From 309534dcd0d7eab179c80f7d2a1a48958819a96c Mon Sep 17 00:00:00 2001 From: Ycros <18012+ycros@users.noreply.github.com> Date: Sun, 2 Jul 2023 18:15:34 +0000 Subject: [PATCH 01/39] implement sampler order, expose sampler order and mirostat in api --- expose.h | 14 ++++++++++++ gpttype_adapter.cpp | 54 +++++++++++++++++++++++++++++++++++++++------ koboldcpp.py | 24 +++++++++++++++++++- 3 files changed, 84 insertions(+), 8 deletions(-) diff --git a/expose.h b/expose.h index b74718eb98918..89471f8c2944f 100644 --- a/expose.h +++ b/expose.h @@ -1,6 +1,18 @@ #pragma once const int stop_token_max = 10; +// match kobold's sampler list and order +enum samplers +{ + KCPP_SAMPLER_TOP_K, + KCPP_SAMPLER_TOP_A, + KCPP_SAMPLER_TOP_P, + KCPP_SAMPLER_TFS, + KCPP_SAMPLER_TYP, + KCPP_SAMPLER_TEMP, + KCPP_SAMPLER_REP_PEN, + KCPP_SAMPLER_MAX +}; struct load_model_inputs { const int threads; @@ -40,6 +52,8 @@ struct generation_inputs const int mirostat = 0; const float mirostat_eta; const float mirostat_tau; + const samplers sampler_order[KCPP_SAMPLER_MAX]; + const int sampler_len; const char * stop_sequence[stop_token_max]; const bool stream_sse; }; diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index 8d49b67b36677..396e53dd56e60 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -219,8 +219,16 @@ void sample_top_a(llama_token_data_array * candidates, float a, size_t min_keep) candidates->size = last_idx; } +void apply_penalties(int n_ctx, int rep_pen_range, float rep_pen, llama_token_data_array & candidates_p) +{ + auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), rep_pen_range), n_ctx); + llama_sample_repetition_penalty(nullptr, &candidates_p, + last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, + last_n_repeat, rep_pen); +} + int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float top_k, float top_a, float top_p, float typical_p, float tfs, float temp, std::mt19937 & rng, -int mirostat, float mirostat_tau, float mirostat_eta) +int mirostat, float mirostat_tau, float mirostat_eta, uint sampler_len, const samplers sampler_order[KCPP_SAMPLER_MAX]) { int id = 0; std::vector candidates; @@ -231,11 +239,11 @@ int mirostat, float mirostat_tau, float mirostat_eta) llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - // Apply penalties - auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), rep_pen_range), n_ctx); - llama_sample_repetition_penalty(nullptr, &candidates_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, rep_pen); + // Run this except for when we are going to do the sampler reordering case below + if (temp <= 0 || mirostat > 0 || sampler_len == 0) + { + apply_penalties(n_ctx, rep_pen_range, rep_pen, candidates_p); + } // llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, @@ -261,6 +269,37 @@ int mirostat, float mirostat_tau, float mirostat_eta) llama_sample_temperature(nullptr, &candidates_p, temp); id = sample_token_mirostat_v2(&candidates_p, rng, mirostat_tau, mirostat_eta, &mirostat_mu); } + else if (sampler_len > 0) + { + for (int i = 0; i < sampler_len; i++) { + switch (sampler_order[i]) { + case KCPP_SAMPLER_TOP_K: + llama_sample_top_k(nullptr, &candidates_p, top_k,1); + break; + case KCPP_SAMPLER_TOP_A: + sample_top_a(&candidates_p,top_a,1); + break; + case KCPP_SAMPLER_TOP_P: + llama_sample_top_p(nullptr, &candidates_p, top_p,1); + break; + case KCPP_SAMPLER_TFS: + llama_sample_tail_free(nullptr, &candidates_p, tfs,1); + break; + case KCPP_SAMPLER_TYP: + llama_sample_typical(nullptr, &candidates_p, typical_p,1); + break; + case KCPP_SAMPLER_TEMP: + llama_sample_temperature(nullptr, &candidates_p, temp); + break; + case KCPP_SAMPLER_REP_PEN: + apply_penalties(n_ctx, rep_pen_range, rep_pen, candidates_p); + break; + default: + break; + } + } + id = sample_token(&candidates_p, rng); + } else { // Temperature sampling @@ -1235,7 +1274,8 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty, top_k, top_a, top_p, typical_p, tfs_z, temp, rng, - params.mirostat,params.mirostat_tau,params.mirostat_eta); + params.mirostat, params.mirostat_tau, params.mirostat_eta, + inputs.sampler_len, inputs.sampler_order); last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(id); diff --git a/koboldcpp.py b/koboldcpp.py index 2041b0d24a7b4..284463441c29c 100644 --- a/koboldcpp.py +++ b/koboldcpp.py @@ -9,6 +9,7 @@ from concurrent.futures import ThreadPoolExecutor stop_token_max = 10 +sampler_order_max = 7 class load_model_inputs(ctypes.Structure): _fields_ = [("threads", ctypes.c_int), @@ -47,6 +48,8 @@ class generation_inputs(ctypes.Structure): ("mirostat", ctypes.c_int), ("mirostat_tau", ctypes.c_float), ("mirostat_eta", ctypes.c_float), + ("sampler_order", ctypes.c_int * sampler_order_max), + ("sampler_len", ctypes.c_int), ("stop_sequence", ctypes.c_char_p * stop_token_max), ("stream_sse", ctypes.c_bool)] @@ -186,7 +189,7 @@ def load_model(model_filename): ret = handle.load_model(inputs) return ret -def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=120, top_a=0.0 ,top_p=0.85, typical_p=1.0, tfs=1.0 ,rep_pen=1.1,rep_pen_range=128,seed=-1,stop_sequence=[],stream_sse=False): +def generate(prompt,max_length=20, max_context_length=512, temperature=0.8, top_k=120, top_a=0.0, top_p=0.85, typical_p=1.0, tfs=1.0, rep_pen=1.1, rep_pen_range=128, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=None, seed=-1, stop_sequence=[], stream_sse=False): inputs = generation_inputs() outputs = ctypes.create_unicode_buffer(ctypes.sizeof(generation_outputs)) inputs.prompt = prompt.encode("UTF-8") @@ -205,8 +208,19 @@ def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k= inputs.mirostat = int(args.usemirostat[0]) inputs.mirostat_tau = float(args.usemirostat[1]) inputs.mirostat_eta = float(args.usemirostat[2]) + elif mirostat in (1, 2): + inputs.mirostat = mirostat + inputs.mirostat_tau = mirostat_tau + inputs.mirostat_eta = mirostat_eta else: inputs.mirostat = inputs.mirostat_tau = inputs.mirostat_eta = 0 + if sampler_order and 0 < len(sampler_order) <= sampler_order_max: + try: + for i, sampler in enumerate(sampler_order): + inputs.sampler_order[i] = sampler + inputs.sampler_len = len(sampler_order) + except TypeError as e: + print("ERROR: sampler_order must be a list of integers: " + str(e)) inputs.seed = seed for n in range(stop_token_max): if not stop_sequence or n >= len(stop_sequence): @@ -272,6 +286,10 @@ def run_blocking(): tfs=genparams.get('tfs', 1.0), rep_pen=genparams.get('rep_pen', 1.1), rep_pen_range=genparams.get('rep_pen_range', 128), + mirostat=genparams.get('mirostat', 0), + mirostat_tau=genparams.get('mirostat_tau', 5.0), + mirostat_eta=genparams.get('mirostat_eta', 0.1), + sampler_order=genparams.get('sampler_order', None), seed=genparams.get('sampler_seed', -1), stop_sequence=genparams.get('stop_sequence', []), stream_sse=stream_flag) @@ -288,6 +306,10 @@ def run_blocking(): tfs=genparams.get('tfs', 1.0), rep_pen=genparams.get('rep_pen', 1.1), rep_pen_range=genparams.get('rep_pen_range', 128), + mirostat=genparams.get('mirostat', 0), + mirostat_tau=genparams.get('mirostat_tau', 5.0), + mirostat_eta=genparams.get('mirostat_eta', 0.1), + sampler_order=genparams.get('sampler_order', None), seed=genparams.get('sampler_seed', -1), stop_sequence=genparams.get('stop_sequence', []), stream_sse=stream_flag) 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 02/39] 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 66e5672917996..96f2228f8677b 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 998d55eacff79..e4ddbe9865506 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 03/39] [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 a869bbac80304..f48a6ca79bec8 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 bfeb3471d765abe4d8f1d593f2fa3780f4dee585 Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Mon, 3 Jul 2023 21:36:42 +0800 Subject: [PATCH 04/39] fix typos --- otherarch/gptj_v3.cpp | 4 ++-- otherarch/mpt_v3.cpp | 4 ++-- otherarch/neox_v3.cpp | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/otherarch/gptj_v3.cpp b/otherarch/gptj_v3.cpp index 13f39b6bfc682..be4ad60daece9 100644 --- a/otherarch/gptj_v3.cpp +++ b/otherarch/gptj_v3.cpp @@ -348,7 +348,7 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g const auto & hparams = model.hparams; size_t vram_total = 0; const int n_gpu = std::min(gpulayers, int(hparams.n_layer)); - fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu); + fprintf(stderr, "%s: [GPU] offloading %d layers to GPU\n", __func__, n_gpu); for (int i = 0; i < n_gpu; ++i) { const auto & layer = model.layers[i]; layer.c_attn_q_proj_w->backend = GGML_BACKEND_GPU; @@ -373,7 +373,7 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g ggml_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w); #endif } - fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); + fprintf(stderr, "%s: [GPU] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); } #endif diff --git a/otherarch/mpt_v3.cpp b/otherarch/mpt_v3.cpp index ef362a051c3d3..e4cf99fd7410c 100644 --- a/otherarch/mpt_v3.cpp +++ b/otherarch/mpt_v3.cpp @@ -301,7 +301,7 @@ bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vo const auto & hparams = model.hparams; size_t vram_total = 0; const int n_gpu = std::min(gpulayers, int(hparams.n_layers)); - fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu); + fprintf(stderr, "%s: [GPU] offloading %d layers to GPU\n", __func__, n_gpu); for (int i = 0; i < n_gpu; ++i) { const auto & layer = model.layers[i]; layer.ffn_up_proj->backend = GGML_BACKEND_GPU; @@ -320,7 +320,7 @@ bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vo ggml_cuda_transform_tensor(layer.c_attn_out_proj_weight->data,layer.c_attn_out_proj_weight); vram_total += ggml_nbytes(layer.c_attn_out_proj_weight); #endif } - fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); + fprintf(stderr, "%s: [GPU] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); } #endif diff --git a/otherarch/neox_v3.cpp b/otherarch/neox_v3.cpp index 6df5d534bda39..29b2d06c8e27e 100644 --- a/otherarch/neox_v3.cpp +++ b/otherarch/neox_v3.cpp @@ -335,7 +335,7 @@ ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model & const auto & hparams = model.hparams; size_t vram_total = 0; const int n_gpu = std::min(gpulayers, int(hparams.n_layer)); - fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu); + fprintf(stderr, "%s: [GPU] offloading %d layers to GPU\n", __func__, n_gpu); for (int i = 0; i < n_gpu; ++i) { const auto & layer = model.layers[i]; layer.c_attn_attn_w->backend = GGML_BACKEND_GPU; @@ -354,7 +354,7 @@ ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model & ggml_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w); #endif } - fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); + fprintf(stderr, "%s: [GPU] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); } #endif From ca9a11697c66e934dedb39faff6e77c7455f6482 Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Tue, 4 Jul 2023 00:35:02 +0800 Subject: [PATCH 05/39] possibly slower, but cannot use larger batches without modifying ggml library. --- gpttype_adapter.cpp | 22 +++++++++++----------- otherarch/rwkv_v3.cpp | 42 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 53 insertions(+), 11 deletions(-) diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index 8d49b67b36677..329d7bb1e459d 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -432,10 +432,10 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in { rwkv_ctx_v3 = rwkv_init_from_file(modelname.c_str(), n_threads); - // if(inputs.gpulayers>0) - // { - // rwkv_gpu_offload_layers(rwkv_ctx_v3,inputs.gpulayers); - // } + if(inputs.gpulayers>0) + { + rwkv_gpu_offload_layers(rwkv_ctx_v3,inputs.gpulayers); + } const struct rwkv_file_header & header = rwkv_ctx_v3->instance->model.header; const size_t n_vocab = header.n_vocab; @@ -1066,15 +1066,15 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o } else { - // if(embd.size()>1) - // { - // evalres = rwkv_eval_sequence(rwkv_ctx_v3, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out); - // } - // else - // { + if(embd.size()>1) + { + evalres = rwkv_eval_sequence(rwkv_ctx_v3, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out); + } + else + { bool ignoreLogits = (!startedsampling && ((int)embd_inp.size() > input_consumed + 2)); evalres = rwkv_eval(rwkv_ctx_v3, embd[0], rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, ignoreLogits?nullptr:rwkv_ctx_v3->logits_out); - //} + } memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * rwkv_vocab.size()); rwkv_ctx_v3->state_in = rwkv_ctx_v3->state_out; diff --git a/otherarch/rwkv_v3.cpp b/otherarch/rwkv_v3.cpp index 809ffcebcfca9..0ccf8d8fac1ce 100644 --- a/otherarch/rwkv_v3.cpp +++ b/otherarch/rwkv_v3.cpp @@ -6,6 +6,13 @@ #include "rwkv_v3.h" #include "ggml.h" +#ifdef GGML_USE_CUBLAS +#include "ggml-cuda.h" +#endif +#if defined(GGML_USE_CLBLAST) +#include "ggml-opencl.h" +#endif + #include #include #include @@ -1058,7 +1065,11 @@ struct rwkv_future_tensor rwkv_future_graph_work(struct rwkv_future_ctx & ctx, const size_t n_threads, const size_t sequence_len = 1 ) { +#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS) + enum ggml_type mul_mat_type = type == GGML_TYPE_F32 ? GGML_TYPE_F32 : GGML_TYPE_F16; +#else enum ggml_type mul_mat_type = ggml_is_quantized(type) ? GGML_TYPE_Q8_1 : type; +#endif return ctx.alloc(GGML_TYPE_I8, rwkv_future_tensor::size(mul_mat_type, ffn_key_height, sequence_len) * n_threads + 64 * (n_threads - 1)); } @@ -1545,7 +1556,38 @@ struct rwkv_context * rwkv_clone_context(struct rwkv_context * ctx, const uint32 } bool rwkv_gpu_offload_layers(struct rwkv_context * ctx, const uint32_t n_layers) { +#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS) + printf("\nOffloading %u (or fewer) layers...",n_layers); + const auto offload = [&](struct ggml_tensor * tensor) { + // TODO support multi-GPU + tensor->backend = GGML_BACKEND_GPU; + #if defined(GGML_USE_CLBLAST) + ggml_cl_transform_tensor(tensor->data, tensor); + #else + ggml_cuda_transform_tensor(tensor->data, tensor); + #endif + }; + + const size_t n_gpu = std::min(n_layers, ctx->instance->model.header.n_layer); + + if (ctx->gpu_layers < n_gpu) { + for (size_t & i = ctx->gpu_layers; i < n_gpu; i++) { + const struct rwkv_layer & layer = ctx->instance->model.layers[i]; + + // TODO also offload other operations to GPU with ggml_cuda_assign_buffers + offload(layer.att_key); + offload(layer.att_value); + offload(layer.att_receptance); + offload(layer.att_output); + + offload(layer.ffn_key); + offload(layer.ffn_value); + offload(layer.ffn_receptance); + } + return true; + } +#endif return false; } From cc45a7feb8412e84ff292207621412fffc0d3d51 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Tue, 4 Jul 2023 02:43:55 +0800 Subject: [PATCH 06/39] 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 50df20edd7a7b..0b12a9e7683f6 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 f48a6ca79bec8..7419b03b61dc3 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 07/39] 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 e4ddbe9865506..3bf98595763e2 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 08/39] [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 fed4ffb0ccd05..fa0bdbefb1de4 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 09/39] 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 b87ea76bc41ee..aec43bd923bb5 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 c6c0afdf18926e4ec2f51e0e1df15d668501db5f Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Tue, 4 Jul 2023 18:35:03 +0800 Subject: [PATCH 10/39] refactor to avoid code duplication --- expose.h | 14 ++--- gpttype_adapter.cpp | 144 ++++++++++++++++++++++++-------------------- koboldcpp.py | 6 +- 3 files changed, 88 insertions(+), 76 deletions(-) diff --git a/expose.h b/expose.h index 89471f8c2944f..3d3677b0d7c88 100644 --- a/expose.h +++ b/expose.h @@ -4,13 +4,13 @@ const int stop_token_max = 10; // match kobold's sampler list and order enum samplers { - KCPP_SAMPLER_TOP_K, - KCPP_SAMPLER_TOP_A, - KCPP_SAMPLER_TOP_P, - KCPP_SAMPLER_TFS, - KCPP_SAMPLER_TYP, - KCPP_SAMPLER_TEMP, - KCPP_SAMPLER_REP_PEN, + KCPP_SAMPLER_TOP_K=0, + KCPP_SAMPLER_TOP_A=1, + KCPP_SAMPLER_TOP_P=2, + KCPP_SAMPLER_TFS=3, + KCPP_SAMPLER_TYP=4, + KCPP_SAMPLER_TEMP=5, + KCPP_SAMPLER_REP_PEN=6, KCPP_SAMPLER_MAX }; struct load_model_inputs diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index 396e53dd56e60..cdbbf3b734bb0 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -219,16 +219,31 @@ void sample_top_a(llama_token_data_array * candidates, float a, size_t min_keep) candidates->size = last_idx; } -void apply_penalties(int n_ctx, int rep_pen_range, float rep_pen, llama_token_data_array & candidates_p) +void sample_rep_pen(int n_ctx, int rep_pen_range, float rep_pen, llama_token_data_array * candidates_p) { auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), rep_pen_range), n_ctx); - llama_sample_repetition_penalty(nullptr, &candidates_p, + llama_sample_repetition_penalty(nullptr, candidates_p, last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, last_n_repeat, rep_pen); } +void sample_temperature(llama_token_data_array * candidates_p, float temp) +{ + if (temp <= 0) + { + // Imitate greedy sampling + temp = 0.01f; //cannot be zero else div0 + llama_sample_temperature(nullptr, candidates_p, temp); + llama_sample_top_k(nullptr, candidates_p, 1, 1); //only want first candidate + } + else + { + llama_sample_temperature(nullptr, candidates_p, temp); + } +} + int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float top_k, float top_a, float top_p, float typical_p, float tfs, float temp, std::mt19937 & rng, -int mirostat, float mirostat_tau, float mirostat_eta, uint sampler_len, const samplers sampler_order[KCPP_SAMPLER_MAX]) +int mirostat, float mirostat_tau, float mirostat_eta, const std::vector & sampler_order) { int id = 0; std::vector candidates; @@ -239,78 +254,54 @@ int mirostat, float mirostat_tau, float mirostat_eta, uint sampler_len, const sa llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - // Run this except for when we are going to do the sampler reordering case below - if (temp <= 0 || mirostat > 0 || sampler_len == 0) - { - apply_penalties(n_ctx, rep_pen_range, rep_pen, candidates_p); - } - - // llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, - // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - // last_n_repeat, alpha_frequency, alpha_presence); - - if (temp <= 0) - { - // Greedy sampling - id = llama_sample_token_greedy(nullptr, &candidates_p); - } - else + if (mirostat == 1 || mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + sample_rep_pen(n_ctx, rep_pen_range, rep_pen, &candidates_p); + sample_temperature(&candidates_p, temp); if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temperature(nullptr, &candidates_p, temp); id = sample_token_mirostat(n_vocab, &candidates_p, rng, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); } - else if (mirostat == 2) + else { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(nullptr, &candidates_p, temp); id = sample_token_mirostat_v2(&candidates_p, rng, mirostat_tau, mirostat_eta, &mirostat_mu); } - else if (sampler_len > 0) + } + else + { + for (int i = 0; i < sampler_order.size(); i++) { - for (int i = 0; i < sampler_len; i++) { - switch (sampler_order[i]) { - case KCPP_SAMPLER_TOP_K: - llama_sample_top_k(nullptr, &candidates_p, top_k,1); - break; - case KCPP_SAMPLER_TOP_A: - sample_top_a(&candidates_p,top_a,1); - break; - case KCPP_SAMPLER_TOP_P: - llama_sample_top_p(nullptr, &candidates_p, top_p,1); - break; - case KCPP_SAMPLER_TFS: - llama_sample_tail_free(nullptr, &candidates_p, tfs,1); - break; - case KCPP_SAMPLER_TYP: - llama_sample_typical(nullptr, &candidates_p, typical_p,1); - break; - case KCPP_SAMPLER_TEMP: - llama_sample_temperature(nullptr, &candidates_p, temp); - break; - case KCPP_SAMPLER_REP_PEN: - apply_penalties(n_ctx, rep_pen_range, rep_pen, candidates_p); - break; - default: - break; - } + switch (sampler_order[i]) + { + case KCPP_SAMPLER_TOP_K: + llama_sample_top_k(nullptr, &candidates_p, top_k,1); + break; + case KCPP_SAMPLER_TOP_A: + sample_top_a(&candidates_p,top_a,1); + break; + case KCPP_SAMPLER_TOP_P: + llama_sample_top_p(nullptr, &candidates_p, top_p,1); + break; + case KCPP_SAMPLER_TFS: + llama_sample_tail_free(nullptr, &candidates_p, tfs,1); + break; + case KCPP_SAMPLER_TYP: + llama_sample_typical(nullptr, &candidates_p, typical_p,1); + break; + case KCPP_SAMPLER_TEMP: + sample_temperature(&candidates_p, temp); + break; + case KCPP_SAMPLER_REP_PEN: + sample_rep_pen(n_ctx, rep_pen_range, rep_pen, &candidates_p); + break; + default: + printf("\nSampleLogits: Unknown Sampler : %d",sampler_order[i]); + break; } - id = sample_token(&candidates_p, rng); - } - else - { - // Temperature sampling - llama_sample_top_k(nullptr, &candidates_p, top_k,1); - sample_top_a(&candidates_p,top_a,1); - llama_sample_tail_free(nullptr, &candidates_p, tfs,1); - llama_sample_typical(nullptr, &candidates_p, typical_p,1); - llama_sample_top_p(nullptr, &candidates_p, top_p,1); - llama_sample_temperature(nullptr, &candidates_p, temp); - id = sample_token(&candidates_p, rng); } + id = sample_token(&candidates_p, rng); } return id; @@ -952,6 +943,28 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o std::mt19937 rng(params.seed); concat_output = ""; + //prepare sampler order + std::vector sampler_order; + if(inputs.sampler_len<=0) //list by value + { + sampler_order = { + KCPP_SAMPLER_REP_PEN, + KCPP_SAMPLER_TOP_K, + KCPP_SAMPLER_TOP_A, + KCPP_SAMPLER_TFS, + KCPP_SAMPLER_TYP, + KCPP_SAMPLER_TOP_P, + KCPP_SAMPLER_TEMP + }; + } + else + { + for(int i=0;i Date: Tue, 4 Jul 2023 20:15:16 +0800 Subject: [PATCH 11/39] Update Makefile: clean simple (#2097) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 03f38bdba04ec..b289d97ed58fe 100644 --- a/Makefile +++ b/Makefile @@ -272,7 +272,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch embd-input-test build-info.h + rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h # # Examples From acc111caf93fc6681450924df9f99679c384c59e Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Tue, 4 Jul 2023 15:38:04 +0300 Subject: [PATCH 12/39] Allow old Make to build server. (#2098) Also make server build by default. Tested with Make 3.82 --- Makefile | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/Makefile b/Makefile index b289d97ed58fe..8966a3590329f 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test - -ifdef LLAMA_BUILD_SERVER - BUILD_TARGETS += server - LLAMA_SERVER_VERBOSE ?= 1 -server: private CXXFLAGS += -DSERVER_VERBOSE=$(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 13/39] 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 34a897327e33b..4ac0f6f4e430c 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 0000000000000..002830cad7962 --- /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, 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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 +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 0000000000000..832e9a3bb045f --- /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, + 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b/examples/server/public/completion.js new file mode 100644 index 0000000000000..4f5005cfb92e1 --- /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(.*)$/gm; + for (const match of text.matchAll(regex)) { + result[match[1]] = match[2] + } + + // since we know this is llama.cpp, let's just decode the json in data + result.data = JSON.parse(result.data); + content += result.data.content; + + // callack + if (callback) { + cont = callback(result) != false; + } + + // if we got a stop token from server, we will break here + if (result.data.stop) { + break; + } + } + } catch (e) { + console.error("llama error: ", e); + throw e; + } + finally { + controller.abort(); + } + + return content; +} diff --git a/examples/server/public/index.html b/examples/server/public/index.html new file mode 100644 index 0000000000000..6393e2e75cf19 --- /dev/null +++ b/examples/server/public/index.html @@ -0,0 +1,359 @@ + + + + + + llama.cpp - chat + + + + + + + + + + diff --git a/examples/server/public/index.js 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createElement,W as createRef,b as effect,F as h,nn as html,lt as hydrate,w as isValidElement,S as options,ut as render,s as signal,z as toChildArray,Dt as useCallback,Qt as useComputed,$t as useContext,Tt as useDebugValue,Et as useEffect,Vt as useErrorBoundary,At as useId,Nt as useImperativeHandle,Ut as useLayoutEffect,Pt as useMemo,Ct as useReducer,Ht as useRef,Kt as useSignal,Xt as useSignalEffect,wt as useState}; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3bf98595763e2..043e49750707b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2,8 +2,6 @@ #include "llama.h" #include "build-info.h" -// single thread -#define CPPHTTPLIB_THREAD_POOL_COUNT 1 #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 @@ -12,6 +10,11 @@ #include "httplib.h" #include "json.hpp" +// auto generated files (update with ./deps.sh) +#include "index.html.hpp" +#include "index.js.hpp" +#include "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 14/39] 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 ba4b2fec9d1df..4ed226e048218 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 0000000000000..aa325a03ee444 --- /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 043e49750707b..a835c398885f6 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 15/39] 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 | 1518 ++++++++++++++---------------------------- ggml.h | 118 ++-- scripts/sync-ggml.sh | 11 +- 3 files changed, 592 insertions(+), 1055 deletions(-) diff --git a/ggml.c b/ggml.c index afeb72ff00ccf..88cbed7d5347c 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] = { "Σx", "Σx_k", "Σx/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) { @@ -6990,26 +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); + + 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_S1_PH; + 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); +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) { + + GGML_ASSERT(b->ne[3] == 1); + GGML_ASSERT(a->ne[2] == b->ne[2]); bool is_node = false; if (a->grad || b->grad) { @@ -7017,43 +7167,42 @@ struct ggml_tensor * ggml_conv_1d_s2_ph( 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); + 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); + + 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_1D_S2_PH; + 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_2d_sk_p0 +// ggml_conv_1d_ph -struct ggml_tensor * ggml_conv_2d_sk_p0( +struct ggml_tensor* ggml_conv_1d_ph( 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) { - GGML_ASSERT(false); // TODO: implement backward - 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); - - result->op = GGML_OP_CONV_2D_SK_P0; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src0 = a; - result->src1 = b; - - return result; + 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++) { @@ -9619,46 +9524,74 @@ static void ggml_compute_forward_mean( } } -// ggml_compute_forward_repeat +// ggml_compute_forward_argmax -static void ggml_compute_forward_repeat_f32( +static void ggml_compute_forward_argmax_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { - GGML_ASSERT(params->ith == 0); - GGML_ASSERT(ggml_can_repeat(src0, dst)); + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { 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]; + 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 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]; + const size_t nb0 = dst->nb[0]; - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); + 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; + } +} - // TODO: support for transposed / permuted tensors +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( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + GGML_ASSERT(params->ith == 0); + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + GGML_TENSOR_UNARY_OP_LOCALS; + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -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 11b51f8bd656a..0af96c76b6d0e 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 e6e39ff8f1dd9..574e5180b4b17 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 16/39] readme : add link web chat PR --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e890dc9c22e75..6c2bb392eab0c 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 17/39] 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 570e273fcfffc..5fa4942be7aaf 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 18/39] Fixed OpenCL offloading prints (#2082) --- llama.cpp | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 7419b03b61dc3..83e93efc1a2d8 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 ea79e549f0982053aad636d832d181dcc3d5863b Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Wed, 5 Jul 2023 17:29:35 +0800 Subject: [PATCH 19/39] fixed refusing to quantize some models --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index b335d2b4ae8d3..6269797412274 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2404,9 +2404,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s int ny = tensor.ne.at(1); if (nx % QK_K != 0 || ny % QK_K != 0) { fprintf(stderr, "\n\n========================= Tensor sizes %d x %d are not divisible by %d\n",nx,ny,QK_K); - fprintf(stderr, "This is required to be able to use k-quants for now!\n"); + fprintf(stderr, "Verify before using\n"); fprintf(stderr, "========================================================================================\n\n"); - throw std::runtime_error("Unsupported tensor size encountered\n"); + // throw std::runtime_error("Unsupported tensor size encountered\n"); } } if (tensor.name == "output.weight") { From 051c70dcd55709c9cbbfa849af035951fe720433 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Wed, 5 Jul 2023 18:31:23 +0800 Subject: [PATCH 20/39] 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 83e93efc1a2d8..e04fbfc0a04de 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 21/39] Quantized dot products for CUDA mul mat vec (#2067) --- CMakeLists.txt | 13 +- Makefile | 13 +- README.md | 3 +- ggml-cuda.cu | 487 ++++++++++++++++++++++++++++++++++++++++--------- 4 files changed, 425 insertions(+), 91 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4ac0f6f4e430c..a2404548f90d4 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: ${CMAKE_CUDA_ARCHITECTURES}") diff --git a/Makefile b/Makefile index 8966a3590329f..71415664bd7e6 100644 --- a/Makefile +++ b/Makefile @@ -164,16 +164,21 @@ ifdef LLAMA_CUBLAS OBJS += ggml-cuda.o NVCC = nvcc NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native +ifdef LLAMA_CUDA_FORCE_DMMV + NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV +endif # LLAMA_CUDA_FORCE_DMMV ifdef LLAMA_CUDA_DMMV_X NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(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 6c2bb392eab0c..32f17c2d1bcde 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 0b12a9e7683f6..7965ff74111f7 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 +#ifdef GGML_CUDA_FORCE_DMMV + const bool use_mul_mat_vec_q = false; +#else + int id; + CUDA_CHECK(cudaGetDevice(&id)); - 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; + 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; - 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); - } + // 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 + 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 22/39] 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 4ed226e048218..160614ba81895 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 23/39] 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 | 590 ++++----------------- ggml.h | 33 +- 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, 174 insertions(+), 550 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index aec43bd923bb5..12481e8be7cf7 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 9cea472dedb82..6aa06ec8fa115 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 88cbed7d5347c..635c32eb5e213 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]; - - 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; + 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) { + 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); -#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; } + } else #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 0af96c76b6d0e..24ca8ae221c75 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; - - quantize_fns_t ggml_internal_get_quantize_fn(size_t i); + 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; + + ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i); #ifdef __cplusplus } diff --git a/llama.cpp b/llama.cpp index e04fbfc0a04de..7a866cb79106e 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 5748c8ac22193..4e0e023575322 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 7b18090d66b6b..48758cda81fdf 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 c40f1b29c7c36..8d3c162d2bfa0 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 c0e361e92313f..0bb9537f693ed 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 24/39] 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 574e5180b4b17..02ea6ec15241b 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 b5a499c1db57e..a3e25214b84eb 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 25/39] 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 635c32eb5e213..d257c3d657b34 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 26/39] 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 160614ba81895..037412d7634c8 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 27/39] 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 | 37 +- examples/server/completion.js.hpp | 548 ++++++--- examples/server/deps.sh | 4 - examples/server/index.html.hpp | 1579 +++++++++++++------------- examples/server/public/completion.js | 119 +- examples/server/public/index.html | 135 ++- examples/server/server.cpp | 825 ++++++++------ llama.cpp | 32 +- llama.h | 15 + 9 files changed, 1926 insertions(+), 1368 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 037412d7634c8..c5139c16bb827 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 002830cad7962..f399fb19a6bc8 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, 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b/examples/server/deps.sh index cf995162a4de0..1e9fe964b961a 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 832e9a3bb045f..42707fad967c2 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, 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0x62, 0x6f, 0x64, + 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a }; -unsigned int index_html_len = 10108; +unsigned int index_html_len = 10752; diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index 4f5005cfb92e1..a43d5a7d56753 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -5,20 +5,29 @@ const paramDefaults = { 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) => { +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 6393e2e75cf19..8ace0b0affe58 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -6,7 +6,6 @@ llama.cpp - chat - +
@@ -280,8 +280,9 @@ Model Choice:
- -
Improve Prompt (Context Injection)
+ +
Improve Prompt (System Message Injection)
+