), + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), + wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), + wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * ggml_fp16_to_fp32(y0->d)))); + } + + *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; i++) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d)); + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); + bx = _mm256_or_si256(bx, bxhi); + + __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps(d, q, acc); + } + + *s = hsum_float_8(acc); +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + __m128i mask = _mm_set1_epi8((char)0xF0); + + // Main loop + for (int i = 0; i < nb; i++) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d)); + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + const __m256i bxhi = bytes_from_bits_32(x[i].qh); + __m128i bxhil = _mm256_castsi256_si128(bxhi); + __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); + bxhil = _mm_andnot_si128(bxhil, mask); + bxhih = _mm_andnot_si128(bxhih, mask); + __m128i bxl = _mm256_castsi256_si128(bx); + __m128i bxh = _mm256_extractf128_si256(bx, 1); + bxl = _mm_or_si128(bxl, bxhil); + bxh = _mm_or_si128(bxh, bxhih); + bx = MM256_SET_M128I(bxh, bxl); + + const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + /* Multiply q with scale and accumulate */ + acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); + } + + *s = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + uint32_t qh; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + // These tempory registers are for masking and shift operations + vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); + vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); + + vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl); + vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); + + for (int i = 0; i < nb; i++) { + memcpy(&qh, x[i].qh, sizeof(uint32_t)); + + // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl); + vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl); + vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); + + // ((qh & (1u << (j + 16))) >> (j + 12)); + vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl); + vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl); + + // narrowing + vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl); + vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); + + vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl); + vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); + + // load + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); + + vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); + vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); + + vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl); + vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi; + } + + *s = sumf; +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + int sumi = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); + + const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; + const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; + + sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); + } + + sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi; + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int qk = QK8_1; + const int nb = n / qk; + + assert(n % qk == 0); + assert(qk == QK5_1); + + const block_q5_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + float summs0 = 0.0f; + float summs1 = 0.0f; + + uint32_t qh0; + uint32_t qh1; + + uint64_t tmp0[4]; + uint64_t tmp1[4]; + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q5_1 * restrict x0 = &x[i]; + const block_q5_1 * restrict x1 = &x[i + 1]; + const block_q8_1 * restrict y0 = &y[i]; + const block_q8_1 * restrict y1 = &y[i + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + summs0 += ggml_fp16_to_fp32(x0->m) * y0->s; + summs1 += ggml_fp16_to_fp32(x1->m) * y1->s; + + // extract the 5th bit via lookup table ((b) << 4) + memcpy(&qh0, x0->qh, sizeof(qh0)); + memcpy(&qh1, x1->qh, sizeof(qh1)); + + tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; + tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; + tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; + tmp0[3] = table_b2b_0[(qh0 >> 24) ]; + + tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; + tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; + tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; + tmp1[3] = table_b2b_0[(qh1 >> 24) ]; + + const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); + const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); + const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); + const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // add high bit + const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); + const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); + const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); + const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + 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; +#elif defined(__wasm_simd128__) + v128_t sumv = wasm_f32x4_splat(0.0f); + + float summs = 0.0f; + + uint32_t qh; + uint64_t tmp[4]; + + // TODO: check if unrolling this is better + for (int i = 0; i < nb; ++i) { + const block_q5_1 * restrict x0 = &x[i]; + const block_q8_1 * restrict y0 = &y[i]; + + summs += ggml_fp16_to_fp32(x0->m) * y0->s; + + const v128_t m4b = wasm_i8x16_splat(0x0F); + + // extract the 5th bit + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_0[(qh >> 24) ]; + + const v128_t qhl = wasm_v128_load(tmp + 0); + const v128_t qhh = wasm_v128_load(tmp + 2); + + const v128_t v0 = wasm_v128_load(x0->qs); + + // 4-bit -> 8-bit + const v128_t v0l = wasm_v128_and (v0, m4b); + const v128_t v0h = wasm_u8x16_shr(v0, 4); + + // add high bit + const v128_t v0lf = wasm_v128_or(v0l, qhl); + const v128_t v0hf = wasm_v128_or(v0h, qhh); + + // load y + const v128_t v1l = wasm_v128_load(y0->qs); + const v128_t v1h = wasm_v128_load(y0->qs + 16); + + // int8x16 -> int16x8 + const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); + const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); + const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); + const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); + + const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); + const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); + const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); + const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); + + // dot product + sumv = wasm_f32x4_add(sumv, + wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), + wasm_i32x4_dot_i16x8(v0lfh, v1lh)), + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), + wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), + wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * y0->d))); + } + + *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.0f; + + // Main loop + for (int i = 0; i < nb; i++) { + const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d)); + + summs += ggml_fp16_to_fp32(x[i].m) * y[i].s; + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); + bx = _mm256_or_si256(bx, bxhi); + + const __m256 dy = _mm256_set1_ps(y[i].d); + const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_us8_pairs_float(bx, by); + + acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); + } + + *s = hsum_float_8(acc) + summs; +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + __m128i mask = _mm_set1_epi8(0x10); + + float summs = 0.0f; + + // Main loop + for (int i = 0; i < nb; i++) { + const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d)); + + summs += ggml_fp16_to_fp32(x[i].m) * y[i].s; + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + const __m256i bxhi = bytes_from_bits_32(x[i].qh); + __m128i bxhil = _mm256_castsi256_si128(bxhi); + __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); + bxhil = _mm_and_si128(bxhil, mask); + bxhih = _mm_and_si128(bxhih, mask); + __m128i bxl = _mm256_castsi256_si128(bx); + __m128i bxh = _mm256_extractf128_si256(bx, 1); + bxl = _mm_or_si128(bxl, bxhil); + bxh = _mm_or_si128(bxh, bxhih); + bx = MM256_SET_M128I(bxh, bxl); + + const __m256 dy = _mm256_set1_ps(y[i].d); + const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_us8_pairs_float(bx, by); + + acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); + } + + *s = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + uint32_t qh; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + // temporary registers for shift operations + vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); + vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); + + for (int i = 0; i < nb; i++) { + memcpy(&qh, x[i].qh, sizeof(uint32_t)); + + // load qh + vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl); + + // ((qh >> (j + 0)) << 4) & 0x10; + vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl); + vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); + vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl); + + // ((qh >> (j + 12)) ) & 0x10; + vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl); + vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl); + + // narrowing + vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl); + vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); + + vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl); + vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); + + // load + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); + + vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); + vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); + + vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + } + + *s = sumf; +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + int sumi = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; + const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; + + const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0; + const int32_t x1 = (x[i].qs[j] >> 4) | xh_1; + + sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); + } + + sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); + + const block_q8_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q8_0 * restrict x0 = &x[i + 0]; + const block_q8_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + const int8x16_t x0_0 = vld1q_s8(x0->qs); + const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); + const int8x16_t x1_0 = vld1q_s8(x1->qs); + const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); + + // load y + const int8x16_t y0_0 = vld1q_s8(y0->qs); + const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); + 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)); + + 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); +#elif defined(__AVX2__) || defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; ++i) { + // Compute combined scale for the block + const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d)); + __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs); + __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + // Multiply q with scale and accumulate +#if defined(__AVX2__) + acc = _mm256_fmadd_ps( d, q, acc ); +#else + acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc ); +#endif + } + + *s = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + size_t vl = __riscv_vsetvl_e8m1(qk); + + for (int i = 0; i < nb; i++) { + // load elements + vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl); + vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl); + + vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); + vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); + + sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)); + } + + *s = sumf; +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + int sumi = 0; + + for (int j = 0; j < qk; j++) { + sumi += x[i].qs[j]*y[i].qs[j]; + } + + sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)); + } + + *s = sumf; +#endif +} + #if QK_K == 256 void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { diff --git a/k_quants.h b/ggml-quants.h similarity index 63% rename from k_quants.h rename to ggml-quants.h index 9de089e7a..d88f99e33 100644 --- a/k_quants.h +++ b/ggml-quants.h @@ -1,20 +1,14 @@ #pragma once +// This is a private API for quantization and dequantization +// Should not be used directly, use ggml.h instead + #include "ggml.h" #include #include #include -// Super-block size -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else -#define QK_K 256 -#define K_SCALE_SIZE 12 -#endif - #ifndef static_assert #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) #define static_assert(cond, msg) _Static_assert(cond, msg) @@ -23,10 +17,66 @@ #endif #endif +#define QK4_0 32 +typedef struct { + ggml_fp16_t d; // delta + uint8_t qs[QK4_0 / 2]; // nibbles / quants +} block_q4_0; +static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); + +#define QK4_1 32 +typedef struct { + ggml_fp16_t d; // delta + ggml_fp16_t m; // min + uint8_t qs[QK4_1 / 2]; // nibbles / quants +} block_q4_1; +static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding"); + +#define QK5_0 32 +typedef struct { + ggml_fp16_t d; // delta + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_0 / 2]; // nibbles / quants +} block_q5_0; +static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); + +#define QK5_1 32 +typedef struct { + ggml_fp16_t d; // delta + ggml_fp16_t m; // min + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_1 / 2]; // nibbles / quants +} block_q5_1; +static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); + +#define QK8_0 32 +typedef struct { + ggml_fp16_t d; // delta + int8_t qs[QK8_0]; // quants +} block_q8_0; +static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); + +#define QK8_1 32 +typedef struct { + float d; // delta + float s; // d * sum(qs[i]) + int8_t qs[QK8_1]; // quants +} block_q8_1; +static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding"); + // // Super-block quantization structures // +// Super-block size +#ifdef GGML_QKK_64 +#define QK_K 64 +#define K_SCALE_SIZE 4 +#else +#define QK_K 256 +#define K_SCALE_SIZE 12 +#endif + // 2-bit quantization // weight is represented as x = a * q + b // 16 blocks of 16 elements each @@ -127,6 +177,13 @@ static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_ // 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_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); @@ -134,6 +191,13 @@ 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_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_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); @@ -142,6 +206,13 @@ 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); // 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_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); @@ -150,16 +221,14 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int void dequantize_row_q8_K(const block_q8_K * 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); +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_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); - -// Quantization with histogram collection -size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist); -size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist); -size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist); -size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); -size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); - diff --git a/ggml.c b/ggml.c index 6f66bab05..95f72c35e 100644 --- a/ggml.c +++ b/ggml.c @@ -1,10 +1,7 @@ #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows #include "ggml.h" - -#ifdef GGML_USE_K_QUANTS -#include "k_quants.h" -#endif +#include "ggml-quants.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW @@ -443,21 +440,6 @@ static ggml_fp16_t table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) static float table_f32_f16[1 << 16]; -#if defined(__ARM_NEON) || defined(__wasm_simd128__) -#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s -#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) -#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) -#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) -#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) -#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) -#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) -#define B8(c,s ) B7(c,s, c), B7(c,s, s) - -// precomputed tables for expanding 8bits to 8 bytes: -static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 -static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 -#endif - // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. // This is also true for POWER9. @@ -587,1071 +569,8 @@ int64_t ggml_cycles_per_ms(void) { static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); -// -// quantization -// - -#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) - -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) -// multiply int8_t, add results pairwise twice -static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { - // Get absolute values of x vectors - const __m128i ax = _mm_sign_epi8(x, x); - // Sign the values of the y vectors - const __m128i sy = _mm_sign_epi8(y, x); - // Perform multiplication and create 16-bit values - const __m128i dot = _mm_maddubs_epi16(ax, sy); - const __m128i ones = _mm_set1_epi16(1); - return _mm_madd_epi16(ones, dot); -} - -#if __AVX__ || __AVX2__ || __AVX512F__ -// horizontally add 8 floats -static inline float hsum_float_8(const __m256 x) { - __m128 res = _mm256_extractf128_ps(x, 1); - res = _mm_add_ps(res, _mm256_castps256_ps128(x)); - res = _mm_add_ps(res, _mm_movehl_ps(res, res)); - res = _mm_add_ss(res, _mm_movehdup_ps(res)); - return _mm_cvtss_f32(res); -} - -// horizontally add 8 int32_t -static inline int hsum_i32_8(const __m256i a) { - const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); - const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); - const __m128i sum64 = _mm_add_epi32(hi64, sum128); - const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); - return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); -} - -// horizontally add 4 int32_t -static inline int hsum_i32_4(const __m128i a) { - const __m128i hi64 = _mm_unpackhi_epi64(a, a); - const __m128i sum64 = _mm_add_epi32(hi64, a); - const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); - return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); -} - -#if defined(__AVX2__) || defined(__AVX512F__) -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m256i shuf_mask = _mm256_set_epi64x( - 0x0303030303030303, 0x0202020202020202, - 0x0101010101010101, 0x0000000000000000); - __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); - const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); - bytes = _mm256_or_si256(bytes, bit_mask); - return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) -{ - const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); - const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); - const __m256i lowMask = _mm256_set1_epi8( 0xF ); - return _mm256_and_si256(lowMask, bytes); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m256i x) { - const __m256i ones = _mm256_set1_epi16(1); - const __m256i summed_pairs = _mm256_madd_epi16(ones, x); - return _mm256_cvtepi32_ps(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { -#if __AVXVNNI__ - const __m256i zero = _mm256_setzero_si256(); - const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); - return _mm256_cvtepi32_ps(summed_pairs); -#else - // Perform multiplication and create 16-bit values - const __m256i dot = _mm256_maddubs_epi16(ax, sy); - return sum_i16_pairs_float(dot); -#endif -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { -#if __AVXVNNIINT8__ - const __m256i zero = _mm256_setzero_si256(); - const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); - return _mm256_cvtepi32_ps(summed_pairs); -#else - // Get absolute values of x vectors - const __m256i ax = _mm256_sign_epi8(x, x); - // Sign the values of the y vectors - const __m256i sy = _mm256_sign_epi8(y, x); - return mul_sum_us8_pairs_float(ax, sy); -#endif -} - -static inline __m128i packNibbles( __m256i bytes ) -{ - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh -#if __AVX512F__ - const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 - bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh - return _mm256_cvtepi16_epi8(bytes); // abcd_efgh -#else - const __m256i lowByte = _mm256_set1_epi16( 0xFF ); - __m256i high = _mm256_andnot_si256( lowByte, bytes ); - __m256i low = _mm256_and_si256( lowByte, bytes ); - high = _mm256_srli_epi16( high, 4 ); - bytes = _mm256_or_si256( low, high ); - - // Compress uint16_t lanes into bytes - __m128i r0 = _mm256_castsi256_si128( bytes ); - __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); - return _mm_packus_epi16( r0, r1 ); -#endif -} -#elif defined(__AVX__) -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); - const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); - __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); - __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); - const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); - bytesl = _mm_or_si128(bytesl, bit_mask); - bytesh = _mm_or_si128(bytesh, bit_mask); - bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); - bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); - return MM256_SET_M128I(bytesh, bytesl); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) -{ - // Load 16 bytes from memory - __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); - __m128i tmph = _mm_srli_epi16(tmpl, 4); - const __m128i lowMask = _mm_set1_epi8(0xF); - tmpl = _mm_and_si128(lowMask, tmpl); - tmph = _mm_and_si128(lowMask, tmph); - return MM256_SET_M128I(tmph, tmpl); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { - const __m128i ones = _mm_set1_epi16(1); - const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); - const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); - const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); - return _mm256_cvtepi32_ps(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { - const __m128i axl = _mm256_castsi256_si128(ax); - const __m128i axh = _mm256_extractf128_si256(ax, 1); - const __m128i syl = _mm256_castsi256_si128(sy); - const __m128i syh = _mm256_extractf128_si256(sy, 1); - // Perform multiplication and create 16-bit values - const __m128i dotl = _mm_maddubs_epi16(axl, syl); - const __m128i doth = _mm_maddubs_epi16(axh, syh); - return sum_i16_pairs_float(doth, dotl); -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { - const __m128i xl = _mm256_castsi256_si128(x); - const __m128i xh = _mm256_extractf128_si256(x, 1); - const __m128i yl = _mm256_castsi256_si128(y); - const __m128i yh = _mm256_extractf128_si256(y, 1); - // Get absolute values of x vectors - const __m128i axl = _mm_sign_epi8(xl, xl); - const __m128i axh = _mm_sign_epi8(xh, xh); - // Sign the values of the y vectors - const __m128i syl = _mm_sign_epi8(yl, xl); - const __m128i syh = _mm_sign_epi8(yh, xh); - // Perform multiplication and create 16-bit values - const __m128i dotl = _mm_maddubs_epi16(axl, syl); - const __m128i doth = _mm_maddubs_epi16(axh, syh); - return sum_i16_pairs_float(doth, dotl); -} - -static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) -{ - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh - const __m128i lowByte = _mm_set1_epi16( 0xFF ); - __m128i high = _mm_andnot_si128( lowByte, bytes1 ); - __m128i low = _mm_and_si128( lowByte, bytes1 ); - high = _mm_srli_epi16( high, 4 ); - bytes1 = _mm_or_si128( low, high ); - high = _mm_andnot_si128( lowByte, bytes2 ); - low = _mm_and_si128( lowByte, bytes2 ); - high = _mm_srli_epi16( high, 4 ); - bytes2 = _mm_or_si128( low, high ); - - return _mm_packus_epi16( bytes1, bytes2); -} -#endif -#elif defined(__SSSE3__) -// horizontally add 4x4 floats -static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { - __m128 res_0 =_mm_hadd_ps(a, b); - __m128 res_1 =_mm_hadd_ps(c, d); - __m128 res =_mm_hadd_ps(res_0, res_1); - res =_mm_hadd_ps(res, res); - res =_mm_hadd_ps(res, res); - - return _mm_cvtss_f32(res); -} -#endif // __AVX__ || __AVX2__ || __AVX512F__ -#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) - -#if defined(__ARM_NEON) - -#if !defined(__aarch64__) - -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); -} - -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); -} - -inline static float vmaxvq_f32(float32x4_t v) { - return - MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), - MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); -} - -inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { - int32x4_t res; - - res[0] = roundf(vgetq_lane_f32(v, 0)); - res[1] = roundf(vgetq_lane_f32(v, 1)); - res[2] = roundf(vgetq_lane_f32(v, 2)); - res[3] = roundf(vgetq_lane_f32(v, 3)); - - return res; -} - -#endif -#endif - -#define QK4_0 32 -typedef struct { - ggml_fp16_t d; // delta - uint8_t qs[QK4_0 / 2]; // nibbles / quants -} block_q4_0; -static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); - -#define QK4_1 32 -typedef struct { - ggml_fp16_t d; // delta - ggml_fp16_t m; // min - uint8_t qs[QK4_1 / 2]; // nibbles / quants -} block_q4_1; -static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding"); - -#define QK5_0 32 -typedef struct { - ggml_fp16_t d; // delta - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_0 / 2]; // nibbles / quants -} block_q5_0; -static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); - -#define QK5_1 32 -typedef struct { - ggml_fp16_t d; // delta - ggml_fp16_t m; // min - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_1 / 2]; // nibbles / quants -} block_q5_1; -static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); - -#define QK8_0 32 -typedef struct { - ggml_fp16_t d; // delta - int8_t qs[QK8_0]; // quants -} block_q8_0; -static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); - -#define QK8_1 32 -typedef struct { - float d; // delta - float s; // d * sum(qs[i]) - int8_t qs[QK8_1]; // quants -} block_q8_1; -static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding"); - -// reference implementation for deterministic creation of model files -static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) { - static const int qk = QK4_0; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - float amax = 0.0f; // absolute max - float max = 0.0f; - - for (int j = 0; j < qk; j++) { - const float v = x[i*qk + j]; - if (amax < fabsf(v)) { - amax = fabsf(v); - max = v; - } - } - - const float d = max / -8; - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < qk/2; ++j) { - const float x0 = x[i*qk + 0 + j]*id; - const float x1 = x[i*qk + qk/2 + j]*id; - - const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f)); - const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f)); - - y[i].qs[j] = xi0; - y[i].qs[j] |= xi1 << 4; - } - } -} - -static void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) { - quantize_row_q4_0_reference(x, y, k); -} - -static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) { - const int qk = QK4_1; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - float min = FLT_MAX; - float max = -FLT_MAX; - - for (int j = 0; j < qk; j++) { - const float v = x[i*qk + j]; - - if (v < min) min = v; - if (v > max) max = v; - } - - const float d = (max - min) / ((1 << 4) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - y[i].m = GGML_FP32_TO_FP16(min); - - for (int j = 0; j < qk/2; ++j) { - const float x0 = (x[i*qk + 0 + j] - min)*id; - const float x1 = (x[i*qk + qk/2 + j] - min)*id; - - const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f)); - const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f)); - - y[i].qs[j] = xi0; - y[i].qs[j] |= xi1 << 4; - } - } -} - -static void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) { - quantize_row_q4_1_reference(x, y, k); -} - -static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) { - static const int qk = QK5_0; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - float amax = 0.0f; // absolute max - float max = 0.0f; - - for (int j = 0; j < qk; j++) { - const float v = x[i*qk + j]; - if (amax < fabsf(v)) { - amax = fabsf(v); - max = v; - } - } - - const float d = max / -16; - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - uint32_t qh = 0; - - for (int j = 0; j < qk/2; ++j) { - const float x0 = x[i*qk + 0 + j]*id; - const float x1 = x[i*qk + qk/2 + j]*id; - - const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f)); - const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f)); - - y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4); - - // get the 5-th bit and store it in qh at the right position - qh |= ((xi0 & 0x10u) >> 4) << (j + 0); - qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2); - } - - memcpy(&y[i].qh, &qh, sizeof(qh)); - } -} - -static void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) { - quantize_row_q5_0_reference(x, y, k); -} - -static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) { - const int qk = QK5_1; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - float min = FLT_MAX; - float max = -FLT_MAX; - - for (int j = 0; j < qk; j++) { - const float v = x[i*qk + j]; - - if (v < min) min = v; - if (v > max) max = v; - } - - const float d = (max - min) / ((1 << 5) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - y[i].m = GGML_FP32_TO_FP16(min); - - uint32_t qh = 0; - - for (int j = 0; j < qk/2; ++j) { - const float x0 = (x[i*qk + 0 + j] - min)*id; - const float x1 = (x[i*qk + qk/2 + j] - min)*id; - - const uint8_t xi0 = (uint8_t)(x0 + 0.5f); - const uint8_t xi1 = (uint8_t)(x1 + 0.5f); - - y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4); - - // get the 5-th bit and store it in qh at the right position - qh |= ((xi0 & 0x10u) >> 4) << (j + 0); - qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2); - } - - memcpy(&y[i].qh, &qh, sizeof(y[i].qh)); - } -} - -static void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) { - quantize_row_q5_1_reference(x, y, k); -} - -// reference implementation for deterministic creation of model files -static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) { - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - for (int i = 0; i < nb; i++) { - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - const float v = x[i*QK8_0 + j]; - amax = MAX(amax, fabsf(v)); - } - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < QK8_0; ++j) { - const float x0 = x[i*QK8_0 + j]*id; - - y[i].qs[j] = roundf(x0); - } - } -} - -static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - for (int i = 0; i < nb; i++) { - float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const float32x4_t v = vmulq_n_f32(srcv[j], id); - const int32x4_t vi = vcvtnq_s32_f32(v); - - y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); - } - } -#elif defined(__wasm_simd128__) - for (int i = 0; i < nb; i++) { - v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), - wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), - wasm_f32x4_extract_lane(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); - const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); - - y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); - y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); - y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); - y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); - } - } -#elif defined(__AVX2__) || defined(__AVX__) - for (int i = 0; i < nb; i++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x ); - __m256 v1 = _mm256_loadu_ps( x + 8 ); - __m256 v2 = _mm256_loadu_ps( x + 16 ); - __m256 v3 = _mm256_loadu_ps( x + 24 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); - - // Quantize these floats - const float d = maxScalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; - const __m256 mul = _mm256_set1_ps( id ); - - // Apply the multiplier - v0 = _mm256_mul_ps( v0, mul ); - v1 = _mm256_mul_ps( v1, mul ); - v2 = _mm256_mul_ps( v2, mul ); - v3 = _mm256_mul_ps( v3, mul ); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 - i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 - - // We got our precious signed bytes, but the order is now wrong - // These AVX2 pack instructions process 16-byte pieces independently - // The following instruction is fixing the order - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)y[i].qs, i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - - _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); -#endif - } -#elif defined(__riscv_v_intrinsic) - - size_t vl = __riscv_vsetvl_e32m4(QK8_0); - - for (int i = 0; i < nb; i++) { - // load elements - vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_0, vl); - - vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); - vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0f, vl); - vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); - float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); - - // convert to integer - vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); - vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); - - // store result - __riscv_vse8_v_i8m1(y[i].qs , vs, vl); - } -#else - // scalar - quantize_row_q8_0_reference(x, y, k); -#endif -} - -// reference implementation for deterministic creation of model files -static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) { - assert(QK8_1 == 32); - assert(k % QK8_1 == 0); - const int nb = k / QK8_1; - - for (int i = 0; i < nb; i++) { - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_1; j++) { - const float v = x[i*QK8_1 + j]; - amax = MAX(amax, fabsf(v)); - } - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = d; - - int sum = 0; - - for (int j = 0; j < QK8_1/2; ++j) { - const float v0 = x[i*QK8_1 + j]*id; - const float v1 = x[i*QK8_1 + QK8_1/2 + j]*id; - - y[i].qs[ j] = roundf(v0); - y[i].qs[QK8_1/2 + j] = roundf(v1); - - sum += y[i].qs[ j]; - sum += y[i].qs[QK8_1/2 + j]; - } - - y[i].s = sum*d; - } -} - -static void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { - assert(k % QK8_1 == 0); - const int nb = k / QK8_1; - - block_q8_1 * restrict y = vy; - -#if defined(__ARM_NEON) - for (int i = 0; i < nb; i++) { - float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = d; - - int32x4_t accv = vdupq_n_s32(0); - - for (int j = 0; j < 8; j++) { - const float32x4_t v = vmulq_n_f32(srcv[j], id); - const int32x4_t vi = vcvtnq_s32_f32(v); - - y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); - - accv = vaddq_s32(accv, vi); - } - - y[i].s = d * vaddvq_s32(accv); - } -#elif defined(__wasm_simd128__) - for (int i = 0; i < nb; i++) { - v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), - wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), - wasm_f32x4_extract_lane(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = d; - - v128_t accv = wasm_i32x4_splat(0); - - for (int j = 0; j < 8; j++) { - const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); - const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); - - y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); - y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); - y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); - y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); - - accv = wasm_i32x4_add(accv, vi); - } - - y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) + - wasm_i32x4_extract_lane(accv, 1) + - wasm_i32x4_extract_lane(accv, 2) + - wasm_i32x4_extract_lane(accv, 3)); - } -#elif defined(__AVX2__) || defined(__AVX__) - for (int i = 0; i < nb; i++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x ); - __m256 v1 = _mm256_loadu_ps( x + 8 ); - __m256 v2 = _mm256_loadu_ps( x + 16 ); - __m256 v3 = _mm256_loadu_ps( x + 24 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); - - // Quantize these floats - const float d = maxScalar / 127.f; - y[i].d = d; - const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; - const __m256 mul = _mm256_set1_ps( id ); - - // Apply the multiplier - v0 = _mm256_mul_ps( v0, mul ); - v1 = _mm256_mul_ps( v1, mul ); - v2 = _mm256_mul_ps( v2, mul ); - v3 = _mm256_mul_ps( v3, mul ); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Compute the sum of the quants and set y[i].s - y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))); - - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 - i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 - - // We got our precious signed bytes, but the order is now wrong - // These AVX2 pack instructions process 16-byte pieces independently - // The following instruction is fixing the order - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)y[i].qs, i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Compute the sum of the quants and set y[i].s - const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); - const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); - y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1)); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - - _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); -#endif - } -#elif defined(__riscv_v_intrinsic) - - size_t vl = __riscv_vsetvl_e32m4(QK8_1); - - for (int i = 0; i < nb; i++) { - // load elements - vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_1, vl); - - vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); - vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0, vl); - vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); - float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = d; - - vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); - - // convert to integer - vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); - vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); - - // store result - __riscv_vse8_v_i8m1(y[i].qs , vs, vl); - - // compute sum for y[i].s - vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl); - vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl); - - // set y[i].s - int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); - y[i].s = sum*d; - } -#else - // scalar - quantize_row_q8_1_reference(x, y, k); -#endif -} - -static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) { - static const int qk = QK4_0; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); - - for (int j = 0; j < qk/2; ++j) { - const int x0 = (x[i].qs[j] & 0x0F) - 8; - const int x1 = (x[i].qs[j] >> 4) - 8; - - y[i*qk + j + 0 ] = x0*d; - y[i*qk + j + qk/2] = x1*d; - } - } -} - -static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) { - static const int qk = QK4_1; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); - const float m = GGML_FP16_TO_FP32(x[i].m); - - for (int j = 0; j < qk/2; ++j) { - const int x0 = (x[i].qs[j] & 0x0F); - const int x1 = (x[i].qs[j] >> 4); - - y[i*qk + j + 0 ] = x0*d + m; - y[i*qk + j + qk/2] = x1*d + m; - } - } -} - -static void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) { - static const int qk = QK5_0; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); - - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; - - const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; - const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; - - y[i*qk + j + 0 ] = x0*d; - y[i*qk + j + qk/2] = x1*d; - } - } -} - -static void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) { - static const int qk = QK5_1; - - assert(k % qk == 0); - - const int nb = k / qk; - - for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); - const float m = GGML_FP16_TO_FP32(x[i].m); - - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; - - const int x0 = (x[i].qs[j] & 0x0F) | xh_0; - const int x1 = (x[i].qs[j] >> 4) | xh_1; - - y[i*qk + j + 0 ] = x0*d + m; - y[i*qk + j + qk/2] = x1*d + m; - } - } -} - -static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) { - static const int qk = QK8_0; - - assert(k % qk == 0); - - const int nb = k / qk; - - const block_q8_0 * restrict x = vx; - - for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); - - for (int j = 0; j < qk; ++j) { - y[i*qk + j] = x[i].qs[j]*d; - } - } -} - 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); -static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); -static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); -static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); -static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); -static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { [GGML_TYPE_I8] = { @@ -1740,7 +659,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .blck_size = QK8_0, .type_size = sizeof(block_q8_0), .is_quantized = true, - .to_float = dequantize_row_q8_0, + .to_float = (ggml_to_float_t) dequantize_row_q8_0, .from_float = quantize_row_q8_0, .from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference, .vec_dot = ggml_vec_dot_q8_0_q8_0, @@ -1755,7 +674,6 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference, .vec_dot_type = GGML_TYPE_Q8_1, }, -#ifdef GGML_USE_K_QUANTS [GGML_TYPE_Q2_K] = { .type_name = "q2_K", .blck_size = QK_K, @@ -1818,7 +736,6 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .is_quantized = true, .from_float = quantize_row_q8_K, } -#endif }; // For internal test use @@ -2442,1218 +1359,6 @@ static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * rest *s = sumf; } -static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); - - const block_q4_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb - for (int i = 0; i < nb; i += 2) { - const block_q4_0 * restrict x0 = &x[i + 0]; - const block_q4_0 * restrict x1 = &x[i + 1]; - const block_q8_0 * restrict y0 = &y[i + 0]; - const block_q8_0 * restrict y1 = &y[i + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - const int8x16_t s8b = vdupq_n_s8(0x8); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // sub 8 - const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); - const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); - const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); - const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - 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); -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (int i = 0; i < nb; ++i) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); - - __m256i bx = bytes_from_nibbles_32(x[i].qs); - - // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. - const __m256i off = _mm256_set1_epi8( 8 ); - bx = _mm256_sub_epi8( bx, off ); - - __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); - - const __m256 q = mul_sum_i8_pairs_float(bx, by); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps( d, q, acc ); - } - - *s = hsum_float_8(acc); -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (int i = 0; i < nb; ++i) { - // Compute combined scale for the block - const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); - - const __m128i lowMask = _mm_set1_epi8(0xF); - const __m128i off = _mm_set1_epi8(8); - - const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs); - - __m128i bx = _mm_and_si128(lowMask, tmp); - __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs); - bx = _mm_sub_epi8(bx, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx, by); - - bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4)); - by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); - bx = _mm_sub_epi8(bx, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx, by); - - // Convert int32_t to float - __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1)); - - // Apply the scale, and accumulate - acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc); - } - - *s = hsum_float_8(acc); -#elif defined(__SSSE3__) - // set constants - const __m128i lowMask = _mm_set1_epi8(0xF); - const __m128i off = _mm_set1_epi8(8); - - // Initialize accumulator with zeros - __m128 acc_0 = _mm_setzero_ps(); - __m128 acc_1 = _mm_setzero_ps(); - __m128 acc_2 = _mm_setzero_ps(); - __m128 acc_3 = _mm_setzero_ps(); - - // First round without accumulation - { - _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) ); - - const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs); - - __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); - __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs); - bx_0 = _mm_sub_epi8(bx_0, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - - __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); - __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16)); - bx_1 = _mm_sub_epi8(bx_1, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); - - _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) ); - - const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs); - - __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); - __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs); - bx_2 = _mm_sub_epi8(bx_2, off); - const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); - - __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); - __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16)); - bx_3 = _mm_sub_epi8(bx_3, off); - const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); - - // Convert int32_t to float - __m128 p0 = _mm_cvtepi32_ps(i32_0); - __m128 p1 = _mm_cvtepi32_ps(i32_1); - __m128 p2 = _mm_cvtepi32_ps(i32_2); - __m128 p3 = _mm_cvtepi32_ps(i32_3); - - // Apply the scale - acc_0 = _mm_mul_ps( d_0_1, p0 ); - acc_1 = _mm_mul_ps( d_0_1, p1 ); - acc_2 = _mm_mul_ps( d_2_3, p2 ); - acc_3 = _mm_mul_ps( d_2_3, p3 ); - } - - // Main loop - GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb - for (int i = 2; i < nb; i+=2) { - _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); - - const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs); - - __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); - __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs); - bx_0 = _mm_sub_epi8(bx_0, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - - __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); - __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); - bx_1 = _mm_sub_epi8(bx_1, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); - - _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) ); - - const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs); - - __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); - __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs); - bx_2 = _mm_sub_epi8(bx_2, off); - const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); - - __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); - __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16)); - bx_3 = _mm_sub_epi8(bx_3, off); - const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); - - // Convert int32_t to float - __m128 p0 = _mm_cvtepi32_ps(i32_0); - __m128 p1 = _mm_cvtepi32_ps(i32_1); - __m128 p2 = _mm_cvtepi32_ps(i32_2); - __m128 p3 = _mm_cvtepi32_ps(i32_3); - - // Apply the scale - __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); - __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); - __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); - __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); - - // Acummulate - acc_0 = _mm_add_ps(p0_d, acc_0); - acc_1 = _mm_add_ps(p1_d, acc_1); - acc_2 = _mm_add_ps(p2_d, acc_2); - acc_3 = _mm_add_ps(p3_d, acc_3); - } - - *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); -#elif defined(__riscv_v_intrinsic) - float sumf = 0.0; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - for (int i = 0; i < nb; i++) { - // load elements - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); - - // mask and store lower part of x, and then upper part - vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - // subtract offset - vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl); - vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); - } - - *s = sumf; -#else - // scalar - float sumf = 0.0; - - for (int i = 0; i < nb; i++) { - int sumi = 0; - - for (int j = 0; j < qk/2; ++j) { - const int v0 = (x[i].qs[j] & 0x0F) - 8; - const int v1 = (x[i].qs[j] >> 4) - 8; - - sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); - } - - sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); - } - - *s = sumf; -#endif -} - -static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int qk = QK8_1; - const int nb = n / qk; - - assert(n % qk == 0); - - const block_q4_1 * restrict x = vx; - const block_q8_1 * restrict y = vy; - - // TODO: add WASM SIMD -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - float summs = 0; - - GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb - for (int i = 0; i < nb; i += 2) { - const block_q4_1 * restrict x0 = &x[i + 0]; - const block_q4_1 * restrict x1 = &x[i + 1]; - const block_q8_1 * restrict y0 = &y[i + 0]; - const block_q8_1 * restrict y1 = &y[i + 1]; - - summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - 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; -#elif defined(__AVX2__) || defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - // Main loop - for (int i = 0; i < nb; ++i) { - const float d0 = GGML_FP16_TO_FP32(x[i].d); - const float d1 = y[i].d; - - summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; - - const __m256 d0v = _mm256_set1_ps( d0 ); - const __m256 d1v = _mm256_set1_ps( d1 ); - - // Compute combined scales - const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); - - // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - const __m256i bx = bytes_from_nibbles_32(x[i].qs); - const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs ); - - const __m256 xy = mul_sum_us8_pairs_float(bx, by); - - // Accumulate d0*d1*x*y -#if defined(__AVX2__) - acc = _mm256_fmadd_ps( d0d1, xy, acc ); -#else - acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); -#endif - } - - *s = hsum_float_8(acc) + summs; -#elif defined(__riscv_v_intrinsic) - float sumf = 0.0; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - for (int i = 0; i < nb; i++) { - // load elements - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); - - // mask and store lower part of x, and then upper part - vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; - } - - *s = sumf; -#else - // scalar - float sumf = 0.0; - - for (int i = 0; i < nb; i++) { - int sumi = 0; - - for (int j = 0; j < qk/2; ++j) { - const int v0 = (x[i].qs[j] & 0x0F); - const int v1 = (x[i].qs[j] >> 4); - - sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); - } - - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; - } - - *s = sumf; -#endif -} - -static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); - assert(qk == QK5_0); - - const block_q5_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - uint32_t qh0; - uint32_t qh1; - - uint64_t tmp0[4]; - uint64_t tmp1[4]; - - GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb - for (int i = 0; i < nb; i += 2) { - const block_q5_0 * restrict x0 = &x[i]; - const block_q5_0 * restrict x1 = &x[i + 1]; - const block_q8_0 * restrict y0 = &y[i]; - const block_q8_0 * restrict y1 = &y[i + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - // extract the 5th bit via lookup table ((!b) << 4) - memcpy(&qh0, x0->qh, sizeof(qh0)); - memcpy(&qh1, x1->qh, sizeof(qh1)); - - tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; - tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; - tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; - tmp0[3] = table_b2b_1[(qh0 >> 24) ]; - - tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; - tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; - tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; - tmp1[3] = table_b2b_1[(qh1 >> 24) ]; - - const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); - const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); - const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); - const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) - const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); - const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); - const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); - const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - 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); -#elif defined(__wasm_simd128__) - v128_t sumv = wasm_f32x4_splat(0.0f); - - uint32_t qh; - uint64_t tmp[4]; - - // TODO: check if unrolling this is better - for (int i = 0; i < nb; ++i) { - const block_q5_0 * restrict x0 = &x[i]; - const block_q8_0 * restrict y0 = &y[i]; - - const v128_t m4b = wasm_i8x16_splat(0x0F); - - // extract the 5th bit - memcpy(&qh, x0->qh, sizeof(qh)); - - tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; - tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; - tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; - tmp[3] = table_b2b_1[(qh >> 24) ]; - - const v128_t qhl = wasm_v128_load(tmp + 0); - const v128_t qhh = wasm_v128_load(tmp + 2); - - const v128_t v0 = wasm_v128_load(x0->qs); - - // 4-bit -> 8-bit - const v128_t v0l = wasm_v128_and (v0, m4b); - const v128_t v0h = wasm_u8x16_shr(v0, 4); - - // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) - const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); - const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); - - // load y - const v128_t v1l = wasm_v128_load(y0->qs); - const v128_t v1h = wasm_v128_load(y0->qs + 16); - - // int8x16 -> int16x8 - const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); - const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); - const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); - const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); - - const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); - const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); - const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); - const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); - - // dot product - sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( - wasm_i32x4_add( - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), - wasm_i32x4_dot_i16x8(v0lfh, v1lh)), - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), - wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); - } - - *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + - wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (int i = 0; i < nb; i++) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); - - __m256i bx = bytes_from_nibbles_32(x[i].qs); - __m256i bxhi = bytes_from_bits_32(x[i].qh); - bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); - bx = _mm256_or_si256(bx, bxhi); - - __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); - - const __m256 q = mul_sum_i8_pairs_float(bx, by); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps(d, q, acc); - } - - *s = hsum_float_8(acc); -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - __m128i mask = _mm_set1_epi8((char)0xF0); - - // Main loop - for (int i = 0; i < nb; i++) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); - - __m256i bx = bytes_from_nibbles_32(x[i].qs); - const __m256i bxhi = bytes_from_bits_32(x[i].qh); - __m128i bxhil = _mm256_castsi256_si128(bxhi); - __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); - bxhil = _mm_andnot_si128(bxhil, mask); - bxhih = _mm_andnot_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx); - __m128i bxh = _mm256_extractf128_si256(bx, 1); - bxl = _mm_or_si128(bxl, bxhil); - bxh = _mm_or_si128(bxh, bxhih); - bx = MM256_SET_M128I(bxh, bxl); - - const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); - - const __m256 q = mul_sum_i8_pairs_float(bx, by); - - /* Multiply q with scale and accumulate */ - acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); - } - - *s = hsum_float_8(acc); -#elif defined(__riscv_v_intrinsic) - float sumf = 0.0; - - uint32_t qh; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - // These tempory registers are for masking and shift operations - vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); - vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); - - vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl); - vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); - - for (int i = 0; i < nb; i++) { - memcpy(&qh, x[i].qh, sizeof(uint32_t)); - - // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl); - vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl); - vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); - - // ((qh & (1u << (j + 16))) >> (j + 12)); - vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl); - vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl); - - // narrowing - vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl); - vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); - - vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl); - vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); - - // load - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); - - vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); - vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); - - vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl); - vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; - } - - *s = sumf; -#else - // scalar - float sumf = 0.0; - - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); - - int sumi = 0; - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); - - const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; - const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; - - sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); - } - - sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; - } - - *s = sumf; -#endif -} - -static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int qk = QK8_1; - const int nb = n / qk; - - assert(n % qk == 0); - assert(qk == QK5_1); - - const block_q5_1 * restrict x = vx; - const block_q8_1 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - float summs0 = 0.0f; - float summs1 = 0.0f; - - uint32_t qh0; - uint32_t qh1; - - uint64_t tmp0[4]; - uint64_t tmp1[4]; - - GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb - for (int i = 0; i < nb; i += 2) { - const block_q5_1 * restrict x0 = &x[i]; - const block_q5_1 * restrict x1 = &x[i + 1]; - const block_q8_1 * restrict y0 = &y[i]; - const block_q8_1 * restrict y1 = &y[i + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s; - summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s; - - // extract the 5th bit via lookup table ((b) << 4) - memcpy(&qh0, x0->qh, sizeof(qh0)); - memcpy(&qh1, x1->qh, sizeof(qh1)); - - tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; - tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; - tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; - tmp0[3] = table_b2b_0[(qh0 >> 24) ]; - - tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; - tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; - tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; - tmp1[3] = table_b2b_0[(qh1 >> 24) ]; - - const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); - const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); - const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); - const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // add high bit - const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); - const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); - const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); - const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - 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; -#elif defined(__wasm_simd128__) - v128_t sumv = wasm_f32x4_splat(0.0f); - - float summs = 0.0f; - - uint32_t qh; - uint64_t tmp[4]; - - // TODO: check if unrolling this is better - for (int i = 0; i < nb; ++i) { - const block_q5_1 * restrict x0 = &x[i]; - const block_q8_1 * restrict y0 = &y[i]; - - summs += GGML_FP16_TO_FP32(x0->m) * y0->s; - - const v128_t m4b = wasm_i8x16_splat(0x0F); - - // extract the 5th bit - memcpy(&qh, x0->qh, sizeof(qh)); - - tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; - tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; - tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; - tmp[3] = table_b2b_0[(qh >> 24) ]; - - const v128_t qhl = wasm_v128_load(tmp + 0); - const v128_t qhh = wasm_v128_load(tmp + 2); - - const v128_t v0 = wasm_v128_load(x0->qs); - - // 4-bit -> 8-bit - const v128_t v0l = wasm_v128_and (v0, m4b); - const v128_t v0h = wasm_u8x16_shr(v0, 4); - - // add high bit - const v128_t v0lf = wasm_v128_or(v0l, qhl); - const v128_t v0hf = wasm_v128_or(v0h, qhh); - - // load y - const v128_t v1l = wasm_v128_load(y0->qs); - const v128_t v1h = wasm_v128_load(y0->qs + 16); - - // int8x16 -> int16x8 - const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); - const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); - const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); - const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); - - const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); - const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); - const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); - const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); - - // dot product - sumv = wasm_f32x4_add(sumv, - wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), - wasm_i32x4_dot_i16x8(v0lfh, v1lh)), - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), - wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d))); - } - - *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + - wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.0f; - - // Main loop - for (int i = 0; i < nb; i++) { - const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); - - summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; - - __m256i bx = bytes_from_nibbles_32(x[i].qs); - __m256i bxhi = bytes_from_bits_32(x[i].qh); - bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); - bx = _mm256_or_si256(bx, bxhi); - - const __m256 dy = _mm256_set1_ps(y[i].d); - const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); - - const __m256 q = mul_sum_us8_pairs_float(bx, by); - - acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); - } - - *s = hsum_float_8(acc) + summs; -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - __m128i mask = _mm_set1_epi8(0x10); - - float summs = 0.0f; - - // Main loop - for (int i = 0; i < nb; i++) { - const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); - - summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; - - __m256i bx = bytes_from_nibbles_32(x[i].qs); - const __m256i bxhi = bytes_from_bits_32(x[i].qh); - __m128i bxhil = _mm256_castsi256_si128(bxhi); - __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); - bxhil = _mm_and_si128(bxhil, mask); - bxhih = _mm_and_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx); - __m128i bxh = _mm256_extractf128_si256(bx, 1); - bxl = _mm_or_si128(bxl, bxhil); - bxh = _mm_or_si128(bxh, bxhih); - bx = MM256_SET_M128I(bxh, bxl); - - const __m256 dy = _mm256_set1_ps(y[i].d); - const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); - - const __m256 q = mul_sum_us8_pairs_float(bx, by); - - acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); - } - - *s = hsum_float_8(acc) + summs; -#elif defined(__riscv_v_intrinsic) - float sumf = 0.0; - - uint32_t qh; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - // temporary registers for shift operations - vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); - vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); - - for (int i = 0; i < nb; i++) { - memcpy(&qh, x[i].qh, sizeof(uint32_t)); - - // load qh - vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl); - - // ((qh >> (j + 0)) << 4) & 0x10; - vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl); - vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); - vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl); - - // ((qh >> (j + 12)) ) & 0x10; - vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl); - vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl); - - // narrowing - vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl); - vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); - - vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl); - vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); - - // load - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl); - - vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); - vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); - - vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; - } - - *s = sumf; -#else - // scalar - float sumf = 0.0; - - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); - - int sumi = 0; - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; - - const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0; - const int32_t x1 = (x[i].qs[j] >> 4) | xh_1; - - sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); - } - - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; - } - - *s = sumf; -#endif -} - -static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); - - const block_q8_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb - for (int i = 0; i < nb; i += 2) { - const block_q8_0 * restrict x0 = &x[i + 0]; - const block_q8_0 * restrict x1 = &x[i + 1]; - const block_q8_0 * restrict y0 = &y[i + 0]; - const block_q8_0 * restrict y1 = &y[i + 1]; - - const int8x16_t x0_0 = vld1q_s8(x0->qs); - const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); - const int8x16_t x1_0 = vld1q_s8(x1->qs); - const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); - - // load y - const int8x16_t y0_0 = vld1q_s8(y0->qs); - const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); - 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)); - - 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); -#elif defined(__AVX2__) || defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (int i = 0; i < nb; ++i) { - // Compute combined scale for the block - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); - __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs); - __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); - - const __m256 q = mul_sum_i8_pairs_float(bx, by); - - // Multiply q with scale and accumulate -#if defined(__AVX2__) - acc = _mm256_fmadd_ps( d, q, acc ); -#else - acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc ); -#endif - } - - *s = hsum_float_8(acc); -#elif defined(__riscv_v_intrinsic) - float sumf = 0.0; - size_t vl = __riscv_vsetvl_e8m1(qk); - - for (int i = 0; i < nb; i++) { - // load elements - vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl); - vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl); - - vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl); - - vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); - vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); - - sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); - } - - *s = sumf; -#else - // scalar - float sumf = 0.0; - - for (int i = 0; i < nb; i++) { - int sumi = 0; - - for (int j = 0; j < qk; j++) { - sumi += x[i].qs[j]*y[i].qs[j]; - } - - sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); - } - - *s = sumf; -#endif -} - // compute GGML_VEC_DOT_UNROLL dot products at once // xs - x row stride in bytes inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { @@ -21001,7 +18706,6 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i block_q8_0 * block = (block_q8_0*)dst + start / QK8_0; result = ggml_quantize_q8_0(src + start, block, n, n, hist); } break; -#ifdef GGML_USE_K_QUANTS case GGML_TYPE_Q2_K: { GGML_ASSERT(start % QK_K == 0); @@ -21032,7 +18736,6 @@ 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; -#endif case GGML_TYPE_F16: { int elemsize = sizeof(ggml_fp16_t); diff --git a/ggml.h b/ggml.h index 08bff5511..8c954904e 100644 --- a/ggml.h +++ b/ggml.h @@ -1930,12 +1930,19 @@ extern "C" { // quantization // + // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist); + 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_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 3d431ee7b..1d1db8fc9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -19,13 +19,11 @@ #ifdef GGML_USE_MPI # include "ggml-mpi.h" #endif -#ifdef GGML_USE_K_QUANTS -# ifndef QK_K -# ifdef GGML_QKK_64 -# define QK_K 64 -# else -# define QK_K 256 -# endif +#ifndef QK_K +# ifdef GGML_QKK_64 +# define QK_K 64 +# else +# define QK_K 256 # endif #endif @@ -8052,7 +8050,7 @@ struct no_init { struct quantize_state_internal { const llama_model & model; const llama_model_quantize_params * params; -#ifdef GGML_USE_K_QUANTS + int n_attention_wv = 0; int n_feed_forward_w2 = 0; int i_attention_wv = 0; @@ -8060,7 +8058,7 @@ struct quantize_state_internal { int n_k_quantized = 0; int n_fallback = 0; -#endif + quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params) : model(model) , params(params) @@ -8125,7 +8123,6 @@ static void llama_convert_tensor_internal( workers.clear(); } -#ifdef GGML_USE_K_QUANTS static ggml_type get_k_quant_type( quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype @@ -8237,7 +8234,6 @@ static ggml_type get_k_quant_type( return new_type; } -#endif static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) { ggml_type quantized_type; @@ -8252,7 +8248,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break; case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break; -#ifdef GGML_USE_K_QUANTS // K-quants case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; case LLAMA_FTYPE_MOSTLY_Q3_K_S: @@ -8263,7 +8258,7 @@ 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; -#endif + default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } @@ -8304,7 +8299,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); gguf_set_val_u32(ctx_out, "general.file_type", ftype); -#ifdef GGML_USE_K_QUANTS for (int i = 0; i < ml.n_tensors; ++i) { struct ggml_tensor * meta = ml.get_tensor_meta(i); @@ -8322,7 +8316,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n", __func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer); } -#endif size_t total_size_org = 0; size_t total_size_new = 0; @@ -8387,9 +8380,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (quantize) { new_type = quantized_type; -#ifdef GGML_USE_K_QUANTS - new_type = get_k_quant_type(qs, new_type, tensor, ftype); -#endif + if (!params->pure) { + new_type = get_k_quant_type(qs, new_type, tensor, ftype); + } + // If we've decided to quantize to the same type the tensor is already // in then there's nothing to do. quantize = tensor->type != new_type; @@ -8514,12 +8508,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_INFO("\n"); } } -#ifdef GGML_USE_K_QUANTS + if (qs.n_fallback > 0) { LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n", __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback); } -#endif } static int llama_apply_lora_from_file_internal( @@ -8844,6 +8837,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { /*.allow_requantize =*/ false, /*.quantize_output_tensor =*/ true, /*.only_copy =*/ false, + /*.pure =*/ false, }; return result; diff --git a/llama.h b/llama.h index d901dcd91..6927bd601 100644 --- a/llama.h +++ b/llama.h @@ -191,6 +191,7 @@ extern "C" { bool allow_requantize; // allow quantizing non-f32/f16 tensors 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 } llama_model_quantize_params; // grammar types From 71a09da301705b9c5ad4ca3cf3fbd966dd3f1ec5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 29 Oct 2023 18:32:51 +0200 Subject: [PATCH 65/95] llama : fix kv shift bug (#3835) ggml-ci --- llama.cpp | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/llama.cpp b/llama.cpp index 1d1db8fc9..d8510a5cf 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1552,14 +1552,14 @@ static void llama_kv_cache_seq_shift( 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.cells[i].pos += delta; + cache.has_shift = true; + cache.cells[i].pos += delta; + cache.cells[i].delta += delta; + if (cache.cells[i].pos < 0) { cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); if (new_head == cache.size) new_head = i; - } else { - cache.has_shift = true; - cache.cells[i].delta = delta; } } } @@ -6073,11 +6073,20 @@ static int llama_decode_internal( #endif // update the kv ring buffer - lctx.kv_self.has_shift = false; - lctx.kv_self.head += n_tokens; - // Ensure kv cache head points to a valid index. - if (lctx.kv_self.head >= lctx.kv_self.size) { - lctx.kv_self.head = 0; + { + if (kv_self.has_shift) { + kv_self.has_shift = false; + for (uint32_t i = 0; i < kv_self.size; ++i) { + kv_self.cells[i].delta = 0; + } + } + + kv_self.head += n_tokens; + + // Ensure kv cache head points to a valid index. + if (kv_self.head >= kv_self.size) { + kv_self.head = 0; + } } #ifdef GGML_PERF From 2046eb4345e62c4575b3cdc0115a51db89f3fb70 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Sun, 29 Oct 2023 12:33:47 -0400 Subject: [PATCH 66/95] make : remove unnecessary dependency on build-info.h (#3842) --- Makefile | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 2cecc2216..c53c1e726 100644 --- a/Makefile +++ b/Makefile @@ -541,10 +541,10 @@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ -COMMON_H_DEPS = common/common.h common/sampling.h build-info.h common/log.h -COMMON_DEPS = $(COMMON_H_DEPS) common.o sampling.o grammar-parser.o +COMMON_H_DEPS = common/common.h common/sampling.h common/log.h +COMMON_DEPS = common.o sampling.o grammar-parser.o -common.o: common/common.cpp $(COMMON_H_DEPS) +common.o: common/common.cpp build-info.h $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ sampling.o: common/sampling.cpp $(COMMON_H_DEPS) From 6e08281e588bbba1a5d180290a94a43f167f3a1a Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 29 Oct 2023 11:31:40 -0600 Subject: [PATCH 67/95] Extend llama_kv_cache_seq_rm to allow matching any sequence (#3843) * Extend llama_kv_cache_seq_rm to allow matichng any sequence * Replace llama_kv_cache_tokens_rm with llama_kv_cache_clear Use llama_kv_cache_clear for cache clearing Change calls to llama_kv_cache_tokens_rm that want to delete by position to use llama_kv_cache_seq_rm functionality --- common/common.cpp | 2 +- examples/batched-bench/batched-bench.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 4 ++-- examples/main/main.cpp | 2 +- examples/perplexity/perplexity.cpp | 6 ++--- examples/server/server.cpp | 2 +- llama.cpp | 29 ++++++++++++------------ llama.h | 15 +++++------- 8 files changed, 30 insertions(+), 32 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index f81f4d354..c187128d6 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -889,7 +889,7 @@ std::tuple llama_init_from_gpt_par std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); - llama_kv_cache_tokens_rm(lctx, -1, -1); + llama_kv_cache_clear(lctx); llama_reset_timings(lctx); } diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 43f9c971d..533c55c17 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -185,7 +185,7 @@ int main(int argc, char ** argv) { const auto t_pp_start = ggml_time_us(); - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); if (!decode_helper(ctx, batch, ctx_params.n_batch)) { LOG_TEE("%s: llama_decode() failed\n", __func__); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 20767d555..780398184 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -1037,7 +1037,7 @@ int main(int argc, char ** argv) { test t(inst, lmodel, ctx); - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); // warmup run if (t.n_prompt > 0) { @@ -1048,7 +1048,7 @@ int main(int argc, char ** argv) { } for (int i = 0; i < params.reps; i++) { - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); uint64_t t_start = get_time_ns(); if (t.n_prompt > 0) { diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 3d9f670b9..8a43b6ab8 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -298,7 +298,7 @@ int main(int argc, char ** argv) { } // remove any "future" tokens that we might have inherited from the previous session - llama_kv_cache_tokens_rm(ctx, n_matching_session_tokens, -1); + llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1); } LOGLN( diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 3c2542e8c..bd2c73d87 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -210,7 +210,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & const auto t_start = std::chrono::high_resolution_clock::now(); // clear the KV cache - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; @@ -339,7 +339,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par const auto t_start = std::chrono::high_resolution_clock::now(); // clear the KV cache - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; @@ -573,7 +573,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } // clear the KV cache - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab); if (logits.empty()) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5b7e4139d..c163c7f8e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -857,7 +857,7 @@ struct llama_server_context void kv_cache_clear() { // clear the entire KV cache - llama_kv_cache_tokens_rm(ctx, -1, -1); + llama_kv_cache_clear(ctx); clean_kv_cache = false; } diff --git a/llama.cpp b/llama.cpp index d8510a5cf..a4340d527 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1466,17 +1466,12 @@ static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { return 0; } -static void llama_kv_cache_tokens_rm(struct llama_kv_cache & cache, int32_t c0, int32_t c1) { - if (c0 < 0) c0 = 0; - if (c1 < 0) c1 = cache.size; - - for (int32_t i = c0; i < c1; ++i) { +static void llama_kv_cache_clear(struct llama_kv_cache & cache) { + for (int32_t i = 0; i < cache.size; ++i) { cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); } - - // Searching for a free slot can start here since we know it will be empty. - cache.head = uint32_t(c0); + cache.head = 0; } static void llama_kv_cache_seq_rm( @@ -1490,8 +1485,14 @@ static void llama_kv_cache_seq_rm( 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.cells[i].seq_id.erase(seq_id); + if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { + if (seq_id < 0) { + cache.cells[i].seq_id.clear(); + } else if (cache.cells[i].has_seq_id(seq_id)) { + cache.cells[i].seq_id.erase(seq_id); + } else { + continue; + } if (cache.cells[i].seq_id.empty()) { cache.cells[i].pos = -1; if (new_head == cache.size) new_head = i; @@ -9207,8 +9208,8 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) { return ctx->kv_self.head; } -void llama_kv_cache_tokens_rm(struct llama_context * ctx, int32_t c0, int32_t c1) { - llama_kv_cache_tokens_rm(ctx->kv_self, c0, c1); +void llama_kv_cache_clear(struct llama_context * ctx) { + llama_kv_cache_clear(ctx->kv_self); } void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { @@ -9654,7 +9655,7 @@ int llama_eval( llama_token * tokens, int32_t n_tokens, int n_past) { - llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1); + 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)); if (ret < 0) { @@ -9669,7 +9670,7 @@ int llama_eval_embd( float * embd, int32_t n_tokens, int n_past) { - llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1); + 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, }; diff --git a/llama.h b/llama.h index 6927bd601..d727dbd9f 100644 --- a/llama.h +++ b/llama.h @@ -334,17 +334,14 @@ extern "C" { LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx), "avoid using this, it will be removed in the future, instead - count the tokens in user code"); - // Remove all tokens data of cells in [c0, c1) - // c0 < 0 : [0, c1] - // c1 < 0 : [c0, inf) - LLAMA_API void llama_kv_cache_tokens_rm( - struct llama_context * ctx, - int32_t c0, - int32_t c1); + // Clear the KV cache + LLAMA_API void llama_kv_cache_clear( + struct llama_context * ctx); // Removes all tokens that belong to the specified sequence and have positions in [p0, p1) - // p0 < 0 : [0, p1] - // p1 < 0 : [p0, inf) + // seq_id < 0 : match any sequence + // p0 < 0 : [0, p1] + // p1 < 0 : [p0, inf) LLAMA_API void llama_kv_cache_seq_rm( struct llama_context * ctx, llama_seq_id seq_id, From 207b51900e15cc7f89763a3bb1c565fe11cbb45d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 30 Oct 2023 19:19:15 +0200 Subject: [PATCH 68/95] ggml : move FP16 <-> FP32 code to ggml-impl.h (#3861) * ggml : move FP16 <-> FP32 stuff to ggml-impl.h ggml-ci * tests : fix ARM build * ggml : explicitly initialize deprecated type traits * ggml : add math.h to ggml-impl.h * ggml : remove duplicate static assert macros * ggml : prefix lookup tables with ggml_ ggml-ci * ggml-impl : move extern "C" to start of file --- ggml-impl.h | 237 ++++++++++++++++++++++++ ggml-quants.c | 350 ++++++++++++++++++------------------ ggml-quants.h | 14 +- ggml.c | 282 +++++------------------------ llama.cpp | 2 +- tests/test-double-float.cpp | 2 +- tests/test-quantize-fns.cpp | 7 + 7 files changed, 470 insertions(+), 424 deletions(-) create mode 100644 ggml-impl.h diff --git a/ggml-impl.h b/ggml-impl.h new file mode 100644 index 000000000..5ec18a50c --- /dev/null +++ b/ggml-impl.h @@ -0,0 +1,237 @@ +#pragma once + +#include "ggml.h" + +// GGML internal header + +#include +#include +#include +#include // memcpy +#include // fabsf + +#ifdef __cplusplus +extern "C" { +#endif + +// static_assert should be a #define, but if it's not, +// fall back to the _Static_assert C11 keyword. +// if C99 - static_assert is noop +// ref: https://stackoverflow.com/a/53923785/4039976 +#ifndef static_assert +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) +#define static_assert(cond, msg) _Static_assert(cond, msg) +#else +#define static_assert(cond, msg) struct global_scope_noop_trick +#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__)) +#ifndef __FMA__ +#define __FMA__ +#endif +#ifndef __F16C__ +#define __F16C__ +#endif +#ifndef __SSE3__ +#define __SSE3__ +#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 +#if defined(__ARM_NEON) && !defined(_MSC_VER) + +// if YCM cannot find , make a symbolic link to it, for example: +// +// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ +// +#include + +#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x)) +#define GGML_COMPUTE_FP32_TO_FP16(x) (x) + +#define GGML_FP16_TO_FP32(x) ((float) (x)) +#define GGML_FP32_TO_FP16(x) (x) + +#else + +#ifdef __wasm_simd128__ +#include +#else +#ifdef __POWER9_VECTOR__ +#include +#undef bool +#define bool _Bool +#else +#if defined(_MSC_VER) || defined(__MINGW32__) +#include +#else +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) +#if !defined(__riscv) +#include +#endif +#endif +#endif +#endif +#endif + +#ifdef __riscv_v_intrinsic +#include +#endif + +#ifdef __F16C__ + +#ifdef _MSC_VER +#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) +#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) +#else +#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) +#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) +#endif + +#elif defined(__POWER9_VECTOR__) + +#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) +#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) +/* the inline asm below is about 12% faster than the lookup method */ +#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) +#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) + +static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + register float f; + register double d; + __asm__( + "mtfprd %0,%2\n" + "xscvhpdp %0,%0\n" + "frsp %1,%0\n" : + /* temp */ "=d"(d), + /* out */ "=f"(f): + /* in */ "r"(h)); + return f; +} + +static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + register double d; + register ggml_fp16_t r; + __asm__( /* xscvdphp can work on double or single precision */ + "xscvdphp %0,%2\n" + "mffprd %1,%0\n" : + /* temp */ "=d"(d), + /* out */ "=r"(r): + /* in */ "f"(f)); + return r; +} + +#else + +// FP16 <-> FP32 +// ref: https://github.com/Maratyszcza/FP16 + +static inline float fp32_from_bits(uint32_t w) { + union { + uint32_t as_bits; + float as_value; + } fp32; + fp32.as_bits = w; + return fp32.as_value; +} + +static inline uint32_t fp32_to_bits(float f) { + union { + float as_value; + uint32_t as_bits; + } fp32; + fp32.as_value = f; + return fp32.as_bits; +} + +static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + const uint32_t w = (uint32_t) h << 16; + const uint32_t sign = w & UINT32_C(0x80000000); + const uint32_t two_w = w + w; + + const uint32_t exp_offset = UINT32_C(0xE0) << 23; +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) + const float exp_scale = 0x1.0p-112f; +#else + const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); +#endif + const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; + + const uint32_t magic_mask = UINT32_C(126) << 23; + const float magic_bias = 0.5f; + const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; + + const uint32_t denormalized_cutoff = UINT32_C(1) << 27; + const uint32_t result = sign | + (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); + return fp32_from_bits(result); +} + +static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) + const float scale_to_inf = 0x1.0p+112f; + const float scale_to_zero = 0x1.0p-110f; +#else + const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); + const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); +#endif + float base = (fabsf(f) * scale_to_inf) * scale_to_zero; + + const uint32_t w = fp32_to_bits(f); + const uint32_t shl1_w = w + w; + const uint32_t sign = w & UINT32_C(0x80000000); + uint32_t bias = shl1_w & UINT32_C(0xFF000000); + if (bias < UINT32_C(0x71000000)) { + bias = UINT32_C(0x71000000); + } + + base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; + const uint32_t bits = fp32_to_bits(base); + const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); + const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); + const uint32_t nonsign = exp_bits + mantissa_bits; + return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); +} + +#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) +#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + +#endif // __F16C__ + +#endif // __ARM_NEON + +// precomputed f32 table for f16 (256 KB) +// defined in ggml.c, initialized in ggml_init() +extern float ggml_table_f32_f16[1 << 16]; + +// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, +// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. +// This is also true for POWER9. +#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) + +inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { + uint16_t s; + memcpy(&s, &f, sizeof(uint16_t)); + return ggml_table_f32_f16[s]; +} + +#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) +#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) + +#endif + + // TODO: backend v2 PR + +#ifdef __cplusplus +} +#endif diff --git a/ggml-quants.c b/ggml-quants.c index fd4ee1be6..721594467 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1,5 +1,5 @@ #include "ggml-quants.h" -#include "ggml.h" +#include "ggml-impl.h" #include #include @@ -352,7 +352,7 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict const float d = max / -8; const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < qk/2; ++j) { const float x0 = x[i*qk + 0 + j]*id; @@ -392,8 +392,8 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict const float d = (max - min) / ((1 << 4) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); - y[i].m = ggml_fp32_to_fp16(min); + y[i].d = GGML_FP32_TO_FP16(d); + y[i].m = GGML_FP32_TO_FP16(min); for (int j = 0; j < qk/2; ++j) { const float x0 = (x[i*qk + 0 + j] - min)*id; @@ -434,7 +434,7 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict const float d = max / -16; const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); uint32_t qh = 0; @@ -481,8 +481,8 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict const float d = (max - min) / ((1 << 5) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); - y[i].m = ggml_fp32_to_fp16(min); + y[i].d = GGML_FP32_TO_FP16(d); + y[i].m = GGML_FP32_TO_FP16(min); uint32_t qh = 0; @@ -524,7 +524,7 @@ void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < QK8_0; ++j) { const float x0 = x[i*QK8_0 + j]*id; @@ -559,7 +559,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < 8; j++) { const float32x4_t v = vmulq_n_f32(srcv[j], id); @@ -592,7 +592,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < 8; j++) { const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); @@ -627,7 +627,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { // Quantize these floats const float d = maxScalar / 127.f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); @@ -704,7 +704,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = ggml_fp32_to_fp16(d); + y[i].d = GGML_FP32_TO_FP16(d); vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); @@ -982,7 +982,7 @@ void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int const int nb = k / qk; for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); + const float d = GGML_FP16_TO_FP32(x[i].d); for (int j = 0; j < qk/2; ++j) { const int x0 = (x[i].qs[j] & 0x0F) - 8; @@ -1002,8 +1002,8 @@ void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int const int nb = k / qk; for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); - const float m = ggml_fp16_to_fp32(x[i].m); + const float d = GGML_FP16_TO_FP32(x[i].d); + const float m = GGML_FP16_TO_FP32(x[i].m); for (int j = 0; j < qk/2; ++j) { const int x0 = (x[i].qs[j] & 0x0F); @@ -1023,7 +1023,7 @@ void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int const int nb = k / qk; for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); + const float d = GGML_FP16_TO_FP32(x[i].d); uint32_t qh; memcpy(&qh, x[i].qh, sizeof(qh)); @@ -1049,8 +1049,8 @@ void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int const int nb = k / qk; for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); - const float m = ggml_fp16_to_fp32(x[i].m); + const float d = GGML_FP16_TO_FP32(x[i].d); + const float m = GGML_FP16_TO_FP32(x[i].m); uint32_t qh; memcpy(&qh, x[i].qh, sizeof(qh)); @@ -1076,7 +1076,7 @@ void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int const int nb = k / qk; for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); + const float d = GGML_FP16_TO_FP32(x[i].d); for (int j = 0; j < qk; ++j) { y[i*qk + j] = x[i].qs[j]*d; @@ -1387,10 +1387,10 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict int l = nearest_int(iscale*scales[j]); y[i].scales[j] = l; } - y[i].d = ggml_fp32_to_fp16(max_scale/q4scale); + y[i].d = GGML_FP32_TO_FP16(max_scale/q4scale); } else { for (int j = 0; j < QK_K/16; ++j) y[i].scales[j] = 0; - y[i].d = ggml_fp32_to_fp16(0.f); + y[i].d = GGML_FP32_TO_FP16(0.f); } if (max_min > 0) { float iscale = q4scale/max_min; @@ -1398,14 +1398,14 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict int l = nearest_int(iscale*mins[j]); y[i].scales[j] |= (l << 4); } - y[i].dmin = ggml_fp32_to_fp16(max_min/q4scale); + y[i].dmin = GGML_FP32_TO_FP16(max_min/q4scale); } else { - y[i].dmin = ggml_fp32_to_fp16(0.f); + y[i].dmin = GGML_FP32_TO_FP16(0.f); } for (int j = 0; j < QK_K/16; ++j) { - const float d = ggml_fp16_to_fp32(y[i].d) * (y[i].scales[j] & 0xF); + const float d = GGML_FP16_TO_FP32(y[i].d) * (y[i].scales[j] & 0xF); if (!d) continue; - const float dm = ggml_fp16_to_fp32(y[i].dmin) * (y[i].scales[j] >> 4); + const float dm = GGML_FP16_TO_FP32(y[i].dmin) * (y[i].scales[j] >> 4); for (int ii = 0; ii < 16; ++ii) { int l = nearest_int((x[16*j + ii] + dm)/d); l = MAX(0, MIN(3, l)); @@ -1436,8 +1436,8 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); - const float min = ggml_fp16_to_fp32(x[i].dmin); + const float d = GGML_FP16_TO_FP32(x[i].d); + const float min = GGML_FP16_TO_FP32(x[i].dmin); const uint8_t * q = x[i].qs; @@ -1526,16 +1526,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict l >>= 4; y[i].scales[j%4 + 8] |= (l << (2*(j/4))); } - y[i].d = ggml_fp32_to_fp16(1/iscale); + y[i].d = GGML_FP32_TO_FP16(1/iscale); } else { - y[i].d = ggml_fp32_to_fp16(0.f); + y[i].d = GGML_FP32_TO_FP16(0.f); } 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; + float d = GGML_FP16_TO_FP32(y[i].d) * sc; if (!d) { continue; } @@ -1555,16 +1555,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict l2 = 8 + MAX(-8, MIN(7, l2)); y[i].scales[j/2] = l1 | (l2 << 4); } - y[i].d = ggml_fp32_to_fp16(1/iscale); + y[i].d = GGML_FP32_TO_FP16(1/iscale); } else { for (int j = 0; j < QK_K/16; j+=2) { y[i].scales[j/2] = 0; } - y[i].d = ggml_fp32_to_fp16(0.f); + y[i].d = GGML_FP32_TO_FP16(0.f); } for (int j = 0; j < QK_K/16; ++j) { int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4; - float d = ggml_fp16_to_fp32(y[i].d) * (s - 8); + float d = GGML_FP16_TO_FP32(y[i].d) * (s - 8); if (!d) { continue; } @@ -1618,7 +1618,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int for (int i = 0; i < nb; i++) { - const float d_all = ggml_fp16_to_fp32(x[i].d); + const float d_all = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q = x[i].qs; const uint8_t * restrict hm = x[i].hmask; @@ -1663,7 +1663,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int for (int i = 0; i < nb; i++) { - const float d_all = ggml_fp16_to_fp32(x[i].d); + const float d_all = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q = x[i].qs; const uint8_t * restrict hm = x[i].hmask; @@ -1753,15 +1753,15 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict 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(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; + 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; + 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)); @@ -1778,17 +1778,17 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict int m2 = nearest_int(inv_min*mins[1]); y[i].scales[0] = d1 | (m1 << 4); y[i].scales[1] = d2 | (m2 << 4); - y[i].d[0] = ggml_fp32_to_fp16(max_scale/s_factor); - y[i].d[1] = ggml_fp32_to_fp16(max_min/s_factor); + y[i].d[0] = GGML_FP32_TO_FP16(max_scale/s_factor); + y[i].d[1] = GGML_FP32_TO_FP16(max_min/s_factor); float sumlx = 0; int suml2 = 0; for (int j = 0; j < QK_K/32; ++j) { const uint8_t sd = y[i].scales[j] & 0xF; const uint8_t sm = y[i].scales[j] >> 4; - const float d = ggml_fp16_to_fp32(y[i].d[0]) * sd; + const float d = GGML_FP16_TO_FP32(y[i].d[0]) * sd; if (!d) continue; - const float m = ggml_fp16_to_fp32(y[i].d[1]) * sm; + const float m = GGML_FP16_TO_FP32(y[i].d[1]) * sm; for (int ii = 0; ii < 32; ++ii) { int l = nearest_int((x[32*j + ii] + m)/d); l = MAX(0, MIN(15, l)); @@ -1798,7 +1798,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } } if (suml2) { - y[i].d[0] = ggml_fp32_to_fp16(sumlx/suml2); + y[i].d[0] = GGML_FP32_TO_FP16(sumlx/suml2); } #endif uint8_t * q = y[i].qs; @@ -1822,8 +1822,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int #if QK_K == 256 - const float d = ggml_fp16_to_fp32(x[i].d); - const float min = ggml_fp16_to_fp32(x[i].dmin); + const float d = GGML_FP16_TO_FP32(x[i].d); + const float min = GGML_FP16_TO_FP32(x[i].dmin); int is = 0; uint8_t sc, m; @@ -1837,8 +1837,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int q += 32; is += 2; } #else - const float dall = ggml_fp16_to_fp32(x[i].d[0]); - const float mall = ggml_fp16_to_fp32(x[i].d[1]); + const float dall = GGML_FP16_TO_FP32(x[i].d[0]); + const float mall = GGML_FP16_TO_FP32(x[i].d[1]); const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4); const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4); for (int l = 0; l < 32; ++l) { @@ -1924,15 +1924,15 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict 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(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; + 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; + 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)); @@ -1976,10 +1976,10 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict int l = nearest_int(iscale*scales[j]); y[i].scales[j] = MAX(-128, MIN(127, l)); } - y[i].d = ggml_fp32_to_fp16(1/iscale); + y[i].d = GGML_FP32_TO_FP16(1/iscale); for (int j = 0; j < QK_K/16; ++j) { - const float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j]; + const 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); @@ -2023,8 +2023,8 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int #if QK_K == 256 - const float d = ggml_fp16_to_fp32(x[i].d); - const float min = ggml_fp16_to_fp32(x[i].dmin); + const float d = GGML_FP16_TO_FP32(x[i].d); + const float min = GGML_FP16_TO_FP32(x[i].dmin); int is = 0; uint8_t sc, m; @@ -2040,7 +2040,7 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int u1 <<= 2; u2 <<= 2; } #else - float d = ggml_fp16_to_fp32(x[i].d); + float d = GGML_FP16_TO_FP32(x[i].d); const int8_t * restrict s = x[i].scales; for (int l = 0; l < 8; ++l) { y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16)); @@ -2103,19 +2103,19 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict if (!max_abs_scale) { memset(&y[i], 0, sizeof(block_q6_K)); - y[i].d = ggml_fp32_to_fp16(0.f); + 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); + 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]; + float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j]; if (!d) { continue; } @@ -2164,7 +2164,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); + const float d = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict ql = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -2371,8 +2371,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, 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)); + 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)); @@ -2389,8 +2389,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, 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)); + 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 } @@ -2402,7 +2402,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, // Main loop for (int i = 0; i < nb; ++i) { /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) ); + const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); __m256i bx = bytes_from_nibbles_32(x[i].qs); @@ -2426,7 +2426,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, // Main loop for (int i = 0; i < nb; ++i) { // Compute combined scale for the block - const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) ); + const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); const __m128i lowMask = _mm_set1_epi8(0xF); const __m128i off = _mm_set1_epi8(8); @@ -2468,7 +2468,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[0].d) * ggml_fp16_to_fp32(y[0].d) ); + const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) ); const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs); @@ -2486,7 +2486,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[1].d) * ggml_fp16_to_fp32(y[1].d) ); + const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) ); const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs); @@ -2521,7 +2521,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) ); + const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs); @@ -2539,7 +2539,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[i + 1].d) * ggml_fp16_to_fp32(y[i + 1].d) ); + const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) ); const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs); @@ -2606,7 +2606,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d); + sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); } *s = sumf; @@ -2624,7 +2624,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); } - sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d); + sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); } *s = sumf; @@ -2655,7 +2655,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri const block_q8_1 * restrict y0 = &y[i + 0]; const block_q8_1 * restrict y1 = &y[i + 1]; - summs += ggml_fp16_to_fp32(x0->m) * y0->s + ggml_fp16_to_fp32(x1->m) * y1->s; + summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s; const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -2679,8 +2679,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri 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); + 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)); @@ -2697,8 +2697,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri 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); + 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 } @@ -2711,10 +2711,10 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri // Main loop for (int i = 0; i < nb; ++i) { - const float d0 = ggml_fp16_to_fp32(x[i].d); + const float d0 = GGML_FP16_TO_FP32(x[i].d); const float d1 = y[i].d; - summs += ggml_fp16_to_fp32(x[i].m) * y[i].s; + summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; const __m256 d0v = _mm256_set1_ps( d0 ); const __m256 d1v = _mm256_set1_ps( d1 ); @@ -2766,7 +2766,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; } *s = sumf; @@ -2784,7 +2784,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); } - sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; } *s = sumf; @@ -2864,10 +2864,10 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri #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)); + 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)); + 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)); @@ -2884,8 +2884,8 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri 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)); + 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 } @@ -2946,7 +2946,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri wasm_i32x4_dot_i16x8(v0lfh, v1lh)), wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * ggml_fp16_to_fp32(y0->d)))); + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); } *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + @@ -2958,7 +2958,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri // Main loop for (int i = 0; i < nb; i++) { /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d)); + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); __m256i bx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); @@ -2982,7 +2982,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri // Main loop for (int i = 0; i < nb; i++) { /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d)); + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); __m256i bx = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); @@ -3066,7 +3066,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi; + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; } *s = sumf; @@ -3090,7 +3090,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); } - sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi; + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; } *s = sumf; @@ -3130,8 +3130,8 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri const uint8x16_t m4b = vdupq_n_u8(0x0F); - summs0 += ggml_fp16_to_fp32(x0->m) * y0->s; - summs1 += ggml_fp16_to_fp32(x1->m) * y1->s; + summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s; + summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s; // extract the 5th bit via lookup table ((b) << 4) memcpy(&qh0, x0->qh, sizeof(qh0)); @@ -3176,10 +3176,10 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri #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); + 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); + 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)); @@ -3196,8 +3196,8 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri 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); + 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 } @@ -3215,7 +3215,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri const block_q5_1 * restrict x0 = &x[i]; const block_q8_1 * restrict y0 = &y[i]; - summs += ggml_fp16_to_fp32(x0->m) * y0->s; + summs += GGML_FP16_TO_FP32(x0->m) * y0->s; const v128_t m4b = wasm_i8x16_splat(0x0F); @@ -3262,7 +3262,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri wasm_i32x4_dot_i16x8(v0lfh, v1lh)), wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * y0->d))); + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d))); } *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + @@ -3275,9 +3275,9 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri // Main loop for (int i = 0; i < nb; i++) { - const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d)); + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); - summs += ggml_fp16_to_fp32(x[i].m) * y[i].s; + summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; __m256i bx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); @@ -3302,9 +3302,9 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri // Main loop for (int i = 0; i < nb; i++) { - const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d)); + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); - summs += ggml_fp16_to_fp32(x[i].m) * y[i].s; + summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; __m256i bx = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); @@ -3385,7 +3385,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; } *s = sumf; @@ -3409,7 +3409,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); } - sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; } *s = sumf; @@ -3451,11 +3451,11 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri #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)); + 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)); + 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)); @@ -3473,8 +3473,8 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri 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)); + 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 } @@ -3486,7 +3486,7 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri // Main loop for (int i = 0; i < nb; ++i) { // Compute combined scale for the block - const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d)); + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs); __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); @@ -3517,7 +3517,7 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); - sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)); + sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); } *s = sumf; @@ -3532,7 +3532,7 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri sumi += x[i].qs[j]*y[i].qs[j]; } - sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)); + sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); } *s = sumf; @@ -3562,8 +3562,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -3641,8 +3641,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -3708,8 +3708,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d); - const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -3816,8 +3816,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int8_t * q8 = y[i].qs; const uint8_t * sc = x[i].scales; - const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d); - const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); size_t vl = 16; @@ -3903,8 +3903,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri summs += y[i].bsums[j] * (sc[j] >> 4); } - const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d); - const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin); + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); int isum = 0; int is = 0; @@ -4021,8 +4021,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -4073,8 +4073,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -4188,8 +4188,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri summs += y[i].bsums[j] * (sc[j] >> 4); } - const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d); - const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin); + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); isum[0] = isum[1] = isum[2] = isum[3] = 0; for (int l = 0; l < 16; ++l) { @@ -4242,7 +4242,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q3 = x[i].qs; const uint8_t * restrict qh = x[i].hmask; @@ -4350,7 +4350,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -4455,7 +4455,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -4676,7 +4676,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; sumf += d*sum_t; @@ -4741,7 +4741,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; q8 += 8; a += 8; } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; } for (int l = 0; l < 8; ++l) sumf += sums[l]; @@ -4843,7 +4843,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -4914,7 +4914,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -5099,7 +5099,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q8 += 8; a += 8; for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l]; } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; } for (int l = 0; l < 8; ++l) sumf += sums[l]; @@ -5139,8 +5139,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); @@ -5222,8 +5222,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); memcpy(utmp, x[i].scales, 12); utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); @@ -5288,8 +5288,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -5371,8 +5371,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri size_t vl = 8; - 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); + 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); vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); @@ -5482,9 +5482,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; q8 += 8; a += 8; } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; sumf -= dmin * sumi; } for (int l = 0; l < 8; ++l) sumf += sums[l]; @@ -5586,8 +5586,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d; - const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; + const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; const __m256 vd = _mm256_set1_ps(d); const uint16_t * a = (const uint16_t *)x[i].scales; @@ -5632,8 +5632,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d; - const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; + const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; const __m256 vd = _mm256_set1_ps(d); const uint16_t * a = (const uint16_t *)x[i].scales; @@ -5689,8 +5689,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri s16[0] = b[0] & 0x0f0f; s16[1] = (b[0] >> 4) & 0x0f0f; - sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]); + sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); size_t vl = 32; @@ -5739,9 +5739,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri s16[0] = b[0] & 0x0f0f; s16[1] = (b[0] >> 4) & 0x0f0f; - sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); + sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); for (int j = 0; j < QK_K/32; ++j) { for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; @@ -5789,8 +5789,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); @@ -5878,8 +5878,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const int8_t * restrict q8 = y[i].qs; #if QK_K == 256 - 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); + 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); memcpy(utmp, x[i].scales, 12); utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); @@ -5960,8 +5960,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri 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); + 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); const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -6065,8 +6065,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict hm = x[i].qh; const int8_t * restrict q8 = y[i].qs; - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; - const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); @@ -6188,9 +6188,9 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; q8 += 8; a += 8; } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; sumf -= dmin * sumi; } for (int l = 0; l < 8; ++l) sumf += sums[l]; @@ -6288,7 +6288,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); @@ -6334,7 +6334,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); @@ -6471,7 +6471,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16); } - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const int8_t * restrict sc = x[i].scales; for (int j = 0; j < QK_K/16; ++j) { @@ -6514,7 +6514,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d_all = ggml_fp16_to_fp32(x[i].d); + const float d_all = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -6646,7 +6646,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q4 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -6726,7 +6726,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q4 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -6838,7 +6838,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri float sumf = 0; for (int i = 0; i < nb; ++i) { - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -6955,7 +6955,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; q8 += 8; a += 8; } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; } for (int l = 0; l < 8; ++l) sumf += sums[l]; @@ -7053,7 +7053,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q4 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -7110,7 +7110,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q4 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -7269,7 +7269,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; q8 += 8; a += 8; } - const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; } for (int l = 0; l < 8; ++l) sumf += sums[l]; diff --git a/ggml-quants.h b/ggml-quants.h index d88f99e33..70c12c274 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -1,22 +1,12 @@ #pragma once -// This is a private API for quantization and dequantization -// Should not be used directly, use ggml.h instead +#include "ggml-impl.h" -#include "ggml.h" +// GGML internal header #include -#include #include -#ifndef static_assert -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) -#define static_assert(cond, msg) _Static_assert(cond, msg) -#else -#define static_assert(cond, msg) struct global_scope_noop_trick -#endif -#endif - #define QK4_0 32 typedef struct { ggml_fp16_t d; // delta diff --git a/ggml.c b/ggml.c index 95f72c35e..84407b122 100644 --- a/ggml.c +++ b/ggml.c @@ -1,6 +1,6 @@ #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows -#include "ggml.h" +#include "ggml-impl.h" #include "ggml-quants.h" #if defined(_MSC_VER) || defined(__MINGW32__) @@ -27,18 +27,6 @@ #include #endif -// static_assert should be a #define, but if it's not, -// fall back to the _Static_assert C11 keyword. -// if C99 - static_assert is noop -// ref: https://stackoverflow.com/a/53923785/4039976 -#ifndef static_assert -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) -#define static_assert(cond, msg) _Static_assert(cond, msg) -#else -#define static_assert(cond, msg) struct global_scope_noop_trick -#endif -#endif - #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) @@ -106,23 +94,11 @@ typedef void * thread_ret_t; #include #endif + #ifdef GGML_USE_CPU_HBM #include #endif -// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 -#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) -#ifndef __FMA__ -#define __FMA__ -#endif -#ifndef __F16C__ -#define __F16C__ -#endif -#ifndef __SSE3__ -#define __SSE3__ -#endif -#endif - /*#define GGML_PERF*/ #define GGML_DEBUG 0 #define GGML_GELU_FP16 @@ -248,213 +224,27 @@ inline static void * ggml_aligned_malloc(size_t size) { #include "ggml-opencl.h" #endif -#undef MIN -#undef MAX -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - // floating point type used to accumulate sums typedef double ggml_float; -// 16-bit float -// on Arm, we use __fp16 -// on x86, we use uint16_t -#if defined(__ARM_NEON) && !defined(_MSC_VER) - -// if YCM cannot find , make a symbolic link to it, for example: -// -// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ -// -#include - -#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x)) -#define GGML_COMPUTE_FP32_TO_FP16(x) (x) - -#define GGML_FP16_TO_FP32(x) ((float) (x)) -#define GGML_FP32_TO_FP16(x) (x) - -#else - -#ifdef __wasm_simd128__ -#include -#else -#ifdef __POWER9_VECTOR__ -#include -#undef bool -#define bool _Bool -#else -#if defined(_MSC_VER) || defined(__MINGW32__) -#include -#else -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) -#if !defined(__riscv) -#include -#endif -#endif -#endif -#endif -#endif - -#ifdef __riscv_v_intrinsic -#include -#endif - -#ifdef __F16C__ - -#ifdef _MSC_VER -#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) -#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) -#else -#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) -#endif - -#elif defined(__POWER9_VECTOR__) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) -/* the inline asm below is about 12% faster than the lookup method */ -#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - register float f; - register double d; - __asm__( - "mtfprd %0,%2\n" - "xscvhpdp %0,%0\n" - "frsp %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=f"(f): - /* in */ "r"(h)); - return f; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - register double d; - register ggml_fp16_t r; - __asm__( /* xscvdphp can work on double or single precision */ - "xscvdphp %0,%2\n" - "mffprd %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=r"(r): - /* in */ "f"(f)); - return r; -} - -#else - -// FP16 <-> FP32 -// ref: https://github.com/Maratyszcza/FP16 - -static inline float fp32_from_bits(uint32_t w) { - union { - uint32_t as_bits; - float as_value; - } fp32; - fp32.as_bits = w; - return fp32.as_value; -} - -static inline uint32_t fp32_to_bits(float f) { - union { - float as_value; - uint32_t as_bits; - } fp32; - fp32.as_value = f; - return fp32.as_bits; -} - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - const uint32_t w = (uint32_t) h << 16; - const uint32_t sign = w & UINT32_C(0x80000000); - const uint32_t two_w = w + w; - - const uint32_t exp_offset = UINT32_C(0xE0) << 23; -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float exp_scale = 0x1.0p-112f; -#else - const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); -#endif - const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; - - const uint32_t magic_mask = UINT32_C(126) << 23; - const float magic_bias = 0.5f; - const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; - - const uint32_t denormalized_cutoff = UINT32_C(1) << 27; - const uint32_t result = sign | - (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); - return fp32_from_bits(result); -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float scale_to_inf = 0x1.0p+112f; - const float scale_to_zero = 0x1.0p-110f; -#else - const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); - const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); -#endif - float base = (fabsf(f) * scale_to_inf) * scale_to_zero; - - const uint32_t w = fp32_to_bits(f); - const uint32_t shl1_w = w + w; - const uint32_t sign = w & UINT32_C(0x80000000); - uint32_t bias = shl1_w & UINT32_C(0xFF000000); - if (bias < UINT32_C(0x71000000)) { - bias = UINT32_C(0x71000000); - } - - base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; - const uint32_t bits = fp32_to_bits(base); - const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); - const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); - const uint32_t nonsign = exp_bits + mantissa_bits; - return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); -} - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#endif // __F16C__ - -#endif // __ARM_NEON - // // global data // // precomputed gelu table for f16 (128 KB) -static ggml_fp16_t table_gelu_f16[1 << 16]; +static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; // precomputed quick gelu table for f16 (128 KB) -static ggml_fp16_t table_gelu_quick_f16[1 << 16]; +static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; // precomputed silu table for f16 (128 KB) -static ggml_fp16_t table_silu_f16[1 << 16]; +static ggml_fp16_t ggml_table_silu_f16[1 << 16]; // precomputed exp table for f16 (128 KB) -static ggml_fp16_t table_exp_f16[1 << 16]; +static ggml_fp16_t ggml_table_exp_f16[1 << 16]; -// precomputed f32 table for f16 (256 KB) -static float table_f32_f16[1 << 16]; - -// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, -// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. -// This is also true for POWER9. -#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) - -inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { - uint16_t s; - memcpy(&s, &f, sizeof(uint16_t)); - return table_f32_f16[s]; -} - -#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) - -#endif +// precomputed f32 table for f16 (256 KB) (ggml-impl.h) +float ggml_table_f32_f16[1 << 16]; // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API @@ -632,6 +422,28 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, + [4] = { // GGML_TYPE_Q4_2 + .type_name = "DEPRECATED", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + .to_float = NULL, + .from_float = NULL, + .from_float_reference = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_COUNT, + }, + [5] = { // GGML_TYPE_Q4_3 + .type_name = "DEPRECATED", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + .to_float = NULL, + .from_float = NULL, + .from_float_reference = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_COUNT, + }, [GGML_TYPE_Q5_0] = { .type_name = "q5_0", .blck_size = QK5_0, @@ -1551,7 +1363,7 @@ inline static float ggml_gelu_f32(float x) { inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { const uint16_t * i16 = (const uint16_t *) x; for (int i = 0; i < n; ++i) { - y[i] = table_gelu_f16[i16[i]]; + y[i] = ggml_table_gelu_f16[i16[i]]; } } @@ -1561,7 +1373,7 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); } } #else @@ -1579,7 +1391,7 @@ inline static float ggml_gelu_quick_f32(float x) { //inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { // const uint16_t * i16 = (const uint16_t *) x; // for (int i = 0; i < n; ++i) { -// y[i] = table_gelu_quick_f16[i16[i]]; +// y[i] = ggml_table_gelu_quick_f16[i16[i]]; // } //} @@ -1589,7 +1401,7 @@ inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(table_gelu_quick_f16[t]); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); } } #else @@ -1608,7 +1420,7 @@ inline static float ggml_silu_f32(float x) { //inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { // const uint16_t * i16 = (const uint16_t *) x; // for (int i = 0; i < n; ++i) { -// y[i] = table_silu_f16[i16[i]]; +// y[i] = ggml_table_silu_f16[i16[i]]; // } //} @@ -1618,7 +1430,7 @@ inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]); + y[i] = GGML_FP16_TO_FP32(ggml_table_silu_f16[t]); } } #else @@ -2334,11 +2146,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { for (int i = 0; i < (1 << 16); ++i) { uint16_t ui = i; memcpy(&ii, &ui, sizeof(ii)); - const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); - table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); - table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); - table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); + const float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); + ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); + ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); + ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); + ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); @@ -10701,7 +10513,7 @@ static void ggml_compute_forward_soft_max_f32( // const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max); ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max); memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); + const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); sum += (ggml_float)val; dp[i] = val; } @@ -12990,7 +12802,7 @@ static void ggml_compute_forward_flash_attn_f32( #else ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); + const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); #endif sump[j] += (ggml_float)val; SS[j] = val; @@ -13192,7 +13004,7 @@ static void ggml_compute_forward_flash_attn_f16( } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); + const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); sump[j] += (ggml_float)val; SS[j] = val; } @@ -13643,7 +13455,7 @@ static void ggml_compute_forward_flash_attn_back_f32( #else ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); + const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); #endif sump[j] += (ggml_float)val; SW[j] = val; @@ -14393,7 +14205,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32( #else ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); + const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); #endif sum += (ggml_float)val; st[i] = val; @@ -14507,7 +14319,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( #else ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); + const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); #endif sum += (ggml_float)val; ds0[i] = val; diff --git a/llama.cpp b/llama.cpp index a4340d527..e599917a8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1467,7 +1467,7 @@ static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { } static void llama_kv_cache_clear(struct llama_kv_cache & cache) { - for (int32_t i = 0; i < cache.size; ++i) { + for (int32_t i = 0; i < (int32_t) cache.size; ++i) { cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); } diff --git a/tests/test-double-float.cpp b/tests/test-double-float.cpp index afd7bf77f..753dae911 100644 --- a/tests/test-double-float.cpp +++ b/tests/test-double-float.cpp @@ -4,7 +4,7 @@ #undef NDEBUG #include -#if !defined(__riscv) && !defined(__s390__) +#if !defined(__riscv) && !defined(__s390__) && !defined(__ARM_NEON) #include #endif #include diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 884af4054..a2459a286 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -129,6 +129,13 @@ int main(int argc, char * argv[]) { ggml_type type = (ggml_type) i; ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); + // deprecated - skip + if (qfns.blck_size == 0) { + continue; + } + + printf("Testing %s\n", ggml_type_name((ggml_type) i)); + if (qfns.from_float && qfns.to_float) { const float total_error = total_quantization_error(qfns, test_size, test_data.data()); const float max_quantization_error = From 07178c98e1b61a5e2af39d347add12e7eb9e08e1 Mon Sep 17 00:00:00 2001 From: Tungsten842 <886724vf@anonaddy.me> Date: Tue, 31 Oct 2023 18:24:03 +0100 Subject: [PATCH 69/95] flake.nix: fix for rocm 5.7 (#3853) --- flake.lock | 12 ++++++------ flake.nix | 10 ++++++---- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/flake.lock b/flake.lock index 070f0e161..0455f6561 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "systems": "systems" }, "locked": { - "lastModified": 1692799911, - "narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=", + "lastModified": 1694529238, + "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=", "owner": "numtide", "repo": "flake-utils", - "rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44", + "rev": "ff7b65b44d01cf9ba6a71320833626af21126384", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1698134075, - "narHash": "sha256-foCD+nuKzfh49bIoiCBur4+Fx1nozo+4C/6k8BYk4sg=", + "lastModified": 1698318101, + "narHash": "sha256-gUihHt3yPD7bVqg+k/UVHgngyaJ3DMEBchbymBMvK1E=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "8efd5d1e283604f75a808a20e6cde0ef313d07d4", + "rev": "63678e9f3d3afecfeafa0acead6239cdb447574c", "type": "github" }, "original": { diff --git a/flake.nix b/flake.nix index fa34394b2..4cf28d5c1 100644 --- a/flake.nix +++ b/flake.nix @@ -11,8 +11,7 @@ meta.mainProgram = "llama"; inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin; buildInputs = with pkgs; [ openmpi ]; - osSpecific = with pkgs; buildInputs ++ - ( + osSpecific = with pkgs; buildInputs ++ ( if isAarch64 && isDarwin then with pkgs.darwin.apple_sdk_11_0.frameworks; [ Accelerate @@ -96,12 +95,15 @@ }; packages.rocm = pkgs.stdenv.mkDerivation { inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ]; + buildInputs = with pkgs.rocmPackages; buildInputs ++ [ clr hipblas rocblas ]; cmakeFlags = cmakeFlags ++ [ "-DLLAMA_HIPBLAS=1" "-DCMAKE_C_COMPILER=hipcc" "-DCMAKE_CXX_COMPILER=hipcc" - "-DCMAKE_POSITION_INDEPENDENT_CODE=ON" + # 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 = { 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 70/95] 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 71/95] 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 72/95] 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 73/95] 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 74/95] 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 75/95] 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 76/95] 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 77/95] 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 78/95] 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 79/95] 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 80/95] 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 81/95] 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 82/95] 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 83/95] 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 84/95] 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 85/95] 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 86/95] 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 87/95] 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 88/95] 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 89/95] 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 90/95] 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 91/95] 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 92/95] 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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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 94/95] 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 95/95] 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) {

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Yusuf Sarıgöz --- .gitignore | 1 + Makefile | 4 +- build.zig | 3 +- examples/llava/CMakeLists.txt | 2 +- examples/llava/clip.cpp | 4 +- examples/server/CMakeLists.txt | 2 +- examples/server/README.md | 36 + examples/server/api_like_OAI.py | 5 +- examples/server/chat.mjs | 11 + examples/server/index.html.hpp | 4052 +++++++++++++++-------------- examples/server/public/index.html | 115 +- examples/server/server.cpp | 2695 ++++++++++++------- 12 files changed, 3980 insertions(+), 2950 deletions(-) diff --git a/.gitignore b/.gitignore index 471cf90d5..545c28726 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ *.gcno *.gcda *.dot +*.bat *.metallib .DS_Store .build/ diff --git a/Makefile b/Makefile index 325ae747b..80179631f 100644 --- a/Makefile +++ b/Makefile @@ -605,8 +605,8 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml. save-load-state: examples/save-load-state/save-load-state.cpp build-info.h 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 build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) +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) + $(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) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/build.zig b/build.zig index 0b74cee48..dcfa3dd6b 100644 --- a/build.zig +++ b/build.zig @@ -131,6 +131,7 @@ pub fn build(b: *std.build.Builder) !void { const sampling = make.obj("sampling", "common/sampling.cpp"); const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); const train = make.obj("train", "common/train.cpp"); + const clip = make.obj("clip", "examples/llava/clip.cpp"); _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, console, grammar_parser }); _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common }); @@ -139,7 +140,7 @@ pub fn build(b: *std.build.Builder) !void { _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index d02e6ab46..2d7979ecd 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -1,7 +1,7 @@ set(TARGET clip) add_library(${TARGET} clip.cpp clip.h) install(TARGETS ${TARGET} LIBRARY) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${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 diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 1ae9077b4..61932e659 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -610,8 +610,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); for (int i = 0; i < 3; ++i) { - new_clip->image_mean[i] = *((float *)gguf_get_arr_data(ctx, idx_mean)); - new_clip->image_std[i] = *((float *)gguf_get_arr_data(ctx, idx_std)); + 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)); } if (verbosity >= 2) { diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 3782f9b80..a23ddcc55 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 ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/README.md b/examples/server/README.md index 9f0ace3d7..715007735 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -24,6 +24,10 @@ Command line options: - `--port`: Set the port to listen. Default: `8080`. - `--path`: path from which to serve static files (default examples/server/public) - `--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) +- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) +- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. ## Build @@ -158,6 +162,8 @@ 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. + *Result JSON:* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. @@ -188,6 +194,12 @@ 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:* @@ -218,8 +230,32 @@ node index.js 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. + ## More examples +### Change system prompt on runtime + +To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it. + +`prompt`: Specify a context that you want all connecting clients to respect. + +`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint. + +`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint. + +```json +{ + "system_prompt": { + "prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:", + "anti_prompt": "User:", + "assistant_name": "Assistant:" + } +} +``` + +**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`. + ### Interactive mode Check the sample in [chat.mjs](chat.mjs). diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py index 14d2dcf65..313e1a965 100755 --- a/examples/server/api_like_OAI.py +++ b/examples/server/api_like_OAI.py @@ -8,6 +8,7 @@ import json app = Flask(__name__) +slot_id = -1 parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n') @@ -77,7 +78,8 @@ def make_postData(body, chat=False, stream=False): if(is_present(body, "stop")): postData["stop"] += body["stop"] postData["n_keep"] = -1 postData["stream"] = stream - + postData["cache_prompt"] = True + postData["slot_id"] = slot_id return postData def make_resData(data, chat=False, promptToken=[]): @@ -128,6 +130,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False): } ] } + slot_id = data["slot_id"] if (chat): if (start): resData["choices"][0]["delta"] = { diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index 87f4d2926..219ebb51a 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -7,6 +7,11 @@ const args = process.argv.slice(2); const grammarJsonSchemaFile = args.find( (_, index) => args[index - 1] === "--grammar-json-schema" ); + +const no_cached_prompt = args.find( + (_, index) => args[index - 1] === "--no-cache-prompt" +) ?? "false"; + const grammarFile = args.find((_, index) => args[index - 1] === "--grammar"); // Example usage: function,arguments @@ -30,6 +35,9 @@ if (grammarFile) { grammar = readFileSync(grammarFile, 'utf-8') } +// for cached prompt +let slot_id = -1; + const API_URL = 'http://127.0.0.1:8080' const chat = [ @@ -76,6 +84,8 @@ async function chat_completion(question) { top_p: 0.9, n_keep: n_keep, n_predict: 256, + cache_prompt: no_cached_prompt === "false", + slot_id: slot_id, stop: ["\n### Human:"], // stop completion after generating this grammar, stream: true, @@ -92,6 +102,7 @@ async function chat_completion(question) { const t = Buffer.from(chunk).toString('utf8') if (t.startsWith('data: ')) { const message = JSON.parse(t.substring(6)) + slot_id = message.slot_id answer += message.content process.stdout.write(message.content) if (message.stop) { diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 58e3387d1..5d3bdfbdd 100644 --- 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+142,8 @@ display: inline; } - header, footer { + header, + footer { text-align: center; } @@ -163,6 +165,7 @@ 0% { background-position: 0%; } + 100% { background-position: 100%; } @@ -181,6 +184,7 @@ --loading-color-1: #22222200; --loading-color-2: #222222ff; } + .popover-content { background-color: black; } @@ -194,6 +198,8 @@ import { llama } from '/completion.js'; import { SchemaConverter } from '/json-schema-to-grammar.mjs'; + let selected_image = false; + var slot_id = -1; const session = signal({ prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.", @@ -203,6 +209,7 @@ type: "chat", // "chat" | "completion" char: "Llama", user: "User", + image_selected: '' }) const params = signal({ @@ -220,7 +227,9 @@ mirostat_tau: 5, // target entropy mirostat_eta: 0.1, // learning rate grammar: '', - n_probs: 0, // no completion_probabilities + n_probs: 0, // no completion_probabilities, + image_data: [], + cache_prompt: true }) /* START: Support for storing prompt templates and parameters in borwser LocalStorage */ @@ -270,6 +279,7 @@ // saved templates were successfuly imported. console.log('Processing saved templates and updating default template') + params.value = { ...params.value, image_data: [] }; //console.log(importedTemplates); savedUserTemplates.value = importedTemplates; @@ -294,7 +304,9 @@ function userTemplateApply(t) { session.value = t.data.session; + session.value = { ...session.value, image_selected: '' }; params.value = t.data.params; + params.value = { ...params.value, image_data: [] }; } function userTemplateResetToDefaultAndApply() { @@ -385,20 +397,25 @@ throw new Error("already running"); } controller.value = new AbortController(); - for await (const chunk of llama(prompt, llamaParams, {controller: controller.value})) { + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) { const data = chunk.data; if (data.stop) { while ( currentMessages.length > 0 && currentMessages[currentMessages.length - 1].content.match(/\n$/) != null - ) { + ) { currentMessages.pop(); } transcriptUpdate([...history, [char, currentMessages]]) console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data); } else { currentMessages.push(data); + slot_id = data.slot_id; + if (selected_image && !data.multimodal) { + alert("The server was not compiled for multimodal or the model projector can't be loaded."); + return; + } transcriptUpdate([...history, [char, currentMessages]]) } @@ -419,7 +436,7 @@ transcriptUpdate([...session.value.transcript, ["{{user}}", msg]]) - const prompt = template(session.value.template, { + let prompt = template(session.value.template, { message: msg, history: session.value.transcript.flatMap( ([name, data]) => @@ -434,9 +451,12 @@ ) ).join("\n"), }); - + if (selected_image) { + prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`; + } await runLlama(prompt, { ...params.value, + slot_id: slot_id, stop: ["", template("{{char}}:"), template("{{user}}:")], }, "{{char}}"); } @@ -446,10 +466,11 @@ console.log('already running...'); return; } - const {prompt} = session.value; + const { prompt } = session.value; transcriptUpdate([...session.value.transcript, ["", prompt]]); await runLlama(prompt, { ...params.value, + slot_id: slot_id, stop: [], }, ""); } @@ -467,6 +488,27 @@ transcriptUpdate([]); } + const uploadImage = (e) => { + e.preventDefault(); + document.getElementById("fileInput").click(); + document.getElementById("fileInput").addEventListener("change", function (event) { + const selectedFile = event.target.files[0]; + if (selectedFile) { + const reader = new FileReader(); + reader.onload = function () { + const image_data = reader.result; + session.value = { ...session.value, image_selected: image_data }; + params.value = { + ...params.value, image_data: [ + { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] + } + }; + selected_image = true; + reader.readAsDataURL(selectedFile); + } + }); + } + function MessageInput() { const message = useSignal("") @@ -497,6 +539,7 @@