From b377bf2266fcc7e98bef0ac2b8318b8b7c523947 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 20 Sep 2023 13:06:34 +0300 Subject: [PATCH] simple : add parallel decoding support --- common/common.cpp | 6 +- examples/embd-input/embd-input-lib.cpp | 8 +- examples/parallel/parallel.cpp | 8 +- examples/server/server.cpp | 6 +- examples/simple/simple.cpp | 186 +++++++++++++++++++------ llama.cpp | 34 ++--- llama.h | 15 +- 7 files changed, 187 insertions(+), 76 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 303b38240..6da466bbe 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -956,11 +956,11 @@ llama_token llama_sample_token( if (mirostat == 1) { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; - llama_sample_temperature(ctx, &cur_p, temp); + llama_sample_temp(ctx, &cur_p, temp); id = llama_sample_token_mirostat(ctx, &cur_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, &cur_p, temp); + llama_sample_temp(ctx, &cur_p, temp); id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else { // Temperature sampling @@ -968,7 +968,7 @@ llama_token llama_sample_token( llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); llama_sample_typical (ctx, &cur_p, typical_p, 1); llama_sample_top_p (ctx, &cur_p, top_p, 1); - llama_sample_temperature(ctx, &cur_p, temp); + llama_sample_temp(ctx, &cur_p, temp); { const int n_top = 10; diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 339612cce..f0089e1f9 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -79,7 +79,7 @@ bool eval_float(void * model, float * input, int N){ if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = { uint32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, }; + llama_batch batch = { int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, }; if (llama_decode(ctx, batch, params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); return false; @@ -183,11 +183,11 @@ llama_token sampling_id(struct MyModel* mymodel) { if (mirostat == 1) { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; - llama_sample_temperature(ctx, &candidates_p, temp); + llama_sample_temp(ctx, &candidates_p, temp); id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); } else if (mirostat == 2) { static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(ctx, &candidates_p, temp); + llama_sample_temp(ctx, &candidates_p, temp); id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else { // Temperature sampling @@ -195,7 +195,7 @@ llama_token sampling_id(struct MyModel* mymodel) { llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); llama_sample_typical(ctx, &candidates_p, typical_p, 1); llama_sample_top_p(ctx, &candidates_p, top_p, 1); - llama_sample_temperature(ctx, &candidates_p, temp); + llama_sample_temp(ctx, &candidates_p, temp); id = llama_sample_token(ctx, &candidates_p); } } diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index e252b0f53..b8bd6d936 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -123,7 +123,7 @@ int main(int argc, char ** argv) { std::vector tokens_system; tokens_system = ::llama_tokenize(ctx, k_system, true); - const uint32_t n_tokens_system = tokens_system.size(); + const int32_t n_tokens_system = tokens_system.size(); llama_seq_id g_seq_id = 0; @@ -144,7 +144,7 @@ int main(int argc, char ** argv) { batch.n_tokens = n_tokens_system; - for (uint32_t i = 0; i < batch.n_tokens; ++i) { + for (int32_t i = 0; i < batch.n_tokens; ++i) { batch.token[i] = tokens_system[i]; batch.pos[i] = i; batch.seq_id[i] = 0; @@ -156,7 +156,7 @@ int main(int argc, char ** argv) { return 1; } - // assign the system KV cachce to all parallel sequences + // assign the system KV cache to all parallel sequences for (int32_t i = 1; i < n_clients; ++i) { llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system); } @@ -248,7 +248,7 @@ int main(int argc, char ** argv) { int32_t n_batch = params.n_batch; for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { - const uint32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { n_tokens, diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6c81bd618..35908b7f0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -523,13 +523,13 @@ struct llama_server_context { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; - llama_sample_temperature(ctx, &candidates_p, temp); + llama_sample_temp(ctx, &candidates_p, temp); 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); + llama_sample_temp(ctx, &candidates_p, temp); result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else @@ -540,7 +540,7 @@ struct llama_server_context 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); + llama_sample_temp(ctx, &candidates_p, temp); result.tok = llama_sample_token(ctx, &candidates_p); } } diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 8a9a1bf54..88d087354 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -32,12 +32,18 @@ int main(int argc, char ** argv) { params.prompt = "Hello my name is"; } + // total length of the sequences including the prompt + const int n_len = 32; + // init LLM llama_backend_init(params.numa); llama_context_params ctx_params = llama_context_default_params(); + ctx_params.seed = 1234; + ctx_params.n_ctx = 2048; + llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params); if (model == NULL) { @@ -47,20 +53,29 @@ int main(int argc, char ** argv) { llama_context * ctx = llama_new_context_with_model(model, ctx_params); + if (ctx == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + // tokenize the prompt std::vector tokens_list; tokens_list = ::llama_tokenize(ctx, params.prompt, true); - const int max_context_size = llama_n_ctx(ctx); - const int max_tokens_list_size = max_context_size - 4; + const int n_ctx = llama_n_ctx(ctx); + const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel; - if ((int) tokens_list.size() > max_tokens_list_size) { - fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size); + LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_parallel, n_kv_req); + + // make sure wi + if (n_kv_req > n_ctx) { + LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__); + LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__); return 1; } - fprintf(stderr, "\n\n"); + fprintf(stderr, "\n"); for (auto id : tokens_list) { fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str()); @@ -68,66 +83,157 @@ int main(int argc, char ** argv) { fflush(stderr); + // create a llama_batch with size 512 + // we use this object to submit token data for decoding + + llama_batch batch = llama_batch_init(512, 0); + + // evaluate the initial prompt + batch.n_tokens = tokens_list.size(); + + for (int32_t i = 0; i < batch.n_tokens; i++) { + batch.token[i] = tokens_list[i]; + batch.pos[i] = i; + batch.seq_id[i] = 0; + batch.logits[i] = false; + } + + // llama_decode will output logits only for the last token of the prompt + batch.logits[batch.n_tokens - 1] = true; + + if (llama_decode(ctx, batch, params.n_threads) != 0) { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return 1; + } + + // assign the system KV cache to all parallel sequences + for (int32_t i = 1; i < n_parallel; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens); + } + + if (n_parallel > 1) { + LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel); + } + // main loop - // The LLM keeps a contextual cache memory of previous token evaluation. - // Usually, once this cache is full, it is required to recompute a compressed context based on previous - // tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist - // example, we will just stop the loop once this cache is full or once an end of stream is detected. + // we will store the parallel decoded sequences in this vector + std::vector streams(n_parallel); - const int n_gen = std::min(32, max_context_size); + // remember the batch index of the last tokenn for each parallel sequence + // we will use this to know which logits to sample from + std::vector i_batch(n_parallel, batch.n_tokens - 1); - int n_cur = 0; + int n_cur = batch.n_tokens; + int n_decode = 0; - while (n_cur < n_gen) { - // evaluate the transformer + const auto t_main_start = ggml_time_us(); - if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), int(tokens_list.size()), n_cur, 0), params.n_threads)) { - fprintf(stderr, "%s : failed to eval\n", __func__); + while (n_cur <= n_len) { + // evaluate the current batch with the transformer model + if (llama_decode(ctx, batch, params.n_threads)) { + fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return 1; } - n_cur += tokens_list.size(); - tokens_list.clear(); + // prepare the next batch + batch.n_tokens = 0; - // sample the next token + // sample the next token for each parallel sequence / stream + for (int32_t i = 0; i < n_parallel; ++i) { + if (i_batch[i] < 0) { + // the stream has already finished + continue; + } - llama_token new_token_id = 0; + auto n_vocab = llama_n_vocab(ctx); + auto logits = llama_get_logits(ctx) + i_batch[i] * n_vocab; - auto logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(ctx); + std::vector candidates; + candidates.reserve(n_vocab); - std::vector candidates; - candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); + } - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + const int top_k = 40; + const float top_p = 0.9f; + const float temp = 0.4f; + + llama_sample_top_k(ctx, &candidates_p, top_k, 1); + llama_sample_top_p(ctx, &candidates_p, top_p, 1); + llama_sample_temp (ctx, &candidates_p, temp); + + const llama_token new_token_id = llama_sample_token(ctx, &candidates_p); + + //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); + + // is it an end of stream ? + // mark this stream as finished + if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) { + i_batch[i] = -1; + LOG_TEE("\n"); + if (n_parallel > 1) { + LOG_TEE("%s: stream %d finished", __func__, i); + } + + continue; + } + + if (n_parallel == 1) { + // print the new token : + LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str()); + fflush(stdout); + } + + streams[i] += llama_token_to_piece(ctx, new_token_id); + + // push this new token for next evaluation + batch.token [batch.n_tokens] = new_token_id; + batch.pos [batch.n_tokens] = n_cur; + batch.seq_id[batch.n_tokens] = i; + batch.logits[batch.n_tokens] = true; + + i_batch[i] = batch.n_tokens; + + batch.n_tokens += 1; + + n_decode += 1; } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - new_token_id = llama_sample_token_greedy(ctx , &candidates_p); - - // is it an end of stream ? - if (new_token_id == llama_token_eos(ctx)) { - fprintf(stderr, " [end of text]\n"); + if (batch.n_tokens == 0) { + // all streams are finished break; } - // print the new token : - printf("%s", llama_token_to_piece(ctx, new_token_id).c_str()); - fflush(stdout); - - // push this new token for next evaluation - tokens_list.push_back(new_token_id); + n_cur += 1; } + LOG_TEE("\n"); + + if (n_parallel > 1) { + LOG_TEE("\n"); + + for (int32_t i = 0; i < n_parallel; ++i) { + LOG_TEE("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str()); + } + } + + const auto t_main_end = ggml_time_us(); + + LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", + __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); + + llama_print_timings(ctx); + + fprintf(stderr, "\n"); + llama_free(ctx); llama_free_model(model); llama_backend_free(); - fprintf(stderr, "\n\n"); - return 0; } diff --git a/llama.cpp b/llama.cpp index f47d9b598..ce3f2c8bb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4185,20 +4185,18 @@ static int llama_decode_internal( { auto & logits_out = lctx.logits; - if (lctx.logits_all) { + if (batch.logits) { logits_out.resize(n_vocab * n_tokens); - if (batch.logits) { - for (uint32_t i = 0; i < n_tokens; i++) { - if (batch.logits[i] == 0) { - continue; - } - memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab); + for (uint32_t i = 0; i < n_tokens; i++) { + if (batch.logits[i] == 0) { + continue; } - } else { - memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens); + memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab); } + } else if (lctx.logits_all) { + logits_out.resize(n_vocab * n_tokens); + memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens); } else { - // return result for just the last token logits_out.resize(n_vocab); memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab); } @@ -5269,7 +5267,7 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c } } -void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) { +void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) { const int64_t t_start_sample_us = ggml_time_us(); for (size_t i = 0; i < candidates_p->size; ++i) { @@ -5281,6 +5279,10 @@ void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array } } +void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) { + llama_sample_temp(ctx, candidates_p, temp); +} + void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty) { if (last_tokens_size == 0 || penalty == 1.0f) { return; @@ -7357,7 +7359,7 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi int llama_eval( struct llama_context * ctx, llama_token * tokens, - uint32_t n_tokens, + int32_t n_tokens, int n_past, int n_threads) { llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1); @@ -7377,7 +7379,7 @@ int llama_eval( int llama_eval_embd( struct llama_context * ctx, float * embd, - uint32_t n_tokens, + int32_t n_tokens, int n_past, int n_threads) { llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1); @@ -7398,7 +7400,7 @@ int llama_eval_embd( struct llama_batch llama_batch_get_one( llama_token * tokens, - uint32_t n_tokens, + int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { return { @@ -7414,8 +7416,8 @@ struct llama_batch llama_batch_get_one( }; } -struct llama_batch llama_batch_init(uint32_t n_tokens, int32_t embd) { - llama_batch batch = { n_tokens, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; +struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd) { + llama_batch batch = { -1, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; if (embd) { batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd); diff --git a/llama.h b/llama.h index 3a46e1ea0..54eab8f08 100644 --- a/llama.h +++ b/llama.h @@ -68,7 +68,7 @@ extern "C" { // data used for batch inference typedef struct llama_batch { - uint32_t n_tokens; + int32_t n_tokens; llama_token * token; float * embd; @@ -370,7 +370,7 @@ extern "C" { LLAMA_API DEPRECATED(int llama_eval( struct llama_context * ctx, llama_token * tokens, - uint32_t n_tokens, + int32_t n_tokens, int n_past, int n_threads), "please use llama_decode() instead"); @@ -380,7 +380,7 @@ extern "C" { LLAMA_API DEPRECATED(int llama_eval_embd( struct llama_context * ctx, float * embd, - uint32_t n_tokens, + int32_t n_tokens, int n_past, int n_threads), "please use llama_decode() instead"); @@ -391,7 +391,7 @@ extern "C" { // LLAMA_API struct llama_batch llama_batch_get_one( llama_token * tokens, - uint32_t n_tokens, + int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id); @@ -401,7 +401,7 @@ extern "C" { // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token // The rest of the llama_batch members are allocated with size n_tokens // All members are left uninitialized - LLAMA_API struct llama_batch llama_batch_init(uint32_t n_tokens, int32_t embd); + LLAMA_API struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd); // Frees a batch of tokens allocated with llama_batch_init() LLAMA_API void llama_batch_free(struct llama_batch batch); @@ -531,7 +531,10 @@ extern "C" { /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep); - LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp); + LLAMA_API void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates, float temp); + + LLAMA_API DEPRECATED(void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp), + "Use llama_sample_temp instead"); /// @details Apply constraints from grammar LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);