From b1ca8f36a9cdbcee5f5c425df717611a1040a897 Mon Sep 17 00:00:00 2001 From: Qingyou Meng Date: Sat, 1 Jul 2023 23:42:43 +0800 Subject: [PATCH 01/84] ggml : disable GGML_TASK_INIT and GGML_TASK_FINALIZE by default (#1995) Will not be scheduled unless explicitly enabled. --- ggml.c | 61 +++++++++++++++++++++++++++++++++++++++++++++++++--------- ggml.h | 3 +++ 2 files changed, 55 insertions(+), 9 deletions(-) diff --git a/ggml.c b/ggml.c index 684caaa37..75cc44baa 100644 --- a/ggml.c +++ b/ggml.c @@ -3846,6 +3846,40 @@ static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); +// WARN: +// Mis-confguration can lead to problem that's hard to reason about: +// * At best it crash or talks nosense. +// * At worst it talks slightly difference but hard to perceive. +// +// An op has to enable INIT or FINALIZE when any of it's branch needs that pass. +// Take care about compile options (e.g., GGML_USE_xxx). +static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; +static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; +static void ggml_setup_op_has_task_pass(void) { + { // INIT + bool * I = GGML_OP_HAS_INIT; + + I[GGML_OP_ACC ] = true; + I[GGML_OP_MUL_MAT ] = true; + I[GGML_OP_OUT_PROD ] = true; + I[GGML_OP_SET ] = true; + I[GGML_OP_GET_ROWS_BACK ] = true; + I[GGML_OP_DIAG_MASK_INF ] = true; + I[GGML_OP_DIAG_MASK_ZERO ] = true; + I[GGML_OP_CONV_1D_S1_PH ] = true; + I[GGML_OP_CONV_1D_S2_PH ] = true; + I[GGML_OP_CONV_2D_SK_P0 ] = true; + I[GGML_OP_FLASH_ATTN_BACK ] = true; + I[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + } + + { // FINALIZE + bool * F = GGML_OP_HAS_FINALIZE; + + F[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + } +} + // // ggml context // @@ -4267,6 +4301,8 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { ggml_cl_init(); #endif + ggml_setup_op_has_task_pass(); + is_first_call = false; } @@ -16791,9 +16827,11 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (node_n != -1) { /* FINALIZE */ struct ggml_tensor * node = state->shared->cgraph->nodes[node_n]; - params.nth = node->n_tasks; - ggml_compute_forward(¶ms, node); - ggml_graph_compute_perf_stats_node(node, state->shared); + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.nth = node->n_tasks; + ggml_compute_forward(¶ms, node); + ggml_graph_compute_perf_stats_node(node, state->shared); + } } // distribute new work or execute it direct if 1T @@ -16805,10 +16843,13 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); + params.nth = node->n_tasks; + /* INIT */ - params.type = GGML_TASK_INIT; - params.nth = node->n_tasks; - ggml_compute_forward(¶ms, node); + if (GGML_OP_HAS_INIT[node->op]) { + params.type = GGML_TASK_INIT; + ggml_compute_forward(¶ms, node); + } if (node->n_tasks == 1) { // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, @@ -16816,9 +16857,11 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { params.type = GGML_TASK_COMPUTE; ggml_compute_forward(¶ms, node); - params.type = GGML_TASK_FINALIZE; - ggml_compute_forward(¶ms, node); - ggml_graph_compute_perf_stats_node(node, state->shared); + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.type = GGML_TASK_FINALIZE; + ggml_compute_forward(¶ms, node); + ggml_graph_compute_perf_stats_node(node, state->shared); + } } else { break; } diff --git a/ggml.h b/ggml.h index 459913222..11b51f8bd 100644 --- a/ggml.h +++ b/ggml.h @@ -444,6 +444,9 @@ extern "C" { // compute types + + // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled. + // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995. enum ggml_task_type { GGML_TASK_INIT = 0, GGML_TASK_COMPUTE, From 04606a159947566b27810508433e6ca5dbc684ba Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 1 Jul 2023 18:45:44 +0300 Subject: [PATCH 02/84] train : fix compile warning --- examples/train-text-from-scratch/train-text-from-scratch.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) 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 05bfa8016..c50eeb343 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -2671,7 +2671,8 @@ struct train_params { const char * fn_checkpoint_out; const char * fn_model_out; - int seed; + uint32_t seed; + int n_ctx; int n_embd; int n_mult; From 79f634a19d1c32a6cfb1befc21551ee684fced6b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 1 Jul 2023 18:46:00 +0300 Subject: [PATCH 03/84] embd-input : fix returning ptr to temporary --- examples/embd-input/embd-input-lib.cpp | 9 ++++++--- examples/embd-input/embd-input.h | 4 +--- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 37de52ad6..570e273fc 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -210,9 +210,12 @@ llama_token sampling_id(struct MyModel* mymodel) { const char * sampling(struct MyModel * mymodel) { llama_context * ctx = mymodel->ctx; int id = sampling_id(mymodel); - std::string ret; - if (id == llama_token_eos()) ret = ""; - else ret = llama_token_to_str(ctx, id); + static std::string ret; + if (id == llama_token_eos()) { + ret = ""; + } else { + ret = llama_token_to_str(ctx, id); + } eval_id(mymodel, id); return ret.c_str(); } diff --git a/examples/embd-input/embd-input.h b/examples/embd-input/embd-input.h index 4fefabd42..efb5ba5e2 100644 --- a/examples/embd-input/embd-input.h +++ b/examples/embd-input/embd-input.h @@ -5,7 +5,6 @@ #include "llama.h" #include "build-info.h" - extern "C" { typedef struct MyModel { @@ -14,14 +13,13 @@ typedef struct MyModel { 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); +const char * sampling(struct MyModel* mymodel); llama_token sampling_id(struct MyModel* mymodel); void free_mymodel(struct MyModel* mymodel); From cb44dbc7de287b3d17772cfb1aa49d55e082ce5b Mon Sep 17 00:00:00 2001 From: Rand Xie Date: Sun, 2 Jul 2023 00:02:58 +0800 Subject: [PATCH 04/84] llama : catch llama_load_session_file_internal exceptions (#2022) * convert checks in llama_load_session_file to throw and handle them * make llama_load_session_file_internal static * address feedbacks to avoid using exceptions --- llama.cpp | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 049f73e44..3a7a0d5da 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3219,7 +3219,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { return nread; } -bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { +static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { llama_file file(path_session, "rb"); // sanity checks @@ -3269,8 +3269,15 @@ bool llama_load_session_file(struct llama_context * ctx, const char * path_sessi llama_set_state_data(ctx, state_data.data()); } +} - return true; +bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { + try { + return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out); + } catch (const std::exception & err) { + fprintf(stderr, "error loading session file: %s\n", err.what()); + return false; + } } bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) { From 463f2f4c4f8dd5ca9594b7d65849f346f0effe05 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 1 Jul 2023 19:05:09 +0300 Subject: [PATCH 05/84] llama : fix return value of llama_load_session_file_internal (#2022) --- llama.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/llama.cpp b/llama.cpp index 3a7a0d5da..69c2ab01b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3269,6 +3269,8 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c llama_set_state_data(ctx, state_data.data()); } + + return true; } bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { From 471aab6e4cb89d8ef6d043f1bc93acb6eb78ab67 Mon Sep 17 00:00:00 2001 From: Judd Date: Sun, 2 Jul 2023 01:00:25 +0800 Subject: [PATCH 06/84] convert : add support of baichuan-7b (#2055) Co-authored-by: Judd --- README.md | 1 + convert.py | 41 ++++++++++++++++++++++++++++++++++++----- 2 files changed, 37 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ee56988c7..e890dc9c2 100644 --- a/README.md +++ b/README.md @@ -85,6 +85,7 @@ as the main playground for developing new features for the [ggml](https://github - [X] [OpenBuddy šŸ¶ (Multilingual)](https://github.com/OpenBuddy/OpenBuddy) - [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b) - [X] [WizardLM](https://github.com/nlpxucan/WizardLM) +- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) **Bindings:** diff --git a/convert.py b/convert.py index e340d2273..142692776 100644 --- a/convert.py +++ b/convert.py @@ -136,7 +136,7 @@ def find_n_mult(n_ff: int, n_embd: int) -> int: calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult if calc_ff == n_ff: return n_mult - return 1 + raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") @dataclass class Params: @@ -321,6 +321,10 @@ class Tensor(metaclass=ABCMeta): @abstractmethod def permute(self, n_head: int) -> 'Tensor': ... @abstractmethod + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... + @abstractmethod + def part(self, n_part: int) -> 'UnquantizedTensor': ... + @abstractmethod def to_ggml(self) -> 'GGMLCompatibleTensor': ... @@ -345,6 +349,14 @@ class UnquantizedTensor(Tensor): def to_ggml(self) -> 'UnquantizedTensor': return self + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head)) + + def part(self, n_part: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) + def permute(self, n_head: int) -> 'UnquantizedTensor': return UnquantizedTensor(permute(self.ndarray, n_head)) @@ -642,6 +654,19 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: return lazy_tensor.load().permute(n_head) return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) +def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().permute_part(n_part, n_head) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + +def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().part(n_part) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out: LazyModel = {} @@ -650,11 +675,17 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out["output.weight"] = model["lm_head.weight"] for i in itertools.count(): - if f"model.layers.{i}.self_attn.q_proj.weight" not in model: + if f"model.layers.{i}.self_attn.q_proj.weight" in model: + out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + elif f"model.layers.{i}.self_attn.W_pack.weight" in model: + out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head) + out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) + else: break - out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) - out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) - out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"] From 2f8cd979ecd1fa582852e7136e92ff8990b98fd8 Mon Sep 17 00:00:00 2001 From: Aaron Miller Date: Sat, 1 Jul 2023 11:14:59 -0700 Subject: [PATCH 07/84] metal : release buffers when freeing metal context (#2062) --- ggml-metal.m | 4 +++- llama.cpp | 8 +++++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 7551231b9..fd69c41fe 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -202,7 +202,9 @@ struct ggml_metal_context * ggml_metal_init(void) { void ggml_metal_free(struct ggml_metal_context * ctx) { fprintf(stderr, "%s: deallocating\n", __func__); - + for (int i = 0; i < ctx->n_buffers; ++i) { + [ctx->buffers[i].metal release]; + } free(ctx); } diff --git a/llama.cpp b/llama.cpp index 69c2ab01b..561accf88 100644 --- a/llama.cpp +++ b/llama.cpp @@ -253,7 +253,13 @@ struct llama_model { struct llama_context { llama_context(const llama_model & model, const llama_vocab & vocab) : model(model), vocab(vocab), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {} - +#ifdef GGML_USE_METAL + ~llama_context() { + if (ctx_metal) { + ggml_metal_free(ctx_metal); + } + } +#endif std::mt19937 rng; bool has_evaluated_once = false; From b2132270678c473f7cd9ba871b03d694126bc33a Mon Sep 17 00:00:00 2001 From: Daniel Drake Date: Sat, 1 Jul 2023 20:31:44 +0200 Subject: [PATCH 08/84] cmake : don't force -mcpu=native on aarch64 (#2063) It's currently not possible to cross-compile llama.cpp for aarch64 because CMakeLists.txt forces -mcpu=native for that target. -mcpu=native doesn't make sense if your build host is not the target architecture, and clang rejects it for that reason, aborting the build. This can be easily reproduced using the current Android NDK to build for aarch64 on an x86_64 host. If there is not a specific CPU-tuning target for aarch64 then -mcpu should be omitted completely. I think that makes sense, there is not enough variance in the aarch64 instruction set to warrant a fixed -mcpu optimization at this point. And if someone is building natively and wishes to enable any possible optimizations for the host device, then there is already the LLAMA_NATIVE option available. Fixes #495. --- CMakeLists.txt | 5 ----- 1 file changed, 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ffda74a70..34a897327 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -386,11 +386,6 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES if (MSVC) # TODO: arm msvc? else() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") - # Apple M1, M2, etc. - # Raspberry Pi 3, 4, Zero 2 (64-bit) - add_compile_options(-mcpu=native) - endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") # Raspberry Pi 1, Zero add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access) From befb3a35627432473f143c90994557d78ff5bc67 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jul 2023 21:47:26 +0200 Subject: [PATCH 09/84] Test-based VRAM scratch size + context adjustment (#2056) --- llama.cpp | 38 +++++++++++++++++++++++++++++++++++--- 1 file changed, 35 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 561accf88..a869bbac8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -66,6 +66,7 @@ enum e_model { MODEL_65B, }; +static const size_t kB = 1024; static const size_t MB = 1024*1024; // computed for n_ctx == 2048 @@ -129,6 +130,34 @@ static const std::map & MEM_REQ_EVAL() return k_sizes; } +// amount of VRAM needed per batch size to hold temporary results +// the values for 3b and 65b are not derived from testing but instead chosen conservatively +static const std::map & VRAM_REQ_SCRATCH_BASE() +{ + static std::map k_sizes = { + { MODEL_3B, 512ull * kB }, + { MODEL_7B, 512ull * kB }, + { MODEL_13B, 640ull * kB }, + { MODEL_30B, 768ull * kB }, + { MODEL_65B, 1536ull * kB }, + }; + return k_sizes; +} + +// amount of VRAM needed per batch size and context to hold temporary results +// the values for 3b and 65b are not derived from testing but instead chosen conservatively +static const std::map & VRAM_REQ_SCRATCH_PER_CONTEXT() +{ + static std::map k_sizes = { + { MODEL_3B, 128ull }, + { MODEL_7B, 128ull }, + { MODEL_13B, 160ull }, + { MODEL_30B, 208ull }, + { MODEL_65B, 416ull }, + }; + return k_sizes; +} + // default hparams (LLaMA 7B) struct llama_hparams { uint32_t n_vocab = 32000; @@ -1118,11 +1147,14 @@ static void llama_model_load_internal( fprintf(stderr, "%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__); ggml_cuda_set_scratch_size(0); // disable scratch } else { - vram_scratch = n_batch * MB; + const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type); + const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type); + vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context); ggml_cuda_set_scratch_size(vram_scratch); if (n_gpu_layers > 0) { - fprintf(stderr, "%s: allocating batch_size x 1 MB = %zd MB VRAM for the scratch buffer\n", - __func__, vram_scratch / MB); + fprintf(stderr, "%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n", + __func__, vram_scratch_base / kB, vram_scratch_per_context, + (vram_scratch + MB - 1) / MB); // round up } } #endif // GGML_USE_CUBLAS From 0bc2cdfc875fa7877d8e01c8bb17066f99c08f21 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jul 2023 21:49:44 +0200 Subject: [PATCH 10/84] Better CUDA synchronization logic (#2057) --- ggml-cuda.cu | 63 ++++++++++++++++++++++++++++++++++++++-------------- ggml-cuda.h | 4 ---- 2 files changed, 46 insertions(+), 21 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 4e0d3dbde..50df20edd 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -214,6 +214,11 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); #endif +struct ggml_tensor_extra_gpu { + void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors + cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs +}; + static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -1970,7 +1975,6 @@ inline void ggml_cuda_op_add( } else { GGML_ASSERT(false); } - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2002,7 +2006,6 @@ inline void ggml_cuda_op_mul( // compute mul_f32_cuda(src0_ddf_i01, src1_ddf_i01, dst_ddf_i01, ne00, ne10, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); } (void) dst; @@ -2023,7 +2026,6 @@ inline void ggml_cuda_op_silu( // compute silu_f32_cuda(src0_ddf_i, dst_ddf_i, ne00*i01_diff, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2046,7 +2048,6 @@ inline void ggml_cuda_op_rms_norm( // compute rms_norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2125,7 +2126,6 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( GGML_ASSERT(false); break; } - CUDA_CHECK(cudaGetLastError()); #ifdef GGML_CUDA_DMMV_F16 if (src1_convert_f16) { @@ -2202,7 +2202,6 @@ inline void ggml_cuda_op_rope( // compute rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) dst; (void) src0_ddq_i; @@ -2226,7 +2225,6 @@ inline void ggml_cuda_op_diag_mask_inf( // compute diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) dst; (void) src0_ddq_i; @@ -2248,7 +2246,6 @@ inline void ggml_cuda_op_soft_max( // compute soft_max_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2344,10 +2341,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0}; size_t dst_asf[GGML_CUDA_MAX_DEVICES] = {0}; - // if multiple GPUs are used they need to wait for the main GPU to finish + // if multiple devices are used they need to wait for the main device + // here an event is recorded that signifies that the main device has finished calculating the input data if (split && g_device_count > 1) { CUDA_CHECK(cudaSetDevice(g_main_device)); - CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device], g_cudaStreams_main[g_main_device])); } for (int id = 0; id < g_device_count; ++id) { @@ -2373,6 +2371,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm int64_t row_diff = row_high - row_low; cudaSetDevice(id); + cudaStream_t cudaStream_main = g_cudaStreams_main[id]; + + // wait for main GPU data if necessary + if (split && id != g_main_device) { + CUDA_CHECK(cudaStreamWaitEvent(cudaStream_main, src0_extra->events[g_main_device])); + } if (src0_on_device && src0_is_contiguous) { if (src0_is_f32) { @@ -2448,8 +2452,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm } const int64_t i11 = i13*ne12 + i12; - cudaStream_t cudaStream_main = g_cudaStreams_main[id]; - // for split tensors the data begins at i0 == i0_offset_low char * src0_ddq_i = src0_ddq[id] + (i0 - i0_offset_low)*src0_stride*src0_ts/src0_bs; float * src0_ddf_i = src0_ddf[id] + (i0 - i0_offset_low)*src0_stride; @@ -2509,6 +2511,7 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm // do the computation op(src0, src1, dst, src0_ddq_i, src0_ddf_i, src1_ddf_i, dst_ddf_i, i02, i01_low, i01_high, i11, cudaStream_main); + CUDA_CHECK(cudaGetLastError()); // copy dst to host or other device if necessary if (!dst_on_device) { @@ -2538,6 +2541,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i, dst_stride*sizeof(float), kind, cudaStream_main)); } } + + // signify to main device that other device is done + if (split && g_device_count > 1 && id != g_main_device) { + CUDA_CHECK(cudaEventRecord(src0_extra->events[id], cudaStream_main)); + } } } } @@ -2549,7 +2557,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm } CUDA_CHECK(cudaSetDevice(id)); - CUDA_CHECK(cudaDeviceSynchronize()); if (src0_asq[id] > 0) { ggml_cuda_pool_free(src0_ddq[id], src0_asq[id]); @@ -2564,6 +2571,21 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm ggml_cuda_pool_free(dst_ddf[id], dst_asf[id]); } } + + // main device waits for all other devices to be finished + if (split && g_device_count > 1) { + CUDA_CHECK(cudaSetDevice(g_main_device)); + for (int id = 0; id < g_device_count; ++id) { + if (id != g_main_device) { + CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams_main[g_main_device], src0_extra->events[id])); + } + } + } + + if (dst->backend == GGML_BACKEND_CPU) { + CUDA_CHECK(cudaSetDevice(g_main_device)); + CUDA_CHECK(cudaDeviceSynchronize()); + } } void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -2803,6 +2825,10 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice); extra->data_device[id] = buf; + + if (backend == GGML_BACKEND_GPU_SPLIT) { + CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id], cudaEventDisableTiming)); + } } tensor->extra = extra; @@ -2816,12 +2842,15 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; for (int id = 0; id < g_device_count; ++id) { - if (extra->data_device[id] == nullptr) { - continue; + if (extra->data_device[id] != nullptr) { + CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaFree(extra->data_device[id])); } - CUDA_CHECK(cudaSetDevice(id)); - CUDA_CHECK(cudaFree(extra->data_device[id])); + if (extra->events[id] != nullptr) { + CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaEventDestroy(extra->events[id])); + } } delete extra; diff --git a/ggml-cuda.h b/ggml-cuda.h index 7a65a3558..3c1e8deb6 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -8,10 +8,6 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 -struct ggml_tensor_extra_gpu { - void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors -}; - void ggml_init_cublas(void); void ggml_cuda_set_tensor_split(const float * tensor_split); From 46088f72318981341a2d646f12f6eee6aec06d65 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 2 Jul 2023 09:46:46 +0300 Subject: [PATCH 11/84] ggml : fix build with OpenBLAS (close #2066) --- ggml.c | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/ggml.c b/ggml.c index 75cc44baa..afeb72ff0 100644 --- a/ggml.c +++ b/ggml.c @@ -3855,28 +3855,29 @@ static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size // Take care about compile options (e.g., GGML_USE_xxx). static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; + static void ggml_setup_op_has_task_pass(void) { { // INIT - bool * I = GGML_OP_HAS_INIT; + bool * p = GGML_OP_HAS_INIT; - I[GGML_OP_ACC ] = true; - I[GGML_OP_MUL_MAT ] = true; - I[GGML_OP_OUT_PROD ] = true; - I[GGML_OP_SET ] = true; - I[GGML_OP_GET_ROWS_BACK ] = true; - I[GGML_OP_DIAG_MASK_INF ] = true; - I[GGML_OP_DIAG_MASK_ZERO ] = true; - I[GGML_OP_CONV_1D_S1_PH ] = true; - I[GGML_OP_CONV_1D_S2_PH ] = true; - I[GGML_OP_CONV_2D_SK_P0 ] = true; - I[GGML_OP_FLASH_ATTN_BACK ] = true; - I[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + p[GGML_OP_ACC ] = true; + p[GGML_OP_MUL_MAT ] = true; + p[GGML_OP_OUT_PROD ] = true; + p[GGML_OP_SET ] = true; + p[GGML_OP_GET_ROWS_BACK ] = true; + p[GGML_OP_DIAG_MASK_INF ] = true; + p[GGML_OP_DIAG_MASK_ZERO ] = true; + p[GGML_OP_CONV_1D_S1_PH ] = true; + p[GGML_OP_CONV_1D_S2_PH ] = true; + p[GGML_OP_CONV_2D_SK_P0 ] = true; + p[GGML_OP_FLASH_ATTN_BACK ] = true; + p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } { // FINALIZE - bool * F = GGML_OP_HAS_FINALIZE; + bool * p = GGML_OP_HAS_FINALIZE; - F[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } } From d7d2e6a0f0c74f7a570dae384dfff371ac744d2a Mon Sep 17 00:00:00 2001 From: WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com> Date: Mon, 3 Jul 2023 05:38:44 +0800 Subject: [PATCH 12/84] server: add option to output probabilities for completion (#1962) * server: add option to output probabilities for completion * server: fix issue when handling probability output for incomplete tokens for multibyte character generation * server: fix llama_sample_top_k order * examples/common.h: put all bool variables in gpt_params together --- examples/common.h | 3 +- examples/server/server.cpp | 150 +++++++++++++++++++++++++++++-------- 2 files changed, 122 insertions(+), 31 deletions(-) diff --git a/examples/common.h b/examples/common.h index 66e567291..96f2228f8 100644 --- a/examples/common.h +++ b/examples/common.h @@ -31,7 +31,7 @@ struct gpt_params { int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - bool low_vram = 0; // if true, reduce VRAM usage at the cost of performance + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. // sampling parameters std::unordered_map logit_bias; // logit bias for specific tokens @@ -59,6 +59,7 @@ struct gpt_params { std::string lora_adapter = ""; // lora adapter path std::string lora_base = ""; // base model path for the lora adapter + bool low_vram = false; // if true, reduce VRAM usage at the cost of performance bool memory_f16 = true; // use f16 instead of f32 for memory kv bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 998d55eac..e4ddbe986 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -26,6 +26,17 @@ struct server_params { int32_t write_timeout = 600; }; +// completion token output with probabilities +struct completion_token_output { + struct token_prob { + llama_token tok; + float prob; + }; + + std::vector probs; + llama_token tok; +}; + static size_t common_part(const std::vector & a, const std::vector & b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} @@ -86,6 +97,40 @@ static void server_log(const char * level, const char * function, int line, fflush(stdout); } +// format incomplete utf-8 multibyte character for output +static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { + std::string out = token == -1 ? "" : llama_token_to_str(ctx, token); + // if first bit is 1, meaning it's a partial character + if (out.size() > 0 && (out[0] & 0x80) == 0x80) { + std::stringstream ss; + ss<< std::hex << (out[0] & 0xff); + std::string res ( ss.str() ); + out = "byte: \\x" + res; + } + return out; +} + +// convert a vector of completion_token_output to json +static json probs_vector_to_json(const llama_context * ctx, const std::vector probs) { + json out = json::array(); + for (const auto & prob : probs) { + json probs_for_token = json::array(); + for (const auto & p : prob.probs) { + std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json { + { "tok_str", tok_str }, + { "prob", p.prob }, + }); + } + std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json { + {"content", tok_str}, + {"probs", probs_for_token}, + }); + } + return out; +} + static bool server_verbose = false; #if SERVER_VERBOSE != 1 @@ -107,6 +152,7 @@ struct llama_server_context { bool stream = false; bool has_next_token = false; std::string generated_text; + std::vector generated_token_probs; size_t num_tokens_predicted = 0; size_t n_past = 0; @@ -142,6 +188,7 @@ struct llama_server_context { num_tokens_predicted = 0; generated_text = ""; generated_text.reserve(params.n_ctx); + generated_token_probs.clear(); truncated = false; stopped_eos = false; stopped_word = false; @@ -221,8 +268,9 @@ struct llama_server_context { llama_set_rng_seed(ctx, params.seed); } - llama_token nextToken() { - llama_token result = -1; + completion_token_output nextToken() { + completion_token_output result; + result.tok = -1; if (embd.size() >= (size_t)params.n_ctx) { // Reset context @@ -261,7 +309,8 @@ struct llama_server_context { if (params.n_predict == 0) { has_next_token = false; - return llama_token_eos(); + result.tok = llama_token_eos(); + return result; } // out of user input, sample next token @@ -278,7 +327,7 @@ struct llama_server_context { const float mirostat_tau = params.mirostat_tau; const float mirostat_eta = params.mirostat_eta; const bool penalize_nl = params.penalize_nl; - llama_token id = 0; + const int32_t n_probs = params.n_probs; { auto * logits = llama_get_logits(ctx); @@ -312,35 +361,42 @@ struct llama_server_context { if (temp <= 0) { // Greedy sampling - id = llama_sample_token_greedy(ctx, &candidates_p); + result.tok = llama_sample_token_greedy(ctx, &candidates_p); + if (n_probs > 0) { + llama_sample_softmax(ctx, &candidates_p); + } } else { if (mirostat == 1) { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + result.tok = 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_temperature(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + result.tok = 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); + size_t min_keep = std::max(1, n_probs); + llama_sample_top_k(ctx, &candidates_p, top_k, min_keep); + llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep); + llama_sample_typical(ctx, &candidates_p, typical_p, min_keep); + llama_sample_top_p(ctx, &candidates_p, top_p, min_keep); llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token(ctx, &candidates_p); + result.tok = llama_sample_token(ctx, &candidates_p); } } + + for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) { + result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p}); + } last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); + last_n_tokens.push_back(result.tok); num_tokens_predicted++; } // add it to the context - embd.push_back(id); - result = id; + embd.push_back(result.tok); // decrement remaining sampling budget --n_remain; @@ -382,12 +438,16 @@ struct llama_server_context { return stop_pos; } - std::string doCompletion() { - const llama_token token = nextToken(); + completion_token_output doCompletion() { + const completion_token_output token_with_probs = nextToken(); - const std::string token_text = token == -1 ? "" : llama_token_to_str(ctx, token); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok); generated_text += token_text; + if (params.n_probs > 0) { + generated_token_probs.push_back(token_with_probs); + } + if (multibyte_pending > 0) { multibyte_pending -= token_text.size(); } else if (token_text.size() == 1) { @@ -416,8 +476,8 @@ struct llama_server_context { } LOG_VERBOSE("next token", { - { "token", token }, - { "token_text", llama_token_to_str(ctx, 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 }, @@ -427,7 +487,7 @@ struct llama_server_context { { "stopping_word", stopping_word }, }); - return token_text; + return token_with_probs; } std::vector getEmbedding() { @@ -669,6 +729,7 @@ static json format_generation_settings(llama_server_context & llama) { { "ignore_eos", ignore_eos }, { "stream", llama.stream }, { "logit_bias", llama.params.logit_bias }, + { "n_probs", llama.params.n_probs }, }; } @@ -678,8 +739,9 @@ static json format_embedding_response(llama_server_context & llama) { }; } -static json format_final_response(llama_server_context & llama, const std::string & content) { - return json { +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 }, @@ -692,13 +754,25 @@ static json format_final_response(llama_server_context & llama, const std::strin { "stopped_limit", llama.stopped_limit }, { "stopping_word", llama.stopping_word }, }; + + if (llama.params.n_probs > 0) { + res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); + } + + return res; } -static json format_partial_response(const std::string & content) { - return json { +static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector & probs) { + json res = json { { "content", content }, { "stop", false }, }; + + if (llama.params.n_probs > 0) { + res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); + } + + return res; } static json format_tokenizer_response(const std::vector & tokens) { @@ -728,6 +802,7 @@ static void parse_options_completion(const json & body, llama_server_context & l llama.params.n_keep = body.value("n_keep", default_params.n_keep); llama.params.seed = body.value("seed", default_params.seed); llama.params.prompt = body.value("prompt", default_params.prompt); + llama.params.n_probs = body.value("n_probs", default_params.n_probs); llama.params.logit_bias.clear(); if (body.value("ignore_eos", false)) { @@ -830,7 +905,8 @@ int main(int argc, char ** argv) { size_t stop_pos = std::string::npos; while (llama.has_next_token) { - const std::string token_text = llama.doCompletion(); + const completion_token_output token_with_probs = llama.doCompletion(); + const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); stop_pos = llama.findStoppingStrings(llama.generated_text, token_text.size(), STOP_FULL); @@ -844,7 +920,7 @@ int main(int argc, char ** argv) { llama.generated_text.end()); } - const json data = format_final_response(llama, llama.generated_text); + const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs); llama_print_timings(llama.ctx); @@ -853,9 +929,11 @@ int main(int argc, char ** argv) { } 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 std::string token_text = llama.doCompletion(); + const completion_token_output token_with_probs = llama.doCompletion(); + const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); if (llama.multibyte_pending > 0) { continue; } @@ -878,10 +956,22 @@ int main(int argc, char ** argv) { const std::string to_send = llama.generated_text.substr(pos, stop_pos); sent_count += to_send.size(); + std::vector probs_output = {}; + + if (llama.params.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 = llama.has_next_token - ? format_partial_response(to_send) + ? format_partial_response(llama, to_send, probs_output) // Generation is done, send extra information. - : format_final_response(llama, to_send); + : format_final_response(llama, to_send, llama.generated_token_probs); const std::string str = "data: " + From 55dbb915cc2a95048f56e667b09dfad38d840421 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Mon, 3 Jul 2023 19:58:58 +0800 Subject: [PATCH 13/84] [llama] No need to check file version when loading vocab score (#2079) --- llama.cpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index a869bbac8..f48a6ca79 100644 --- a/llama.cpp +++ b/llama.cpp @@ -481,9 +481,7 @@ struct llama_file_loader { std::string word = file.read_string(len); float score = 0.0f; - if (file_version >= LLAMA_FILE_VERSION_GGMF_V1) { - file.read_raw(&score, sizeof(score)); - } + file.read_raw(&score, sizeof(score)); vocab.token_to_id[word] = i; From cc45a7feb8412e84ff292207621412fffc0d3d51 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Tue, 4 Jul 2023 02:43:55 +0800 Subject: [PATCH 14/84] Fix crash of test-tokenizer-0 under Debug build (#2064) * Fix crash of test-tokenizer-0 under Debug build * Change per comment --- ggml-cuda.cu | 2 +- llama.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 50df20edd..0b12a9e76 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2835,7 +2835,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { } void ggml_cuda_free_data(struct ggml_tensor * tensor) { - if (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) { + if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { return; } diff --git a/llama.cpp b/llama.cpp index f48a6ca79..7419b03b6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -194,8 +194,8 @@ struct llama_layer { }; struct llama_kv_cache { - struct ggml_tensor * k; - struct ggml_tensor * v; + struct ggml_tensor * k = NULL; + struct ggml_tensor * v = NULL; struct ggml_context * ctx = NULL; From 1cf14ccef12e19c5a5b0b17ab456242d1f8c7fdd Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Tue, 4 Jul 2023 00:05:23 +0300 Subject: [PATCH 15/84] fix server crashes (#2076) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e4ddbe986..3bf985957 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -906,7 +906,7 @@ int main(int argc, char ** argv) { while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok); stop_pos = llama.findStoppingStrings(llama.generated_text, token_text.size(), STOP_FULL); @@ -933,7 +933,7 @@ int main(int argc, char ** argv) { while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok); if (llama.multibyte_pending > 0) { continue; } From 14a2cc71f62e45803ae70890ffbdeb0a172e6210 Mon Sep 17 00:00:00 2001 From: Govlzkoy Date: Tue, 4 Jul 2023 07:50:00 +0800 Subject: [PATCH 16/84] [ggml] fix index for ne03 value in ggml_cl_mul_f32 (#2088) --- ggml-opencl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index fed4ffb0c..fa0bdbefb 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1376,7 +1376,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = 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]; From 698efad5fbbf326f01288649b123eff5f79b417e Mon Sep 17 00:00:00 2001 From: Erik Scholz Date: Tue, 4 Jul 2023 01:50:12 +0200 Subject: [PATCH 17/84] CI: make the brew update temporarily optional. (#2092) until they decide to fix the brew installation in the macos runners. see the open issues. eg https://github.com/actions/runner-images/pull/7710 --- .github/workflows/build.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b87ea76bc..aec43bd92 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -111,6 +111,7 @@ jobs: - name: Dependencies id: depends + continue-on-error: true run: | brew update @@ -129,6 +130,7 @@ jobs: - name: Dependencies id: depends + continue-on-error: true run: | brew update From 23c7c6fc9182b041f006b86ea1e7f99911ecf344 Mon Sep 17 00:00:00 2001 From: ZhouYuChen Date: Tue, 4 Jul 2023 20:15:16 +0800 Subject: [PATCH 18/84] Update Makefile: clean simple (#2097) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 03f38bdba..b289d97ed 100644 --- a/Makefile +++ b/Makefile @@ -272,7 +272,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch embd-input-test build-info.h + rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h # # Examples From acc111caf93fc6681450924df9f99679c384c59e Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Tue, 4 Jul 2023 15:38:04 +0300 Subject: [PATCH 19/84] Allow old Make to build server. (#2098) Also make server build by default. Tested with Make 3.82 --- Makefile | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/Makefile b/Makefile index b289d97ed..8966a3590 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test - -ifdef LLAMA_BUILD_SERVER - BUILD_TARGETS += server - LLAMA_SERVER_VERBOSE ?= 1 -server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) -endif +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test default: $(BUILD_TARGETS) @@ -61,6 +55,10 @@ else CXXFLAGS += -DNDEBUG endif +ifdef LLAMA_SERVER_VERBOSE + CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) +endif + # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar From 7ee76e45afae7f9a7a53e93393accfb5b36684e1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20L=C3=BCtke?= Date: Tue, 4 Jul 2023 10:05:27 -0400 Subject: [PATCH 20/84] Simple webchat for server (#1998) * expose simple web interface on root domain * embed index and add --path for choosing static dir * allow server to multithread because web browsers send a lot of garbage requests we want the server to multithread when serving 404s for favicon's etc. To avoid blowing up llama we just take a mutex when it's invoked. * let's try this with the xxd tool instead and see if msvc is happier with that * enable server in Makefiles * add /completion.js file to make it easy to use the server from js * slightly nicer css * rework state management into session, expose historyTemplate to settings --------- Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 2 +- examples/server/completion.js.hpp | 193 +++ examples/server/deps.sh | 22 + examples/server/index.html.hpp | 846 ++++++++++++ examples/server/index.js.hpp | 1851 ++++++++++++++++++++++++++ examples/server/public/completion.js | 81 ++ examples/server/public/index.html | 359 +++++ examples/server/public/index.js | 1 + examples/server/server.cpp | 69 +- 9 files changed, 3416 insertions(+), 8 deletions(-) create mode 100644 examples/server/completion.js.hpp create mode 100755 examples/server/deps.sh create mode 100644 examples/server/index.html.hpp create mode 100644 examples/server/index.js.hpp create mode 100644 examples/server/public/completion.js create mode 100644 examples/server/public/index.html create mode 100644 examples/server/public/index.js diff --git a/CMakeLists.txt b/CMakeLists.txt index 34a897327..4ac0f6f4e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -79,7 +79,7 @@ option(LLAMA_QKK_64 "llama: use super-block size of 64 option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) -option(LLAMA_BUILD_SERVER "llama: build server example" OFF) +option(LLAMA_BUILD_SERVER "llama: build server example" ON) # # Build info header diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp new file mode 100644 index 000000000..002830cad --- /dev/null +++ b/examples/server/completion.js.hpp @@ -0,0 +1,193 @@ +unsigned char completion_js[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72, + 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x3a, 0x20, 0x35, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, + 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, + 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, + 0x3a, 0x20, 0x5b, 0x22, 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+echo "download js bundle files" +curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js +echo >> $PUBLIC/index.js # add newline + +FILES=$(ls $PUBLIC) + +for FILE in $FILES; do + func=$(echo $FILE | tr '.' '_') + echo "generate $FILE.hpp ($func)" + xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp +done diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp new file mode 100644 index 000000000..832e9a3bb --- /dev/null +++ b/examples/server/index.html.hpp @@ -0,0 +1,846 @@ +unsigned char index_html[] = { + 0x3c, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a, 0x0a, 0x3c, 0x68, 0x65, 0x61, + 0x64, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, 0x63, + 0x68, 0x61, 0x72, 0x73, 0x65, 0x74, 0x3d, 0x22, 0x55, 0x54, 0x46, 0x2d, + 0x38, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, + 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x76, 0x69, 0x65, 0x77, 0x70, 0x6f, + 0x72, 0x74, 0x22, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3d, + 0x22, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3d, 0x64, 0x65, 0x76, 0x69, 0x63, + 0x65, 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x2c, 0x20, 0x69, 0x6e, 0x69, + 0x74, 0x69, 0x61, 0x6c, 0x2d, 0x73, 0x63, 0x61, 0x6c, 0x65, 0x3d, 0x31, + 0x2c, 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mode 100644 index 000000000..4f5005cfb --- /dev/null +++ b/examples/server/public/completion.js @@ -0,0 +1,81 @@ +const paramDefaults = { + stream: true, + n_predict: 500, + temperature: 0.2, + stop: [""] +}; + +/** + * This function completes the input text using a llama dictionary. + * @param {object} params - The parameters for the completion request. + * @param {object} controller - an instance of AbortController if you need one, or null. + * @param {function} callback - The callback function to call when the completion is done. + * @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback. + */ +export const llamaComplete = async (params, controller, callback) => { + if (!controller) { + controller = new AbortController(); + } + const completionParams = { ...paramDefaults, ...params }; + + // we use fetch directly here becasue the built in fetchEventSource does not support POST + const response = await fetch("/completion", { + method: 'POST', + body: JSON.stringify(completionParams), + headers: { + 'Connection': 'keep-alive', + 'Content-Type': 'application/json', + 'Accept': 'text/event-stream' + }, + signal: controller.signal, + }); + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + + let content = ""; + + try { + + let cont = true; + + while (cont) { + const result = await reader.read(); + if (result.done) { + break; + } + + // sse answers in the form multiple lines of: value\n with data always present as a key. in our case we + // mainly care about the data: key here, which we expect as json + const text = decoder.decode(result.value); + + // parse all sse events and add them to result + const regex = /^(\S+):\s(.*)$/gm; + for (const match of text.matchAll(regex)) { + result[match[1]] = match[2] + } + + // since we know this is llama.cpp, let's just decode the json in data + result.data = JSON.parse(result.data); + content += result.data.content; + + // callack + if (callback) { + cont = callback(result) != false; + } + + // if we got a stop token from server, we will break here + if (result.data.stop) { + break; + } + } + } catch (e) { + console.error("llama error: ", e); + throw e; + } + finally { + controller.abort(); + } + + return content; +} diff --git a/examples/server/public/index.html b/examples/server/public/index.html new file mode 100644 index 000000000..6393e2e75 --- /dev/null +++ b/examples/server/public/index.html @@ -0,0 +1,359 @@ + + + + + + llama.cpp - chat + + + + + + + + + + diff --git a/examples/server/public/index.js 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"completion.js.hpp" + #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif @@ -21,6 +24,7 @@ using json = nlohmann::json; struct server_params { std::string hostname = "127.0.0.1"; + std::string public_path = "examples/server/public"; int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; @@ -172,6 +176,12 @@ struct llama_server_context { std::string stopping_word; int32_t multibyte_pending = 0; + std::mutex mutex; + + std::unique_lock lock() { + return std::unique_lock(mutex); + } + ~llama_server_context() { if (ctx) { llama_free(ctx); @@ -539,6 +549,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port); + fprintf(stderr, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); fprintf(stderr, "\n"); @@ -565,6 +576,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } sparams.hostname = argv[i]; + } else if (arg == "--path") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.public_path = argv[i]; } else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) { invalid_param = true; @@ -839,17 +856,24 @@ static void parse_options_completion(const json & body, llama_server_context & l LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); } + static void log_server_request(const Request & req, const Response & res) { LOG_INFO("request", { { "remote_addr", req.remote_addr }, { "remote_port", req.remote_port }, { "status", res.status }, + { "method", req.method }, { "path", req.path }, + { "params", req.params }, + }); + + LOG_VERBOSE("request", { { "request", req.body }, { "response", res.body }, }); } + int main(int argc, char ** argv) { // own arguments required by this example gpt_params params; @@ -884,16 +908,34 @@ int main(int argc, char ** argv) { 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.js is found in the public --path + svr.Get("/index.js", [](const Request &, Response & res) { + 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("/", [](const Request &, Response & res) { - res.set_content("

llama.cpp server works

", "text/html"); + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); + return false; + }); + + // this is only called if no index.html is found in the public --path + svr.Get("/completion.js", [](const Request &, Response & res) { + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); + return false; }); svr.Post("/completion", [&llama](const Request & req, Response & res) { + auto lock = llama.lock(); + llama.rewind(); + llama_reset_timings(llama.ctx); parse_options_completion(json::parse(req.body), llama); @@ -1002,6 +1044,8 @@ int main(int argc, char ** argv) { }); svr.Post("/tokenize", [&llama](const Request & req, Response & res) { + auto lock = llama.lock(); + const json body = json::parse(req.body); const std::string content = body.value("content", ""); const std::vector tokens = llama_tokenize(llama.ctx, content, false); @@ -1010,6 +1054,8 @@ int main(int argc, char ** argv) { }); svr.Post("/embedding", [&llama](const Request & req, Response & res) { + auto lock = llama.lock(); + const json body = json::parse(req.body); llama.rewind(); @@ -1040,18 +1086,27 @@ int main(int argc, char ** argv) { res.status = 500; }); + svr.set_error_handler([](const Request &, Response & res) { + 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_write_timeout(sparams.write_timeout); if (!svr.bind_to_port(sparams.hostname, sparams.port)) { - LOG_ERROR("couldn't bind to server socket", { - { "hostname", sparams.hostname }, - { "port", sparams.port }, - }); + fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } + // Set the base directory for serving static files + svr.set_base_dir(sparams.public_path); + + // to make it ctrl+clickable: + fprintf(stdout, "\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 }, From f257fd255044decffad93dee2502875ce66ad80c Mon Sep 17 00:00:00 2001 From: jwj7140 <32943891+jwj7140@users.noreply.github.com> Date: Wed, 5 Jul 2023 03:06:12 +0900 Subject: [PATCH 21/84] Add an API example using server.cpp similar to OAI. (#2009) * add api_like_OAI.py * add evaluated token count to server * add /v1/ endpoints binding --- examples/server/README.md | 16 +++ examples/server/api_like_OAI.py | 219 ++++++++++++++++++++++++++++++++ examples/server/server.cpp | 14 +- 3 files changed, 244 insertions(+), 5 deletions(-) create mode 100755 examples/server/api_like_OAI.py diff --git a/examples/server/README.md b/examples/server/README.md index ba4b2fec9..4ed226e04 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -190,3 +190,19 @@ Run with bash: ```sh bash chat.sh ``` + +### API like OAI + +API example using Python Flask: [api_like_OAI.py](api_like_OAI.py) +This example must be used with server.cpp + +```sh +python api_like_OAI.py +``` + +After running the API server, you can use it in Python by setting the API base URL. +```python +openai.api_base = "http://:port" +``` + +Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py new file mode 100755 index 000000000..aa325a03e --- /dev/null +++ b/examples/server/api_like_OAI.py @@ -0,0 +1,219 @@ +import argparse +from flask import Flask, jsonify, request, Response +import urllib.parse +import requests +import time +import json + + +app = Flask(__name__) + +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') +parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ") +parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ") +parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ") +parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '')", default="") +parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080') +parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="") +parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1') +parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081) + +args = parser.parse_args() + +def is_present(json, key): + try: + buf = json[key] + except KeyError: + return False + return True + + + +#convert chat to prompt +def convert_chat(messages): + prompt = "" + args.chat_prompt.replace("\\n", "\n") + + system_n = args.system_name.replace("\\n", "\n") + user_n = args.user_name.replace("\\n", "\n") + ai_n = args.ai_name.replace("\\n", "\n") + stop = args.stop.replace("\\n", "\n") + + + for line in messages: + if (line["role"] == "system"): + prompt += f"{system_n}{line['content']}" + if (line["role"] == "user"): + prompt += f"{user_n}{line['content']}" + if (line["role"] == "assistant"): + prompt += f"{ai_n}{line['content']}{stop}" + prompt += ai_n.rstrip() + + return prompt + +def make_postData(body, chat=False, stream=False): + postData = {} + if (chat): + postData["prompt"] = convert_chat(body["messages"]) + else: + postData["prompt"] = body["prompt"] + if(is_present(body, "temperature")): postData["temperature"] = body["temperature"] + if(is_present(body, "top_k")): postData["top_k"] = body["top_k"] + if(is_present(body, "top_p")): postData["top_p"] = body["top_p"] + if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"] + if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"] + if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"] + if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"] + if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"] + if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"] + if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"] + if(is_present(body, "seed")): postData["seed"] = body["seed"] + if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()] + if (args.stop != ""): + postData["stop"] = [args.stop] + else: + postData["stop"] = [] + if(is_present(body, "stop")): postData["stop"] += body["stop"] + postData["n_keep"] = -1 + postData["stream"] = stream + + return postData + +def make_resData(data, chat=False, promptToken=[]): + resData = { + "id": "chatcmpl" if (chat) else "cmpl", + "object": "chat.completion" if (chat) else "text_completion", + "created": int(time.time()), + "truncated": data["truncated"], + "model": "LLaMA_CPP", + "usage": { + "prompt_tokens": data["tokens_evaluated"], + "completion_tokens": data["tokens_predicted"], + "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"] + } + } + if (len(promptToken) != 0): + resData["promptToken"] = promptToken + if (chat): + #only one choice is supported + resData["choices"] = [{ + "index": 0, + "message": { + "role": "assistant", + "content": data["content"], + }, + "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + }] + else: + #only one choice is supported + resData["choices"] = [{ + "text": data["content"], + "index": 0, + "logprobs": None, + "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + }] + return resData + +def make_resData_stream(data, chat=False, time_now = 0, start=False): + resData = { + "id": "chatcmpl" if (chat) else "cmpl", + "object": "chat.completion.chunk" if (chat) else "text_completion.chunk", + "created": time_now, + "model": "LLaMA_CPP", + "choices": [ + { + "finish_reason": None, + "index": 0 + } + ] + } + if (chat): + if (start): + resData["choices"][0]["delta"] = { + "role": "assistant" + } + else: + resData["choices"][0]["delta"] = { + "content": data["content"] + } + if (data["stop"]): + resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + else: + resData["choices"][0]["text"] = data["content"] + if (data["stop"]): + resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + + return resData + + +@app.route('/chat/completions', methods=['POST']) +@app.route('/v1/chat/completions', methods=['POST']) +def chat_completions(): + if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): + return Response(status=403) + body = request.get_json() + stream = False + tokenize = False + if(is_present(body, "stream")): stream = body["stream"] + if(is_present(body, "tokenize")): tokenize = body["tokenize"] + postData = make_postData(body, chat=True, stream=stream) + + promptToken = [] + if (tokenize): + tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() + promptToken = tokenData["tokens"] + + if (not stream): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) + print(data.json()) + resData = make_resData(data.json(), chat=True, promptToken=promptToken) + return jsonify(resData) + else: + def generate(): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) + time_now = int(time.time()) + resData = make_resData_stream({}, chat=True, time_now=time_now, start=True) + yield 'data: {}\n'.format(json.dumps(resData)) + for line in data.iter_lines(): + if line: + decoded_line = line.decode('utf-8') + resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now) + yield 'data: {}\n'.format(json.dumps(resData)) + return Response(generate(), mimetype='text/event-stream') + + +@app.route('/completions', methods=['POST']) +@app.route('/v1/completions', methods=['POST']) +def completion(): + if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): + return Response(status=403) + body = request.get_json() + stream = False + tokenize = False + if(is_present(body, "stream")): stream = body["stream"] + if(is_present(body, "tokenize")): tokenize = body["tokenize"] + postData = make_postData(body, chat=False, stream=stream) + + promptToken = [] + if (tokenize): + tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() + promptToken = tokenData["tokens"] + + if (not stream): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) + print(data.json()) + resData = make_resData(data.json(), chat=False, promptToken=promptToken) + return jsonify(resData) + else: + def generate(): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) + time_now = int(time.time()) + for line in data.iter_lines(): + if line: + decoded_line = line.decode('utf-8') + resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now) + yield 'data: {}\n'.format(json.dumps(resData)) + return Response(generate(), mimetype='text/event-stream') + +if __name__ == '__main__': + app.run(args.host, port=args.port) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 043e49750..a835c3988 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -158,6 +158,7 @@ struct llama_server_context { std::string generated_text; std::vector generated_token_probs; + size_t num_prompt_tokens = 0; size_t num_tokens_predicted = 0; size_t n_past = 0; size_t n_remain = 0; @@ -195,6 +196,7 @@ struct llama_server_context { void rewind() { params.antiprompt.clear(); + num_prompt_tokens = 0; num_tokens_predicted = 0; generated_text = ""; generated_text.reserve(params.n_ctx); @@ -226,17 +228,18 @@ struct llama_server_context { void loadPrompt() { params.prompt.insert(0, 1, ' '); // always add a first space std::vector prompt_tokens = ::llama_tokenize(ctx, params.prompt, true); + num_prompt_tokens = prompt_tokens.size(); if (params.n_keep < 0) { - params.n_keep = (int)prompt_tokens.size(); + params.n_keep = (int)num_prompt_tokens; } params.n_keep = std::min(params.n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal - if (prompt_tokens.size() >= (size_t)params.n_ctx) { + if (num_prompt_tokens>= (size_t)params.n_ctx) { 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 = (prompt_tokens.size() - params.n_keep - n_left - 1) / n_left; + 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()); @@ -250,7 +253,7 @@ struct llama_server_context { truncated = true; prompt_tokens = new_tokens; } else { - const size_t ps = prompt_tokens.size(); + 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); } @@ -258,7 +261,7 @@ struct llama_server_context { // compare the evaluated prompt with the new prompt n_past = common_part(embd, prompt_tokens); embd = prompt_tokens; - if (n_past == prompt_tokens.size()) { + if (n_past == num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. n_past--; } @@ -763,6 +766,7 @@ static json format_final_response(llama_server_context & llama, const std::strin { "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.params.prompt }, { "truncated", llama.truncated }, From ed9a54e5129a11c2a5b555e1dc65e875e3c37b4f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 4 Jul 2023 21:54:11 +0300 Subject: [PATCH 22/84] ggml : sync latest (new ops, macros, refactoring) (#2106) - add ggml_argmax() - add ggml_tanh() - add ggml_elu() - refactor ggml_conv_1d() and variants - refactor ggml_conv_2d() and variants - add helper macros to reduce code duplication in ggml.c --- ggml.c | 1512 ++++++++++++++---------------------------- ggml.h | 118 ++-- scripts/sync-ggml.sh | 11 +- 3 files changed, 589 insertions(+), 1052 deletions(-) diff --git a/ggml.c b/ggml.c index afeb72ff0..88cbed7d5 100644 --- a/ggml.c +++ b/ggml.c @@ -220,9 +220,27 @@ inline static void* ggml_aligned_malloc(size_t size) { #define GGML_ALIGNED_FREE(ptr) free(ptr) #endif -#define UNUSED(x) (void)(x) +#define UNUSED GGML_UNUSED #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0) +// +// tensor access macros +// + +#define GGML_TENSOR_UNARY_OP_LOCALS \ + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \ + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); \ + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); \ + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); + +#define GGML_TENSOR_BINARY_OP_LOCALS \ + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \ + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); \ + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); \ + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); \ + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); \ + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); + #if defined(GGML_USE_ACCELERATE) #include #if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions @@ -3447,6 +3465,8 @@ inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } +inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } +inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } static const float GELU_COEF_A = 0.044715f; @@ -3598,6 +3618,16 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x *s = 1.f/(*s); } +inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { + float max = -INFINITY; + int idx = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + if (max == x[i]) { idx = i; } + } + *s = idx; +} + // // data types // @@ -3707,12 +3737,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "SUM", "SUM_ROWS", "MEAN", + "ARGMAX", "REPEAT", "REPEAT_BACK", "ABS", "SGN", "NEG", "STEP", + "TANH", + "ELU", "RELU", "GELU", "GELU_QUICK", @@ -3744,9 +3777,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ROPE_BACK", "ALIBI", "CLAMP", - "CONV_1D_S1_PH", - "CONV_1D_S2_PH", - "CONV_2D_SK_P0", + "CONV_1D", + "CONV_2D", "FLASH_ATTN", "FLASH_FF", @@ -3765,7 +3797,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); +static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -3783,12 +3815,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "Ī£x", "Ī£x_k", "Ī£x/n", + "argmax(x)", "repeat(x)", "repeat_back(x)", "abs(x)", "sgn(x)", "-x", "step(x)", + "tanh(x)", + "elu(x)", "relu(x)", "gelu(x)", "gelu_quick(x)", @@ -3820,9 +3855,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "rope_back(x)", "alibi(x)", "clamp(x)", - "conv_1d_s1_ph(x)", - "conv_1d_s2_ph(x)", - "conv_2d_sk_p0(x)", + "conv_1d(x)", + "conv_2d(x)", "flash_attn(x)", "flash_ff(x)", @@ -3841,7 +3875,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); +static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); @@ -3867,9 +3901,8 @@ static void ggml_setup_op_has_task_pass(void) { p[GGML_OP_GET_ROWS_BACK ] = true; p[GGML_OP_DIAG_MASK_INF ] = true; p[GGML_OP_DIAG_MASK_ZERO ] = true; - p[GGML_OP_CONV_1D_S1_PH ] = true; - p[GGML_OP_CONV_1D_S2_PH ] = true; - p[GGML_OP_CONV_2D_SK_P0 ] = true; + p[GGML_OP_CONV_1D ] = true; + p[GGML_OP_CONV_2D ] = true; p[GGML_OP_FLASH_ATTN_BACK ] = true; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } @@ -5440,6 +5473,30 @@ struct ggml_tensor * ggml_mean( return result; } +// ggml_argmax + +struct ggml_tensor * ggml_argmax( + struct ggml_context * ctx, + struct ggml_tensor * a) { + GGML_ASSERT(ggml_is_matrix(a)); + bool is_node = false; + + if (a->grad) { + GGML_ASSERT(false); + is_node = true; + } + + int64_t ne[GGML_MAX_DIMS] = { a->ne[1], 1, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, ne); + + result->op = GGML_OP_ARGMAX; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + // ggml_repeat struct ggml_tensor * ggml_repeat( @@ -5633,6 +5690,74 @@ struct ggml_tensor * ggml_step_inplace( return ggml_step_impl(ctx, a, true); } +// ggml_tanh + +struct ggml_tensor * ggml_tanh_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad)) { + is_node = true; + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_TANH; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + +struct ggml_tensor * ggml_tanh( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_tanh_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_tanh_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_tanh_impl(ctx, a, true); +} + +// ggml_elu + +struct ggml_tensor * ggml_elu_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad)) { + is_node = true; + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_ELU; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + +struct ggml_tensor * ggml_elu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_elu_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_elu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_elu_impl(ctx, a, true); +} + // ggml_relu struct ggml_tensor * ggml_relu_impl( @@ -6874,6 +6999,8 @@ struct ggml_tensor * ggml_rope_back( int n_dims, int mode) { GGML_ASSERT(n_past >= 0); + GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); + bool is_node = false; if (a->grad) { @@ -6974,15 +7101,21 @@ struct ggml_tensor * ggml_clamp( return result; } -// ggml_conv_1d_s1_ph +// ggml_conv_1d -struct ggml_tensor * ggml_conv_1d_s1_ph( +static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) { + return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; +} + +GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * b, + int s0, + int p0, + int d0) { GGML_ASSERT(ggml_is_matrix(b)); GGML_ASSERT(a->ne[1] == b->ne[1]); - GGML_ASSERT(a->ne[3] == 1); bool is_node = false; if (a->grad || b->grad) { @@ -6990,54 +7123,43 @@ struct ggml_tensor * ggml_conv_1d_s1_ph( is_node = true; } - const int64_t ne[4] = { b->ne[0], a->ne[2], 1, 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + const int64_t ne[4] = { + ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), + a->ne[2], 1, 1, + }; + struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - result->op = GGML_OP_CONV_1D_S1_PH; + ggml_scratch_save(ctx); + struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); + ((int32_t*)c->data)[0] = s0; + ((int32_t*)c->data)[1] = p0; + ((int32_t*)c->data)[2] = d0; + ggml_scratch_load(ctx); + + result->op = GGML_OP_CONV_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; + result->opt[0] = c; return result; } -// ggml_conv_1d_s2_ph +// ggml_conv_2d -struct ggml_tensor * ggml_conv_1d_s2_ph( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - GGML_ASSERT(ggml_is_matrix(b)); - GGML_ASSERT(a->ne[1] == b->ne[1]); - GGML_ASSERT(a->ne[3] == 1); - bool is_node = false; +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) { - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - - result->op = GGML_OP_CONV_1D_S2_PH; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src0 = a; - result->src1 = b; - - return result; -} - -// ggml_conv_2d_sk_p0 - -struct ggml_tensor * ggml_conv_2d_sk_p0( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { GGML_ASSERT(b->ne[3] == 1); GGML_ASSERT(a->ne[2] == b->ne[2]); - GGML_ASSERT(b->ne[0] % a->ne[0] == 0); - GGML_ASSERT(b->ne[1] % a->ne[1] == 0); bool is_node = false; if (a->grad || b->grad) { @@ -7045,15 +7167,42 @@ struct ggml_tensor * ggml_conv_2d_sk_p0( is_node = true; } - const int64_t ne[4] = { b->ne[0]/a->ne[0], b->ne[1]/a->ne[1], a->ne[3], 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + 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], 1, + }; + struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_CONV_2D_SK_P0; + ggml_scratch_save(ctx); + struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6); + ((int32_t*)c->data)[0] = s0; + ((int32_t*)c->data)[1] = s1; + ((int32_t*)c->data)[2] = p0; + ((int32_t*)c->data)[3] = p1; + ((int32_t*)c->data)[4] = d0; + ((int32_t*)c->data)[5] = d1; + ggml_scratch_load(ctx); + + result->op = GGML_OP_CONV_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; + result->opt[0] = c; return result; + +} + +// ggml_conv_1d_ph + +struct ggml_tensor* ggml_conv_1d_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s, + int d) { + return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d); } // ggml_flash_attn @@ -7603,25 +7752,7 @@ static void ggml_compute_forward_dup_f16( return; } - const int64_t ne00 = src0->ne[0]; - 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 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads @@ -7892,25 +8023,7 @@ static void ggml_compute_forward_dup_f32( return; } - const int64_t ne00 = src0->ne[0]; - 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 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads @@ -8208,24 +8321,8 @@ static void ggml_compute_forward_add_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -8294,28 +8391,12 @@ static void ggml_compute_forward_add_f16_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); @@ -8364,24 +8445,8 @@ static void ggml_compute_forward_add_f16_f16( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); @@ -8431,25 +8496,8 @@ static void ggml_compute_forward_add_q_f32( } const int nr = ggml_nrows(src0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -8570,19 +8618,8 @@ static void ggml_compute_forward_add1_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -8636,23 +8673,12 @@ static void ggml_compute_forward_add1_f16_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); @@ -8697,23 +8723,12 @@ static void ggml_compute_forward_add1_f16_f16( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); @@ -8758,19 +8773,8 @@ static void ggml_compute_forward_add1_q_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const enum ggml_type type = src0->type; dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; @@ -8902,15 +8906,8 @@ static void ggml_compute_forward_acc_f32( const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; - 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 size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // src0 and dst as viewed during acc const size_t nb0 = ggml_element_size(src0); @@ -8999,24 +8996,8 @@ static void ggml_compute_forward_sub_f32( } const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -9106,29 +9087,7 @@ static void ggml_compute_forward_mul_f32( const int64_t nr = ggml_nrows(src0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - 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 size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -9216,24 +9175,8 @@ static void ggml_compute_forward_div_f32( } const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -9440,14 +9383,8 @@ static void ggml_compute_forward_sum_f32( assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); ggml_float sum = 0; ggml_float row_sum = 0; @@ -9496,29 +9433,13 @@ static void ggml_compute_forward_sum_rows_f32( GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(dst->nb[0] == sizeof(float)); - const int64_t ne00 = src0->ne[0]; - 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 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(ne0 == 1); GGML_ASSERT(ne1 == ne01); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - for (int64_t i3 = 0; i3 < ne03; i3++) { for (int64_t i2 = 0; i2 < ne02; i2++) { for (int64_t i1 = 0; i1 < ne01; i1++) { @@ -9562,19 +9483,7 @@ static void ggml_compute_forward_mean_f32( assert(src0->nb[0] == sizeof(float)); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; + GGML_TENSOR_UNARY_OP_LOCALS; assert(ne0 == 1); assert(ne1 == ne01); @@ -9586,10 +9495,6 @@ static void ggml_compute_forward_mean_f32( UNUSED(ne2); UNUSED(ne3); - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { @@ -9619,6 +9524,52 @@ static void ggml_compute_forward_mean( } } +// ggml_compute_forward_argmax + +static void ggml_compute_forward_argmax_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + assert(dst->nb[0] == sizeof(float)); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + const size_t nb01 = src0->nb[1]; + const size_t nb0 = dst->nb[0]; + + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src = (float *) ((char *) src0->data + i1*nb01); + int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); + int v = 0; + ggml_vec_argmax_f32(ne00, &v, src); + dst_[0] = v; + } +} + +static void ggml_compute_forward_argmax( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argmax_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_repeat static void ggml_compute_forward_repeat_f32( @@ -9632,25 +9583,7 @@ static void ggml_compute_forward_repeat_f32( return; } - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); @@ -9711,25 +9644,7 @@ static void ggml_compute_forward_repeat_back_f32( return; } - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne00/ne0); @@ -9959,6 +9874,90 @@ static void ggml_compute_forward_step( } } +// ggml_compute_forward_tanh + +static void ggml_compute_forward_tanh_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_tanh_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_tanh( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_tanh_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_elu + +static void ggml_compute_forward_elu_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_elu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_elu( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_elu_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_relu static void ggml_compute_forward_relu_f32( @@ -10260,18 +10259,7 @@ static void ggml_compute_forward_norm_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const float eps = 1e-5f; // TODO: make this a parameter @@ -10337,18 +10325,7 @@ static void ggml_compute_forward_rms_norm_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const float eps = 1e-6f; // TODO: make this a parameter @@ -10413,22 +10390,7 @@ static void ggml_compute_forward_rms_norm_back_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const float eps = 1e-6f; // TODO: make this a parameter @@ -10624,41 +10586,7 @@ static void ggml_compute_forward_mul_mat_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - const int64_t ne10 = src1->ne[0]; -#endif - const int64_t ne11 = src1->ne[1]; -#ifndef NDEBUG - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nb00 = src0->nb[0]; -#endif - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - -#ifndef NDEBUG - const int nb10 = src1->nb[0]; -#endif - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -10795,37 +10723,10 @@ static void ggml_compute_forward_mul_mat_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; + GGML_TENSOR_BINARY_OP_LOCALS; - const int64_t 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 ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - const int ith = params->ith; const int nth = params->nth; @@ -10995,35 +10896,7 @@ static void ggml_compute_forward_mul_mat_q_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - 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 ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -11039,7 +10912,7 @@ static void ggml_compute_forward_mul_mat_q_f32( enum ggml_type const vec_dot_type = quantize_fns[type].vec_dot_type; // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]); + GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted @@ -11233,35 +11106,7 @@ static void ggml_compute_forward_out_prod_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - 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 ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -11496,15 +11341,8 @@ static void ggml_compute_forward_set_f32( const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; - 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 size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // src0 and dst as viewed during set const size_t nb0 = ggml_element_size(src0); @@ -11895,29 +11733,14 @@ static void ggml_compute_forward_diag_f32( // TODO: handle transposed/permuted matrices - const int ne00 = src0->ne[0]; - const int ne01 = src0->ne[1]; - const int ne02 = src0->ne[2]; - const int ne03 = src0->ne[3]; - const int ne0 = dst->ne[0]; - const int ne1 = dst->ne[1]; - const int ne2 = dst->ne[2]; - const int ne3 = dst->ne[3]; + GGML_TENSOR_UNARY_OP_LOCALS; + GGML_ASSERT(ne00 == ne0); GGML_ASSERT(ne00 == ne1); GGML_ASSERT(ne01 == 1); GGML_ASSERT(ne02 == ne2); GGML_ASSERT(ne03 == ne3); - const int nb00 = src0->nb[0]; - //const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); @@ -12494,20 +12317,7 @@ static void ggml_compute_forward_rope_f32( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -12634,20 +12444,7 @@ static void ggml_compute_forward_rope_f16( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -12800,21 +12597,7 @@ static void ggml_compute_forward_rope_back_f32( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -12913,21 +12696,7 @@ static void ggml_compute_forward_rope_back_f16( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -13025,7 +12794,7 @@ static void ggml_compute_forward_rope_back( } } -// ggml_compute_forward_conv_1d_s1_ph +// ggml_compute_forward_conv_1d static void ggml_compute_forward_conv_1d_s1_ph_f16_f32( const struct ggml_compute_params * params, @@ -13039,36 +12808,7 @@ static void ggml_compute_forward_conv_1d_s1_ph_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - 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 ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13159,36 +12899,7 @@ static void ggml_compute_forward_conv_1d_s1_ph_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - 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 ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13288,8 +12999,6 @@ static void ggml_compute_forward_conv_1d_s1_ph( } } -// ggml_compute_forward_conv_1d_s2_ph - static void ggml_compute_forward_conv_1d_s2_ph_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -13302,36 +13011,7 @@ static void ggml_compute_forward_conv_1d_s2_ph_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - 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 ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13422,36 +13102,7 @@ static void ggml_compute_forward_conv_1d_s2_ph_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - 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 ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13551,6 +13202,28 @@ static void ggml_compute_forward_conv_1d_s2_ph( } } +// ggml_compute_forward_conv_1d + +static void ggml_compute_forward_conv_1d( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + const struct ggml_tensor * opt0, + struct ggml_tensor * dst) { + const int32_t s0 = ((const int32_t*)(opt0->data))[0]; + const int32_t p0 = ((const int32_t*)(opt0->data))[1]; + const int32_t d0 = ((const int32_t*)(opt0->data))[2]; + GGML_ASSERT(d0 == 1); // dilation not supported + GGML_ASSERT(p0 == src0->ne[0]/2); // only half padding supported + if (s0 == 1) { + ggml_compute_forward_conv_1d_s1_ph(params, src0, src1, dst); + } else if (s0 == 2) { + ggml_compute_forward_conv_1d_s2_ph(params, src0, src1, dst); + } else { + GGML_ASSERT(false); // only stride 1 and 2 supported + }; +} + // ggml_compute_forward_conv_2d_sk_p0 static void ggml_compute_forward_conv_2d_sk_p0_f16_f32( @@ -13565,36 +13238,7 @@ static void ggml_compute_forward_conv_2d_sk_p0_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int ne00 = src0->ne[0]; - const int ne01 = src0->ne[1]; - const int ne02 = src0->ne[2]; - //const int ne03 = src0->ne[3]; - - const int ne10 = src1->ne[0]; - //const int ne11 = src1->ne[1]; - const int ne12 = src1->ne[2]; - //const int ne13 = src1->ne[3]; - - const int ne0 = dst->ne[0]; - const int ne1 = dst->ne[1]; - const int ne2 = dst->ne[2]; - //const int ne3 = dst->ne[3]; - //const int ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - //const int nb01 = src0->nb[1]; - //const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - //const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - //const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13687,6 +13331,34 @@ static void ggml_compute_forward_conv_2d_sk_p0( } } +// ggml_compute_forward_conv_2d + +static void ggml_compute_forward_conv_2d( + const struct ggml_compute_params* params, + const struct ggml_tensor* src0, + const struct ggml_tensor* src1, + const struct ggml_tensor* opt0, + struct ggml_tensor* dst) { + const int32_t s0 = ((const int32_t*)(opt0->data))[0]; + const int32_t s1 = ((const int32_t*)(opt0->data))[1]; + const int32_t p0 = ((const int32_t*)(opt0->data))[2]; + const int32_t p1 = ((const int32_t*)(opt0->data))[3]; + const int32_t d0 = ((const int32_t*)(opt0->data))[4]; + const int32_t d1 = ((const int32_t*)(opt0->data))[5]; + GGML_ASSERT(d0 == 1); // dilation not supported + GGML_ASSERT(d1 == 1); + GGML_ASSERT(p0 == 0); // padding not supported + GGML_ASSERT(p1 == 0); + + if (s0 == src0->ne[0] && s1 == src0->ne[1]) { + ggml_compute_forward_conv_2d_sk_p0(params, src0, src1, dst); + } + else { + GGML_ASSERT(false); // only stride equal to kernel size is supported + }; +} + + // ggml_compute_forward_flash_attn static void ggml_compute_forward_flash_attn_f32( @@ -13699,45 +13371,14 @@ static void ggml_compute_forward_flash_attn_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t neq0 = q->ne[0]; - const int64_t neq1 = q->ne[1]; - const int64_t neq2 = q->ne[2]; - const int64_t neq3 = q->ne[3]; - - const int64_t nek0 = k->ne[0]; - const int64_t nek1 = k->ne[1]; - //const int64_t nek2 = k->ne[2]; - //const int64_t nek3 = k->ne[3]; - - //const int64_t nev0 = v->ne[0]; - const int64_t nev1 = v->ne[1]; - //const int64_t nev2 = v->ne[2]; - //const int64_t nev3 = v->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - - const int nbk0 = k->nb[0]; - const int nbk1 = k->nb[1]; - const int nbk2 = k->nb[2]; - const int nbk3 = k->nb[3]; - - const int nbq0 = q->nb[0]; - const int nbq1 = q->nb[1]; - const int nbq2 = q->nb[2]; - const int nbq3 = q->nb[3]; - - const int nbv0 = v->nb[0]; - const int nbv1 = v->nb[1]; - const int nbv2 = v->nb[2]; - const int nbv3 = v->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, neq, q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, v, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -13908,45 +13549,14 @@ static void ggml_compute_forward_flash_attn_f16( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t neq0 = q->ne[0]; - const int64_t neq1 = q->ne[1]; - const int64_t neq2 = q->ne[2]; - const int64_t neq3 = q->ne[3]; - - const int64_t nek0 = k->ne[0]; - const int64_t nek1 = k->ne[1]; - //const int64_t nek2 = k->ne[2]; - //const int64_t nek3 = k->ne[3]; - - //const int64_t nev0 = v->ne[0]; - const int64_t nev1 = v->ne[1]; - //const int64_t nev2 = v->ne[2]; - //const int64_t nev3 = v->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - - const int nbk0 = k->nb[0]; - const int nbk1 = k->nb[1]; - const int nbk2 = k->nb[2]; - const int nbk3 = k->nb[3]; - - const int nbq0 = q->nb[0]; - const int nbq1 = q->nb[1]; - const int nbq2 = q->nb[2]; - const int nbq3 = q->nb[3]; - - const int nbv0 = v->nb[0]; - const int nbv1 = v->nb[1]; - const int nbv2 = v->nb[2]; - const int nbv3 = v->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, neq, q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, v, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -14180,65 +13790,18 @@ static void ggml_compute_forward_flash_ff_f16( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t nea0 = a->ne[0]; - const int64_t nea1 = a->ne[1]; - const int64_t nea2 = a->ne[2]; - const int64_t nea3 = a->ne[3]; - - const int64_t neb00 = b0->ne[0]; - const int64_t neb01 = b0->ne[1]; - //const int64_t neb02 = b0->ne[2]; - //const int64_t neb03 = b0->ne[3]; - - const int64_t neb10 = b1->ne[0]; - const int64_t neb11 = b1->ne[1]; - //const int64_t neb12 = b1->ne[2]; - //const int64_t neb13 = b1->ne[3]; - - const int64_t nec00 = c0->ne[0]; - const int64_t nec01 = c0->ne[1]; - //const int64_t nec02 = c0->ne[2]; - //const int64_t nec03 = c0->ne[3]; - - const int64_t nec10 = c1->ne[0]; - const int64_t nec11 = c1->ne[1]; - //const int64_t nec12 = c1->ne[2]; - //const int64_t nec13 = c1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - - const int nba0 = a->nb[0]; - const int nba1 = a->nb[1]; - const int nba2 = a->nb[2]; - const int nba3 = a->nb[3]; - - const int nbb00 = b0->nb[0]; - const int nbb01 = b0->nb[1]; - const int nbb02 = b0->nb[2]; - const int nbb03 = b0->nb[3]; - - const int nbb10 = b1->nb[0]; - //const int nbb11 = b1->nb[1]; - //const int nbb12 = b1->nb[2]; - //const int nbb13 = b1->nb[3]; - - const int nbc00 = c0->nb[0]; - const int nbc01 = c0->nb[1]; - const int nbc02 = c0->nb[2]; - const int nbc03 = c0->nb[3]; - - const int nbc10 = c1->nb[0]; - //const int nbc11 = c1->nb[1]; - //const int nbc12 = c1->nb[2]; - //const int nbc13 = c1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, nea, a, ne); + GGML_TENSOR_LOCALS(size_t, nba, a, nb); + GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne); + GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb); + GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne); + GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb); + GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne); + GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb); + GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne); + GGML_TENSOR_LOCALS(size_t, nbc1, c1, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -14386,55 +13949,16 @@ static void ggml_compute_forward_flash_attn_back_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t neq0 = q->ne[0]; - const int64_t neq1 = q->ne[1]; - const int64_t neq2 = q->ne[2]; - const int64_t neq3 = q->ne[3]; - - const int64_t nek0 = k->ne[0]; - const int64_t nek1 = k->ne[1]; - //const int64_t nek2 = k->ne[2]; - //const int64_t nek3 = k->ne[3]; - - const int64_t nev0 = v->ne[0]; - const int64_t nev1 = v->ne[1]; - //const int64_t nev2 = v->ne[2]; - //const int64_t nev3 = v->ne[3]; - - const int64_t ned0 = d->ne[0]; - const int64_t ned1 = d->ne[1]; - //const int64_t ned2 = d->ne[2]; - //const int64_t ned3 = d->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nbk0 = k->nb[0]; - const int nbk1 = k->nb[1]; - const int nbk2 = k->nb[2]; - const int nbk3 = k->nb[3]; - - const int nbq0 = q->nb[0]; - const int nbq1 = q->nb[1]; - const int nbq2 = q->nb[2]; - const int nbq3 = q->nb[3]; - - const int nbv0 = v->nb[0]; - const int nbv1 = v->nb[1]; - const int nbv2 = v->nb[2]; - const int nbv3 = v->nb[3]; - - const int nbd0 = d->nb[0]; - const int nbd1 = d->nb[1]; - const int nbd2 = d->nb[2]; - const int nbd3 = d->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, neq, q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, v, nb); + GGML_TENSOR_LOCALS(int64_t, ned, d, ne); + GGML_TENSOR_LOCALS(size_t, nbd, d, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -14792,15 +14316,8 @@ static void ggml_compute_forward_win_part_f32( return; } - const int64_t ne00 = src0->ne[0]; UNUSED(ne00); - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; UNUSED(ne03); - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; UNUSED(ne3); + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); const int32_t nep0 = ((const int32_t *)(opt0->data))[0]; const int32_t nep1 = ((const int32_t *)(opt0->data))[1]; @@ -14863,14 +14380,8 @@ static void ggml_compute_forward_win_unpart_f32( return; } - const int64_t ne00 = src0->ne[0]; - 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 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); const int32_t w = ((const int32_t *)(opt0->data))[0]; @@ -15468,6 +14979,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_mean(params, tensor->src0, tensor); } break; + case GGML_OP_ARGMAX: + { + ggml_compute_forward_argmax(params, tensor->src0, tensor); + } break; case GGML_OP_REPEAT: { ggml_compute_forward_repeat(params, tensor->src0, tensor); @@ -15492,6 +15007,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_step(params, tensor->src0, tensor); } break; + case GGML_OP_TANH: + { + ggml_compute_forward_tanh(params, tensor->src0, tensor); + } break; + case GGML_OP_ELU: + { + ggml_compute_forward_elu(params, tensor->src0, tensor); + } break; case GGML_OP_RELU: { ggml_compute_forward_relu(params, tensor->src0, tensor); @@ -15608,17 +15131,13 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor); } break; - case GGML_OP_CONV_1D_S1_PH: + case GGML_OP_CONV_1D: { - ggml_compute_forward_conv_1d_s1_ph(params, tensor->src0, tensor->src1, tensor); + ggml_compute_forward_conv_1d(params, tensor->src0, tensor->src1, tensor->opt[0], tensor); } break; - case GGML_OP_CONV_1D_S2_PH: + case GGML_OP_CONV_2D: { - ggml_compute_forward_conv_1d_s2_ph(params, tensor->src0, tensor->src1, tensor); - } break; - case GGML_OP_CONV_2D_SK_P0: - { - ggml_compute_forward_conv_2d_sk_p0(params, tensor->src0, tensor->src1, tensor); + ggml_compute_forward_conv_2d(params, tensor->src0, tensor->src1, tensor->opt[0], tensor); } break; case GGML_OP_FLASH_ATTN: { @@ -15867,6 +15386,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor } } break; case GGML_OP_MEAN: + case GGML_OP_ARGMAX: { GGML_ASSERT(false); // TODO: implement } break; @@ -15920,6 +15440,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // noop } } break; + case GGML_OP_TANH: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_ELU: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_RELU: { if (src0->grad) { @@ -15939,14 +15467,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CLAMP: - { - GGML_ASSERT(false); // TODO: not implemented - } break; case GGML_OP_SILU: { // necessary for llama @@ -16263,7 +15783,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // necessary for llama if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 3); + assert(ggml_nelements(src1) == 4); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; @@ -16303,15 +15823,19 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // noop } } break; - case GGML_OP_CONV_1D_S1_PH: + case GGML_OP_ALIBI: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_1D_S2_PH: + case GGML_OP_CLAMP: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_2D_SK_P0: + case GGML_OP_CONV_1D: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_CONV_2D: { GGML_ASSERT(false); // TODO: not implemented } break; @@ -16968,12 +16492,15 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) case GGML_OP_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: + case GGML_OP_ARGMAX: case GGML_OP_REPEAT: case GGML_OP_REPEAT_BACK: case GGML_OP_ABS: case GGML_OP_SGN: case GGML_OP_NEG: case GGML_OP_STEP: + case GGML_OP_TANH: + case GGML_OP_ELU: case GGML_OP_RELU: { node->n_tasks = 1; @@ -17087,8 +16614,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { node->n_tasks = 1; //TODO } break; - case GGML_OP_CONV_1D_S1_PH: - case GGML_OP_CONV_1D_S2_PH: + case GGML_OP_CONV_1D: { node->n_tasks = n_threads; @@ -17117,7 +16643,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) work_size = MAX(work_size, cur); } break; - case GGML_OP_CONV_2D_SK_P0: + case GGML_OP_CONV_2D: { node->n_tasks = n_threads; @@ -17479,13 +17005,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { fwrite(&nb, sizeof(uint64_t), 1, fout); } - // store the pointer address - { - const uint64_t ptr = (uint64_t) tensor->data; - - fwrite(&ptr, sizeof(uint64_t), 1, fout); - } - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); // dump the data @@ -17519,13 +17038,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { fwrite(&nb, sizeof(uint64_t), 1, fout); } - // store the pointer address - { - const uint64_t ptr = (uint64_t) tensor->data; - - fwrite(&ptr, sizeof(uint64_t), 1, fout); - } - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); // output the op arguments @@ -17710,8 +17222,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor->op = (enum ggml_op) op; - uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); - memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME; tensor->data = (void *) ptr; @@ -17757,8 +17267,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** nb[j] = nb_cur; } - uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); // TODO: not yet used - const char * ptr_name = ptr; ptr += GGML_MAX_NAME; const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += (2 + GGML_MAX_OPT)*sizeof(int32_t); diff --git a/ggml.h b/ggml.h index 11b51f8bd..0af96c76b 100644 --- a/ggml.h +++ b/ggml.h @@ -201,6 +201,8 @@ #define GGML_MAX_NAME 48 #define GGML_DEFAULT_N_THREADS 4 +#define GGML_UNUSED(x) (void)(x) + #define GGML_ASSERT(x) \ do { \ if (!(x)) { \ @@ -209,6 +211,30 @@ } \ } while (0) +// used to copy the number of elements and stride in bytes of tensors into local variables. +// main purpose is to reduce code duplication and improve readability. +// +// example: +// +// GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); +// GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); +// +#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \ + const type prefix##0 = (pointer)->array[0]; \ + GGML_UNUSED(prefix##0); +#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \ + GGML_TENSOR_LOCALS_1 (type, prefix, pointer, array) \ + const type prefix##1 = (pointer)->array[1]; \ + GGML_UNUSED(prefix##1); +#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \ + GGML_TENSOR_LOCALS_2 (type, prefix, pointer, array) \ + const type prefix##2 = (pointer)->array[2]; \ + GGML_UNUSED(prefix##2); +#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \ + GGML_TENSOR_LOCALS_3 (type, prefix, pointer, array) \ + const type prefix##3 = (pointer)->array[3]; \ + GGML_UNUSED(prefix##3); + #ifdef __cplusplus extern "C" { #endif @@ -295,12 +321,15 @@ extern "C" { GGML_OP_SUM, GGML_OP_SUM_ROWS, GGML_OP_MEAN, + GGML_OP_ARGMAX, GGML_OP_REPEAT, GGML_OP_REPEAT_BACK, GGML_OP_ABS, GGML_OP_SGN, GGML_OP_NEG, GGML_OP_STEP, + GGML_OP_TANH, + GGML_OP_ELU, GGML_OP_RELU, GGML_OP_GELU, GGML_OP_GELU_QUICK, @@ -332,9 +361,8 @@ extern "C" { GGML_OP_ROPE_BACK, GGML_OP_ALIBI, GGML_OP_CLAMP, - GGML_OP_CONV_1D_S1_PH, - GGML_OP_CONV_1D_S2_PH, - GGML_OP_CONV_2D_SK_P0, + GGML_OP_CONV_1D, + GGML_OP_CONV_2D, GGML_OP_FLASH_ATTN, GGML_OP_FLASH_FF, @@ -690,6 +718,11 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + // argmax along rows + GGML_API struct ggml_tensor * ggml_argmax( + struct ggml_context * ctx, + struct ggml_tensor * a); + // if a is the same shape as b, and a is not parameter, return a // otherwise, return a new tensor: repeat(a) to fit in b GGML_API struct ggml_tensor * ggml_repeat( @@ -734,6 +767,22 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_tanh( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_tanh_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_elu( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_elu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a); @@ -1084,58 +1133,33 @@ extern "C" { float min, float max); - // TODO: implement general-purpose convolutions - // GGML_API struct ggml_tensor * ggml_conv_1d( - // struct ggml_context * ctx, - // struct ggml_tensor * a, - // struct ggml_tensor * b, - // int s0 - // int p0, - // int d0); - // - // GGML_API 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); - - // padding = half - // TODO: we don't support extra parameters for now - // that's why we are hard-coding the stride, padding, and dilation - // not great .. - // example: - // a: 3 80 768 1 - // b: 3000 80 1 1 - // res: 3000 768 1 1 - // used in whisper - GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph( + GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int s0, // stride + int p0, // padding + int d0); // dilation - // used in whisper - GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph( + GGML_API struct ggml_tensor * ggml_conv_2d( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1); - // kernel size is a->ne[0] x a->ne[1] - // stride is equal to kernel size - // padding is zero - // example: - // a: 16 16 3 768 - // b: 1024 1024 3 1 - // res: 64 64 768 1 - // used in sam - GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0( + // conv_1d with padding = half + // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d) + GGML_API struct ggml_tensor* ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int s, + int d); GGML_API struct ggml_tensor * ggml_flash_attn( struct ggml_context * ctx, diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index e6e39ff8f..574e5180b 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -1,6 +1,11 @@ #!/bin/bash -cp -rpv ../ggml/src/ggml.c ./ggml.c -cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu -cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h +cp -rpv ../ggml/src/ggml.c ./ggml.c +cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h +cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu +cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h +cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp +cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h +cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m +cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h From b472f3fca558b6335adbd87210ed58cfb5da37cb Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 4 Jul 2023 22:25:22 +0300 Subject: [PATCH 23/84] readme : add link web chat PR --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e890dc9c2..6c2bb392e 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** +- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998 - k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001 - New roadmap: https://github.com/users/ggerganov/projects/7 - Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985 From 7f0e9a775ecc4c6ade271c217f63d6dc93e79eaa Mon Sep 17 00:00:00 2001 From: Nigel Bosch Date: Tue, 4 Jul 2023 18:33:33 -0500 Subject: [PATCH 24/84] embd-input: Fix input embedding example unsigned int seed (#2105) --- examples/embd-input/embd-input-lib.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 570e273fc..5fa4942be 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -29,7 +29,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) { fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); From 9e4475f5cf639315f61ed7b8da6258bb0c7c5ca9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 5 Jul 2023 08:58:05 +0200 Subject: [PATCH 25/84] Fixed OpenCL offloading prints (#2082) --- llama.cpp | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 7419b03b6..83e93efc1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1156,6 +1156,7 @@ static void llama_model_load_internal( } } #endif // GGML_USE_CUBLAS + #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); @@ -1164,6 +1165,10 @@ static void llama_model_load_internal( fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__); } size_t vram_kv_cache = 0; + +#ifdef GGML_USE_CUBLAS + const int max_backend_supported_layers = hparams.n_layer + 3; + const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3; if (n_gpu_layers > (int) hparams.n_layer + 1) { if (low_vram) { fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__); @@ -1180,14 +1185,18 @@ static void llama_model_load_internal( vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2; } } - const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3; +#elif defined(GGML_USE_CLBLAST) + const int max_backend_supported_layers = hparams.n_layer + 1; + const int max_offloadable_layers = hparams.n_layer + 1; +#endif // GGML_USE_CUBLAS + fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n", - __func__, std::min(n_gpu_layers, max_offloadable_layers), hparams.n_layer + 3); + __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); fprintf(stderr, "%s: total VRAM used: %zu MB\n", __func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up #else (void) n_gpu_layers; -#endif +#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) } // populate `tensors_by_name` From 051c70dcd55709c9cbbfa849af035951fe720433 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Wed, 5 Jul 2023 18:31:23 +0800 Subject: [PATCH 26/84] llama: Don't double count the sampling time (#2107) --- llama.cpp | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 83e93efc1..e04fbfc0a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1905,10 +1905,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can return; } - const int64_t t_start_sample_us = ggml_time_us(); - llama_sample_softmax(ctx, candidates); + const int64_t t_start_sample_us = ggml_time_us(); + // Compute the cumulative probabilities float cum_sum = 0.0f; size_t last_idx = candidates->size; @@ -1937,9 +1937,8 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * return; } - const int64_t t_start_sample_us = ggml_time_us(); - llama_sample_softmax(nullptr, candidates); + const int64_t t_start_sample_us = ggml_time_us(); // Compute the first and second derivatives std::vector first_derivatives(candidates->size - 1); @@ -1991,11 +1990,11 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c return; } - const int64_t t_start_sample_us = ggml_time_us(); - // Compute the softmax of logits and calculate entropy llama_sample_softmax(nullptr, candidates); + const int64_t t_start_sample_us = ggml_time_us(); + float entropy = 0.0f; for (size_t i = 0; i < candidates->size; ++i) { entropy += -candidates->data[i].p * logf(candidates->data[i].p); @@ -2164,13 +2163,11 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ if (ctx) { ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - ctx->n_sample++; } return X; } llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) { - assert(ctx); int64_t t_start_sample_us; t_start_sample_us = ggml_time_us(); @@ -2185,13 +2182,14 @@ llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_tok candidates->size = 1; } + if (ctx) { + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + } + // Normalize the probabilities of the remaining words llama_sample_softmax(ctx, candidates); // Sample the next word X from the remaining words - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } llama_token X = llama_sample_token(ctx, candidates); t_start_sample_us = ggml_time_us(); From 924dd22fd3ba93e097f8d19ba5cda919ca2fe2fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 5 Jul 2023 14:19:42 +0200 Subject: [PATCH 27/84] Quantized dot products for CUDA mul mat vec (#2067) --- CMakeLists.txt | 13 +- Makefile | 13 +- README.md | 3 +- ggml-cuda.cu | 491 ++++++++++++++++++++++++++++++++++++++++--------- 4 files changed, 427 insertions(+), 93 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4ac0f6f4e..a2404548f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -68,8 +68,9 @@ option(LLAMA_ACCELERATE "llama: enable Accelerate framework option(LLAMA_BLAS "llama: use BLAS" OFF) set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUBLAS "llama: use cuBLAS" OFF) +option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF) set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels") -set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels") +set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels") option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv CUDA kernels" OFF) set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") option(LLAMA_CLBLAST "llama: use CLBlast" OFF) @@ -246,8 +247,14 @@ if (LLAMA_CUBLAS) set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h) add_compile_definitions(GGML_USE_CUBLAS) + if (LLAMA_CUDA_FORCE_DMMV) + add_compile_definitions(GGML_CUDA_FORCE_DMMV) + endif() add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y}) + add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) + if (DEFINED LLAMA_CUDA_DMMV_Y) + add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility + endif() if (LLAMA_CUDA_DMMV_F16) add_compile_definitions(GGML_CUDA_DMMV_F16) endif() @@ -263,7 +270,7 @@ if (LLAMA_CUBLAS) if (LLAMA_CUDA_DMMV_F16) set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics else() - set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard + set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics endif() endif() message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") diff --git a/Makefile b/Makefile index 8966a3590..71415664b 100644 --- a/Makefile +++ b/Makefile @@ -164,16 +164,21 @@ ifdef LLAMA_CUBLAS OBJS += ggml-cuda.o NVCC = nvcc NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native +ifdef LLAMA_CUDA_FORCE_DMMV + NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV +endif # LLAMA_CUDA_FORCE_DMMV ifdef LLAMA_CUDA_DMMV_X NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) else NVCCFLAGS += -DGGML_CUDA_DMMV_X=32 endif # LLAMA_CUDA_DMMV_X -ifdef LLAMA_CUDA_DMMV_Y - NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y) +ifdef LLAMA_CUDA_MMV_Y + NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) +else ifdef LLAMA_CUDA_DMMV_Y + NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility else - NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1 -endif # LLAMA_CUDA_DMMV_Y + NVCCFLAGS += -DGGML_CUDA_MMV_Y=1 +endif # LLAMA_CUDA_MMV_Y ifdef LLAMA_CUDA_DMMV_F16 NVCCFLAGS += -DGGML_CUDA_DMMV_F16 endif # LLAMA_CUDA_DMMV_F16 diff --git a/README.md b/README.md index 6c2bb392e..32f17c2d1 100644 --- a/README.md +++ b/README.md @@ -345,8 +345,9 @@ Building the program with BLAS support may lead to some performance improvements | Option | Legal values | Default | Description | |-------------------------|------------------------|---------|-------------| + | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_DMMV_Y | Positive integer | 1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0b12a9e76..7965ff741 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -70,9 +70,11 @@ typedef void (*ggml_cuda_op_t)( // QK = number of values after dequantization // QR = QK / number of values before dequantization +// QI = number of 32 bit integers before dequantization #define QK4_0 32 #define QR4_0 2 +#define QI4_0 4 typedef struct { half d; // delta uint8_t qs[QK4_0 / 2]; // nibbles / quants @@ -81,6 +83,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 #define QK4_1 32 #define QR4_1 2 +#define QI4_1 4 typedef struct { half d; // delta half m; // min @@ -90,6 +93,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong #define QK5_0 32 #define QR5_0 2 +#define QI5_0 4 typedef struct { half d; // delta uint8_t qh[4]; // 5-th bit of quants @@ -99,6 +103,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5 #define QK5_1 32 #define QR5_1 2 +#define QI5_1 4 typedef struct { half d; // delta half m; // min @@ -109,12 +114,25 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + #define QK8_0 32 #define QR8_0 1 +#define QI8_0 8 typedef struct { half 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 +#define QR8_1 1 +#define QI8_1 8 +typedef struct { + half d; // delta + half s; // unquantized sum + int8_t qs[QK8_0]; // quants +} block_q8_1; +static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding"); + +typedef float (*vec_dot_q_cuda_t)(const void * vbq, const block_q8_1 * bq8_1, const int iqs); + //================================= k-quants #ifdef GGML_QKK_64 @@ -198,14 +216,15 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define CUDA_SCALE_BLOCK_SIZE 256 #define CUDA_ROPE_BLOCK_SIZE 256 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32 +#define CUDA_QUANTIZE_BLOCK_SIZE 256 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256 // dmmv = dequantize_mul_mat_vec #ifndef GGML_CUDA_DMMV_X #define GGML_CUDA_DMMV_X 32 #endif -#ifndef GGML_CUDA_DMMV_Y -#define GGML_CUDA_DMMV_Y 1 +#ifndef GGML_CUDA_MMV_Y +#define GGML_CUDA_MMV_Y 1 #endif #ifndef K_QUANTS_PER_ITERATION @@ -270,7 +289,6 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol } // sum up partial sums - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -714,7 +732,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -819,7 +836,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -923,7 +939,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1028,7 +1043,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1139,7 +1153,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1158,6 +1171,41 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs, v.y = x[ib + iqs + 1]; } +static __global__ void quantize_q8_1(const float * x, void * vy, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + + block_q8_1 * y = (block_q8_1 *) vy; + + const int ib = i / QK8_0; // block index + const int iqs = i % QK8_0; // quant index + + const float xi = x[i]; + float amax = fabsf(xi); + float sum = xi; + +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32)); + sum += __shfl_xor_sync(0xffffffff, sum, mask, 32); + } + + const float d = amax / 127; + const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); + + y[ib].qs[iqs] = q; + + if (iqs > 0) { + return; + } + + y[ib].d = d; + y[ib].s = sum; +} + template static __global__ void dequantize_block(const void * vx, float * y, const int k) { const int i = blockDim.x*blockIdx.x + 2*threadIdx.x; @@ -1179,6 +1227,182 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k) y[iybs + iqs + y_offset] = v.y; } +static __device__ __forceinline__ float vec_dot_q4_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq; + + int vi; + memcpy(&vi, &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int)); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]); + + const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d); + + // subtract 8 from each quantized value + const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808); + const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808); + + // SIMD dot product of quantized values + int sumi = __dp4a(vi0, ui0, 0); + sumi = __dp4a(vi1, ui1, sumi); + + return sumi*d; +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q4_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq; + + const int vi = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]); + + const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d); + const float m = bq4_1->m; + const float s = bq8_1->s; + + const int vi0 = (vi >> 0) & 0x0F0F0F0F; + const int vi1 = (vi >> 4) & 0x0F0F0F0F; + + // SIMD dot product of quantized values + int sumi = __dp4a(vi0, ui0, 0); + sumi = __dp4a(vi1, ui1, sumi); + + return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q5_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq; + + int qs; + memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int)); + const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2); + const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]); + + const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d); + + int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits + vi0 |= (qh0 << 4) & 0x00000010; // 1 -> 5 + vi0 |= (qh0 << 11) & 0x00001000; // 2 -> 13 + vi0 |= (qh0 << 18) & 0x00100000; // 3 -> 21 + vi0 |= (qh0 << 25) & 0x10000000; // 4 -> 29 + vi0 = __vsub4(vi0, 0x10101010); // subtract 16 from quantized values + int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values + + int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits + vi1 |= (qh1 << 4) & 0x00000010; // 1 -> 5 + vi1 |= (qh1 << 11) & 0x00001000; // 2 -> 13 + vi1 |= (qh1 << 18) & 0x00100000; // 3 -> 21 + vi1 |= (qh1 << 25) & 0x10000000; // 4 -> 29 + vi1 = __vsub4(vi1, 0x10101010); // subtract 16 from quantized values + sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values + + return sumi*d; +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q5_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq; + + const int qs = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]); + const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2); + const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]); + + const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d); + const float m = bq5_1->m; + const float s = bq8_1->s; + + int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits + vi0 |= (qh0 << 4) & 0x00000010; // 1 -> 5 + vi0 |= (qh0 << 11) & 0x00001000; // 2 -> 13 + vi0 |= (qh0 << 18) & 0x00100000; // 3 -> 21 + vi0 |= (qh0 << 25) & 0x10000000; // 4 -> 29 + int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values + + int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits + vi1 |= (qh1 << 4) & 0x00000010; // 1 -> 5 + vi1 |= (qh1 << 11) & 0x00001000; // 2 -> 13 + vi1 |= (qh1 << 18) & 0x00100000; // 3 -> 21 + vi1 |= (qh1 << 25) & 0x10000000; // 4 -> 29 + sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values + + return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q8_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq; + + int vi; + memcpy(&vi, &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int)); + const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + + const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d); + + // SIMD dot product of quantized values + int sumi = __dp4a(vi, ui, 0); + + return sumi*d; +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +template +static __global__ void mul_mat_vec_q(const void * vx, const void * vy, float * dst, const int ncols, const int nrows) { + const int row = blockIdx.y*blockDim.y + threadIdx.y; + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = 0; i < blocks_per_row; i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i + threadIdx.x / qi; // x block index + + const int iby = i + threadIdx.x / qi; // y block index + + const int iqs = threadIdx.x % qi; // x block quant index when casting the quants to int + + tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); + } + + if (threadIdx.x == 0) { + dst[row] = tmp; + } +} + template static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows) { // qk = quantized weights per x block @@ -1233,7 +1457,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, } // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1284,7 +1507,6 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl const int idst = channel*nrows_dst + row_dst; // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1330,7 +1552,6 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous } // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1440,7 +1661,6 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol } // sum up partial sums - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1494,6 +1714,11 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con rms_norm_f32<<>>(x, dst, ncols); } +static void quantize_row_q8_1_cuda(const float * x, void * vy, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; + quantize_q8_1<<>>(x, vy, k); +} + static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; dequantize_block<<>>(vx, y, k); @@ -1562,45 +1787,45 @@ static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cu static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -1647,6 +1872,51 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); } +static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; dequantize_block<1, 1, convert_f16><<>>(vx, y, k); @@ -1654,9 +1924,9 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec<1, 1, convert_f16> <<>>(vx, y, dst, ncols, nrows); } @@ -1822,6 +2092,7 @@ static size_t g_scratch_offset = 0; 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 cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; @@ -1839,9 +2110,12 @@ void ggml_init_cublas() { for (int id = 0; id < g_device_count; ++id) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s\n", id, prop.name); + 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; + + g_compute_capabilities[id] = 100*prop.major + 10*prop.minor; } for (int id = 0; id < g_device_count; ++id) { g_tensor_split[id] /= total_vram; @@ -2057,7 +2331,7 @@ inline void ggml_cuda_op_rms_norm( (void) i1; } -inline void ggml_cuda_op_dequantize_mul_mat_vec( +inline void ggml_cuda_op_mul_mat_vec( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i, float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1, cudaStream_t & cudaStream_main){ @@ -2069,69 +2343,116 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( const int64_t ne00 = src0->ne[0]; const int64_t nrows = i01_high - i01_low; -// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics -#ifdef GGML_CUDA_DMMV_F16 - size_t ash; - dfloat * src1_dfloat = nullptr; // dfloat == half - - bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || - src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || - src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; - - if (src1_convert_f16) { - src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash); - ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00, - ne00, 1, sizeof(float), 0, 0, - ne00, 1, sizeof(half), 0, 0, cudaStream_main); - } +#ifdef GGML_CUDA_FORCE_DMMV + const bool use_mul_mat_vec_q = false; #else - dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + const bool mul_mat_vec_q_implemented = src0->type == GGML_TYPE_Q4_0 || + src0->type == GGML_TYPE_Q4_1 || + src0->type == GGML_TYPE_Q5_0 || + src0->type == GGML_TYPE_Q5_1 || + src0->type == GGML_TYPE_Q8_0; + + // The integer intrinsics used in mul_mat_vec_q are available with compute capability 6. + // However, they have bad performance with Pascal cards. + // Therefore, in a multi GPU setting decide at runtime which GPUs should use mul_mat_vec_q. + const bool use_mul_mat_vec_q = g_compute_capabilities[id] >= 700 && mul_mat_vec_q_implemented; +#endif + + if (use_mul_mat_vec_q) { + size_t as; + void * src1_q8_1 = ggml_cuda_pool_malloc(ne00*sizeof(block_q8_1)/QK8_1, &as); + quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, cudaStream_main); + + switch (src0->type) { + case GGML_TYPE_Q4_0: + mul_mat_vec_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q4_1: + mul_mat_vec_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_0: + mul_mat_vec_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_1: + mul_mat_vec_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q8_0: + mul_mat_vec_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + default: + GGML_ASSERT(false); + break; + } + + ggml_cuda_pool_free(src1_q8_1, as); + } else { + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics +#ifdef GGML_CUDA_DMMV_F16 + size_t ash; + dfloat * src1_dfloat = nullptr; // dfloat == half + + bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || + src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || + src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; + + if (src1_convert_f16) { + src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash); + ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00, + ne00, 1, sizeof(float), 0, 0, + ne00, 1, sizeof(half), 0, 0, cudaStream_main); + } +#else + dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion #endif // GGML_CUDA_DMMV_F16 - switch (src0->type) { - case GGML_TYPE_Q4_0: - dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q4_1: - dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q5_0: - dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q5_1: - dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q8_0: - dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q2_K: - dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q3_K: - dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q4_K: - dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q5_K: - dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q6_K: - dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_F16: - convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - default: - GGML_ASSERT(false); - break; - } + switch (src0->type) { + case GGML_TYPE_Q4_0: + dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q4_1: + dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_0: + dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_1: + dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q8_0: + dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q2_K: + dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q3_K: + dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q4_K: + dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_K: + dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q6_K: + dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_F16: + convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + default: + GGML_ASSERT(false); + break; + } #ifdef GGML_CUDA_DMMV_F16 - if (src1_convert_f16) { - ggml_cuda_pool_free(src1_dfloat, ash); - } + if (src1_convert_f16) { + ggml_cuda_pool_free(src1_dfloat, ash); + } #endif // GGML_CUDA_DMMV_F16 + } (void) src1; (void) dst; @@ -2701,8 +3022,8 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_ }else if (src0->type == GGML_TYPE_F32) { ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, 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 && src0->ne[1] % GGML_CUDA_DMMV_Y == 0) { - ggml_cuda_op(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false, false); + if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) { + ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false); } else { ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false); } From 8567c76b5326e862be0755a8dc1dd988223fcae3 Mon Sep 17 00:00:00 2001 From: Jesse Jojo Johnson Date: Wed, 5 Jul 2023 15:13:35 +0000 Subject: [PATCH 28/84] Update server instructions for web front end (#2103) Co-authored-by: Jesse Johnson --- examples/server/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 4ed226e04..160614ba8 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -1,6 +1,6 @@ # llama.cpp/example/server -This example demonstrates a simple HTTP API server to interact with llama.cpp. +This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp. Command line options: @@ -21,6 +21,7 @@ Command line options: - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. +- `--public`: path from which to serve static files (default examples/server/public) - `--embedding`: Enable embedding extraction, Default: disabled. ## Build @@ -59,7 +60,7 @@ server.exe -m models\7B\ggml-model.bin -c 2048 ``` The above command will start a server that by default listens on `127.0.0.1:8080`. -You can consume the endpoints with Postman or NodeJS with axios library. +You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url. ## Testing with CURL From 1b107b8550dced48dc5f41184640061354226b96 Mon Sep 17 00:00:00 2001 From: Stephan Walter Date: Wed, 5 Jul 2023 16:13:06 +0000 Subject: [PATCH 29/84] ggml : generalize `quantize_fns` for simpler FP16 handling (#1237) * Generalize quantize_fns for simpler FP16 handling * Remove call to ggml_cuda_mul_mat_get_wsize * ci : disable FMA for mac os actions --------- Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 3 +- examples/quantize-stats/quantize-stats.cpp | 14 +- ggml.c | 588 ++++----------------- ggml.h | 31 +- llama.cpp | 10 +- pocs/vdot/q8dot.cpp | 6 +- pocs/vdot/vdot.cpp | 13 +- tests/test-quantize-fns.cpp | 30 +- tests/test-quantize-perf.cpp | 25 +- 9 files changed, 172 insertions(+), 548 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index aec43bd92..12481e8be 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -137,9 +137,10 @@ jobs: - name: Build id: cmake_build run: | + sysctl -a mkdir build cd build - cmake -DLLAMA_AVX2=OFF .. + cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF .. cmake --build . --config Release - name: Test diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 9cea472de..6aa06ec8f 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -147,7 +147,7 @@ void test_roundtrip_on_chunk( const ggml_tensor * layer, int64_t offset, int64_t chunk_size, - const quantize_fns_t & qfns, + const ggml_type_traits_t & qfns, bool use_reference, float * input_scratch, char * quantized_scratch, @@ -163,11 +163,11 @@ void test_roundtrip_on_chunk( } if (use_reference) { - qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size); + qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size); } else { - qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size); + qfns.from_float(input_scratch, quantized_scratch, chunk_size); } - qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size); + qfns.to_float(quantized_scratch, output_scratch, chunk_size); update_error_stats(chunk_size, input_scratch, output_scratch, stats); } @@ -177,7 +177,7 @@ void test_roundtrip_on_chunk( void test_roundtrip_on_layer( std::string & name, bool print_layer_stats, - const quantize_fns_t & qfns, + const ggml_type_traits_t & qfns, bool use_reference, const ggml_tensor * layer, std::vector & input_scratch, @@ -388,8 +388,8 @@ int main(int argc, char ** argv) { if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) { continue; } - quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); + if (qfns.from_float && qfns.to_float) { if (params.verbose) { printf("testing %s ...\n", ggml_type_name(type)); } diff --git a/ggml.c b/ggml.c index 88cbed7d5..635c32eb5 100644 --- a/ggml.c +++ b/ggml.c @@ -481,14 +481,14 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); } -void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) { - for (size_t i = 0; i < n; i++) { +void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) { + for (int i = 0; i < n; i++) { y[i] = GGML_FP16_TO_FP32(x[i]); } } -void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) { - size_t i = 0; +void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) { + int i = 0; #if defined(__F16C__) for (; i + 7 < n; i += 8) { __m256 x_vec = _mm256_loadu_ps(x + i); @@ -1627,109 +1627,112 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in } } +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 quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = { +static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { + [GGML_TYPE_F32] = { + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, + .vec_dot_type = GGML_TYPE_F32, + }, + [GGML_TYPE_F16] = { + .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, + .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, + .vec_dot_type = GGML_TYPE_F16, + }, [GGML_TYPE_Q4_0] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_0, - .quantize_row_q = quantize_row_q4_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference, - .quantize_row_q_dot = quantize_row_q8_0, - .vec_dot_q = ggml_vec_dot_q4_0_q8_0, + .to_float = (ggml_to_float_t) dequantize_row_q4_0, + .from_float = quantize_row_q4_0, + .from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference, + .vec_dot = ggml_vec_dot_q4_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q4_1] = { - .dequantize_row_q = (dequantize_row_q_t)dequantize_row_q4_1, - .quantize_row_q = quantize_row_q4_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference, - .quantize_row_q_dot = quantize_row_q8_1, - .vec_dot_q = ggml_vec_dot_q4_1_q8_1, + .to_float = (ggml_to_float_t) dequantize_row_q4_1, + .from_float = quantize_row_q4_1, + .from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference, + .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, [GGML_TYPE_Q5_0] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_0, - .quantize_row_q = quantize_row_q5_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference, - .quantize_row_q_dot = quantize_row_q8_0, - .vec_dot_q = ggml_vec_dot_q5_0_q8_0, + .to_float = (ggml_to_float_t) dequantize_row_q5_0, + .from_float = quantize_row_q5_0, + .from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference, + .vec_dot = ggml_vec_dot_q5_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q5_1] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_1, - .quantize_row_q = quantize_row_q5_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference, - .quantize_row_q_dot = quantize_row_q8_1, - .vec_dot_q = ggml_vec_dot_q5_1_q8_1, + .to_float = (ggml_to_float_t) dequantize_row_q5_1, + .from_float = quantize_row_q5_1, + .from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference, + .vec_dot = ggml_vec_dot_q5_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, [GGML_TYPE_Q8_0] = { - .dequantize_row_q = dequantize_row_q8_0, - .quantize_row_q = quantize_row_q8_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference, - .quantize_row_q_dot = quantize_row_q8_0, - .vec_dot_q = ggml_vec_dot_q8_0_q8_0, + .to_float = 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, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q8_1] = { - .dequantize_row_q = NULL, // TODO - .quantize_row_q = quantize_row_q8_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_1_reference, - .quantize_row_q_dot = quantize_row_q8_1, - .vec_dot_q = NULL, // TODO + .from_float = quantize_row_q8_1, + .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] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q2_K, - .quantize_row_q = quantize_row_q2_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q2_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q2_K, + .from_float = quantize_row_q2_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference, + .vec_dot = ggml_vec_dot_q2_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q3_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q3_K, - .quantize_row_q = quantize_row_q3_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q3_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q3_K, + .from_float = quantize_row_q3_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference, + .vec_dot = ggml_vec_dot_q3_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q4_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_K, - .quantize_row_q = quantize_row_q4_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q4_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q4_K, + .from_float = quantize_row_q4_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference, + .vec_dot = ggml_vec_dot_q4_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q5_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_K, - .quantize_row_q = quantize_row_q5_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q5_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q5_K, + .from_float = quantize_row_q5_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference, + .vec_dot = ggml_vec_dot_q5_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q6_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q6_K, - .quantize_row_q = quantize_row_q6_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q6_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q6_K, + .from_float = quantize_row_q6_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference, + .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, + [GGML_TYPE_Q8_K] = { + .from_float = quantize_row_q8_K, + } #endif }; // For internal test use -quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { +ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) { GGML_ASSERT(i < GGML_TYPE_COUNT); - return quantize_fns[i]; + return type_traits[i]; } @@ -2275,7 +2278,7 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } -inline 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_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { #ifdef GGML_SIMD float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); @@ -2312,7 +2315,7 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float *s = sumf; } -inline 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_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { ggml_float sumf = 0.0; #if defined(GGML_SIMD) @@ -7825,8 +7828,8 @@ static void ggml_compute_forward_dup_f16( id += ne00 * (ne01 - ir1); } } - } else if (ggml_is_quantized(dst->type)) { - quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; + } else if (type_traits[dst->type].from_float) { + ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; @@ -8078,26 +8081,8 @@ static void ggml_compute_forward_dup_f32( id += rs * (ne01 - ir1); } } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (ggml_is_quantized(dst->type)) { - quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; + } else if (type_traits[dst->type].from_float) { + ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; size_t id = 0; size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); @@ -8503,8 +8488,8 @@ static void ggml_compute_forward_add_q_f32( const int nth = params->nth; const enum ggml_type type = src0->type; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; - quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; + ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; + ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); @@ -8777,8 +8762,8 @@ static void ggml_compute_forward_add1_q_f32( GGML_TENSOR_UNARY_OP_LOCALS; const enum ggml_type type = src0->type; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; - quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; + ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; + ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); @@ -10578,317 +10563,7 @@ static bool ggml_compute_forward_mul_mat_use_blas( } #endif -static void ggml_compute_forward_mul_mat_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int ith = params->ith; - const int nth = params->nth; - - assert(ne02 == ne12); - assert(ne03 == ne13); - assert(ne2 == ne12); - assert(ne3 == ne13); - - // we don't support permuted src0 or src1 - assert(nb00 == sizeof(float)); - assert(nb10 == sizeof(float)); - - // dst cannot be transposed or permuted - assert(nb0 == sizeof(float)); - assert(nb0 <= nb1); - assert(nb1 <= nb2); - assert(nb2 <= nb3); - - assert(ne0 == ne01); - assert(ne1 == ne11); - assert(ne2 == ne02); - assert(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } - return; - } -#endif - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { - if (params->ith != 0) { - return; - } - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03); - const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - ne11, ne01, ne10, - 1.0f, y, ne10, - x, ne00, - 0.0f, d, ne01); - } - } - //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); - - return; - } -#endif - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // parallelize by src0 rows using ggml_vec_dot_f32 - - // total rows in src0 - const int nr = ne01*ne02*ne03; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - for (int64_t ic = 0; ic < ne11; ++ic) { - // src1 indices - const int i13 = i03; - const int i12 = i02; - const int i11 = ic; - - // dst indices - const int i0 = i01; - const int i1 = i11; - const int i2 = i02; - const int i3 = i03; - - ggml_vec_dot_f32(ne00, - (float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - (float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)), - (float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13))); - } - } - - //int64_t t1 = ggml_perf_time_us(); - //static int64_t acc = 0; - //acc += t1 - t0; - //if (t1 - t0 > 10) { - // printf("\n"); - // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); - // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); - // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); - // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); - - // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); - //} -} - -static void ggml_compute_forward_mul_mat_f16_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS; - - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int ith = params->ith; - const int nth = params->nth; - - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // TODO: we don't support permuted src0 - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } - return; - } -#endif - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->ith != 0) { - return; - } - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - float * const wdata = params->wdata; - { - size_t id = 0; - for (int64_t i01 = 0; i01 < ne01; ++i01) { - for (int64_t i00 = 0; i00 < ne00; ++i00) { - wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00)); - } - } - - assert(id*sizeof(float) <= params->wsize); - } - - const float * x = wdata; - const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); - - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - - // zT = y * xT - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - ne11, ne01, ne10, - 1.0f, y, ne10, - x, ne00, - 0.0f, d, ne01); - } - } - - /*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/ - - return; - } -#endif - - if (params->type == GGML_TASK_INIT) { - ggml_fp16_t * const wdata = params->wdata; - - size_t id = 0; - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - for (int64_t i10 = 0; i10 < ne10; ++i10) { - wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10)); - } - } - } - } - - GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize); - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // fp16 -> half the size, so divide by 2 - // TODO: do not support transposed src1 - assert(nb10/2 == sizeof(ggml_fp16_t)); - - // parallelize by src0 rows using ggml_vec_dot_f16 - - // total rows in src0 - const int nr = ne01*ne02*ne03; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * wdata = params->wdata; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int i13 = i03; - const int i12 = i02; - - const int i0 = i01; - const int i2 = i02; - const int i3 = i03; - - ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); - ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00; - - float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3)); - - for (int64_t ic = 0; ic < ne11; ++ic) { - ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00); - } - } - - //int64_t t1 = ggml_time_us(); - //static int64_t acc = 0; - //acc += t1 - t0; - //if (t1 - t0 > 10) { - // printf("\n"); - // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); - // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); - // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); - - // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); - //} -} - -static void ggml_compute_forward_mul_mat_q_f32( +static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -10907,9 +10582,10 @@ static void ggml_compute_forward_mul_mat_q_f32( GGML_ASSERT(ne3 == ne13); const enum ggml_type type = src0->type; - quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot; - vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q; - enum ggml_type const vec_dot_type = quantize_fns[type].vec_dot_type; + + ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; + ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); @@ -10952,27 +10628,27 @@ static void ggml_compute_forward_mul_mat_q_f32( return; } - float * const wdata = params->wdata; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; - for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { + const void * x = (char *) src0->data + i03*nb03 + i02*nb02; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - { + if (type != GGML_TYPE_F32) { + float * const wdata = params->wdata; + ggml_to_float_t const to_float = type_traits[type].to_float; + size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { - dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00); + to_float((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00); id += ne00; } assert(id*sizeof(float) <= params->wsize); + x = wdata; } - const float * x = wdata; - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, @@ -10988,14 +10664,16 @@ static void ggml_compute_forward_mul_mat_q_f32( #endif if (params->type == GGML_TASK_INIT) { - char * wdata = params->wdata; - const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; + if (src1->type != vec_dot_type) { + char * wdata = params->wdata; + const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); - wdata += row_size; + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); + wdata += row_size; + } } } } @@ -11019,7 +10697,7 @@ static void ggml_compute_forward_mul_mat_q_f32( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - void * wdata = params->wdata; + void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ne00*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; for (int ir = ir0; ir < ir1; ++ir) { @@ -11043,7 +10721,7 @@ static void ggml_compute_forward_mul_mat_q_f32( assert(ne00 % 32 == 0); for (int64_t ic = 0; ic < ne11; ++ic) { - vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size)); + vec_dot(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size)); } } @@ -11060,40 +10738,6 @@ static void ggml_compute_forward_mul_mat_q_f32( //} } -static void ggml_compute_forward_mul_mat( - 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_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - { - ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_mul_mat_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} // ggml_compute_forward_out_prod @@ -11483,7 +11127,7 @@ static void ggml_compute_forward_get_rows_q( const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); const enum ggml_type type = src0->type; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; + ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); @@ -16529,6 +16173,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks); size_t cur = 0; + const enum ggml_type vec_dot_type = type_traits[node->src0->type].vec_dot_type; #if defined(GGML_USE_CUBLAS) if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) { @@ -16544,37 +16189,18 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) } else #endif - if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { - node->n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning + if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { + node->n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + if (node->src0->type != GGML_TYPE_F32) { // here we need memory just for single 2D matrix from src0 cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); - } else { - cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1); } -#else - cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1); + } else #endif - } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) { - cur = 0; -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { - node->n_tasks = 1; - } -#endif - } else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { - node->n_tasks = 1; - cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); - } else -#endif - { - const enum ggml_type type_q = quantize_fns[node->src0->type].vec_dot_type; - cur = GGML_TYPE_SIZE[type_q]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[type_q]; - } + if (node->src1->type != vec_dot_type) { + cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type]; } else { GGML_ASSERT(false); } diff --git a/ggml.h b/ggml.h index 0af96c76b..24ca8ae22 100644 --- a/ggml.h +++ b/ggml.h @@ -250,8 +250,8 @@ extern "C" { GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x); GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x); - GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n); - GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n); + GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n); + GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n); struct ggml_object; struct ggml_context; @@ -1514,26 +1514,19 @@ extern "C" { // Internal types and functions exposed for tests and benchmarks // -#ifdef __cplusplus - // restrict not standard in C++ -#define GGML_RESTRICT -#else -#define GGML_RESTRICT restrict -#endif - typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); - typedef void (*quantize_row_q_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); - typedef void (*vec_dot_q_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y); + typedef void (*ggml_to_float_t)(const void * x, float * y, int k); + typedef void (*ggml_from_float_t)(const float * x, void * y, int k); + typedef void (*ggml_vec_dot_t)(const int n, float * s, const void * x, const void * y); typedef struct { - dequantize_row_q_t dequantize_row_q; - quantize_row_q_t quantize_row_q; - quantize_row_q_t quantize_row_q_reference; - quantize_row_q_t quantize_row_q_dot; - vec_dot_q_t vec_dot_q; - enum ggml_type vec_dot_type; - } quantize_fns_t; + ggml_to_float_t to_float; + ggml_from_float_t from_float; + ggml_from_float_t from_float_reference; + ggml_vec_dot_t vec_dot; + enum ggml_type vec_dot_type; + } ggml_type_traits_t; - quantize_fns_t ggml_internal_get_quantize_fn(size_t i); + ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i); #ifdef __cplusplus } diff --git a/llama.cpp b/llama.cpp index e04fbfc0a..7a866cb79 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2257,10 +2257,10 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam } float * f32_output = (float *) output.addr; - quantize_fns_t qtype; + ggml_type_traits_t qtype; if (ggml_is_quantized(tensor.type)) { - qtype = ggml_internal_get_quantize_fn(tensor.type); - if (qtype.dequantize_row_q == NULL) { + qtype = ggml_internal_get_type_traits(tensor.type); + if (qtype.to_float == NULL) { throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type))); } } else if (tensor.type != GGML_TYPE_F16) { @@ -2271,7 +2271,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam if (tensor.type == GGML_TYPE_F16) { ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements); } else if (ggml_is_quantized(tensor.type)) { - qtype.dequantize_row_q(tensor.data, f32_output, nelements); + qtype.to_float(tensor.data, f32_output, nelements); } else { LLAMA_ASSERT(false); // unreachable } @@ -2296,7 +2296,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam if (typ == GGML_TYPE_F16) { ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels); } else { - qtype.dequantize_row_q(inbuf, outbuf, nels); + qtype.to_float(inbuf, outbuf, nels); } }; workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems)); diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp index 5748c8ac2..4e0e02357 100644 --- a/pocs/vdot/q8dot.cpp +++ b/pocs/vdot/q8dot.cpp @@ -136,7 +136,7 @@ int main(int argc, char** argv) { auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1; - auto funcs = ggml_internal_get_quantize_fn(ggml_type); + auto funcs = ggml_internal_get_type_traits(ggml_type); Stat simple, ggml; @@ -156,8 +156,8 @@ int main(int argc, char** argv) { t1 = std::chrono::high_resolution_clock::now(); float fs; - if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data()); - else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data()); + if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data()); + else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data()); t2 = std::chrono::high_resolution_clock::now(); t = 1e-3*std::chrono::duration_cast(t2-t1).count(); if (iloop > 3) ggml.addResult(fs, t); diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index 7b18090d6..48758cda8 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -235,7 +235,7 @@ int main(int argc, char** argv) { int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64); int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64); - auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0); + auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0); std::vector q40; std::vector q41; @@ -261,9 +261,9 @@ int main(int argc, char** argv) { // Note, we do not include this in the timing as in practical application // we already have the quantized model weights. if (useQ4_1) { - funcs.quantize_row_q(x1.data(), q41.data(), kVecSize); + funcs.from_float(x1.data(), q41.data(), kVecSize); } else { - funcs.quantize_row_q(x1.data(), q40.data(), kVecSize); + funcs.from_float(x1.data(), q40.data(), kVecSize); } // Now measure time the dot product needs using the "scalar" version above @@ -282,9 +282,10 @@ int main(int argc, char** argv) { dot_q4_q8(kVecSize, &result, q40.data(), q8.data()); } else { - funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize); - if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data()); - else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data()); + auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type); + vdot.from_float(y1.data(), q8.data(), kVecSize); + if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data()); + else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data()); } sumq += result; t2 = std::chrono::high_resolution_clock::now(); diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index c40f1b29c..8d3c162d2 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -40,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) { } // Total quantization error on test data -float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) { +float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); - qfns.quantize_row_q(test_data, tmp_q.data(), test_size); - qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size); + qfns.from_float(test_data, tmp_q.data(), test_size); + qfns.to_float(tmp_q.data(), tmp_out.data(), test_size); return array_rmse(test_data, tmp_out.data(), test_size); } // Total quantization error on test data -float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) { +float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); std::vector tmp_out_ref(test_size); - qfns.quantize_row_q(test_data, tmp_q.data(), test_size); - qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size); + qfns.from_float(test_data, tmp_q.data(), test_size); + qfns.to_float(tmp_q.data(), tmp_out.data(), test_size); - qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size); - qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size); + qfns.from_float_reference(test_data, tmp_q.data(), test_size); + qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size); return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size); } @@ -73,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) { } // Total dot product error -float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) { +float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) { std::vector tmp_q1(2*test_size); std::vector tmp_q2(2*test_size); - qfns.quantize_row_q (test_data1, tmp_q1.data(), test_size); - qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), test_size); + auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type); + + qfns.from_float(test_data1, tmp_q1.data(), test_size); + vdot.from_float(test_data2, tmp_q2.data(), test_size); float result = INFINITY; - qfns.vec_dot_q(test_size, &result, tmp_q1.data(), tmp_q2.data()); + qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data()); const float dot_ref = dot_product(test_data1, test_data2, test_size); @@ -123,9 +125,9 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + 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 = type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index c0e361e92..0bb9537f6 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -123,9 +123,9 @@ void usage(char * argv[]) { printf(" --type TYPE set test type as"); for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - quantize_fns_t qfns = ggml_internal_get_quantize_fn(type); + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); if (ggml_type_name(type) != NULL) { - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + if (qfns.from_float && qfns.to_float) { printf(" %s", ggml_type_name(type)); } } @@ -271,12 +271,12 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) { continue; } - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + if (qfns.from_float && qfns.to_float) { printf("%s\n", ggml_type_name(type)); if (params.op_quantize_row_q_reference) { @@ -284,7 +284,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.quantize_row_q_reference(test_data1, test_q1, size); + qfns.from_float_reference(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -298,7 +298,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.quantize_row_q(test_data1, test_q1, size); + qfns.from_float(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -309,11 +309,11 @@ int main(int argc, char * argv[]) { if (params.op_dequantize_row_q) { printf(" dequantize_row_q\n"); - qfns.quantize_row_q(test_data1, test_q1, largest); + qfns.from_float(test_data1, test_q1, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.dequantize_row_q(test_q1, test_out, size); + qfns.to_float(test_q1, test_out, size); return test_out[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -327,7 +327,8 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.quantize_row_q_dot(test_data1, test_q1, size); + auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type); + vdot.from_float(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -338,13 +339,13 @@ int main(int argc, char * argv[]) { if (params.op_vec_dot_q) { printf(" vec_dot_q\n"); - qfns.quantize_row_q(test_data1, test_q1, largest); - qfns.quantize_row_q(test_data2, test_q2, largest); + qfns.from_float(test_data1, test_q1, largest); + qfns.from_float(test_data2, test_q2, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { float result; - qfns.vec_dot_q(size, &result, test_q1, test_q2); + qfns.vec_dot(size, &result, test_q1, test_q2); return result; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); From 1b6efeab829f3eeda5b39bd47624bb60b3531b88 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 5 Jul 2023 20:20:05 +0300 Subject: [PATCH 30/84] tests : fix test-grad0 --- scripts/sync-ggml.sh | 5 ++++- tests/test-grad0.c | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index 574e5180b..02ea6ec15 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -8,4 +8,7 @@ cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal -cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h +cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h + +cp -rpv ../ggml/tests/test-opt.c ./tests/test-opt.c +cp -rpv ../ggml/tests/test-grad0.c ./tests/test-grad0.c diff --git a/tests/test-grad0.c b/tests/test-grad0.c index b5a499c1d..a3e25214b 100644 --- a/tests/test-grad0.c +++ b/tests/test-grad0.c @@ -1154,7 +1154,7 @@ int main(int argc, const char ** argv) { continue; } - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode)); + struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0)); GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY); From ec326d350c72afd23709a409944728a607188cc0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 5 Jul 2023 20:44:11 +0300 Subject: [PATCH 31/84] ggml : fix bug introduced in #1237 --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 635c32eb5..d257c3d65 100644 --- a/ggml.c +++ b/ggml.c @@ -16202,7 +16202,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) if (node->src1->type != vec_dot_type) { cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type]; } else { - GGML_ASSERT(false); + cur = 0; } work_size = MAX(work_size, cur); From 983b555e9ddb36703cee4d22642afe958de093b7 Mon Sep 17 00:00:00 2001 From: Jesse Jojo Johnson Date: Wed, 5 Jul 2023 18:03:19 +0000 Subject: [PATCH 32/84] Update Server Instructions (#2113) * Update server instructions for web front end * Update server README * Remove duplicate OAI instructions * Fix duplicate text --------- Co-authored-by: Jesse Johnson --- examples/server/README.md | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 160614ba8..037412d76 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -21,7 +21,7 @@ Command line options: - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. -- `--public`: path from which to serve static files (default examples/server/public) +- `--path`: path from which to serve static files (default examples/server/public) - `--embedding`: Enable embedding extraction, Default: disabled. ## Build @@ -207,3 +207,27 @@ openai.api_base = "http://:port" ``` Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API + +### Extending the Web Front End + +The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. A simple example is below: + +``` + + + 
+      
+
+ + +``` From 31cfbb1013a482e89c72146e2063ac4362becae7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20L=C3=BCtke?= Date: Wed, 5 Jul 2023 16:51:13 -0400 Subject: [PATCH 33/84] Expose generation timings from server & update completions.js (#2116) * use javascript generators as much cleaner API Also add ways to access completion as promise and EventSource * export llama_timings as struct and expose them in server * update readme, update baked includes * llama : uniform variable names + struct init --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 35 +- examples/server/completion.js.hpp | 548 ++++++--- examples/server/deps.sh | 4 - examples/server/index.html.hpp | 1581 +++++++++++++------------- examples/server/public/completion.js | 119 +- examples/server/public/index.html | 129 ++- examples/server/server.cpp | 821 ++++++++----- llama.cpp | 32 +- llama.h | 15 + 9 files changed, 1921 insertions(+), 1363 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 037412d76..c5139c16b 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -26,20 +26,17 @@ Command line options: ## Build -Build llama.cpp with server from repository root with either make or CMake. +server is build alongside everything else from the root of the project - Using `make`: ```bash - LLAMA_BUILD_SERVER=1 make + make ``` - Using `CMake`: ```bash - mkdir build-server - cd build-server - cmake -DLLAMA_BUILD_SERVER=ON .. cmake --build . --config Release ``` @@ -208,24 +205,30 @@ openai.api_base = "http://:port" Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API -### Extending the Web Front End +### Extending or building alternative Web Front End -The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. A simple example is below: +The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. -``` +Read the documentation in `/completion.js` to see convenient ways to access llama. + +A simple example is below: + +```html
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 002830cad..f399fb19a 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -7,187 +7,369 @@ unsigned char completion_js[] = { 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x5d, 0x0a, 0x7d, - 0x3b, 0x0a, 0x0a, 0x2f, 0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x54, 0x68, - 0x69, 0x73, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x73, 0x20, 0x74, 0x68, - 0x65, 0x20, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x65, 0x78, 0x74, - 0x20, 0x75, 0x73, 0x69, 0x6e, 0x67, 0x20, 0x61, 0x20, 0x6c, 0x6c, 0x61, - 0x6d, 0x61, 0x20, 0x64, 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x61, 0x72, - 0x79, 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x20, 0x7b, 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b/examples/server/deps.sh index cf995162a..1e9fe964b 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -4,10 +4,6 @@ # get the directory of this script file DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" PUBLIC=$DIR/public -OUTPUT=$DIR/templats.hpp - -echo "// Generated file, do not edit" > $OUTPUT -echo "" > $OUTPUT echo "download js bundle files" curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 832e9a3bb..42707fad9 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -13,138 +13,143 @@ unsigned char index_html[] = { 0x3c, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x20, 0x2d, 0x20, 0x63, 0x68, 0x61, 0x74, 0x3c, 0x2f, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x3c, - 0x73, 0x74, 0x79, 0x6c, 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Ideally ignored, and you get at it via the callback. - */ -export const llamaComplete = async (params, controller, callback) => { +let generation_settings = null; + + +// Completes the prompt as a generator. Recommended for most use cases. +// +// Example: +// +// import { llama } from '/completion.js' +// +// const request = llama("Tell me a joke", {n_predict: 800}) +// for await (const chunk of request) { +// document.write(chunk.data.content) +// } +// +export async function* llama(prompt, params = {}, config = {}) { + let controller = config.controller; + if (!controller) { controller = new AbortController(); } - const completionParams = { ...paramDefaults, ...params }; - // we use fetch directly here becasue the built in fetchEventSource does not support POST + const completionParams = { ...paramDefaults, ...params, prompt }; + const response = await fetch("/completion", { method: 'POST', body: JSON.stringify(completionParams), @@ -36,7 +45,6 @@ export const llamaComplete = async (params, controller, callback) => { let content = ""; try { - let cont = true; while (cont) { @@ -59,18 +67,21 @@ export const llamaComplete = async (params, controller, callback) => { result.data = JSON.parse(result.data); content += result.data.content; - // callack - if (callback) { - cont = callback(result) != false; - } + // yield + yield result; // if we got a stop token from server, we will break here if (result.data.stop) { + if (result.data.generation_settings) { + generation_settings = result.data.generation_settings; + } break; } } } catch (e) { - console.error("llama error: ", e); + if (e.name !== 'AbortError') { + console.error("llama error: ", e); + } throw e; } finally { @@ -79,3 +90,79 @@ export const llamaComplete = async (params, controller, callback) => { return content; } + +// Call llama, return an event target that you can subcribe to +// +// Example: +// +// import { llamaEventTarget } from '/completion.js' +// +// const conn = llamaEventTarget(prompt) +// conn.addEventListener("message", (chunk) => { +// document.write(chunk.detail.content) +// }) +// +export const llamaEventTarget = (prompt, params = {}, config = {}) => { + const eventTarget = new EventTarget(); + (async () => { + let content = ""; + for await (const chunk of llama(prompt, params, config)) { + if (chunk.data) { + content += chunk.data.content; + eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); + } + if (chunk.data.generation_settings) { + eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); + } + if (chunk.data.timings) { + eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); + } + } + eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); + })(); + return eventTarget; +} + +// Call llama, return a promise that resolves to the completed text. This does not support streaming +// +// Example: +// +// llamaPromise(prompt).then((content) => { +// document.write(content) +// }) +// +// or +// +// const content = await llamaPromise(prompt) +// document.write(content) +// +export const llamaPromise = (prompt, params = {}, config = {}) => { + return new Promise(async (resolve, reject) => { + let content = ""; + try { + for await (const chunk of llama(prompt, params, config)) { + content += chunk.data.content; + } + resolve(content); + } catch (error) { + reject(error); + } + }); +}; + +/** + * (deprecated) + */ +export const llamaComplete = async (params, controller, callback) => { + for await (const chunk of llama(params.prompt, params, { controller })) { + callback(chunk); + } +} + +// Get the model info from the server. This is useful for getting the context window and so on. +export const llamaModelInfo = async () => { + if (!generation_settings) { + generation_settings = await fetch("/model.json").then(r => r.json()); + } + return generation_settings; +} diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 6393e2e75..8ace0b0af 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -6,7 +6,6 @@ llama.cpp - chat