From 2cb8469e7f905bb3d3915c9b3c3fea4235013314 Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Wed, 19 Jul 2023 23:45:40 +0300 Subject: [PATCH] refactor evaluation logic --- examples/server/server.cpp | 415 ++++++++++++++++++------------------- 1 file changed, 200 insertions(+), 215 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 57fef514f..0ea5b6d0a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -21,6 +21,7 @@ using namespace httplib; using json = nlohmann::json; +using ordered_json = nlohmann::ordered_json; struct server_params { std::string hostname = "127.0.0.1"; @@ -82,9 +83,19 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { return ret; } +#define SERVER_LOG_PRETTY 0 + static void server_log(const char * level, const char * function, int line, - const char * message, const nlohmann::ordered_json & extra) { - nlohmann::ordered_json log { + const char * message, const ordered_json & extra) { + + #if SERVER_LOG_PRETTY == 1 + fprintf(stdout, ANSI_COLOR_MAGENTA ANSI_BOLD "[%s] " ANSI_COLOR_RESET " %s@%d: %s\n", level, function, line, message); + for (auto & it : extra.items()) { + fprintf(stdout, "%s=" ANSI_COLOR_YELLOW ANSI_BOLD "%s " ANSI_COLOR_RESET, it.key().c_str(), it.value().dump().c_str()); + } + fprintf(stdout, "\n\n"); + #else + ordered_json log { { "timestamp", time(nullptr) }, { "level", level }, { "function", function }, @@ -98,6 +109,8 @@ static void server_log(const char * level, const char * function, int line, const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace); fprintf(stdout, "%.*s\n", (int)str.size(), str.data()); + #endif + fflush(stdout); } @@ -128,8 +141,8 @@ static json probs_vector_to_json(const llama_context * ctx, const 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_past_guidance = 0; - int n_keep_guidance = 0; - size_t n_remain = 0; - bool cfg_enabled = false; - +struct prompt_evaluator { + llama_context * ctx; + size_t n_ctx = 0; + //std::string prompt; std::vector embd; - std::vector embd_guidance; std::vector last_n_tokens; - - llama_model * model = nullptr; - llama_context * ctx = nullptr; - llama_context * ctx_guidance = nullptr; - gpt_params params; - + size_t num_prompt_tokens = 0; + //size_t num_tokens_predicted = 0; + //size_t n_remain = 0; + size_t n_past = 0; + size_t n_keep = 0; bool truncated = false; - bool stopped_eos = false; - bool stopped_word = false; - bool stopped_limit = false; - std::string stopping_word; - int32_t multibyte_pending = 0; - std::mutex mutex; - - std::unique_lock lock() { - return std::unique_lock(mutex); + void set_context(llama_context * ctx) { + this->ctx = ctx; + this->n_ctx = llama_n_ctx(ctx); } - ~llama_server_context() { + ~prompt_evaluator() { if (ctx) { llama_free(ctx); ctx = nullptr; } - if (ctx_guidance) { - llama_free(ctx_guidance); - ctx_guidance = nullptr; - } - if (model) { - llama_free_model(model); - model = nullptr; - } } void rewind() { - params.antiprompt.clear(); num_prompt_tokens = 0; - num_tokens_predicted = 0; - generated_text = ""; - generated_text.reserve(params.n_ctx); - generated_token_probs.clear(); + //num_tokens_predicted = 0; truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - - n_remain = 0; + //n_remain = 0; n_past = 0; - cfg_enabled = false; - n_past_guidance = 0; } - bool loadModel(const gpt_params & params_) { - params = params_; - std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (model == nullptr) { - LOG_ERROR("unable to load model", {{ "model", params_.model }}); - return false; - } - - struct llama_context_params lparams = llama_context_params_from_gpt_params(params); - ctx_guidance = llama_new_context_with_model(model, lparams); - - last_n_tokens.resize(params.n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); - return true; - } - - void loadPrompt() { - params.prompt.insert(0, 1, ' '); // always add a first space - std::vector prompt_tokens = ::llama_tokenize(ctx, params.prompt, true); + void load_prompt(std::string &prompt, int keep, size_t n_last) { + prompt.insert(0, 1, ' '); // always add a first space + std::vector prompt_tokens = ::llama_tokenize(ctx, prompt, true); num_prompt_tokens = prompt_tokens.size(); - if (params.n_keep < 0) { - params.n_keep = (int)num_prompt_tokens; + if (keep < 0) { + keep = (int)num_prompt_tokens; } - params.n_keep = std::min(params.n_ctx - 4, params.n_keep); + n_keep = std::min(n_ctx - 4, (size_t)keep); // if input prompt is too big, truncate like normal - 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 = (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()); + if (num_prompt_tokens >= n_ctx) { + const size_t n_left = (n_ctx - n_keep) / 2; + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + n_keep); + const size_t erased_blocks = (num_prompt_tokens - n_keep - n_left - 1) / n_left; + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + n_keep + erased_blocks * n_left, prompt_tokens.end()); LOG_VERBOSE("input truncated", { - { "n_ctx", params.n_ctx }, - { "n_keep", params.n_keep }, + { "n_ctx", n_ctx }, + { "n_keep", n_keep }, { "n_left", n_left }, { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) }, }); truncated = true; prompt_tokens = new_tokens; - } else { - const size_t ps = num_prompt_tokens; - std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); + } + + last_n_tokens.resize(n_last); + if (n_last > 0) { + const size_t s = std::min(n_last, num_prompt_tokens); + std::fill(last_n_tokens.begin(), last_n_tokens.end() - s, 0); + std::copy(prompt_tokens.end() - s, prompt_tokens.end(), last_n_tokens.begin()); } // compare the evaluated prompt with the new prompt @@ -285,50 +246,141 @@ struct llama_server_context { { "cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past) }, { "to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) }, }); + } + bool evaluate(size_t n_threads, size_t n_batch) { + if (embd.size() >= n_ctx) { + // Reset context + const size_t n_left = (n_ctx - n_keep) / 2; + + std::vector new_tokens(embd.begin(), embd.begin() + n_keep); + new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end()); + embd = new_tokens; + n_past = n_keep; + truncated = true; + LOG_VERBOSE("input truncated", { + { "n_ctx", n_ctx }, + { "n_keep", n_keep }, + { "n_left", n_left }, + { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) }, + }); + } + + while (n_past < embd.size()) { + size_t n_eval = embd.size() - n_past; + if (n_eval > n_batch) { + n_eval = n_batch; + } + + //LOG_VERBOSE("eval", { + // { "n_eval", n_eval }, + // { "n_past", n_past }, + // { "n_threads", n_threads }, + // { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) }, + //}); + + if (llama_eval(ctx, &embd[n_past], n_eval, n_past, n_threads)) { + LOG_ERROR("failed to eval", { + { "n_eval", n_eval }, + { "n_past", n_past }, + { "n_threads", n_threads }, + { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) }, + }); + return false; + } + n_past += n_eval; + } + + return true; + } + + void append_token(llama_token id) { + if (last_n_tokens.size() > 0) { + last_n_tokens.erase(last_n_tokens.begin()); + last_n_tokens.push_back(id); + } + embd.push_back(id); + } +}; + +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; + int n_keep_guidance = 0; + size_t n_remain = 0; + bool cfg_enabled = false; + + llama_model * model = nullptr; + llama_context * ctx = nullptr; + gpt_params params; + + prompt_evaluator evaluator; + prompt_evaluator evaluator_guidance; + + bool stopped_eos = false; + bool stopped_word = false; + bool stopped_limit = false; + 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 (model) { + llama_free_model(model); + model = nullptr; + } + } + + void rewind() { + params.antiprompt.clear(); + num_tokens_predicted = 0; + generated_text = ""; + generated_text.reserve(params.n_ctx); + generated_token_probs.clear(); + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + multibyte_pending = 0; + + n_remain = 0; + cfg_enabled = false; + evaluator.rewind(); + evaluator_guidance.rewind(); + } + + bool loadModel(const gpt_params & params_) { + params = params_; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr) { + LOG_ERROR("unable to load model", {{ "model", params_.model }}); + return false; + } + evaluator.set_context(ctx); + + struct llama_context_params lparams = llama_context_params_from_gpt_params(params); + llama_context * ctx_guidance = llama_new_context_with_model(model, lparams); + evaluator_guidance.set_context(ctx_guidance); + + return true; + } + + void loadPrompt() { + evaluator.load_prompt(params.prompt, params.n_keep, params.repeat_last_n); has_next_token = true; } void loadGuidancePrompt() { - params.cfg_negative_prompt.insert(0, 1, ' '); // always add a first space - std::vector prompt_tokens = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, true); - num_prompt_tokens = prompt_tokens.size(); - - if (n_keep_guidance < 0) { - n_keep_guidance = (int)num_prompt_tokens; - } - n_keep_guidance = std::min(params.n_ctx - 4, n_keep_guidance); - - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)params.n_ctx) { - const int n_left = (params.n_ctx - n_keep_guidance) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + n_keep_guidance); - const int erased_blocks = (num_prompt_tokens - n_keep_guidance - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + n_keep_guidance + erased_blocks * n_left, prompt_tokens.end()); - - LOG_VERBOSE("guidance truncated", { - { "n_ctx", params.n_ctx }, - { "n_keep", n_keep_guidance }, - { "n_left", n_left }, - { "new_tokens", tokens_to_str(ctx_guidance, new_tokens.cbegin(), new_tokens.cend()) }, - }); - - prompt_tokens = new_tokens; - } - - // compare the evaluated prompt with the new prompt - n_past_guidance = common_part(embd_guidance, prompt_tokens); - embd_guidance = prompt_tokens; - if (n_past_guidance == num_prompt_tokens) { - // we have to evaluate at least 1 token to generate logits. - n_past_guidance--; - } - - LOG_VERBOSE("guidance prompt ingested", { - { "n_past", n_past_guidance }, - { "cached", tokens_to_str(ctx_guidance, embd.cbegin(), embd.cbegin() + n_past) }, - { "to_eval", tokens_to_str(ctx_guidance, embd.cbegin() + n_past, embd.cend()) }, - }); + evaluator_guidance.load_prompt(params.cfg_negative_prompt, n_keep_guidance, 0); } void beginCompletion() { @@ -341,80 +393,14 @@ struct llama_server_context { completion_token_output result; result.tok = -1; - if (embd.size() >= (size_t)params.n_ctx) { - // Reset context - const int n_left = (params.n_ctx - params.n_keep) / 2; - - std::vector new_tokens(embd.begin(), embd.begin() + params.n_keep); - new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end()); - embd = new_tokens; - n_past = params.n_keep; - truncated = true; - LOG_VERBOSE("input truncated", { - { "n_ctx", params.n_ctx }, - { "n_keep", params.n_keep }, - { "n_left", n_left }, - { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) }, - }); - } - - while (n_past < embd.size()) { - int n_eval = (int)embd.size() - n_past; - if (n_eval > params.n_batch) { - n_eval = params.n_batch; - } - if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) { - LOG_ERROR("failed to eval", { - { "n_eval", n_eval }, - { "n_past", n_past }, - { "n_threads", params.n_threads }, - { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) }, - }); - has_next_token = false; - return result; - } - n_past += n_eval; - } + evaluator.evaluate(params.n_threads, params.n_batch); if (cfg_enabled) { - if (embd_guidance.size() >= (size_t)params.n_ctx) { - // Reset context - const int n_left = (params.n_ctx - n_keep_guidance) / 2; - - std::vector new_tokens(embd.begin(), embd.begin() + n_keep_guidance); - new_tokens.insert(new_tokens.end(), embd_guidance.end() - n_left, embd_guidance.end()); - embd_guidance = new_tokens; - n_past_guidance = n_keep_guidance; - LOG_VERBOSE("guidance truncated", { - { "n_ctx", params.n_ctx }, - { "n_keep", n_keep_guidance }, - { "n_left", n_left }, - { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) }, - }); - } - - while (n_past_guidance < embd_guidance.size()) { - int n_eval = (int)embd_guidance.size() - n_past_guidance; - if (n_eval > params.n_batch) { - n_eval = params.n_batch; - } - if (llama_eval(ctx_guidance, &embd_guidance[n_past_guidance], n_eval, n_past_guidance, params.n_threads)) { - LOG_ERROR("failed to eval", { - { "n_eval", n_eval }, - { "n_past", n_past_guidance }, - { "n_threads", params.n_threads }, - { "embd", tokens_to_str(ctx_guidance, embd_guidance.cbegin() + n_past_guidance, embd_guidance.cend()) }, - }); - has_next_token = false; - return result; - } - n_past_guidance += n_eval; - } + evaluator_guidance.evaluate(params.n_threads, params.n_batch); } if (params.n_predict == 0) { has_next_token = false; - //result.tok = llama_token_eos(); return result; } @@ -424,7 +410,7 @@ struct llama_server_context { const float top_p = params.top_p; const float tfs_z = params.tfs_z; const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n; + //const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n; const float repeat_penalty = params.repeat_penalty; const float alpha_presence = params.presence_penalty; const float alpha_frequency = params.frequency_penalty; @@ -435,7 +421,7 @@ struct llama_server_context { const int32_t n_probs = params.n_probs; { - auto * logits = llama_get_logits(ctx); + auto logits = llama_get_logits(ctx); auto n_vocab = llama_n_vocab(ctx); // Apply params.logit_bias map @@ -453,18 +439,18 @@ struct llama_server_context { if (cfg_enabled) { llama_sample_classifier_free_guidance( - ctx, &candidates_p, ctx_guidance, params.cfg_scale, params.cfg_smooth_factor); + ctx, &candidates_p, evaluator_guidance.ctx, params.cfg_scale, params.cfg_smooth_factor); } // Apply penalties float nl_logit = logits[llama_token_nl()]; - auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx); + //auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx); llama_sample_repetition_penalty(ctx, &candidates_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, repeat_penalty); + evaluator.last_n_tokens.data(), evaluator.last_n_tokens.size(), + repeat_penalty); llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); + evaluator.last_n_tokens.data(), evaluator.last_n_tokens.size(), + alpha_frequency, alpha_presence); if (!penalize_nl) { logits[llama_token_nl()] = nl_logit; } @@ -500,21 +486,19 @@ struct llama_server_context { 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(result.tok); num_tokens_predicted++; } // add it to the context - embd.push_back(result.tok); + evaluator.append_token(result.tok); if (cfg_enabled) { - embd_guidance.push_back(result.tok); + evaluator_guidance.append_token(result.tok); } // decrement remaining sampling budget --n_remain; - if (!embd.empty() && embd.back() == llama_token_eos()) { - //stopping_word = llama_token_to_str(ctx, embd.back()); + if (result.tok == llama_token_eos()) { + stopping_word = ""; has_next_token = false; stopped_eos = true; LOG_VERBOSE("eos token found", {}); @@ -591,6 +575,7 @@ struct llama_server_context { LOG_VERBOSE("next token", { { "token", token_with_probs.tok }, { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) }, + { "n_past", evaluator.n_past }, { "has_next_token", has_next_token }, { "n_remain", n_remain }, { "num_tokens_predicted", num_tokens_predicted }, @@ -884,16 +869,16 @@ 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 }, + { "tokens_evaluated", llama.evaluator.num_prompt_tokens }, { "generation_settings", format_generation_settings(llama) }, { "prompt", llama.params.prompt }, { "cfg_negative_prompt", llama.params.cfg_negative_prompt }, - { "truncated", llama.truncated }, + { "truncated", llama.evaluator.truncated }, { "stopped_eos", llama.stopped_eos }, { "stopped_word", llama.stopped_word }, { "stopped_limit", llama.stopped_limit }, { "stopping_word", llama.stopping_word }, - { "tokens_cached", llama.n_past }, + { "tokens_cached", llama.evaluator.n_past }, { "timings", format_timings(llama) }, };