From 63f99b1ea622cfe4f9ddeda2ac7a8231416e0dbb Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Wed, 11 Oct 2023 18:14:11 -0400 Subject: [PATCH 01/64] implementing parallel decoding in server example --- examples/server/server.cpp | 1770 +++++++++++++++++++++--------------- 1 file changed, 1037 insertions(+), 733 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8c5318c650ae8..00b46bc3d8331 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -18,6 +18,8 @@ #include "json-schema-to-grammar.mjs.hpp" #include +#include +#include #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 @@ -35,6 +37,68 @@ struct server_params int32_t write_timeout = 600; }; +static bool server_verbose = false; + +#if SERVER_VERBOSE != 1 +#define LOG_VERBOSE(MSG, ...) +#else +#define LOG_VERBOSE(MSG, ...) \ + do \ + { \ + if (server_verbose) \ + { \ + server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ + } \ + } while (0) +#endif + +#define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) + + +// parallel +enum slot_state +{ + IDLE, + SLEEPING, + PROCESSING +}; + +enum slot_command { + NONE, + LOAD_PROMPT, + RELEASE +}; + +struct slot_params { + bool stream = true; + uint32_t seed = -1; // RNG seed + int32_t n_predict = 128; // new tokens to predict + + // sampler params + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float tfs_z = 1.00f; // 1.0 = disabled + float typical_p = 1.00f; // 1.0 = disabled + float temp = 0.80f; // 1.0 = disabled + float repeat_penalty = 1.10f; // 1.0 = disabled + int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float frequency_penalty = 0.00f; // 0.0 = disabled + float presence_penalty = 0.00f; // 0.0 = disabled + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate + int n_probs = 0; + bool penalize_nl = false; + + std::unordered_map logit_bias; // logit bias for specific tokens + + std::string grammar = ""; // optional BNF-like grammar to constrain sampling + bool remember_generation = false; // remember a part of the prompt to avoid reprocessing all prompt + std::vector antiprompt; +}; + // completion token output with probabilities struct completion_token_output { @@ -69,6 +133,23 @@ static bool ends_with(const std::string &str, const std::string &suffix) 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } +static void slot_params_to_gpt_params(const slot_params &src, gpt_params & dst) +{ + dst.frequency_penalty = src.frequency_penalty; + dst.temp = src.temp; + dst.top_k = src.top_k; + dst.top_p = src.top_p; + dst.grammar = src.grammar; + dst.logit_bias = src.logit_bias; + dst.mirostat = src.mirostat; + dst.mirostat_eta = src.mirostat_eta; + dst.mirostat_tau = src.mirostat_tau; + dst.typical_p = src.typical_p; + dst.repeat_penalty = src.repeat_penalty; + dst.repeat_last_n = src.repeat_last_n; + dst.presence_penalty = src.presence_penalty; +} + static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { @@ -162,55 +243,146 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector context_tokens; + std::vector last_n_tokens; + std::vector generated_token_probs; + int sent_tokens = 0; + slot_state state = IDLE; + slot_command command = NONE; + bool truncated = false; + bool stopped_eos = false; + bool stopped_word = false; + bool stopped_limit = false; + std::string stopping_word; + int32_t multibyte_pending = 0; -#if SERVER_VERBOSE != 1 -#define LOG_VERBOSE(MSG, ...) -#else -#define LOG_VERBOSE(MSG, ...) \ - do \ - { \ - if (server_verbose) \ - { \ - server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ - } \ - } while (0) -#endif + slot_params params; -#define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) + // grammar props + grammar_parser::parse_state parsed_grammar; + llama_grammar *grammar = nullptr; + + void reset() { + state = IDLE; + command = NONE; + num_prompt_tokens = 0; + num_tokens_predicted = 0; + generated_text = ""; + generated_token_probs.clear(); + truncated = false; + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + multibyte_pending = 0; + n_past = 0; + + if (grammar != nullptr) { + llama_grammar_free(grammar); + grammar = nullptr; + } + + // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? + } + + bool loadGrammar() + { + if (!params.grammar.empty()) { + parsed_grammar = grammar_parser::parse(params.grammar.c_str()); + // will be empty (default) if there are parse errors + if (parsed_grammar.rules.empty()) { + LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); + return false; + } + grammar_parser::print_grammar(stderr, parsed_grammar); + + // TODO: fix this comment + // { + // auto it = params.logit_bias.find(llama_token_eos(ctx)); + // if (it != params.logit_bias.end() && it->second == -INFINITY) { + // LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); + // } + // } + + std::vector grammar_rules(parsed_grammar.c_rules()); + grammar = llama_grammar_init( + grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + } + return true; + } + + bool hasNewToken() { + return generated_token_probs.size() > sent_tokens; + } + + bool available() { + return state == IDLE && + command == NONE && + !params.remember_generation; + } + + bool isProcessing() { + return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + } + + completion_token_output next() { + completion_token_output tkn = generated_token_probs.at(sent_tokens); + sent_tokens++; + return tkn; + } + + void addTokenString(completion_token_output token) { + if(command == RELEASE) { + generated_token_probs.clear(); + sent_tokens = 0; + return; + } + context_tokens.push_back(token.tok); + generated_token_probs.push_back(token); + num_tokens_predicted++; + } + + void release() { + if(state == PROCESSING) { + command = RELEASE; + } + } +}; struct llama_server_context { - bool stream = false; - bool has_next_token = false; - std::string generated_text; - std::vector generated_token_probs; + std::vector slots; - size_t num_prompt_tokens = 0; - size_t num_tokens_predicted = 0; - size_t n_past = 0; - size_t n_remain = 0; + // system prompt + std::string system_prompt = ""; + bool update_system_prompt = false; + std::vector tokens_system; + int32_t n_tokens_system = 0; - json prompt; - std::vector embd; - std::vector last_n_tokens; + // broadcast to all clients to keep the same prompt format + std::string user_name = ""; // this should be the anti prompt + std::string assistant_name = ""; // this is for generate the prompt llama_model *model = nullptr; llama_context *ctx = nullptr; + llama_batch batch; + std::vector candidates; + bool all_slots_are_idle = false; gpt_params params; int n_ctx; - - grammar_parser::parse_state parsed_grammar; - llama_grammar *grammar = nullptr; - - bool truncated = false; - bool stopped_eos = false; - bool stopped_word = false; - bool stopped_limit = false; - std::string stopping_word; - int32_t multibyte_pending = 0; + int n_vocab; std::mutex mutex; @@ -231,29 +403,11 @@ struct llama_server_context llama_free_model(model); model = nullptr; } - } - - void rewind() - { - params.antiprompt.clear(); - params.grammar.clear(); - num_prompt_tokens = 0; - num_tokens_predicted = 0; - generated_text = ""; - generated_text.reserve(n_ctx); - generated_token_probs.clear(); - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - n_remain = 0; - n_past = 0; - if (grammar != nullptr) { - llama_grammar_free(grammar); - grammar = nullptr; + for(auto &slot : slots) { + if(slot.grammar) { + llama_grammar_free(slot.grammar); + } } } @@ -267,11 +421,31 @@ struct llama_server_context return false; } n_ctx = llama_n_ctx(ctx); - last_n_tokens.resize(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); + n_vocab = llama_n_vocab(model); + candidates.reserve(n_vocab); return true; } + void initialize() { + // create slots + LOG_TEE("Available slots:\n"); + for (int i = 0; i < params.n_parallel; i++) + { + llama_client_slot slot; + slot.id = i; + slot.last_n_tokens.resize(params.n_predict); // max prediction per slot + slot.reset(); + std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + LOG_TEE(" - slot %i\n", slot.id); + slots.push_back(slot); + } + batch = llama_batch_init(params.n_ctx, 0); + + // empty system prompt + system_prompt = ""; + all_slots_are_idle = true; + } + std::vector tokenize(const json & json_prompt, bool add_bos) const { // If `add_bos` is true, we only add BOS, when json_prompt is a string, @@ -317,305 +491,236 @@ struct llama_server_context return prompt_tokens; } - bool loadGrammar() - { - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); - return false; - } - grammar_parser::print_grammar(stderr, parsed_grammar); - - { - auto it = params.logit_bias.find(llama_token_eos(ctx)); - if (it != params.logit_bias.end() && it->second == -INFINITY) { - LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); - } - } - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - } - return true; - } - - void loadInfill() - { - bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; - } + void processPrompt() { - auto prefix_tokens = tokenize(params.input_prefix, false); - auto suffix_tokens = tokenize(params.input_suffix, false); - const int space_token = 29871; - if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { - suffix_tokens.erase(suffix_tokens.begin()); - } - prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - prefix_tokens.push_back(llama_token_middle(ctx)); - auto prompt_tokens = prefix_tokens; - - num_prompt_tokens = prompt_tokens.size(); - if (params.n_keep < 0) - { - params.n_keep = (int)num_prompt_tokens; - } - params.n_keep = std::min(params.n_ctx - 4, params.n_keep); + //params.n_keep = std::min(n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)params.n_ctx) - { - printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens); - // todo we probably want to cut from both sides - const int n_left = (params.n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", params.n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; - } - 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); - } + // if (num_prompt_tokens >= (size_t)n_ctx) + // { + // const int n_left = (n_ctx - params.n_keep) / 2; + // std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + // const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; + // new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); + // std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); + + // LOG_VERBOSE("input truncated", { + // {"n_ctx", n_ctx}, + // {"n_keep", params.n_keep}, + // {"n_left", n_left}, + // {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + // }); + + // truncated = true; + // prompt_tokens = new_tokens; + // } + // 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); + // } // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - embd = prompt_tokens; - if (n_past == num_prompt_tokens) - { - // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); - n_past--; - } - - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - - has_next_token = true; } - void loadPrompt() - { - auto prompt_tokens = tokenize(prompt, true); // always add BOS - num_prompt_tokens = prompt_tokens.size(); - - if (params.n_keep < 0) - { - params.n_keep = (int)num_prompt_tokens; - } - params.n_keep = std::min(n_ctx - 4, params.n_keep); - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)n_ctx) - { - const int n_left = (n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; - } - else + llama_client_slot* getSlot(int id) { + for (llama_client_slot & slot : slots) { - 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); + if ((id == -1 && slot.available()) || slot.id == id) + { + return &slot; + } } + return nullptr; + } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); - - embd = prompt_tokens; - if (n_past == num_prompt_tokens) - { - // we have to evaluate at least 1 token to generate logits. - n_past--; + bool launchSlot(llama_client_slot* &slot) { + if(!slot->loadGrammar()) { + return false; } - - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - - has_next_token = true; + all_slots_are_idle = false; + slot->command = LOAD_PROMPT; + LOG_TEE("slot %i is processing\n", slot->id); + return true; } - void beginCompletion() + void loadInfill() { - // number of tokens to keep when resetting context - n_remain = params.n_predict; - llama_set_rng_seed(ctx, params.seed); + // bool suff_rm_leading_spc = true; + // if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + // params.input_suffix.erase(0, 1); + // suff_rm_leading_spc = false; + // } + + // auto prefix_tokens = tokenize(params.input_prefix, false); + // auto suffix_tokens = tokenize(params.input_suffix, false); + // const int space_token = 29871; + // if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { + // suffix_tokens.erase(suffix_tokens.begin()); + // } + // prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); + // prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS + // prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); + // prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + // prefix_tokens.push_back(llama_token_middle(ctx)); + // auto prompt_tokens = prefix_tokens; + + // num_prompt_tokens = prompt_tokens.size(); + + // if (params.n_keep < 0) + // { + // 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 (num_prompt_tokens >= (size_t)params.n_ctx) + // { + // printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens); + // // todo we probably want to cut from both sides + // const int n_left = (params.n_ctx - params.n_keep) / 2; + // std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + // const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; + // new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); + // std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); + + // LOG_VERBOSE("input truncated", { + // {"n_ctx", params.n_ctx}, + // {"n_keep", params.n_keep}, + // {"n_left", n_left}, + // {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + // }); + + // truncated = true; + // prompt_tokens = new_tokens; + // } + // 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); + // } + + // // compare the evaluated prompt with the new prompt + // n_past = common_part(embd, prompt_tokens); + // embd = prompt_tokens; + // if (n_past == num_prompt_tokens) + // { + // // we have to evaluate at least 1 token to generate logits. + // printf("we have to evaluate at least 1 token to generate logits\n"); + // n_past--; + // } + + // LOG_VERBOSE("prompt ingested", { + // {"n_past", n_past}, + // {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, + // {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, + // }); + + // has_next_token = true; } - completion_token_output nextToken() - { - completion_token_output result; - result.tok = -1; - - if (embd.size() >= (size_t)n_ctx) - { - // Shift context - - const int n_left = n_past - params.n_keep - 1; - const int n_discard = n_left/2; - - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + void updateSystemPrompt() { + tokens_system = ::llama_tokenize(ctx, system_prompt, true); + n_tokens_system = tokens_system.size(); - for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++) - { - embd[i - n_discard] = embd[i]; - } - embd.resize(embd.size() - n_discard); + batch.n_tokens = n_tokens_system; - n_past -= n_discard; - - truncated = true; - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); + // clear the entire KV cache + for (int i = 0; i < params.n_parallel; ++i) + { + llama_kv_cache_seq_rm(ctx, i, 0, -1); } - bool tg = true; - while (n_past < embd.size()) + for (int32_t i = 0; i < batch.n_tokens; ++i) { - int n_eval = (int)embd.size() - n_past; - tg = n_eval == 1; - if (n_eval > params.n_batch) - { - n_eval = params.n_batch; - } - - if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0))) - { - LOG_ERROR("failed to eval", { - {"n_eval", n_eval}, - {"n_past", n_past}, - {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - has_next_token = false; - return result; - } - n_past += n_eval; + batch.token[i] = tokens_system[i]; + batch.pos[i] = i; + batch.seq_id[i] = 0; + batch.logits[i] = false; } - if (params.n_predict == 0) + if (llama_decode(ctx, batch) != 0) { - has_next_token = false; - result.tok = llama_token_eos(ctx); - return result; + LOG_TEE("%s: llama_decode() failed\n", __func__); + return; } + // assign the system KV cache to all parallel sequences + for (int32_t i = 1; i < params.n_parallel; ++i) { - // out of user input, sample next token - std::vector candidates; - candidates.reserve(llama_n_vocab(model)); + llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system); + } - result.tok = llama_sample_token(ctx, NULL, grammar, params, last_n_tokens, candidates); + LOG_TEE("system prompt updated\n"); + update_system_prompt = false; + } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + void notifySystemPromptChanged() { + // release all slots + for (llama_client_slot &slot : slots) + { + slot.release(); + } + waitAllAreIdle(); + all_slots_are_idle = true; + // wait until system prompt load + update_system_prompt = true; + while(update_system_prompt) { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + // system prompt loaded, continue + } - const int32_t n_probs = params.n_probs; - if (params.temp <= 0 && n_probs > 0) - { - // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &candidates_p); - } + void processSystemPromptData(json sys_props) { + system_prompt = sys_props.value("system_prompt", ""); + user_name = sys_props.value("anti_prompt", ""); + assistant_name = sys_props.value("assistant_name", ""); + notifySystemPromptChanged(); + } - for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i) + void waitAllAreIdle() { + bool wait = true; + while(wait) { + wait = false; + for (auto &slot : slots) { - 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); - if (tg) { - num_tokens_predicted++; + if (!slot.available()) + { + wait = true; + break; + } } } - - // add it to the context - embd.push_back(result.tok); - // decrement remaining sampling budget - --n_remain; - - if (!embd.empty() && embd.back() == llama_token_eos(ctx)) - { - // stopping_word = llama_token_to_piece(ctx, embd.back()); - has_next_token = false; - stopped_eos = true; - LOG_VERBOSE("eos token found", {}); - return result; - } - - has_next_token = params.n_predict == -1 || n_remain != 0; - return result; } - size_t findStoppingStrings(const std::string &text, const size_t last_token_size, - const stop_type type) + size_t findStoppingStrings(const size_t last_token_size, + const stop_type type, llama_client_slot & slot) { size_t stop_pos = std::string::npos; - for (const std::string &word : params.antiprompt) + for (const std::string &word : slot.params.antiprompt) { size_t pos; if (type == STOP_FULL) { const size_t tmp = word.size() + last_token_size; - const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; - pos = text.find(word, from_pos); + const size_t from_pos = slot.generated_text.size() > tmp ? slot.generated_text.size() - tmp : 0; + pos = slot.generated_text.find(word, from_pos); } else { - pos = find_partial_stop_string(word, text); + pos = find_partial_stop_string(word, slot.generated_text); } if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { if (type == STOP_FULL) { - stopping_word = word; - stopped_word = true; - has_next_token = false; + slot.stopping_word = word; + slot.stopped_word = true; } stop_pos = pos; } @@ -623,70 +728,255 @@ struct llama_server_context return stop_pos; } - completion_token_output doCompletion() - { - auto token_with_probs = nextToken(); + bool processToken(completion_token_output & result, llama_client_slot & slot) { + // remember which tokens were sampled - used for repetition penalties during sampling + slot.last_n_tokens.erase(slot.last_n_tokens.begin()); + slot.last_n_tokens.push_back(result.tok); + const std::string token_str = llama_token_to_piece(ctx, result.tok); + slot.sampled = result.tok; + + size_t stop_pos = + findStoppingStrings(token_str.size(), STOP_FULL, slot); + + slot.addTokenString(result); + + slot.generated_text += token_str; - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); - generated_text += token_text; + bool has_next_token = !(slot.n_decoded > 2 && + (result.tok == llama_token_eos(ctx) || + (slot.n_decoded + slot.n_past >= + params.n_predict) || + stop_pos != std::string::npos)); - if (params.n_probs > 0) + if (slot.params.n_probs > 0) { - generated_token_probs.push_back(token_with_probs); + slot.generated_token_probs.push_back(result); } - if (multibyte_pending > 0) + if (slot.multibyte_pending > 0) { - multibyte_pending -= token_text.size(); + slot.multibyte_pending -= token_str.size(); } - else if (token_text.size() == 1) + else if (token_str.size() == 1) { - const char c = token_text[0]; + const char c = token_str[0]; // 2-byte characters: 110xxxxx 10xxxxxx if ((c & 0xE0) == 0xC0) { - multibyte_pending = 1; + slot.multibyte_pending = 1; // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx } else if ((c & 0xF0) == 0xE0) { - multibyte_pending = 2; + slot.multibyte_pending = 2; // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx } else if ((c & 0xF8) == 0xF0) { - multibyte_pending = 3; + slot.multibyte_pending = 3; } else { - multibyte_pending = 0; + slot.multibyte_pending = 0; } } - if (multibyte_pending > 0 && !has_next_token) + if (slot.multibyte_pending > 0 && !has_next_token) { has_next_token = true; - n_remain++; } - if (!has_next_token && n_remain == 0) + if (!has_next_token && (slot.n_decoded + slot.n_past >= params.n_predict)) { - stopped_limit = true; + slot.stopped_limit = true; + } + + if (!slot.context_tokens.empty() && result.tok == llama_token_eos(ctx)){ + slot.stopped_eos = true; + LOG_VERBOSE("eos token found", {}); } LOG_VERBOSE("next token", { - {"token", token_with_probs.tok}, - {"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)}, + {"token", result.tok}, + {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, {"has_next_token", has_next_token}, - {"n_remain", n_remain}, - {"num_tokens_predicted", num_tokens_predicted}, - {"stopped_eos", stopped_eos}, - {"stopped_word", stopped_word}, - {"stopped_limit", stopped_limit}, - {"stopping_word", stopping_word}, + {"n_remain", (params.n_predict - slot.n_decoded + slot.n_past)}, + {"num_tokens_predicted", slot.num_tokens_predicted}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, }); + return has_next_token; // continue + } + + bool updateSlots() { + + // update the system prompt wait until all slots are idle state + if(update_system_prompt) { + updateSystemPrompt(); + } + + batch.n_tokens = 0; + int kv_cache_free = (n_ctx - n_tokens_system); + + if(all_slots_are_idle) { + // avoid 100% usage of cpu all time + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + + // decode any currently ongoing sequences + for (auto & slot : slots) { + // release the slot + if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken()) + { + LOG_TEE("slot %i released\n", slot.id); + if(!slot.params.remember_generation) { + llama_kv_cache_seq_rm(ctx, slot.id, n_tokens_system, n_ctx); + slot.state = IDLE; + slot.command = NONE; + slot.num_prompt_tokens = 0; + slot.num_tokens_predicted = 0; + } else { + slot.state = SLEEPING; + slot.command = NONE; + } + continue; + } + + kv_cache_free -= slot.num_prompt_tokens; + + if (slot.state == IDLE || slot.command == RELEASE) { + continue; + } + + batch.token [batch.n_tokens] = slot.sampled; + batch.pos [batch.n_tokens] = n_tokens_system + slot.n_past + slot.n_decoded; + batch.seq_id[batch.n_tokens] = slot.id; + batch.logits[batch.n_tokens] = true; + + slot.n_decoded += 1; + slot.i_batch = batch.n_tokens; + + batch.n_tokens += 1; + } + + // assign workload to the slots + if (params.cont_batching || batch.n_tokens == 0) { + for (auto & slot : slots) { + // need process the prompt + bool keep_gen = slot.state == SLEEPING; // remember generation + if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) { + slot.state = PROCESSING; + slot.command = NONE; + + auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt + slot.num_prompt_tokens = prompt_tokens.size(); + + slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0; + + slot.context_tokens = prompt_tokens; + + LOG_VERBOSE("prompt ingested", { + {"n_past", slot.n_past}, + {"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)}, + {"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())}, + }); + + std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + + for (size_t i = slot.n_past; i < slot.context_tokens.size(); ++i) { + batch.token [batch.n_tokens] = slot.context_tokens[i]; + batch.pos [batch.n_tokens] = i + n_tokens_system; + batch.seq_id[batch.n_tokens] = slot.id; + batch.logits[batch.n_tokens] = false; + batch.n_tokens += 1; + } + + // extract the logits only for the last token + if (batch.n_tokens > 0) { + batch.logits[batch.n_tokens - 1] = true; + } + + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; + } + } + } + + if (batch.n_tokens == 0) { + all_slots_are_idle = true; + return true; + } - return token_with_probs; + // process in chunks of params.n_batch + int32_t n_batch = params.n_batch; + + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + + const int ret = llama_decode(ctx, batch_view); + if (ret != 0) { + if (n_batch == 1 || ret < 0) { + // if you get here, it means the KV cache is full - try increasing it via the context size + LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); + return false; + } + + LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2); + + // retry with half the batch size to try to find a free slot in the KV cache + n_batch /= 2; + i -= n_batch; + continue; + } + + for (auto & slot : slots) { + if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { + continue; + } + + slot_params_to_gpt_params(slot.params, params); + completion_token_output result; + const llama_token id = llama_sample_token(ctx, NULL, NULL, params, slot.last_n_tokens, candidates, slot.i_batch - i); + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + result.tok = id; + const int32_t n_probs = params.n_probs; + if (params.temp <= 0 && n_probs > 0) + { + // For llama_sample_token_greedy we need to sort candidates + llama_sample_softmax(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}); + } + + if (!processToken(result, slot)) { + slot.generated_text.clear(); + slot.release(); + } + kv_cache_free -= slot.num_tokens_predicted; + slot.i_batch = -1; + } + } + + if(kv_cache_free < 0) { + LOG_TEE("\nError: kv cache is full, increase context size."); + return false; + } + return true; } std::vector getEmbedding() @@ -999,6 +1289,29 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, else if (arg == "--embedding") { params.embedding = true; + } else if (arg == "-cb" || arg == "--cont-batching") + { + params.cont_batching = true; + } + else if (arg == "-np" || arg == "--parallel") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_parallel = std::stoi(argv[i]); + } else if (arg == "-n" || arg == "--n-predict") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_predict = std::stoi(argv[i]); + if(params.n_predict <= 128) { // this example don't support long prompts + params.n_predict = 128; + } } else { @@ -1016,37 +1329,37 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } -static json format_generation_settings(llama_server_context &llama) +static json format_generation_settings(llama_server_context &llama, llama_client_slot* &slot) { - const auto eos_bias = llama.params.logit_bias.find(llama_token_eos(llama.ctx)); - const bool ignore_eos = eos_bias != llama.params.logit_bias.end() && + const auto eos_bias = slot->params.logit_bias.find(llama_token_eos(llama.ctx)); + const bool ignore_eos = eos_bias != slot->params.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); return json{ {"n_ctx", llama.n_ctx}, {"model", llama.params.model_alias}, - {"seed", llama.params.seed}, - {"temp", llama.params.temp}, - {"top_k", llama.params.top_k}, - {"top_p", llama.params.top_p}, - {"tfs_z", llama.params.tfs_z}, - {"typical_p", llama.params.typical_p}, - {"repeat_last_n", llama.params.repeat_last_n}, - {"repeat_penalty", llama.params.repeat_penalty}, - {"presence_penalty", llama.params.presence_penalty}, - {"frequency_penalty", llama.params.frequency_penalty}, - {"mirostat", llama.params.mirostat}, - {"mirostat_tau", llama.params.mirostat_tau}, - {"mirostat_eta", llama.params.mirostat_eta}, - {"penalize_nl", llama.params.penalize_nl}, - {"stop", llama.params.antiprompt}, - {"n_predict", llama.params.n_predict}, - {"n_keep", llama.params.n_keep}, + {"seed", slot->params.seed}, + {"temp", slot->params.temp}, + {"top_k", slot->params.top_k}, + {"top_p", slot->params.top_p}, + {"tfs_z", slot->params.tfs_z}, + {"typical_p", slot->params.typical_p}, + {"repeat_last_n", slot->params.repeat_last_n}, + {"repeat_penalty", slot->params.repeat_penalty}, + {"presence_penalty",slot->params.presence_penalty}, + {"frequency_penalty", slot->params.frequency_penalty}, + {"mirostat", slot->params.mirostat}, + {"mirostat_tau", slot->params.mirostat_tau}, + {"mirostat_eta", slot->params.mirostat_eta}, + {"penalize_nl", slot->params.penalize_nl}, + {"stop", slot->params.antiprompt}, + {"n_predict", slot->params.n_predict}, + // {"n_keep", slot.params.n_keep}, {"ignore_eos", ignore_eos}, - {"stream", llama.stream}, - {"logit_bias", llama.params.logit_bias}, - {"n_probs", llama.params.n_probs}, - {"grammar", llama.params.grammar}, + {"stream", slot->params.stream}, + {"logit_bias", slot->params.logit_bias}, + {"n_probs", slot->params.n_probs}, + {"grammar", slot->params.grammar}, }; } @@ -1074,27 +1387,27 @@ static json format_timings(llama_server_context &llama) }; } -static json format_final_response(llama_server_context &llama, const std::string &content, const std::vector &probs) +static json format_final_response(llama_server_context &llama, llama_client_slot* &slot, const std::string &content, const std::vector &probs) { json res = json{ {"content", content}, {"stop", true}, {"model", llama.params.model_alias}, - {"tokens_predicted", llama.num_tokens_predicted}, - {"tokens_evaluated", llama.num_prompt_tokens}, - {"generation_settings", format_generation_settings(llama)}, - {"prompt", llama.prompt}, - {"truncated", llama.truncated}, - {"stopped_eos", llama.stopped_eos}, - {"stopped_word", llama.stopped_word}, - {"stopped_limit", llama.stopped_limit}, - {"stopping_word", llama.stopping_word}, - {"tokens_cached", llama.n_past}, - {"timings", format_timings(llama)}, + {"tokens_predicted", slot->num_tokens_predicted}, + {"tokens_evaluated", slot->num_prompt_tokens}, + {"generation_settings", format_generation_settings(llama, slot)}, + {"prompt", slot->prompt}, + {"truncated", slot->truncated}, + {"stopped_eos", slot->stopped_eos}, + {"stopped_word", slot->stopped_word}, + {"stopped_limit", slot->stopped_limit}, + {"stopping_word", slot->stopping_word}, + {"tokens_cached", slot->n_past}, + // {"timings", format_timings(llama)}, }; - if (llama.params.n_probs > 0) + if (slot->params.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1103,14 +1416,15 @@ static json format_final_response(llama_server_context &llama, const std::string } static json format_partial_response( - llama_server_context &llama, const std::string &content, const std::vector &probs + llama_server_context &llama, llama_client_slot* &slot, const std::string &content, const std::vector &probs ) { json res = json{ {"content", content}, {"stop", false}, + { "slot_id", slot->id } }; - if (llama.params.n_probs > 0) + if (slot->params.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1139,43 +1453,43 @@ static T json_value(const json &body, const std::string &key, const T &default_v : default_value; } -static void parse_options_completion(const json &body, llama_server_context &llama) +static void parse_options_completion(const json &body, llama_client_slot* &slot, llama_server_context &llama) { - gpt_params default_params; - - llama.stream = json_value(body, "stream", false); - llama.params.n_predict = json_value(body, "n_predict", default_params.n_predict); - llama.params.top_k = json_value(body, "top_k", default_params.top_k); - llama.params.top_p = json_value(body, "top_p", default_params.top_p); - llama.params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z); - llama.params.typical_p = json_value(body, "typical_p", default_params.typical_p); - llama.params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n); - llama.params.temp = json_value(body, "temperature", default_params.temp); - llama.params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty); - llama.params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty); - llama.params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty); - llama.params.mirostat = json_value(body, "mirostat", default_params.mirostat); - llama.params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau); - llama.params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta); - llama.params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl); - llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); - llama.params.seed = json_value(body, "seed", default_params.seed); - llama.params.grammar = json_value(body, "grammar", default_params.grammar); - llama.params.n_probs = json_value(body, "n_probs", default_params.n_probs); + slot_params default_params; + + slot->params.stream = json_value(body, "stream", false); + slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict); + slot->params.top_k = json_value(body, "top_k", default_params.top_k); + slot->params.top_p = json_value(body, "top_p", default_params.top_p); + slot->params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z); + slot->params.typical_p = json_value(body, "typical_p", default_params.typical_p); + slot->params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n); + slot->params.temp = json_value(body, "temperature", default_params.temp); + slot->params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty); + slot->params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty); + slot->params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty); + slot->params.mirostat = json_value(body, "mirostat", default_params.mirostat); + slot->params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau); + slot->params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta); + slot->params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl); + //llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); + slot->params.seed = json_value(body, "seed", default_params.seed); + slot->params.grammar = json_value(body, "grammar", default_params.grammar); + slot->params.n_probs = json_value(body, "n_probs", default_params.n_probs); if (body.count("prompt") != 0) { - llama.prompt = body["prompt"]; + slot->prompt = body["prompt"]; } else { - llama.prompt = ""; + slot->prompt = ""; } - llama.params.logit_bias.clear(); + slot->params.logit_bias.clear(); if (json_value(body, "ignore_eos", false)) { - llama.params.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; + slot->params.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; } const auto &logit_bias = body.find("logit_bias"); @@ -1191,18 +1505,18 @@ static void parse_options_completion(const json &body, llama_server_context &lla { if (el[1].is_number()) { - llama.params.logit_bias[tok] = el[1].get(); + slot->params.logit_bias[tok] = el[1].get(); } else if (el[1].is_boolean() && !el[1].get()) { - llama.params.logit_bias[tok] = -INFINITY; + slot->params.logit_bias[tok] = -INFINITY; } } } } } - llama.params.antiprompt.clear(); + slot->params.antiprompt.clear(); const auto &stop = body.find("stop"); if (stop != body.end() && stop->is_array()) { @@ -1210,34 +1524,34 @@ static void parse_options_completion(const json &body, llama_server_context &lla { if (!word.empty()) { - llama.params.antiprompt.push_back(word); + slot->params.antiprompt.push_back(word); } } } - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); + LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); } -static void parse_options_infill(const json &body, llama_server_context &llama) -{ - if (body.count("input_prefix") != 0) - { - llama.params.input_prefix = body["input_prefix"]; - } - else - { - llama.params.input_prefix = ""; - } - if (body.count("input_suffix") != 0) - { - llama.params.input_suffix = body["input_suffix"]; - } - else - { - llama.params.input_suffix = ""; - } - parse_options_completion(body, llama); -} +// static void parse_options_infill(const json &body, llama_server_context &llama) +// { +// if (body.count("input_prefix") != 0) +// { +// llama.params.input_prefix = body["input_prefix"]; +// } +// else +// { +// llama.params.input_prefix = ""; +// } +// if (body.count("input_suffix") != 0) +// { +// llama.params.input_suffix = body["input_suffix"]; +// } +// else +// { +// llama.params.input_suffix = ""; +// } +// parse_options_completion(body, slot, llama); +// } static void log_server_request(const Request &req, const Response &res) { @@ -1266,32 +1580,35 @@ static bool is_at_eob(llama_server_context &server_context, const llama_token *t // * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. // This is also called when the stop condition is met. // Collect tokens into std::vector response which is pointed to by callback_data. -static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { - auto & llama = *static_cast(callback_data); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - llama.generated_token_probs.resize(llama.generated_token_probs.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; - std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); - printf("%zu", n); - } - fflush(stdout); -#if 0 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams:\n"; - for (size_t i=0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["<(callback_data); +// // Mark beams as EOS as needed. +// for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { +// llama_beam_view& beam_view = beams_state.beam_views[i]; +// if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { +// beam_view.eob = true; +// } +// } +// printf(","); // Show progress +// if (const size_t n = beams_state.common_prefix_length) { +// llama.generated_token_probs.resize(llama.generated_token_probs.size() + n); +// assert(0u < beams_state.n_beams); +// const llama_token * tokens = beams_state.beam_views[0].tokens; +// const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; +// std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); +// printf("%zu", n); +// } +// fflush(stdout); +// #if 0 // DEBUG: print current beams for this iteration +// std::cout << "\n\nCurrent beams:\n"; +// for (size_t i=0 ; i < beams_state.n_beams ; ++i) { +// std::cout << "beams["<(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); return false; }); - svr.Post("/completion", [&llama](const Request &req, Response &res) + svr.Get("/props", [&llama](const Request & /*req*/, Response &res) + { + res.set_header("Access-Control-Allow-Origin", "*"); + json data = { + { "user_name", llama.user_name.c_str() }, + { "assistant_name", llama.assistant_name.c_str() } + }; + res.set_content(data.dump(), "application/json"); }); + + svr.Post("/completion", [&](const Request &req, Response &res) { - auto lock = llama.lock(); + //auto lock = llama.lock(); - llama.rewind(); + json data = json::parse(req.body); - llama_reset_timings(llama.ctx); + llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1)); - parse_options_completion(json::parse(req.body), llama); - - if (!llama.loadGrammar()) - { - res.status = 400; + if(slot == nullptr) { + LOG_TEE("slot unavailable\n"); + res.status = 404; + res.set_content("slot_error", "text/plain"); return; } - llama.loadPrompt(); - llama.beginCompletion(); - - if (!llama.stream) { - if (llama.params.n_beams) { - // Fill llama.generated_token_probs vector with final beam. - llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, - llama.n_past, llama.n_remain); - // Translate llama.generated_token_probs to llama.generated_text. - append_to_generated_text_from_generated_token_probs(llama); - } else { - size_t stop_pos = std::string::npos; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok); - - stop_pos = llama.findStoppingStrings(llama.generated_text, - token_text.size(), STOP_FULL); - } + if(data.contains("system_prompt")) { + llama.processSystemPromptData(data["system_prompt"]); + } - if (stop_pos == std::string::npos) { - stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL); - } - if (stop_pos != std::string::npos) { - llama.generated_text.erase(llama.generated_text.begin() + stop_pos, - llama.generated_text.end()); - } - } + // llama_reset_timings(llama.ctx); - auto probs = llama.generated_token_probs; - if (llama.params.n_probs > 0 && llama.stopped_word) { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); - probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); - } + slot->reset(); - const json data = format_final_response(llama, llama.generated_text, probs); + parse_options_completion(json::parse(req.body), slot, llama); - llama_print_timings(llama.ctx); + if (!llama.launchSlot(slot)) + { + res.status = 400; + return; + } - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), - "application/json"); + if (!slot->params.stream) { + // if (llama.params.n_beams) { + // // Fill llama.generated_token_probs vector with final beam. + // llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, + // llama.n_past, llama.n_remain); + // // Translate llama.generated_token_probs to llama.generated_text. + // append_to_generated_text_from_generated_token_probs(llama); + // } else { + // size_t stop_pos = std::string::npos; + + // while (llama.has_next_token) { + // const completion_token_output token_with_probs = llama.doCompletion(); + // const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok); + + // stop_pos = llama.findStoppingStrings(llama.generated_text, + // token_text.size(), STOP_FULL); + // } + + // if (stop_pos == std::string::npos) { + // stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL); + // } + // if (stop_pos != std::string::npos) { + // llama.generated_text.erase(llama.generated_text.begin() + stop_pos, + // llama.generated_text.end()); + // } + // } + + // auto probs = llama.generated_token_probs; + // if (llama.params.n_probs > 0 && llama.stopped_word) { + // const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); + // probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); + // } + + // const json data = format_final_response(llama, llama.generated_text, probs); + + // llama_print_timings(llama.ctx); + + // res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), + // "application/json"); } else { - const auto chunked_content_provider = [&](size_t, DataSink & sink) { - size_t sent_count = 0; - size_t sent_token_probs_index = 0; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - continue; - } - const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); - } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); - } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector probs_output = {}; - - if (llama.params.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); + auto chunked_content_provider = [&](size_t /*offset*/, DataSink &sink) { + size_t sent_count = 0; + size_t sent_token_probs_index = 0; + while(slot->isProcessing()) { + if(slot->hasNewToken()) { // new token notification + const completion_token_output token = slot->next(); + std::string token_str = llama_token_to_piece(llama.ctx, token.tok); + + std::vector probs_output = {}; + + const json data = format_partial_response(llama, slot, token_str, probs_output); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if(!sink.write(str.c_str(), str.size())) { + slot->release(); + return false; } - sent_token_probs_index = probs_stop_pos; - } - - const json data = format_partial_response(llama, to_send, probs_output); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; + } else { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); } } - - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, + const json data = format_final_response( + llama, slot, "", - std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) + std::vector( + slot->generated_token_probs.begin(), + slot->generated_token_probs.begin() + sent_token_probs_index) ); const std::string str = @@ -1525,145 +1824,144 @@ int main(int argc, char **argv) llama_print_timings(llama.ctx); return false; } - } - } - - llama_print_timings(llama.ctx); - sink.done(); - return true; - }; - const auto on_complete = [&](bool) { - llama.mutex.unlock(); + sink.done(); + return true; + }; + auto on_complete = [&] (bool) { + //llama.mutex.unlock(); + slot->sent_tokens = 0; + slot->generated_token_probs.clear(); + slot->release(); }; - lock.release(); + //lock.release(); res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); - svr.Post("/infill", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - llama.rewind(); - - llama_reset_timings(llama.ctx); - - parse_options_infill(json::parse(req.body), llama); - - if (!llama.loadGrammar()) - { - res.status = 400; - return; - } - llama.loadInfill(); - llama.beginCompletion(); - const auto chunked_content_provider = [&](size_t, DataSink & sink) { - size_t sent_count = 0; - size_t sent_token_probs_index = 0; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - continue; - } - const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); - } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); - } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector probs_output = {}; - - if (llama.params.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); - } - sent_token_probs_index = probs_stop_pos; - } - - const json data = format_partial_response(llama, to_send, probs_output); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } - } - - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, - "", - std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - ); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } - } - } - - llama_print_timings(llama.ctx); - sink.done(); - return true; - }; - const auto on_complete = [&](bool) { - llama.mutex.unlock(); - }; - lock.release(); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - }); - - svr.Get("/model.json", [&llama](const Request &, Response &res) - { - const json data = format_generation_settings(llama); - return res.set_content(data.dump(), "application/json"); }); + // svr.Post("/infill", [&llama](const Request &req, Response &res) + // { + // auto lock = llama.lock(); + + // llama.rewind(); + + // llama_reset_timings(llama.ctx); + + // parse_options_infill(json::parse(req.body), llama); + + // if (!llama.loadGrammar()) + // { + // res.status = 400; + // return; + // } + // llama.loadInfill(); + // llama.beginCompletion(); + // const auto chunked_content_provider = [&](size_t, DataSink & sink) { + // size_t sent_count = 0; + // size_t sent_token_probs_index = 0; + + // while (llama.has_next_token) { + // const completion_token_output token_with_probs = llama.doCompletion(); + // if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { + // continue; + // } + // const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); + + // size_t pos = std::min(sent_count, llama.generated_text.size()); + + // const std::string str_test = llama.generated_text.substr(pos); + // bool is_stop_full = false; + // size_t stop_pos = + // llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); + // if (stop_pos != std::string::npos) { + // is_stop_full = true; + // llama.generated_text.erase( + // llama.generated_text.begin() + pos + stop_pos, + // llama.generated_text.end()); + // pos = std::min(sent_count, llama.generated_text.size()); + // } else { + // is_stop_full = false; + // stop_pos = llama.findStoppingStrings(str_test, token_text.size(), + // STOP_PARTIAL); + // } + + // if ( + // stop_pos == std::string::npos || + // // Send rest of the text if we are at the end of the generation + // (!llama.has_next_token && !is_stop_full && stop_pos > 0) + // ) { + // const std::string to_send = llama.generated_text.substr(pos, std::string::npos); + + // sent_count += to_send.size(); + + // std::vector probs_output = {}; + + // if (llama.params.n_probs > 0) { + // const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); + // size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); + // size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); + // if (probs_pos < probs_stop_pos) { + // probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + // } + // sent_token_probs_index = probs_stop_pos; + // } + + // const json data = format_partial_response(llama, to_send, probs_output); + + // const std::string str = + // "data: " + + // data.dump(-1, ' ', false, json::error_handler_t::replace) + + // "\n\n"; + + // LOG_VERBOSE("data stream", { + // { "to_send", str } + // }); + + // if (!sink.write(str.data(), str.size())) { + // LOG_VERBOSE("stream closed", {}); + // llama_print_timings(llama.ctx); + // return false; + // } + // } + + // if (!llama.has_next_token) { + // // Generation is done, send extra information. + // const json data = format_final_response( + // llama, + // "", + // std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) + // ); + + // const std::string str = + // "data: " + + // data.dump(-1, ' ', false, json::error_handler_t::replace) + + // "\n\n"; + + // LOG_VERBOSE("data stream", { + // { "to_send", str } + // }); + + // if (!sink.write(str.data(), str.size())) { + // LOG_VERBOSE("stream closed", {}); + // llama_print_timings(llama.ctx); + // return false; + // } + // } + // } + + // llama_print_timings(llama.ctx); + // sink.done(); + // return true; + // }; + // const auto on_complete = [&](bool) { + // llama.mutex.unlock(); + // }; + // lock.release(); + // res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + // }); + + // svr.Get("/model.json", [&llama](const Request &, Response &res) + // { + // const json data = format_generation_settings(llama); + // return res.set_content(data.dump(), "application/json"); }); svr.Options(R"(/.*)", [](const Request &, Response &res) { return res.set_content("", "application/json"); }); @@ -1696,29 +1994,29 @@ int main(int argc, char **argv) const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json"); }); - svr.Post("/embedding", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - const json body = json::parse(req.body); - - llama.rewind(); - llama_reset_timings(llama.ctx); - if (body.count("content") != 0) - { - llama.prompt = body["content"]; - } - else - { - llama.prompt = ""; - } - llama.params.n_predict = 0; - llama.loadPrompt(); - llama.beginCompletion(); - llama.doCompletion(); - - const json data = format_embedding_response(llama); - return res.set_content(data.dump(), "application/json"); }); + // svr.Post("/embedding", [&llama](const Request &req, Response &res) + // { + // auto lock = llama.lock(); + + // const json body = json::parse(req.body); + + // llama.rewind(); + // llama_reset_timings(llama.ctx); + // if (body.count("content") != 0) + // { + // llama.prompt = body["content"]; + // } + // else + // { + // llama.prompt = ""; + // } + // llama.params.n_predict = 0; + // llama.loadPrompt(); + // llama.beginCompletion(); + // llama.doCompletion(); + + // const json data = format_embedding_response(llama); + // return res.set_content(data.dump(), "application/json"); }); svr.set_logger(log_server_request); @@ -1755,6 +2053,16 @@ int main(int argc, char **argv) return 1; } + if(!params.embedding) { + std::thread t([&llama]() + { + bool running = true; + while (running) + { + running = llama.updateSlots(); + } }); + } + // Set the base directory for serving static files svr.set_base_dir(sparams.public_path); @@ -1770,10 +2078,6 @@ int main(int argc, char **argv) { return 1; } - - if (llama.grammar != nullptr) { - llama_grammar_free(llama.grammar); - } llama_backend_free(); return 0; From 471230202daa8d838f95e3b3c25c42ff2db9ec20 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Wed, 11 Oct 2023 19:48:15 -0400 Subject: [PATCH 02/64] crash fixed --- examples/server/server.cpp | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 00b46bc3d8331..1bb3418f69b10 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -32,7 +32,7 @@ struct server_params { std::string hostname = "127.0.0.1"; std::string public_path = "examples/server/public"; - int32_t port = 8080; + int32_t port = 8040; int32_t read_timeout = 600; int32_t write_timeout = 600; }; @@ -329,8 +329,7 @@ struct llama_client_slot bool available() { return state == IDLE && - command == NONE && - !params.remember_generation; + command == NONE && !params.remember_generation; } bool isProcessing() { @@ -811,7 +810,6 @@ struct llama_server_context } bool updateSlots() { - // update the system prompt wait until all slots are idle state if(update_system_prompt) { updateSystemPrompt(); @@ -1704,7 +1702,7 @@ int main(int argc, char **argv) }; res.set_content(data.dump(), "application/json"); }); - svr.Post("/completion", [&](const Request &req, Response &res) + svr.Post("/completion", [&llama](const Request &req, Response &res) { //auto lock = llama.lock(); @@ -2053,16 +2051,6 @@ int main(int argc, char **argv) return 1; } - if(!params.embedding) { - std::thread t([&llama]() - { - bool running = true; - while (running) - { - running = llama.updateSlots(); - } }); - } - // Set the base directory for serving static files svr.set_base_dir(sparams.public_path); @@ -2073,12 +2061,17 @@ int main(int argc, char **argv) {"hostname", sparams.hostname}, {"port", sparams.port}, }); - + std::thread t([&llama]() + { + bool running = true; + while (running) + { + running = llama.updateSlots(); + } }); if (!svr.listen_after_bind()) { return 1; } llama_backend_free(); - return 0; } From 78504218b9e0f337871ff556ba4d729c7a760898 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Thu, 12 Oct 2023 12:51:48 -0400 Subject: [PATCH 03/64] save dev progress --- .gitignore | 1 + examples/server/server.cpp | 113 +++++++++++++++++++++---------------- 2 files changed, 65 insertions(+), 49 deletions(-) diff --git a/.gitignore b/.gitignore index 420e0d6d016a2..8bf420994c950 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ *.gcno *.gcda *.dot +*.bat *.metallib .DS_Store .build/ diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1bb3418f69b10..02c67a28174f6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -382,7 +382,7 @@ struct llama_server_context gpt_params params; int n_ctx; int n_vocab; - + bool clean_kv_cache = true; std::mutex mutex; std::unique_lock lock() @@ -484,6 +484,7 @@ struct llama_server_context else { auto s = json_prompt.template get(); + printf("----------------------\nprompt:\n%s-----------------------\n", s.c_str()); prompt_tokens = ::llama_tokenize(ctx, s, add_bos); } @@ -622,17 +623,22 @@ struct llama_server_context // has_next_token = true; } + void cleanKVCache() { + // clear the entire KV cache + for (int i = 0; i < params.n_parallel; ++i) + { + llama_kv_cache_seq_rm(ctx, i, 0, -1); + } + clean_kv_cache = false; + } + void updateSystemPrompt() { tokens_system = ::llama_tokenize(ctx, system_prompt, true); n_tokens_system = tokens_system.size(); batch.n_tokens = n_tokens_system; - // clear the entire KV cache - for (int i = 0; i < params.n_parallel; ++i) - { - llama_kv_cache_seq_rm(ctx, i, 0, -1); - } + cleanKVCache(); for (int32_t i = 0; i < batch.n_tokens; ++i) { @@ -732,6 +738,7 @@ struct llama_server_context slot.last_n_tokens.erase(slot.last_n_tokens.begin()); slot.last_n_tokens.push_back(result.tok); const std::string token_str = llama_token_to_piece(ctx, result.tok); + printf("%s", token_str.c_str()); slot.sampled = result.tok; size_t stop_pos = @@ -819,6 +826,9 @@ struct llama_server_context int kv_cache_free = (n_ctx - n_tokens_system); if(all_slots_are_idle) { + if(system_prompt.empty() && clean_kv_cache) { + cleanKVCache(); + } // avoid 100% usage of cpu all time std::this_thread::sleep_for(std::chrono::milliseconds(5)); } @@ -865,6 +875,7 @@ struct llama_server_context // need process the prompt bool keep_gen = slot.state == SLEEPING; // remember generation if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) { + LOG_TEE("processing prompt\n"); slot.state = PROCESSING; slot.command = NONE; @@ -881,8 +892,12 @@ struct llama_server_context {"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())}, }); - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + if(system_prompt.empty()) { + LOG_TEE("cleaning kv: %i\n", slot.n_past); + llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1); + } + std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); for (size_t i = slot.n_past; i < slot.context_tokens.size(); ++i) { batch.token [batch.n_tokens] = slot.context_tokens[i]; batch.pos [batch.n_tokens] = i + n_tokens_system; @@ -912,7 +927,6 @@ struct llama_server_context for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); - llama_batch batch_view = { n_tokens, batch.token + i, @@ -1773,55 +1787,56 @@ int main(int argc, char **argv) // res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), // "application/json"); } else { - auto chunked_content_provider = [&](size_t /*offset*/, DataSink &sink) { + printf("processing -> %s\n", slot->isProcessing() ? "true" : "false"); + const auto chunked_content_provider = [slot](size_t, DataSink & sink) { size_t sent_count = 0; size_t sent_token_probs_index = 0; while(slot->isProcessing()) { if(slot->hasNewToken()) { // new token notification - const completion_token_output token = slot->next(); - std::string token_str = llama_token_to_piece(llama.ctx, token.tok); - - std::vector probs_output = {}; - - const json data = format_partial_response(llama, slot, token_str, probs_output); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if(!sink.write(str.c_str(), str.size())) { - slot->release(); - return false; - } + // const completion_token_output token = slot->next(); + // std::string token_str = llama_token_to_piece(llama.ctx, token.tok); + + // std::vector probs_output = {}; + + // const json data = format_partial_response(llama, slot, token_str, probs_output); + // const std::string str = + // "data: " + + // data.dump(-1, ' ', false, json::error_handler_t::replace) + + // "\n\n"; + + // LOG_VERBOSE("data stream", { + // { "to_send", str } + // }); + // if(!sink.write(str.c_str(), str.size())) { + // slot->release(); + // return false; + // } } else { std::this_thread::sleep_for(std::chrono::milliseconds(5)); } } - const json data = format_final_response( - llama, slot, - "", - std::vector( - slot->generated_token_probs.begin(), - slot->generated_token_probs.begin() + sent_token_probs_index) - ); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } + // const json data = format_final_response( + // llama, slot, + // "", + // std::vector( + // slot->generated_token_probs.begin(), + // slot->generated_token_probs.begin() + sent_token_probs_index) + // ); + + // const std::string str = + // "data: " + + // data.dump(-1, ' ', false, json::error_handler_t::replace) + + // "\n\n"; + + // LOG_VERBOSE("data stream", { + // { "to_send", str } + // }); + + // if (!sink.write(str.data(), str.size())) { + // LOG_VERBOSE("stream closed", {}); + // llama_print_timings(llama.ctx); + // return false; + // } sink.done(); return true; }; From 29c8cdd65d1c6ced44f9f0d4f4c2d03a215ebed5 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Thu, 12 Oct 2023 15:02:19 -0400 Subject: [PATCH 04/64] refactored sampling function --- examples/server/server.cpp | 724 ++++++++++++++----------------------- 1 file changed, 276 insertions(+), 448 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index de10af2b0f2dd..ad2ae4ee41a05 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -75,25 +75,6 @@ struct slot_params { bool stream = true; uint32_t seed = -1; // RNG seed int32_t n_predict = 128; // new tokens to predict - - // sampler params - int32_t top_k = 40; // <= 0 to use vocab size - float top_p = 0.95f; // 1.0 = disabled - float tfs_z = 1.00f; // 1.0 = disabled - float typical_p = 1.00f; // 1.0 = disabled - float temp = 0.80f; // 1.0 = disabled - float repeat_penalty = 1.10f; // 1.0 = disabled - int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float frequency_penalty = 0.00f; // 0.0 = disabled - float presence_penalty = 0.00f; // 0.0 = disabled - int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate - int n_probs = 0; - bool penalize_nl = false; - - std::unordered_map logit_bias; // logit bias for specific tokens - std::string grammar = ""; // optional BNF-like grammar to constrain sampling bool remember_generation = false; // remember a part of the prompt to avoid reprocessing all prompt std::vector antiprompt; @@ -133,23 +114,6 @@ static bool ends_with(const std::string &str, const std::string &suffix) 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } -static void slot_params_to_gpt_params(const slot_params &src, gpt_params & dst) -{ - dst.frequency_penalty = src.frequency_penalty; - dst.temp = src.temp; - dst.top_k = src.top_k; - dst.top_p = src.top_p; - dst.grammar = src.grammar; - dst.logit_bias = src.logit_bias; - dst.mirostat = src.mirostat; - dst.mirostat_eta = src.mirostat_eta; - dst.mirostat_tau = src.mirostat_tau; - dst.typical_p = src.typical_p; - dst.repeat_penalty = src.repeat_penalty; - dst.repeat_last_n = src.repeat_last_n; - dst.presence_penalty = src.presence_penalty; -} - static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { @@ -247,10 +211,10 @@ struct llama_client_slot { int id; // generation props - int32_t num_prompt_tokens = 0; + int32_t n_past = 0; int32_t n_decoded = 0; int32_t i_batch = -1; - size_t n_past = 0; + int32_t num_prompt_tokens = 0; json prompt; std::string generated_text = ""; int num_tokens_predicted = 0; @@ -268,7 +232,9 @@ struct llama_client_slot std::string stopping_word; int32_t multibyte_pending = 0; - slot_params params; + struct slot_params params; + struct llama_sampling_params sparams; + llama_sampling_context ctx_sampling; // grammar props grammar_parser::parse_state parsed_grammar; @@ -292,12 +258,14 @@ struct llama_client_slot if (grammar != nullptr) { llama_grammar_free(grammar); grammar = nullptr; + ctx_sampling.params = sparams; + ctx_sampling.grammar = NULL; } // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } - bool loadGrammar() + bool loadGrammar(llama_token eos) { if (!params.grammar.empty()) { parsed_grammar = grammar_parser::parse(params.grammar.c_str()); @@ -308,18 +276,19 @@ struct llama_client_slot } grammar_parser::print_grammar(stderr, parsed_grammar); - // TODO: fix this comment - // { - // auto it = params.logit_bias.find(llama_token_eos(ctx)); - // if (it != params.logit_bias.end() && it->second == -INFINITY) { - // LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); - // } - // } + { + auto it = sparams.logit_bias.find(eos); + if (it != sparams.logit_bias.end() && it->second == -INFINITY) { + LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); + } + } std::vector grammar_rules(parsed_grammar.c_rules()); grammar = llama_grammar_init( grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); } + ctx_sampling.params = sparams; + ctx_sampling.grammar = grammar; return true; } @@ -368,7 +337,7 @@ struct llama_server_context std::string system_prompt = ""; bool update_system_prompt = false; std::vector tokens_system; - int32_t n_tokens_system = 0; + int32_t num_tokens_system; // broadcast to all clients to keep the same prompt format std::string user_name = ""; // this should be the anti prompt @@ -380,7 +349,6 @@ struct llama_server_context std::vector candidates; bool all_slots_are_idle = false; gpt_params params; - llama_sampling_context ctx_sampling; int n_ctx; int n_vocab; bool clean_kv_cache = true; @@ -403,29 +371,10 @@ struct llama_server_context llama_free_model(model); model = nullptr; } - } - - void rewind() - { - params.antiprompt.clear(); - params.grammar.clear(); - num_prompt_tokens = 0; - num_tokens_predicted = 0; - generated_text = ""; - generated_text.reserve(n_ctx); - generated_token_probs.clear(); - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - n_remain = 0; - n_past = 0; - - if (grammar != nullptr) { - llama_grammar_free(grammar); - grammar = nullptr; + for(auto &slot : slots) { + if(slot.grammar) { + llama_grammar_free(slot.grammar); + } } } @@ -447,6 +396,7 @@ struct llama_server_context void initialize() { // create slots LOG_TEE("Available slots:\n"); + all_slots_are_idle = true; for (int i = 0; i < params.n_parallel; i++) { llama_client_slot slot; @@ -457,11 +407,11 @@ struct llama_server_context LOG_TEE(" - slot %i\n", slot.id); slots.push_back(slot); } + LOG_TEE("Context Size: %i\n", params.n_ctx); batch = llama_batch_init(params.n_ctx, 0); - // empty system prompt system_prompt = ""; - all_slots_are_idle = true; + num_tokens_system = 0; } std::vector tokenize(const json & json_prompt, bool add_bos) const @@ -510,28 +460,57 @@ struct llama_server_context return prompt_tokens; } - bool loadGrammar() - { - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); - return false; - } - grammar_parser::print_grammar(stderr, parsed_grammar); + void processPrompt() { + //params.n_keep = std::min(n_ctx - 4, params.n_keep); + + // if input prompt is too big, truncate like normal + // if (num_prompt_tokens >= (size_t)n_ctx) + // { + // const int n_left = (n_ctx - params.n_keep) / 2; + // std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + // const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; + // new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); + // std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); + + // LOG_VERBOSE("input truncated", { + // {"n_ctx", n_ctx}, + // {"n_keep", params.n_keep}, + // {"n_left", n_left}, + // {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + // }); + // truncated = true; + // prompt_tokens = new_tokens; + // } + // 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); + // } + + // compare the evaluated prompt with the new prompt + } + + + llama_client_slot* getSlot(int id) { + for (llama_client_slot & slot : slots) + { + if ((id == -1 && slot.available()) || slot.id == id) { - auto it = params.logit_bias.find(llama_token_eos(ctx)); - if (it != params.logit_bias.end() && it->second == -INFINITY) { - LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); - } + return &slot; } + } + return nullptr; + } - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + bool launchSlot(llama_client_slot* &slot) { + if(!slot->loadGrammar(llama_token_eos(ctx))) { + return false; } + all_slots_are_idle = false; + slot->command = LOAD_PROMPT; + LOG_TEE("slot %i is processing\n", slot->id); return true; } @@ -592,15 +571,15 @@ struct llama_server_context // std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); // } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - embd = prompt_tokens; - if (n_past == num_prompt_tokens) - { - // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); - n_past--; - } + // // compare the evaluated prompt with the new prompt + // n_past = common_part(embd, prompt_tokens); + // embd = prompt_tokens; + // if (n_past == num_prompt_tokens) + // { + // // we have to evaluate at least 1 token to generate logits. + // printf("we have to evaluate at least 1 token to generate logits\n"); + // n_past--; + // } // LOG_VERBOSE("prompt ingested", { // {"n_past", n_past}, @@ -617,172 +596,81 @@ struct llama_server_context { llama_kv_cache_seq_rm(ctx, i, 0, -1); } - params.n_keep = std::min(n_ctx - 4, params.n_keep); + clean_kv_cache = false; + } - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)n_ctx) - { - const int n_left = (n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; - } - 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); - } + void updateSystemPrompt() { + tokens_system = ::llama_tokenize(ctx, system_prompt, true); + num_tokens_system = tokens_system.size(); - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); + batch.n_tokens = num_tokens_system; - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + cleanKVCache(); - embd = prompt_tokens; - if (n_past == num_prompt_tokens) + for (int32_t i = 0; i < batch.n_tokens; ++i) { - // we have to evaluate at least 1 token to generate logits. - n_past--; + batch.token[i] = tokens_system[i]; + batch.pos[i] = i; + batch.seq_id[i] = 0; + batch.logits[i] = false; } - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - - has_next_token = true; - } - - void beginCompletion() - { - // number of tokens to keep when resetting context - n_remain = params.n_predict; - llama_set_rng_seed(ctx, params.seed); - } - - completion_token_output nextToken() - { - completion_token_output result; - result.tok = -1; - - if (embd.size() >= (size_t)n_ctx) + if (llama_decode(ctx, batch) != 0) { - // Shift context - - const int n_left = n_past - params.n_keep - 1; - const int n_discard = n_left/2; - - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); - - for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++) - { - embd[i - n_discard] = embd[i]; - } - embd.resize(embd.size() - n_discard); - - n_past -= n_discard; - - truncated = true; - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); + LOG_TEE("%s: llama_decode() failed\n", __func__); + return; } - bool tg = true; - while (n_past < embd.size()) + // assign the system KV cache to all parallel sequences + for (int32_t i = 1; i < params.n_parallel; ++i) { - int n_eval = (int)embd.size() - n_past; - tg = n_eval == 1; - if (n_eval > params.n_batch) - { - n_eval = params.n_batch; - } - - if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0))) - { - LOG_ERROR("failed to eval", { - {"n_eval", n_eval}, - {"n_past", n_past}, - {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - has_next_token = false; - return result; - } - n_past += n_eval; + llama_kv_cache_seq_cp(ctx, 0, i, 0, num_tokens_system); } - if (params.n_predict == 0) - { - has_next_token = false; - result.tok = llama_token_eos(ctx); - return result; - } + LOG_TEE("system prompt updated\n"); + update_system_prompt = false; + } + void notifySystemPromptChanged() { + // release all slots + for (llama_client_slot &slot : slots) { - // out of user input, sample next token - std::vector candidates; - candidates.reserve(llama_n_vocab(model)); - - result.tok = llama_sample_token(ctx, NULL, grammar, params, last_n_tokens, candidates); - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + slot.release(); + } + waitAllAreIdle(); + all_slots_are_idle = true; + // wait until system prompt load + update_system_prompt = true; + while(update_system_prompt) { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + // system prompt loaded, continue + } - const int32_t n_probs = params.n_probs; - if (params.temp <= 0 && n_probs > 0) - { - // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &candidates_p); - } + void processSystemPromptData(json sys_props) { + system_prompt = sys_props.value("system_prompt", ""); + user_name = sys_props.value("anti_prompt", ""); + assistant_name = sys_props.value("assistant_name", ""); + notifySystemPromptChanged(); + } - for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i) + void waitAllAreIdle() { + bool wait = true; + while(wait) { + wait = false; + for (auto &slot : slots) { - 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); - if (tg) { - num_tokens_predicted++; + if (!slot.available()) + { + wait = true; + break; + } } } - - // add it to the context - embd.push_back(result.tok); - // decrement remaining sampling budget - --n_remain; - - if (!embd.empty() && embd.back() == llama_token_eos(ctx)) - { - // stopping_word = llama_token_to_piece(ctx, embd.back()); - has_next_token = false; - stopped_eos = true; - LOG_VERBOSE("eos token found", {}); - return result; - } - - has_next_token = params.n_predict == -1 || n_remain != 0; - return result; } - size_t findStoppingStrings(const size_t last_token_size, - const stop_type type, llama_client_slot & slot) + size_t findStoppingStrings(const std::string &text, const size_t last_token_size, + const stop_type type, llama_client_slot &slot) { size_t stop_pos = std::string::npos; for (const std::string &word : slot.params.antiprompt) @@ -791,22 +679,19 @@ struct llama_server_context if (type == STOP_FULL) { const size_t tmp = word.size() + last_token_size; - const size_t from_pos = slot.generated_text.size() > tmp ? slot.generated_text.size() - tmp : 0; - pos = slot.generated_text.find(word, from_pos); + const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; + pos = text.find(word, from_pos); } else { - pos = find_partial_stop_string(word, slot.generated_text); + pos = find_partial_stop_string(word, text); } if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { - if (type == STOP_FULL) - { - slot.stopping_word = word; - slot.stopped_word = true; - } stop_pos = pos; + slot.stopped_word = true; + slot.stopping_word = word; } } return stop_pos; @@ -817,23 +702,19 @@ struct llama_server_context slot.last_n_tokens.erase(slot.last_n_tokens.begin()); slot.last_n_tokens.push_back(result.tok); const std::string token_str = llama_token_to_piece(ctx, result.tok); - printf("%s", token_str.c_str()); slot.sampled = result.tok; - - size_t stop_pos = - findStoppingStrings(token_str.size(), STOP_FULL, slot); - slot.addTokenString(result); - slot.generated_text += token_str; + size_t stop_pos = findStoppingStrings(slot.generated_text, token_str.size(), STOP_FULL, slot); + bool has_next_token = !(slot.n_decoded > 2 && (result.tok == llama_token_eos(ctx) || (slot.n_decoded + slot.n_past >= params.n_predict) || stop_pos != std::string::npos)); - if (params.n_probs > 0) + if (slot.sparams.n_probs > 0) { slot.generated_token_probs.push_back(result); } @@ -902,7 +783,7 @@ struct llama_server_context } batch.n_tokens = 0; - int kv_cache_free = (n_ctx - n_tokens_system); + int kv_cache_free = (n_ctx - num_tokens_system); if(all_slots_are_idle) { if(system_prompt.empty() && clean_kv_cache) { @@ -919,18 +800,16 @@ struct llama_server_context { LOG_TEE("slot %i released\n", slot.id); if(!slot.params.remember_generation) { - llama_kv_cache_seq_rm(ctx, slot.id, n_tokens_system, n_ctx); + llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system, n_ctx); slot.state = IDLE; slot.command = NONE; - slot.num_prompt_tokens = 0; - slot.num_tokens_predicted = 0; + slot.generated_text.clear(); } else { slot.state = SLEEPING; slot.command = NONE; } continue; } - kv_cache_free -= slot.num_prompt_tokens; if (slot.state == IDLE || slot.command == RELEASE) { @@ -938,7 +817,7 @@ struct llama_server_context } batch.token [batch.n_tokens] = slot.sampled; - batch.pos [batch.n_tokens] = n_tokens_system + slot.n_past + slot.n_decoded; + batch.pos [batch.n_tokens] = num_tokens_system + slot.n_past + slot.n_decoded; batch.seq_id[batch.n_tokens] = slot.id; batch.logits[batch.n_tokens] = true; @@ -954,13 +833,11 @@ struct llama_server_context // need process the prompt bool keep_gen = slot.state == SLEEPING; // remember generation if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) { - LOG_TEE("processing prompt\n"); slot.state = PROCESSING; slot.command = NONE; auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt slot.num_prompt_tokens = prompt_tokens.size(); - slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0; slot.context_tokens = prompt_tokens; @@ -972,14 +849,14 @@ struct llama_server_context }); if(system_prompt.empty()) { - LOG_TEE("cleaning kv: %i\n", slot.n_past); llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1); } std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); - for (size_t i = slot.n_past; i < slot.context_tokens.size(); ++i) { - batch.token [batch.n_tokens] = slot.context_tokens[i]; - batch.pos [batch.n_tokens] = i + n_tokens_system; + for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { + printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str()); + batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; + batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; batch.seq_id[batch.n_tokens] = slot.id; batch.logits[batch.n_tokens] = false; batch.n_tokens += 1; @@ -1037,13 +914,12 @@ struct llama_server_context continue; } - slot_params_to_gpt_params(slot.params, params); completion_token_output result; - const llama_token id = llama_sample_token(ctx, NULL, NULL, params, slot.last_n_tokens, candidates, slot.i_batch - i); + const llama_token id = llama_sampling_sample(ctx, NULL, slot.ctx_sampling, slot.last_n_tokens, candidates, slot.i_batch - i); llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; result.tok = id; - const int32_t n_probs = params.n_probs; - if (params.temp <= 0 && n_probs > 0) + const int32_t n_probs = slot.sparams.n_probs; + if (slot.sparams.temp <= 0 && n_probs > 0) { // For llama_sample_token_greedy we need to sort candidates llama_sample_softmax(ctx, &candidates_p); @@ -1055,7 +931,6 @@ struct llama_server_context } if (!processToken(result, slot)) { - slot.generated_text.clear(); slot.release(); } kv_cache_free -= slot.num_tokens_predicted; @@ -1092,16 +967,15 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("usage: %s [options]\n", argv0); printf("\n"); printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); + printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); + printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); + printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); + printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -1246,15 +1120,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads = std::stoi(argv[i]); } - else if (arg == "--threads-batch" || arg == "-tb") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -1432,35 +1297,35 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, static json format_generation_settings(llama_server_context &llama, llama_client_slot* &slot) { - const auto eos_bias = llama.params.logit_bias.find(llama_token_eos(llama.ctx)); - const bool ignore_eos = eos_bias != llama.params.logit_bias.end() && + const auto eos_bias = slot->sparams.logit_bias.find(llama_token_eos(llama.ctx)); + const bool ignore_eos = eos_bias != slot->sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); return json{ {"n_ctx", llama.n_ctx}, {"model", llama.params.model_alias}, - {"seed", llama.params.seed}, - {"temp", llama.params.temp}, - {"top_k", llama.params.top_k}, - {"top_p", llama.params.top_p}, - {"tfs_z", llama.params.tfs_z}, - {"typical_p", llama.params.typical_p}, - {"repeat_last_n", llama.params.repeat_last_n}, - {"repeat_penalty", llama.params.repeat_penalty}, - {"presence_penalty", llama.params.presence_penalty}, - {"frequency_penalty", llama.params.frequency_penalty}, - {"mirostat", llama.params.mirostat}, - {"mirostat_tau", llama.params.mirostat_tau}, - {"mirostat_eta", llama.params.mirostat_eta}, - {"penalize_nl", llama.params.penalize_nl}, - {"stop", llama.params.antiprompt}, - {"n_predict", llama.params.n_predict}, - {"n_keep", llama.params.n_keep}, + {"seed", slot->params.seed}, + {"temp", slot->sparams.temp}, + {"top_k", slot->sparams.top_k}, + {"top_p", slot->sparams.top_p}, + {"tfs_z", slot->sparams.tfs_z}, + {"typical_p", slot->sparams.typical_p}, + {"repeat_last_n", slot->sparams.repeat_last_n}, + {"repeat_penalty", slot->sparams.repeat_penalty}, + {"presence_penalty",slot->sparams.presence_penalty}, + {"frequency_penalty", slot->sparams.frequency_penalty}, + {"mirostat", slot->sparams.mirostat}, + {"mirostat_tau", slot->sparams.mirostat_tau}, + {"mirostat_eta", slot->sparams.mirostat_eta}, + {"penalize_nl", slot->sparams.penalize_nl}, + {"stop", slot->params.antiprompt}, + {"n_predict", slot->params.n_predict}, + // {"n_keep", slot.params.n_keep}, {"ignore_eos", ignore_eos}, - {"stream", llama.stream}, - {"logit_bias", llama.params.logit_bias}, - {"n_probs", llama.params.n_probs}, - {"grammar", llama.params.grammar}, + {"stream", slot->params.stream}, + {"logit_bias", slot->sparams.logit_bias}, + {"n_probs", slot->sparams.n_probs}, + {"grammar", slot->params.grammar}, }; } @@ -1488,7 +1353,7 @@ static json format_timings(llama_server_context &llama) }; } -static json format_final_response(llama_server_context &llama, llama_client_slot* &slot, const std::string &content, const std::vector &probs) +static json format_final_response(llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs) { json res = json{ @@ -1508,7 +1373,7 @@ static json format_final_response(llama_server_context &llama, llama_client_slot // {"timings", format_timings(llama)}, }; - if (llama.params.n_probs > 0) + if (slot->sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1517,7 +1382,7 @@ static json format_final_response(llama_server_context &llama, llama_client_slot } static json format_partial_response( - llama_server_context &llama, llama_client_slot* &slot, const std::string &content, const std::vector &probs + llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs ) { json res = json{ {"content", content}, @@ -1525,7 +1390,7 @@ static json format_partial_response( { "slot_id", slot->id } }; - if (llama.params.n_probs > 0) + if (slot->sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1556,27 +1421,28 @@ static T json_value(const json &body, const std::string &key, const T &default_v static void parse_options_completion(const json &body, llama_client_slot* &slot, llama_server_context &llama) { - gpt_params default_params; - - llama.stream = json_value(body, "stream", false); - llama.params.n_predict = json_value(body, "n_predict", default_params.n_predict); - llama.params.top_k = json_value(body, "top_k", default_params.top_k); - llama.params.top_p = json_value(body, "top_p", default_params.top_p); - llama.params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z); - llama.params.typical_p = json_value(body, "typical_p", default_params.typical_p); - llama.params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n); - llama.params.temp = json_value(body, "temperature", default_params.temp); - llama.params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty); - llama.params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty); - llama.params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty); - llama.params.mirostat = json_value(body, "mirostat", default_params.mirostat); - llama.params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau); - llama.params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta); - llama.params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl); - llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); - llama.params.seed = json_value(body, "seed", default_params.seed); - llama.params.grammar = json_value(body, "grammar", default_params.grammar); - llama.params.n_probs = json_value(body, "n_probs", default_params.n_probs); + slot_params default_params; + llama_sampling_params default_sparams; + + slot->params.stream = json_value(body, "stream", false); + slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(body, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(body, "top_p", default_sparams.top_p); + slot->sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); + slot->sparams.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n); + slot->sparams.temp = json_value(body, "temperature", default_sparams.temp); + slot->sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty); + slot->sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty); + slot->sparams.frequency_penalty = json_value(body, "frequency_penalty", default_sparams.frequency_penalty); + slot->sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); + //llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); + slot->params.seed = json_value(body, "seed", default_params.seed); + slot->params.grammar = json_value(body, "grammar", default_params.grammar); + slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); if (body.count("prompt") != 0) { @@ -1587,10 +1453,10 @@ static void parse_options_completion(const json &body, llama_client_slot* &slot, slot->prompt = ""; } - llama.params.logit_bias.clear(); + slot->sparams.logit_bias.clear(); if (json_value(body, "ignore_eos", false)) { - llama.params.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; + slot->sparams.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; } const auto &logit_bias = body.find("logit_bias"); @@ -1606,11 +1472,11 @@ static void parse_options_completion(const json &body, llama_client_slot* &slot, { if (el[1].is_number()) { - llama.params.logit_bias[tok] = el[1].get(); + slot->sparams.logit_bias[tok] = el[1].get(); } else if (el[1].is_boolean() && !el[1].get()) { - llama.params.logit_bias[tok] = -INFINITY; + slot->sparams.logit_bias[tok] = -INFINITY; } } } @@ -1630,8 +1496,6 @@ static void parse_options_completion(const json &body, llama_client_slot* &slot, } } - llama.ctx_sampling = llama_sampling_context_init(llama.params, llama.grammar); - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); } @@ -1809,7 +1673,7 @@ int main(int argc, char **argv) svr.Post("/completion", [&llama](const Request &req, Response &res) { - //auto lock = llama.lock(); + auto lock = llama.lock(); json data = json::parse(req.body); @@ -1865,11 +1729,11 @@ int main(int argc, char **argv) // } // } - auto probs = llama.generated_token_probs; - if (llama.params.n_probs > 0 && llama.stopped_word) { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); - probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); - } + // auto probs = llama.generated_token_probs; + // if (llama.params.n_probs > 0 && llama.stopped_word) { + // const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); + // probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); + // } // const json data = format_final_response(llama, llama.generated_text, probs); @@ -1878,104 +1742,67 @@ int main(int argc, char **argv) // res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), // "application/json"); } else { - printf("processing -> %s\n", slot->isProcessing() ? "true" : "false"); - const auto chunked_content_provider = [slot](size_t, DataSink & sink) { + const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { size_t sent_count = 0; size_t sent_token_probs_index = 0; while(slot->isProcessing()) { if(slot->hasNewToken()) { // new token notification - // const completion_token_output token = slot->next(); - // std::string token_str = llama_token_to_piece(llama.ctx, token.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); - } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); - } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector probs_output = {}; - - if (llama.params.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + const completion_token_output token = slot->next(); + std::string token_str = llama_token_to_piece(llama.ctx, token.tok); + std::vector probs_output = {}; + if (slot->sparams.n_probs > 0) { + const std::vector to_send_toks = llama_tokenize(llama.ctx, token_str, false); + size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); + size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); + if (probs_pos < probs_stop_pos) { + probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); + } + sent_token_probs_index = probs_stop_pos; } - sent_token_probs_index = probs_stop_pos; - } - - const json data = format_partial_response(llama, to_send, probs_output); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; + const json data = format_partial_response(llama, slot, token_str, probs_output); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if(!sink.write(str.c_str(), str.size())) { + slot->release(); + return false; + } + } else { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); } } - - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, - "", - std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - ); - - // const std::string str = - // "data: " + - // data.dump(-1, ' ', false, json::error_handler_t::replace) + - // "\n\n"; - - // LOG_VERBOSE("data stream", { - // { "to_send", str } - // }); - - // if (!sink.write(str.data(), str.size())) { - // LOG_VERBOSE("stream closed", {}); - // llama_print_timings(llama.ctx); - // return false; - // } + const json data = format_final_response( + llama, slot, + "", + std::vector( + slot->generated_token_probs.begin(), + slot->generated_token_probs.begin() + sent_token_probs_index) + ); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.data(), str.size())) { + slot->release(); + return false; + } sink.done(); return true; - }; + }; auto on_complete = [&] (bool) { - //llama.mutex.unlock(); + llama.mutex.unlock(); slot->sent_tokens = 0; slot->generated_token_probs.clear(); slot->release(); }; - //lock.release(); + lock.release(); res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); @@ -2036,15 +1863,15 @@ int main(int argc, char **argv) // 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; - } + // 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 = format_partial_response(llama, to_send, probs_output); @@ -2212,6 +2039,7 @@ int main(int argc, char **argv) { running = llama.updateSlots(); } }); + if (!svr.listen_after_bind()) { return 1; From 81484805f036b6fb622f380aa02dc0f14b2e32d0 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Thu, 12 Oct 2023 16:17:27 -0400 Subject: [PATCH 05/64] completion endpoint working --- examples/server/server.cpp | 49 ++++++++++++++++++++++++++------------ 1 file changed, 34 insertions(+), 15 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ad2ae4ee41a05..3e073de1c341a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -91,6 +91,7 @@ struct completion_token_output std::vector probs; llama_token tok; + std::string text_to_send; }; static size_t common_part(const std::vector &a, const std::vector &b) @@ -231,6 +232,7 @@ struct llama_client_slot bool stopped_limit = false; std::string stopping_word; int32_t multibyte_pending = 0; + size_t sent_count = 0; struct slot_params params; struct llama_sampling_params sparams; @@ -453,7 +455,6 @@ struct llama_server_context else { auto s = json_prompt.template get(); - printf("----------------------\nprompt:\n%s-----------------------\n", s.c_str()); prompt_tokens = ::llama_tokenize(ctx, s, add_bos); } @@ -492,7 +493,6 @@ struct llama_server_context // compare the evaluated prompt with the new prompt } - llama_client_slot* getSlot(int id) { for (llama_client_slot & slot : slots) { @@ -703,17 +703,36 @@ struct llama_server_context slot.last_n_tokens.push_back(result.tok); const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; - slot.addTokenString(result); slot.generated_text += token_str; - size_t stop_pos = findStoppingStrings(slot.generated_text, token_str.size(), STOP_FULL, slot); - - bool has_next_token = !(slot.n_decoded > 2 && - (result.tok == llama_token_eos(ctx) || - (slot.n_decoded + slot.n_past >= - params.n_predict) || - stop_pos != std::string::npos)); - + size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + + const std::string str_test = slot.generated_text.substr(pos); + bool is_stop_full = false; + size_t stop_pos = findStoppingStrings(str_test, token_str.size(), STOP_FULL, slot); + if (stop_pos != std::string::npos) { + is_stop_full = true; + slot.generated_text.erase( + slot.generated_text.begin() + pos + stop_pos, + slot.generated_text.end()); + pos = std::min(slot.sent_count, slot.generated_text.size()); + result.tok = -1; + } else { + is_stop_full = false; + stop_pos = findStoppingStrings(str_test, token_str.size(), + STOP_PARTIAL, slot); + } + bool has_next_token = !is_stop_full && stop_pos > 0; + if(stop_pos == std::string::npos) { + result.text_to_send = slot.generated_text.substr(pos, std::string::npos); + slot.sent_count += result.text_to_send.size(); + has_next_token = true; + } + slot.addTokenString(result); + if(slot.n_decoded > 2 && (result.tok == llama_token_eos(ctx) || + slot.n_past + slot.n_decoded >= params.n_predict)) { + has_next_token = false; + } if (slot.sparams.n_probs > 0) { slot.generated_token_probs.push_back(result); @@ -804,6 +823,7 @@ struct llama_server_context slot.state = IDLE; slot.command = NONE; slot.generated_text.clear(); + return true; } else { slot.state = SLEEPING; slot.command = NONE; @@ -1748,10 +1768,9 @@ int main(int argc, char **argv) while(slot->isProcessing()) { if(slot->hasNewToken()) { // new token notification const completion_token_output token = slot->next(); - std::string token_str = llama_token_to_piece(llama.ctx, token.tok); std::vector probs_output = {}; if (slot->sparams.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, token_str, false); + const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); if (probs_pos < probs_stop_pos) { @@ -1759,7 +1778,7 @@ int main(int argc, char **argv) } sent_token_probs_index = probs_stop_pos; } - const json data = format_partial_response(llama, slot, token_str, probs_output); + const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); const std::string str = "data: " + data.dump(-1, ' ', false, json::error_handler_t::replace) + @@ -1796,7 +1815,7 @@ int main(int argc, char **argv) sink.done(); return true; }; - auto on_complete = [&] (bool) { + auto on_complete = [slot, &llama] (bool) { llama.mutex.unlock(); slot->sent_tokens = 0; slot->generated_token_probs.clear(); From 5b8e29de533110d6fc47491ba4e16d602fbc4529 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Thu, 12 Oct 2023 17:09:12 -0400 Subject: [PATCH 06/64] multiple client support --- examples/server/server.cpp | 53 +++++++++++++------------------------- 1 file changed, 18 insertions(+), 35 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3e073de1c341a..53a20973657b1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -76,7 +76,7 @@ struct slot_params { uint32_t seed = -1; // RNG seed int32_t n_predict = 128; // new tokens to predict std::string grammar = ""; // optional BNF-like grammar to constrain sampling - bool remember_generation = false; // remember a part of the prompt to avoid reprocessing all prompt + bool remember_generation = false; // remember a the prompt to avoid reprocessing all prompt std::vector antiprompt; }; @@ -256,6 +256,7 @@ struct llama_client_slot stopping_word = ""; multibyte_pending = 0; n_past = 0; + sent_count = 0; if (grammar != nullptr) { llama_grammar_free(grammar); @@ -299,8 +300,7 @@ struct llama_client_slot } bool available() { - return state == IDLE && - command == NONE && !params.remember_generation; + return state == IDLE && command == NONE; } bool isProcessing() { @@ -354,12 +354,6 @@ struct llama_server_context int n_ctx; int n_vocab; bool clean_kv_cache = true; - std::mutex mutex; - - std::unique_lock lock() - { - return std::unique_lock(mutex); - } ~llama_server_context() { @@ -406,7 +400,7 @@ struct llama_server_context slot.last_n_tokens.resize(params.n_predict); // max prediction per slot slot.reset(); std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); - LOG_TEE(" - slot %i\n", slot.id); + LOG_TEE(" -> Slot %i\n", slot.id); slots.push_back(slot); } LOG_TEE("Context Size: %i\n", params.n_ctx); @@ -716,7 +710,6 @@ struct llama_server_context slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.sent_count, slot.generated_text.size()); - result.tok = -1; } else { is_stop_full = false; stop_pos = findStoppingStrings(str_test, token_str.size(), @@ -737,7 +730,6 @@ struct llama_server_context { slot.generated_token_probs.push_back(result); } - if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -780,7 +772,6 @@ struct llama_server_context slot.stopped_eos = true; LOG_VERBOSE("eos token found", {}); } - LOG_VERBOSE("next token", { {"token", result.tok}, {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, @@ -818,18 +809,11 @@ struct llama_server_context if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken()) { LOG_TEE("slot %i released\n", slot.id); - if(!slot.params.remember_generation) { - llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system, n_ctx); - slot.state = IDLE; - slot.command = NONE; - slot.generated_text.clear(); - return true; - } else { - slot.state = SLEEPING; - slot.command = NONE; - } + slot.state = slot.params.remember_generation ? SLEEPING : IDLE; + slot.command = NONE; continue; } + kv_cache_free -= slot.num_prompt_tokens; if (slot.state == IDLE || slot.command == RELEASE) { @@ -858,23 +842,28 @@ struct llama_server_context auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt slot.num_prompt_tokens = prompt_tokens.size(); + slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0; slot.context_tokens = prompt_tokens; + if (slot.n_past == slot.num_prompt_tokens) { + // we have to evaluate at least 1 token to generate logits. + printf("we have to evaluate at least 1 token to generate logits\n"); + slot.n_past--; + } + + llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); + LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, {"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())}, }); - if(system_prompt.empty()) { - llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1); - } - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { - printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str()); + //printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str()); batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; batch.seq_id[batch.n_tokens] = slot.id; @@ -1693,7 +1682,6 @@ int main(int argc, char **argv) svr.Post("/completion", [&llama](const Request &req, Response &res) { - auto lock = llama.lock(); json data = json::parse(req.body); @@ -1763,13 +1751,12 @@ int main(int argc, char **argv) // "application/json"); } else { const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { - size_t sent_count = 0; size_t sent_token_probs_index = 0; while(slot->isProcessing()) { if(slot->hasNewToken()) { // new token notification const completion_token_output token = slot->next(); std::vector probs_output = {}; - if (slot->sparams.n_probs > 0) { + if (slot->sparams.n_probs > 0) { const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); @@ -1816,12 +1803,10 @@ int main(int argc, char **argv) return true; }; auto on_complete = [slot, &llama] (bool) { - llama.mutex.unlock(); slot->sent_tokens = 0; slot->generated_token_probs.clear(); slot->release(); }; - lock.release(); res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); @@ -1956,7 +1941,6 @@ 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); std::vector tokens; @@ -1969,7 +1953,6 @@ int main(int argc, char **argv) svr.Post("/detokenize", [&llama](const Request &req, Response &res) { - auto lock = llama.lock(); const json body = json::parse(req.body); std::string content; From 83c2b3553a8a87ded6df7e3bb4b96ec822e4ca4e Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Thu, 12 Oct 2023 18:43:57 -0400 Subject: [PATCH 07/64] grammar + no stream completion --- examples/server/server.cpp | 486 +++++++++++++++---------------------- 1 file changed, 198 insertions(+), 288 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 53a20973657b1..2f3c3fe4f4c2f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -32,7 +32,7 @@ struct server_params { std::string hostname = "127.0.0.1"; std::string public_path = "examples/server/public"; - int32_t port = 8040; + int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; }; @@ -78,6 +78,8 @@ struct slot_params { std::string grammar = ""; // optional BNF-like grammar to constrain sampling bool remember_generation = false; // remember a the prompt to avoid reprocessing all prompt std::vector antiprompt; + json input_prefix; + json input_suffix; }; // completion token output with probabilities @@ -233,6 +235,7 @@ struct llama_client_slot std::string stopping_word; int32_t multibyte_pending = 0; size_t sent_count = 0; + bool infill = false; struct slot_params params; struct llama_sampling_params sparams; @@ -257,6 +260,7 @@ struct llama_client_slot multibyte_pending = 0; n_past = 0; sent_count = 0; + infill = false; if (grammar != nullptr) { llama_grammar_free(grammar); @@ -508,82 +512,6 @@ struct llama_server_context return true; } - void loadInfill() - { - // bool suff_rm_leading_spc = true; - // if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { - // params.input_suffix.erase(0, 1); - // suff_rm_leading_spc = false; - // } - - // auto prefix_tokens = tokenize(params.input_prefix, false); - // auto suffix_tokens = tokenize(params.input_suffix, false); - // const int space_token = 29871; - // if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { - // suffix_tokens.erase(suffix_tokens.begin()); - // } - // prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); - // prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS - // prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); - // prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - // prefix_tokens.push_back(llama_token_middle(ctx)); - // auto prompt_tokens = prefix_tokens; - - // num_prompt_tokens = prompt_tokens.size(); - - // if (params.n_keep < 0) - // { - // 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 (num_prompt_tokens >= (size_t)params.n_ctx) - // { - // printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens); - // // todo we probably want to cut from both sides - // const int n_left = (params.n_ctx - params.n_keep) / 2; - // std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - // const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - // new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - // std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - // LOG_VERBOSE("input truncated", { - // {"n_ctx", params.n_ctx}, - // {"n_keep", params.n_keep}, - // {"n_left", n_left}, - // {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - // }); - - // truncated = true; - // prompt_tokens = new_tokens; - // } - // 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); - // } - - // // compare the evaluated prompt with the new prompt - // n_past = common_part(embd, prompt_tokens); - // embd = prompt_tokens; - // if (n_past == num_prompt_tokens) - // { - // // we have to evaluate at least 1 token to generate logits. - // printf("we have to evaluate at least 1 token to generate logits\n"); - // n_past--; - // } - - // LOG_VERBOSE("prompt ingested", { - // {"n_past", n_past}, - // {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - // {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - // }); - - // has_next_token = true; - } - void cleanKVCache() { // clear the entire KV cache for (int i = 0; i < params.n_parallel; ++i) @@ -839,8 +767,29 @@ struct llama_server_context if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) { slot.state = PROCESSING; slot.command = NONE; + std::vector prompt_tokens; + if(slot.infill) { + bool suff_rm_leading_spc = true; + if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + params.input_suffix.erase(0, 1); + suff_rm_leading_spc = false; + } + auto prefix_tokens = tokenize(slot.params.input_prefix, false); + auto suffix_tokens = tokenize(slot.params.input_suffix, false); + const int space_token = 29871; + if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { + suffix_tokens.erase(suffix_tokens.begin()); + } + prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); + prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.push_back(llama_token_middle(ctx)); + prompt_tokens = prefix_tokens; + } else { + prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt + } - auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt slot.num_prompt_tokens = prompt_tokens.size(); slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0; @@ -1304,7 +1253,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } -static json format_generation_settings(llama_server_context &llama, llama_client_slot* &slot) +static json format_generation_settings(llama_server_context &llama, llama_client_slot* slot) { const auto eos_bias = slot->sparams.logit_bias.find(llama_token_eos(llama.ctx)); const bool ignore_eos = eos_bias != slot->sparams.logit_bias.end() && @@ -1428,7 +1377,7 @@ static T json_value(const json &body, const std::string &key, const T &default_v : default_value; } -static void parse_options_completion(const json &body, llama_client_slot* &slot, llama_server_context &llama) +static void parse_options_completion(const json &body, llama_client_slot* slot, llama_server_context &llama) { slot_params default_params; llama_sampling_params default_sparams; @@ -1508,26 +1457,26 @@ static void parse_options_completion(const json &body, llama_client_slot* &slot, LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); } -// static void parse_options_infill(const json &body, llama_server_context &llama) -// { -// if (body.count("input_prefix") != 0) -// { -// llama.params.input_prefix = body["input_prefix"]; -// } -// else -// { -// llama.params.input_prefix = ""; -// } -// if (body.count("input_suffix") != 0) -// { -// llama.params.input_suffix = body["input_suffix"]; -// } -// else -// { -// llama.params.input_suffix = ""; -// } -// parse_options_completion(body, slot, llama); -// } +static void parse_options_infill(const json &body, llama_server_context &llama, llama_client_slot *slot) +{ + if (body.count("input_prefix") != 0) + { + slot->params.input_prefix = body["input_prefix"]; + } + else + { + slot->params.input_prefix = ""; + } + if (body.count("input_suffix") != 0) + { + slot->params.input_suffix = body["input_suffix"]; + } + else + { + slot->params.input_suffix = ""; + } + parse_options_completion(body, slot, llama); +} static void log_server_request(const Request &req, const Response &res) { @@ -1682,7 +1631,6 @@ int main(int argc, char **argv) svr.Post("/completion", [&llama](const Request &req, Response &res) { - json data = json::parse(req.body); llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1)); @@ -1702,7 +1650,7 @@ int main(int argc, char **argv) slot->reset(); - parse_options_completion(json::parse(req.body), slot, llama); + parse_options_completion(data, slot, llama); if (!llama.launchSlot(slot)) { @@ -1711,44 +1659,36 @@ int main(int argc, char **argv) } if (!slot->params.stream) { - // if (llama.params.n_beams) { - // // Fill llama.generated_token_probs vector with final beam. - // llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, - // llama.n_past, llama.n_remain); - // // Translate llama.generated_token_probs to llama.generated_text. - // append_to_generated_text_from_generated_token_probs(llama); - // } else { - // size_t stop_pos = std::string::npos; - - // while (llama.has_next_token) { - // const completion_token_output token_with_probs = llama.doCompletion(); - // const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok); - - // stop_pos = llama.findStoppingStrings(llama.generated_text, - // token_text.size(), STOP_FULL); - // } - - // if (stop_pos == std::string::npos) { - // stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL); - // } - // if (stop_pos != std::string::npos) { - // llama.generated_text.erase(llama.generated_text.begin() + stop_pos, - // llama.generated_text.end()); - // } - // } - - // auto probs = llama.generated_token_probs; - // if (llama.params.n_probs > 0 && llama.stopped_word) { - // const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); - // probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); - // } - - // const json data = format_final_response(llama, llama.generated_text, probs); - - // llama_print_timings(llama.ctx); - - // res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), - // "application/json"); + std::string completion_text = ""; + if (llama.params.n_beams) { + // // Fill llama.generated_token_probs vector with final beam. + // llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, + // slot->n_past, llama.n_remain); + // // Translate llama.generated_token_probs to llama.generated_text. + // append_to_generated_text_from_generated_token_probs(llama); + } else { + + while (slot->isProcessing()) { + if(slot->hasNewToken()) { + completion_text += slot->next().text_to_send; + } else { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + } + } + + auto probs = slot->generated_token_probs; + if (slot->sparams.n_probs > 0 && slot->stopped_word) { + const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); + probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); + } + + const json data = format_final_response(llama, slot, completion_text, probs); + + //llama_print_timings(llama.ctx); + + res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), + "application/json"); } else { const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { size_t sent_token_probs_index = 0; @@ -1810,131 +1750,101 @@ int main(int argc, char **argv) res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); - // svr.Post("/infill", [&llama](const Request &req, Response &res) - // { - // auto lock = llama.lock(); - - // llama.rewind(); - - // llama_reset_timings(llama.ctx); - - // parse_options_infill(json::parse(req.body), llama); - - // if (!llama.loadGrammar()) - // { - // res.status = 400; - // return; - // } - // llama.loadInfill(); - // llama.beginCompletion(); - // const auto chunked_content_provider = [&](size_t, DataSink & sink) { - // size_t sent_count = 0; - // size_t sent_token_probs_index = 0; - - // while (llama.has_next_token) { - // const completion_token_output token_with_probs = llama.doCompletion(); - // if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - // continue; - // } - // const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - // size_t pos = std::min(sent_count, llama.generated_text.size()); - - // const std::string str_test = llama.generated_text.substr(pos); - // bool is_stop_full = false; - // size_t stop_pos = - // llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - // if (stop_pos != std::string::npos) { - // is_stop_full = true; - // llama.generated_text.erase( - // llama.generated_text.begin() + pos + stop_pos, - // llama.generated_text.end()); - // pos = std::min(sent_count, llama.generated_text.size()); - // } else { - // is_stop_full = false; - // stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - // STOP_PARTIAL); - // } - - // if ( - // stop_pos == std::string::npos || - // // Send rest of the text if we are at the end of the generation - // (!llama.has_next_token && !is_stop_full && stop_pos > 0) - // ) { - // const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - // sent_count += to_send.size(); - - // std::vector probs_output = {}; - - // if (llama.params.n_probs > 0) { - // const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); - // size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - // size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - // if (probs_pos < probs_stop_pos) { - // probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); - // } - // sent_token_probs_index = probs_stop_pos; - // } - - // const json data = format_partial_response(llama, to_send, probs_output); - - // const std::string str = - // "data: " + - // data.dump(-1, ' ', false, json::error_handler_t::replace) + - // "\n\n"; - - // LOG_VERBOSE("data stream", { - // { "to_send", str } - // }); - - // if (!sink.write(str.data(), str.size())) { - // LOG_VERBOSE("stream closed", {}); - // llama_print_timings(llama.ctx); - // return false; - // } - // } - - // if (!llama.has_next_token) { - // // Generation is done, send extra information. - // const json data = format_final_response( - // llama, - // "", - // std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - // ); - - // const std::string str = - // "data: " + - // data.dump(-1, ' ', false, json::error_handler_t::replace) + - // "\n\n"; - - // LOG_VERBOSE("data stream", { - // { "to_send", str } - // }); - - // if (!sink.write(str.data(), str.size())) { - // LOG_VERBOSE("stream closed", {}); - // llama_print_timings(llama.ctx); - // return false; - // } - // } - // } - - // llama_print_timings(llama.ctx); - // sink.done(); - // return true; - // }; - // const auto on_complete = [&](bool) { - // llama.mutex.unlock(); - // }; - // lock.release(); - // res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - // }); - - // svr.Get("/model.json", [&llama](const Request &, Response &res) - // { - // const json data = format_generation_settings(llama); - // return res.set_content(data.dump(), "application/json"); }); + svr.Post("/infill", [&llama](const Request &req, Response &res) + { + + json data = json::parse(req.body); + + llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1)); + + if(slot == nullptr) { + LOG_TEE("slot unavailable\n"); + res.status = 404; + res.set_content("slot_error", "text/plain"); + return; + } + + if(data.contains("system_prompt")) { + llama.processSystemPromptData(data["system_prompt"]); + } + + // llama_reset_timings(llama.ctx); + + slot->reset(); + slot->infill = true; + + parse_options_infill(data, llama, slot); + + if (!llama.launchSlot(slot)) + { + res.status = 400; + return; + } + + const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { + size_t sent_token_probs_index = 0; + while(slot->isProcessing()) { + if(slot->hasNewToken()) { // new token notification + const completion_token_output token = slot->next(); + std::vector probs_output = {}; + if (slot->sparams.n_probs > 0) { + const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); + size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); + size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); + if (probs_pos < probs_stop_pos) { + probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); + } + sent_token_probs_index = probs_stop_pos; + } + const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if(!sink.write(str.c_str(), str.size())) { + slot->release(); + return false; + } + } else { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + } + const json data = format_final_response( + llama, slot, + "", + std::vector( + slot->generated_token_probs.begin(), + slot->generated_token_probs.begin() + sent_token_probs_index) + ); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.data(), str.size())) { + slot->release(); + return false; + } + sink.done(); + return true; + }; + auto on_complete = [slot, &llama] (bool) { + slot->sent_tokens = 0; + slot->generated_token_probs.clear(); + slot->release(); + }; + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + }); + + svr.Get("/model.json", [&llama](const Request &, Response &res) + { + const json data = format_generation_settings(llama, llama.getSlot(0)); + return res.set_content(data.dump(), "application/json"); }); svr.Options(R"(/.*)", [](const Request &, Response &res) { return res.set_content("", "application/json"); }); @@ -1965,29 +1875,29 @@ int main(int argc, char **argv) const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json"); }); - // svr.Post("/embedding", [&llama](const Request &req, Response &res) - // { - // auto lock = llama.lock(); - - // const json body = json::parse(req.body); - - // llama.rewind(); - // llama_reset_timings(llama.ctx); - // if (body.count("content") != 0) - // { - // llama.prompt = body["content"]; - // } - // else - // { - // llama.prompt = ""; - // } - // llama.params.n_predict = 0; - // llama.loadPrompt(); - // llama.beginCompletion(); - // llama.doCompletion(); - - // const json data = format_embedding_response(llama); - // return res.set_content(data.dump(), "application/json"); }); + svr.Post("/embedding", [&llama](const Request &req, Response &res) + { + const json body = json::parse(req.body); + + llama_client_slot* slot = llama.getSlot(-1); + + slot->reset(); + //llama_reset_timings(llama.ctx); + if (body.count("content") != 0) + { + slot->prompt = body["content"]; + } + else + { + slot->prompt = ""; + } + llama.params.n_predict = 0; + llama.launchSlot(slot); + while(slot->isProcessing()) { + std::this_thread::sleep_for(std::chrono::microseconds(10)); + } + const json data = format_embedding_response(llama); + return res.set_content(data.dump(), "application/json"); }); svr.set_logger(log_server_request); From 500ac7120e84055baa06e74232c27a05aa0a8820 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Thu, 12 Oct 2023 21:16:12 -0400 Subject: [PATCH 08/64] cached prompt support --- examples/server/server.cpp | 124 +++++++++++++++++++++++++------------ 1 file changed, 83 insertions(+), 41 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2f3c3fe4f4c2f..18ee4b5beebe2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -37,6 +37,11 @@ struct server_params int32_t write_timeout = 600; }; +// struct beam_search_callback_data { +// llama_server_context* ctx; +// llama_client_slot* slot; +// }; + static bool server_verbose = false; #if SERVER_VERBOSE != 1 @@ -76,7 +81,7 @@ struct slot_params { uint32_t seed = -1; // RNG seed int32_t n_predict = 128; // new tokens to predict std::string grammar = ""; // optional BNF-like grammar to constrain sampling - bool remember_generation = false; // remember a the prompt to avoid reprocessing all prompt + bool cache_prompt = false; // remember a the prompt to avoid reprocessing all prompt std::vector antiprompt; json input_prefix; json input_suffix; @@ -246,12 +251,9 @@ struct llama_client_slot llama_grammar *grammar = nullptr; void reset() { - state = IDLE; - command = NONE; num_prompt_tokens = 0; num_tokens_predicted = 0; generated_text = ""; - generated_token_probs.clear(); truncated = false; stopped_eos = false; stopped_word = false; @@ -261,6 +263,7 @@ struct llama_client_slot n_past = 0; sent_count = 0; infill = false; + clean_tokens(); if (grammar != nullptr) { llama_grammar_free(grammar); @@ -300,7 +303,7 @@ struct llama_client_slot } bool hasNewToken() { - return generated_token_probs.size() > sent_tokens; + return num_tokens_predicted > sent_tokens; } bool available() { @@ -308,7 +311,7 @@ struct llama_client_slot } bool isProcessing() { - return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + return (state == IDLE || state == SLEEPING) && command == LOAD_PROMPT || state == PROCESSING; } completion_token_output next() { @@ -319,8 +322,6 @@ struct llama_client_slot void addTokenString(completion_token_output token) { if(command == RELEASE) { - generated_token_probs.clear(); - sent_tokens = 0; return; } context_tokens.push_back(token.tok); @@ -333,6 +334,11 @@ struct llama_client_slot command = RELEASE; } } + + void clean_tokens() { + sent_tokens = 0; + generated_token_probs.clear(); + } }; struct llama_server_context @@ -626,9 +632,7 @@ struct llama_server_context const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; slot.generated_text += token_str; - size_t pos = std::min(slot.sent_count, slot.generated_text.size()); - const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = findStoppingStrings(str_test, token_str.size(), STOP_FULL, slot); @@ -737,14 +741,20 @@ struct llama_server_context if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken()) { LOG_TEE("slot %i released\n", slot.id); - slot.state = slot.params.remember_generation ? SLEEPING : IDLE; + slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; + if(slot.state == SLEEPING) { + printf("%i has cached prompt."); + } slot.command = NONE; continue; } kv_cache_free -= slot.num_prompt_tokens; - if (slot.state == IDLE || slot.command == RELEASE) { + if ( + slot.state == IDLE || + slot.state == SLEEPING || + slot.command == RELEASE) { continue; } @@ -765,8 +775,6 @@ struct llama_server_context // need process the prompt bool keep_gen = slot.state == SLEEPING; // remember generation if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) { - slot.state = PROCESSING; - slot.command = NONE; std::vector prompt_tokens; if(slot.infill) { bool suff_rm_leading_spc = true; @@ -794,6 +802,9 @@ struct llama_server_context slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0; + printf("n_past: %i, context: %i, prompt: %i, cache: %s\n", + slot.n_past ,slot.context_tokens.size(), prompt_tokens.size(), keep_gen ? "true" : "false"); + slot.context_tokens = prompt_tokens; if (slot.n_past == slot.num_prompt_tokens) { @@ -812,7 +823,7 @@ struct llama_server_context std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { - //printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str()); + printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str()); batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; batch.seq_id[batch.n_tokens] = slot.id; @@ -827,6 +838,8 @@ struct llama_server_context slot.n_decoded = 0; slot.i_batch = batch.n_tokens - 1; + slot.state = PROCESSING; + slot.command = NONE; } } } @@ -868,10 +881,18 @@ struct llama_server_context } for (auto & slot : slots) { + if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { continue; } + // prompt evaluated for embedding + if(params.embedding) { + slot.release(); + slot.i_batch = -1; + return true; + } + completion_token_output result; const llama_token id = llama_sampling_sample(ctx, NULL, slot.ctx_sampling, slot.last_n_tokens, candidates, slot.i_batch - i); llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; @@ -1316,6 +1337,7 @@ static json format_final_response(llama_server_context &llama, llama_client_slot json res = json{ {"content", content}, + {"slot_id", slot->id}, {"stop", true}, {"model", llama.params.model_alias}, {"tokens_predicted", slot->num_tokens_predicted}, @@ -1327,7 +1349,7 @@ static json format_final_response(llama_server_context &llama, llama_client_slot {"stopped_word", slot->stopped_word}, {"stopped_limit", slot->stopped_limit}, {"stopping_word", slot->stopping_word}, - {"tokens_cached", slot->n_past}, + {"tokens_cached", slot->n_past} // {"timings", format_timings(llama)}, }; @@ -1383,6 +1405,7 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, llama_sampling_params default_sparams; slot->params.stream = json_value(body, "stream", false); + slot->params.cache_prompt = json_value(body, "cache_prompt", false); slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict); slot->sparams.top_k = json_value(body, "top_k", default_sparams.top_k); slot->sparams.top_p = json_value(body, "top_p", default_sparams.top_p); @@ -1495,8 +1518,8 @@ static void log_server_request(const Request &req, const Response &res) }); } -static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx); +static bool is_at_eob(llama_server_context * server_context, const llama_token *tokens, const size_t n_tokens) { + return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context->ctx); } // Function matching type llama_beam_search_callback_fn_t. @@ -1509,21 +1532,21 @@ static bool is_at_eob(llama_server_context &server_context, const llama_token *t // AVOID HEADACHES unnecessaries // static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { -// auto & llama = *static_cast(callback_data); +// auto & llama = *static_cast(callback_data); // // Mark beams as EOS as needed. // for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { // llama_beam_view& beam_view = beams_state.beam_views[i]; -// if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { +// if (!beam_view.eob && is_at_eob(llama.ctx, beam_view.tokens, beam_view.n_tokens)) { // beam_view.eob = true; // } // } // printf(","); // Show progress // if (const size_t n = beams_state.common_prefix_length) { -// llama.generated_token_probs.resize(llama.generated_token_probs.size() + n); +// llama.slot->generated_token_probs.resize(llama.slot->generated_token_probs.size() + n); // assert(0u < beams_state.n_beams); // const llama_token * tokens = beams_state.beam_views[0].tokens; // const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; -// std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); +// std::transform(tokens, tokens + n, llama.slot->generated_token_probs.end() - n, map); // printf("%zu", n); // } // fflush(stdout); @@ -1541,17 +1564,17 @@ struct token_translator { std::string operator()(const completion_token_output & cto) const { return (*this)(cto.tok); } }; -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot & slot) +static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot* slot) { - auto & gtps = slot.generated_token_probs; + auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); - if (slot.generated_text.capacity() < slot.generated_text.size() + len) { - slot.generated_text.reserve(slot.generated_text.size() + len); + if (slot->generated_text.capacity() < slot->generated_text.size() + len) { + slot->generated_text.reserve(slot->generated_text.size() + len); } for (const completion_token_output & cto : gtps) { - slot.generated_text += translator(cto); + slot->generated_text += translator(cto); } } @@ -1662,17 +1685,19 @@ int main(int argc, char **argv) std::string completion_text = ""; if (llama.params.n_beams) { // // Fill llama.generated_token_probs vector with final beam. - // llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, - // slot->n_past, llama.n_remain); + // beam_search_callback_data data_; + // data_.slot = slot; + // data_.ctx = &llama; + // llama_beam_search(llama.ctx, beam_search_callback, &data_, llama.params.n_beams, + // slot->n_past, llama.params.n_predict); // // Translate llama.generated_token_probs to llama.generated_text. - // append_to_generated_text_from_generated_token_probs(llama); + // append_to_generated_text_from_generated_token_probs(llama, slot); } else { - while (slot->isProcessing()) { if(slot->hasNewToken()) { completion_text += slot->next().text_to_send; } else { - std::this_thread::sleep_for(std::chrono::milliseconds(5)); + std::this_thread::sleep_for(std::chrono::microseconds(5)); } } } @@ -1686,7 +1711,7 @@ int main(int argc, char **argv) const json data = format_final_response(llama, slot, completion_text, probs); //llama_print_timings(llama.ctx); - + slot->release(); res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { @@ -1743,8 +1768,7 @@ int main(int argc, char **argv) return true; }; auto on_complete = [slot, &llama] (bool) { - slot->sent_tokens = 0; - slot->generated_token_probs.clear(); + slot->clean_tokens(); slot->release(); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); @@ -1781,7 +1805,27 @@ int main(int argc, char **argv) return; } - const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { + if(!slot->params.stream) { + std::string completion_text = ""; + while (slot->isProcessing()) { + if(slot->hasNewToken()) { + completion_text += slot->next().text_to_send; + } else { + std::this_thread::sleep_for(std::chrono::microseconds(5)); + } + } + auto probs = slot->generated_token_probs; + if (slot->sparams.n_probs > 0 && slot->stopped_word) { + const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); + probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); + } + + const json data = format_final_response(llama, slot, completion_text, probs); + //llama_print_timings(llama.ctx); + res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), + "application/json"); + } else { + const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { size_t sent_token_probs_index = 0; while(slot->isProcessing()) { if(slot->hasNewToken()) { // new token notification @@ -1834,11 +1878,11 @@ int main(int argc, char **argv) return true; }; auto on_complete = [slot, &llama] (bool) { - slot->sent_tokens = 0; - slot->generated_token_probs.clear(); + slot->clean_tokens(); slot->release(); }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } }); svr.Get("/model.json", [&llama](const Request &, Response &res) @@ -1878,9 +1922,7 @@ int main(int argc, char **argv) svr.Post("/embedding", [&llama](const Request &req, Response &res) { const json body = json::parse(req.body); - llama_client_slot* slot = llama.getSlot(-1); - slot->reset(); //llama_reset_timings(llama.ctx); if (body.count("content") != 0) From 4ba5a5013dd585afeb17a1e5860cdba6a4e140de Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 11:06:41 -0400 Subject: [PATCH 09/64] chat.mjs support cached prompt + some fixes --- examples/server/chat.mjs | 13 +++++- examples/server/server.cpp | 90 +++++++++++++++++--------------------- 2 files changed, 52 insertions(+), 51 deletions(-) diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index 87f4d2926ca87..103116bf28999 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -7,6 +7,11 @@ const args = process.argv.slice(2); const grammarJsonSchemaFile = args.find( (_, index) => args[index - 1] === "--grammar-json-schema" ); + +const no_cached_prompt = args.find( + (_, index) => args[index - 1] === "--no-cache-prompt" +) ?? "false"; + const grammarFile = args.find((_, index) => args[index - 1] === "--grammar"); // Example usage: function,arguments @@ -30,6 +35,9 @@ if (grammarFile) { grammar = readFileSync(grammarFile, 'utf-8') } +// for cached prompt +let slot_id = -1; + const API_URL = 'http://127.0.0.1:8080' const chat = [ @@ -76,7 +84,9 @@ async function chat_completion(question) { top_p: 0.9, n_keep: n_keep, n_predict: 256, - stop: ["\n### Human:"], // stop completion after generating this + cache_prompt: no_cached_prompt === "false", + slot_id: slot_id, + stop: ["### Human:"], // stop completion after generating this grammar, stream: true, }) @@ -92,6 +102,7 @@ async function chat_completion(question) { const t = Buffer.from(chunk).toString('utf8') if (t.startsWith('data: ')) { const message = JSON.parse(t.substring(6)) + slot_id = message.slot_id answer += message.content process.stdout.write(message.content) if (message.stop) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 18ee4b5beebe2..dbeb5fe5a177f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -407,14 +407,13 @@ struct llama_server_context { llama_client_slot slot; slot.id = i; - slot.last_n_tokens.resize(params.n_predict); // max prediction per slot - slot.reset(); + slot.last_n_tokens.resize(n_ctx); // a slot can fill context size std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + slot.reset(); LOG_TEE(" -> Slot %i\n", slot.id); slots.push_back(slot); } - LOG_TEE("Context Size: %i\n", params.n_ctx); - batch = llama_batch_init(params.n_ctx, 0); + batch = llama_batch_init(n_ctx, 0); // empty system prompt system_prompt = ""; num_tokens_system = 0; @@ -465,38 +464,6 @@ struct llama_server_context return prompt_tokens; } - void processPrompt() { - //params.n_keep = std::min(n_ctx - 4, params.n_keep); - - // if input prompt is too big, truncate like normal - // if (num_prompt_tokens >= (size_t)n_ctx) - // { - // const int n_left = (n_ctx - params.n_keep) / 2; - // std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - // const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - // new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - // std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - // LOG_VERBOSE("input truncated", { - // {"n_ctx", n_ctx}, - // {"n_keep", params.n_keep}, - // {"n_left", n_left}, - // {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - // }); - - // truncated = true; - // prompt_tokens = new_tokens; - // } - // 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); - // } - - // compare the evaluated prompt with the new prompt - } - llama_client_slot* getSlot(int id) { for (llama_client_slot & slot : slots) { @@ -740,10 +707,11 @@ struct llama_server_context // release the slot if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken()) { - LOG_TEE("slot %i released\n", slot.id); slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; if(slot.state == SLEEPING) { - printf("%i has cached prompt."); + LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, slot.n_past); + } else { + LOG_TEE("slot %i released\n", slot.id); } slot.command = NONE; continue; @@ -773,8 +741,9 @@ struct llama_server_context if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { // need process the prompt - bool keep_gen = slot.state == SLEEPING; // remember generation - if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) { + if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) { + slot.state = PROCESSING; + slot.command = NONE; std::vector prompt_tokens; if(slot.infill) { bool suff_rm_leading_spc = true; @@ -800,10 +769,7 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); - slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0; - - printf("n_past: %i, context: %i, prompt: %i, cache: %s\n", - slot.n_past ,slot.context_tokens.size(), prompt_tokens.size(), keep_gen ? "true" : "false"); + slot.n_past = slot.params.cache_prompt ? common_part(slot.context_tokens, prompt_tokens) : 0; slot.context_tokens = prompt_tokens; @@ -813,6 +779,35 @@ struct llama_server_context slot.n_past--; } + if(!slot.params.cache_prompt) { + std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + } else { + LOG_TEE("slot %i - cached: %i tokens | to eval: %i tokens\n", slot.id, slot.n_past, (slot.num_prompt_tokens - slot.n_past)); + //if input prompt is too big, truncate like normal + if (slot.num_prompt_tokens >= (size_t)n_ctx) + { + const int n_left = (n_ctx - params.n_keep) / 2; + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + const int erased_blocks = (slot.num_prompt_tokens - params.n_keep - n_left - 1) / n_left; + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); + std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), slot.last_n_tokens.begin()); + + LOG_VERBOSE("input truncated", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); + + slot.truncated = true; + prompt_tokens = new_tokens; + } else { + const size_t ps = slot.num_prompt_tokens; + std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end() - ps, 0); + std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps); + } + } + llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); LOG_VERBOSE("prompt ingested", { @@ -820,10 +815,7 @@ struct llama_server_context {"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())}, }); - - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { - printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str()); batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; batch.seq_id[batch.n_tokens] = slot.id; @@ -838,8 +830,6 @@ struct llama_server_context slot.n_decoded = 0; slot.i_batch = batch.n_tokens - 1; - slot.state = PROCESSING; - slot.command = NONE; } } } @@ -1743,7 +1733,7 @@ int main(int argc, char **argv) return false; } } else { - std::this_thread::sleep_for(std::chrono::milliseconds(5)); + std::this_thread::sleep_for(std::chrono::microseconds(5)); } } const json data = format_final_response( From 6358ae5f48a93f51e846f8072a8b1591924beb9d Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 12:22:54 -0400 Subject: [PATCH 10/64] server ui now support multiple clients --- examples/server/index.html.hpp | 4078 +++++++++-------- .../server/json-schema-to-grammar.mjs.hpp | 580 +-- examples/server/public/completion.js | 1 - examples/server/public/index.html | 7 +- examples/server/server.cpp | 9 +- 5 files changed, 2354 insertions(+), 2321 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 58e3387d124b7..2305fda77f662 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -288,20 +288,22 @@ unsigned char index_html[] = { 0x20, 0x20, 0x3c, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x6d, 0x6f, 0x64, 0x75, 0x6c, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, - 0x7b, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x2c, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x2c, 0x20, 0x68, 0x2c, - 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x2c, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, - 0x72, 0x2c, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x20, 0x75, - 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x20, 0x75, 0x73, - 0x65, 0x52, 0x65, 0x66, 0x2c, 0x20, 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a/examples/server/json-schema-to-grammar.mjs.hpp +++ b/examples/server/json-schema-to-grammar.mjs.hpp @@ -1,192 +1,198 @@ unsigned char json_schema_to_grammar_mjs[] = { 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x53, 0x50, 0x41, 0x43, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, 0x20, 0x3d, 0x20, 0x27, 0x22, 0x20, 0x22, 0x3f, - 0x27, 0x3b, 0x0a, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x50, 0x52, - 0x49, 0x4d, 0x49, 0x54, 0x49, 0x56, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, - 0x53, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x62, 0x6f, 0x6f, 0x6c, - 0x65, 0x61, 0x6e, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x74, 0x72, 0x75, 0x65, - 0x22, 0x20, 0x7c, 0x20, 0x22, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x22, 0x29, - 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x6e, - 0x75, 0x6d, 0x62, 0x65, 0x72, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x2d, 0x22, - 0x3f, 0x20, 0x28, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x20, 0x7c, 0x20, 0x5b, - 0x31, 0x2d, 0x39, 0x5d, 0x20, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x2a, 0x29, - 0x29, 0x20, 0x28, 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+101,6 @@ export async function* llama(prompt, params = {}, config = {}) { if (e.name !== 'AbortError') { console.error("llama error: ", e); } - throw e; } finally { controller.abort(); diff --git a/examples/server/public/index.html b/examples/server/public/index.html index f539884e23a06..b70be2035346a 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -220,7 +220,9 @@ mirostat_tau: 5, // target entropy mirostat_eta: 0.1, // learning rate grammar: '', - n_probs: 0, // no completion_probabilities + n_probs: 0, // no completion_probabilities, + slot_id: -1, + cache_prompt: true }) /* START: Support for storing prompt templates and parameters in borwser LocalStorage */ @@ -385,6 +387,7 @@ throw new Error("already running"); } controller.value = new AbortController(); + let slot_id = -1; for await (const chunk of llama(prompt, llamaParams, {controller: controller.value})) { const data = chunk.data; @@ -396,9 +399,11 @@ currentMessages.pop(); } transcriptUpdate([...history, [char, currentMessages]]) + params.value = {...params.value, slot_id} console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data); } else { currentMessages.push(data); + slot_id = data.slot_id; transcriptUpdate([...history, [char, currentMessages]]) } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index dbeb5fe5a177f..f8d8a0c80d414 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -252,7 +252,6 @@ struct llama_client_slot void reset() { num_prompt_tokens = 0; - num_tokens_predicted = 0; generated_text = ""; truncated = false; stopped_eos = false; @@ -322,6 +321,7 @@ struct llama_client_slot void addTokenString(completion_token_output token) { if(command == RELEASE) { + num_tokens_predicted = 0; return; } context_tokens.push_back(token.tok); @@ -338,6 +338,7 @@ struct llama_client_slot void clean_tokens() { sent_tokens = 0; generated_token_probs.clear(); + num_tokens_predicted = 0; } }; @@ -1355,7 +1356,7 @@ static json format_partial_response( llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs ) { json res = json{ - {"content", content}, + {"content", content }, {"stop", false}, { "slot_id", slot->id } }; @@ -1705,7 +1706,7 @@ int main(int argc, char **argv) res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { - const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { + const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { size_t sent_token_probs_index = 0; while(slot->isProcessing()) { if(slot->hasNewToken()) { // new token notification @@ -1758,8 +1759,8 @@ int main(int argc, char **argv) return true; }; auto on_complete = [slot, &llama] (bool) { - slot->clean_tokens(); slot->release(); + slot->clean_tokens(); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); From a410a9e30063a65a5500ddef647eb6a967c9f994 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 12:23:58 -0400 Subject: [PATCH 11/64] unused change reverted --- examples/server/public/completion.js | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index 21da5e1371683..0c9bd5f1021db 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -101,6 +101,7 @@ export async function* llama(prompt, params = {}, config = {}) { if (e.name !== 'AbortError') { console.error("llama error: ", e); } + throw e; } finally { controller.abort(); From b6d9e212e5dae233eada2d1f56d6a142df2bc722 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 13:10:38 -0400 Subject: [PATCH 12/64] fixed timings per slot --- examples/server/server.cpp | 65 ++++++++++++++++++++++++-------------- 1 file changed, 41 insertions(+), 24 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f8d8a0c80d414..a130f5e09115f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -223,11 +223,13 @@ struct llama_client_slot int32_t n_decoded = 0; int32_t i_batch = -1; int32_t num_prompt_tokens = 0; + int32_t num_prompt_tokens_processed = 0; + json prompt; std::string generated_text = ""; int num_tokens_predicted = 0; llama_token sampled; - std::vector context_tokens; + std::vector cache_tokens; std::vector last_n_tokens; std::vector generated_token_probs; int sent_tokens = 0; @@ -241,6 +243,11 @@ struct llama_client_slot int32_t multibyte_pending = 0; size_t sent_count = 0; bool infill = false; + int64_t t_start_process_prompt; + int64_t t_start_genereration; + + double t_prompt_processing; // ms + double t_token_generation; // ms struct slot_params params; struct llama_sampling_params sparams; @@ -324,13 +331,14 @@ struct llama_client_slot num_tokens_predicted = 0; return; } - context_tokens.push_back(token.tok); + cache_tokens.push_back(token.tok); generated_token_probs.push_back(token); num_tokens_predicted++; } void release() { if(state == PROCESSING) { + t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; command = RELEASE; } } @@ -668,7 +676,7 @@ struct llama_server_context slot.stopped_limit = true; } - if (!slot.context_tokens.empty() && result.tok == llama_token_eos(ctx)){ + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)){ slot.stopped_eos = true; LOG_VERBOSE("eos token found", {}); } @@ -710,7 +718,7 @@ struct llama_server_context { slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; if(slot.state == SLEEPING) { - LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, slot.n_past); + LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, slot.cache_tokens.size()); } else { LOG_TEE("slot %i released\n", slot.id); } @@ -745,6 +753,8 @@ struct llama_server_context if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) { slot.state = PROCESSING; slot.command = NONE; + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_genereration = 0; std::vector prompt_tokens; if(slot.infill) { bool suff_rm_leading_spc = true; @@ -770,9 +780,9 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); - slot.n_past = slot.params.cache_prompt ? common_part(slot.context_tokens, prompt_tokens) : 0; + slot.n_past = slot.params.cache_prompt ? common_part(slot.cache_tokens, prompt_tokens) : 0; - slot.context_tokens = prompt_tokens; + slot.cache_tokens = prompt_tokens; if (slot.n_past == slot.num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. @@ -780,10 +790,13 @@ struct llama_server_context slot.n_past--; } + + slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + if(!slot.params.cache_prompt) { std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); } else { - LOG_TEE("slot %i - cached: %i tokens | to eval: %i tokens\n", slot.id, slot.n_past, (slot.num_prompt_tokens - slot.n_past)); + LOG_TEE("slot %i - in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); //if input prompt is too big, truncate like normal if (slot.num_prompt_tokens >= (size_t)n_ctx) { @@ -813,8 +826,8 @@ struct llama_server_context LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)}, - {"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())}, + {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; @@ -886,6 +899,12 @@ struct llama_server_context completion_token_output result; const llama_token id = llama_sampling_sample(ctx, NULL, slot.ctx_sampling, slot.last_n_tokens, candidates, slot.i_batch - i); + + if (slot.n_decoded == 1) { + slot.t_start_genereration = ggml_time_us(); + slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; + } + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; result.tok = id; const int32_t n_probs = slot.sparams.n_probs; @@ -1306,20 +1325,18 @@ static json format_embedding_response(llama_server_context &llama) }; } -static json format_timings(llama_server_context &llama) +static json format_timings(llama_client_slot* slot) { - const auto timings = llama_get_timings(llama.ctx); - return json{ - {"prompt_n", timings.n_p_eval}, - {"prompt_ms", timings.t_p_eval_ms}, - {"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval}, - {"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval}, - - {"predicted_n", timings.n_eval}, - {"predicted_ms", timings.t_eval_ms}, - {"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval}, - {"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval}, + {"prompt_n", slot->num_prompt_tokens_processed}, + {"prompt_ms", slot->t_prompt_processing}, + {"prompt_per_token_ms",slot->t_prompt_processing / slot->num_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / slot->t_prompt_processing * slot->num_prompt_tokens_processed}, + + {"predicted_n", slot->n_decoded}, + {"predicted_ms", slot->t_token_generation}, + {"predicted_per_token_ms",slot->t_token_generation / slot->n_decoded}, + {"predicted_per_second", 1e3 / slot->t_token_generation * slot->n_decoded}, }; } @@ -1331,7 +1348,7 @@ static json format_final_response(llama_server_context &llama, llama_client_slot {"slot_id", slot->id}, {"stop", true}, {"model", llama.params.model_alias}, - {"tokens_predicted", slot->num_tokens_predicted}, + {"tokens_predicted", slot->n_decoded}, {"tokens_evaluated", slot->num_prompt_tokens}, {"generation_settings", format_generation_settings(llama, slot)}, {"prompt", slot->prompt}, @@ -1340,8 +1357,8 @@ static json format_final_response(llama_server_context &llama, llama_client_slot {"stopped_word", slot->stopped_word}, {"stopped_limit", slot->stopped_limit}, {"stopping_word", slot->stopping_word}, - {"tokens_cached", slot->n_past} - // {"timings", format_timings(llama)}, + {"tokens_cached", slot->n_past}, + {"timings", format_timings(slot)} }; if (slot->sparams.n_probs > 0) From a2c2d98c1675ac75074ad6b83451a0dbc7c46133 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 14:12:50 -0400 Subject: [PATCH 13/64] add context swap --- examples/server/server.cpp | 71 +++++++++++++++++++++++++++++++------- 1 file changed, 59 insertions(+), 12 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a130f5e09115f..d839d6ce5ebdc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -79,7 +79,7 @@ enum slot_command { struct slot_params { bool stream = true; uint32_t seed = -1; // RNG seed - int32_t n_predict = 128; // new tokens to predict + int32_t n_predict = -1; // new tokens to predict std::string grammar = ""; // optional BNF-like grammar to constrain sampling bool cache_prompt = false; // remember a the prompt to avoid reprocessing all prompt std::vector antiprompt; @@ -224,6 +224,7 @@ struct llama_client_slot int32_t i_batch = -1; int32_t num_prompt_tokens = 0; int32_t num_prompt_tokens_processed = 0; + int32_t n_remaining = -1; json prompt; std::string generated_text = ""; @@ -308,6 +309,16 @@ struct llama_client_slot return true; } + bool hasBudget(gpt_params &global_params) { + n_remaining = -1; + if(params.n_predict != -1) { + n_remaining = params.n_predict - n_decoded; + } else if(global_params.n_predict != -1) { + n_remaining = global_params.n_predict - n_decoded; + } + return n_remaining > 0 || n_remaining == -1; // no budget || limitless + } + bool hasNewToken() { return num_tokens_predicted > sent_tokens; } @@ -607,6 +618,8 @@ struct llama_server_context slot.last_n_tokens.push_back(result.tok); const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; + + // search stop word and delete it slot.generated_text += token_str; size_t pos = std::min(slot.sent_count, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); @@ -623,17 +636,17 @@ struct llama_server_context stop_pos = findStoppingStrings(str_test, token_str.size(), STOP_PARTIAL, slot); } + + // check if there is any token to predict bool has_next_token = !is_stop_full && stop_pos > 0; if(stop_pos == std::string::npos) { + // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.sent_count += result.text_to_send.size(); has_next_token = true; } + // add the token to slot queue and cache slot.addTokenString(result); - if(slot.n_decoded > 2 && (result.tok == llama_token_eos(ctx) || - slot.n_past + slot.n_decoded >= params.n_predict)) { - has_next_token = false; - } if (slot.sparams.n_probs > 0) { slot.generated_token_probs.push_back(result); @@ -671,20 +684,25 @@ struct llama_server_context has_next_token = true; } - if (!has_next_token && (slot.n_decoded + slot.n_past >= params.n_predict)) + // check the limits + if ( + slot.n_decoded > 2 && has_next_token && !slot.hasBudget(params)) { slot.stopped_limit = true; + has_next_token = false; } if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)){ slot.stopped_eos = true; + has_next_token = false; LOG_VERBOSE("eos token found", {}); } + LOG_VERBOSE("next token", { {"token", result.tok}, {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, {"has_next_token", has_next_token}, - {"n_remain", (params.n_predict - slot.n_decoded + slot.n_past)}, + {"n_remain", slot.n_remaining}, {"num_tokens_predicted", slot.num_tokens_predicted}, {"stopped_eos", slot.stopped_eos}, {"stopped_word", slot.stopped_word}, @@ -736,12 +754,13 @@ struct llama_server_context } batch.token [batch.n_tokens] = slot.sampled; - batch.pos [batch.n_tokens] = num_tokens_system + slot.n_past + slot.n_decoded; + batch.pos [batch.n_tokens] = num_tokens_system + slot.n_past; batch.seq_id[batch.n_tokens] = slot.id; batch.logits[batch.n_tokens] = true; slot.n_decoded += 1; slot.i_batch = batch.n_tokens; + slot.n_past += 1; batch.n_tokens += 1; } @@ -853,6 +872,37 @@ struct llama_server_context return true; } + // context shift + if(slots.size() == 1) { + llama_client_slot slot = slots[0]; + if (slot.cache_tokens.size() >= (size_t)n_ctx) + { + // Shift context + const int n_left = slot.n_past - params.n_keep - 1; + const int n_discard = n_left / 2; + + llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + + for (size_t i = params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + { + slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + } + + slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + + slot.n_past -= n_discard; + + slot.truncated = true; + + LOG_VERBOSE("input truncated", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + }); + } + } + // process in chunks of params.n_batch int32_t n_batch = params.n_batch; @@ -1264,9 +1314,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.n_predict = std::stoi(argv[i]); - if(params.n_predict <= 128) { // this example don't support long prompts - params.n_predict = 128; - } } else { @@ -1428,7 +1475,7 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - //llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); + llama.params.n_keep = json_value(body, "n_keep", -1); slot->params.seed = json_value(body, "seed", default_params.seed); slot->params.grammar = json_value(body, "grammar", default_params.grammar); slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); From eb082012278036b020f117b69545b85b5fa12870 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 14:28:06 -0400 Subject: [PATCH 14/64] add changes to README.md --- examples/server/README.md | 8 ++++++++ examples/server/server.cpp | 8 ++++---- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 8a079ae261cfa..e28e5845b60af 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -24,6 +24,8 @@ Command line options: - `--port`: Set the port to listen. Default: `8080`. - `--path`: path from which to serve static files (default examples/server/public) - `--embedding`: Enable embedding extraction, Default: disabled. +- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) +- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) ## Build @@ -158,6 +160,12 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) + + `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) + + `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. + - **POST** `/tokenize`: Tokenize a given text. *Options:* diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d839d6ce5ebdc..0741ebf117ba1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -78,8 +78,8 @@ enum slot_command { struct slot_params { bool stream = true; - uint32_t seed = -1; // RNG seed - int32_t n_predict = -1; // new tokens to predict + uint32_t seed = -1; // RNG seed + int32_t n_predict = -1; // new tokens to predict std::string grammar = ""; // optional BNF-like grammar to constrain sampling bool cache_prompt = false; // remember a the prompt to avoid reprocessing all prompt std::vector antiprompt; @@ -563,7 +563,7 @@ struct llama_server_context } void processSystemPromptData(json sys_props) { - system_prompt = sys_props.value("system_prompt", ""); + system_prompt = sys_props.value("prompt", ""); user_name = sys_props.value("anti_prompt", ""); assistant_name = sys_props.value("assistant_name", ""); notifySystemPromptChanged(); @@ -872,7 +872,7 @@ struct llama_server_context return true; } - // context shift + // context shift takes effect only when there is a single slot if(slots.size() == 1) { llama_client_slot slot = slots[0]; if (slot.cache_tokens.size() >= (size_t)n_ctx) From 9d98cdda2cf95cefcdfd84347645c2bc2ba379ad Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 18:42:44 -0400 Subject: [PATCH 15/64] llava multimodal integration --- examples/server/CMakeLists.txt | 2 +- examples/server/server.cpp | 312 ++++++++++++++++++++++++++++----- 2 files changed, 274 insertions(+), 40 deletions(-) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 3782f9b80ab82..a23ddcc550274 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0741ebf117ba1..f3e6b6e39f385 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,6 +3,13 @@ #include "build-info.h" #include "grammar-parser.h" +// #define SERVER_MULTIMODAL_SUPPORT + +#ifdef SERVER_MULTIMODAL_SUPPORT +#include "../llava/clip.h" +#include "stb_image.h" +#endif + #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 @@ -61,6 +68,57 @@ static bool server_verbose = false; #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) +#ifdef SERVER_MULTIMODAL_SUPPORT +static const std::string base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; + + +static inline bool is_base64(uint8_t c) { + return (isalnum(c) || (c == '+') || (c == '/')); +} + +std::vector base64_decode(std::string const& encoded_string) { + int in_len = encoded_string.size(); + int i = 0; + int j = 0; + int in_ = 0; + BYTE char_array_4[4], char_array_3[3]; + std::vector ret; + while (in_len-- && ( encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { + char_array_4[i++] = encoded_string[in_]; in_++; + if (i ==4) { + for (i = 0; i <4; i++) + char_array_4[i] = base64_chars.find(char_array_4[i]); + + char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) + ret.push_back(char_array_3[i]); + i = 0; + } + } + + if (i) { + for (j = i; j <4; j++) + char_array_4[j] = 0; + + for (j = 0; j <4; j++) + char_array_4[j] = base64_chars.find(char_array_4[j]); + + char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (j = 0; (j < i - 1); j++) ret.push_back(char_array_3[j]); + } + + return ret; +} +#endif // parallel enum slot_state @@ -258,6 +316,13 @@ struct llama_client_slot grammar_parser::parse_state parsed_grammar; llama_grammar *grammar = nullptr; +#ifdef SERVER_MULTIMODAL_SUPPORT + clip_image_u8 img_data; + bool request_encode_image = false; + float* image_embedding = nullptr; + int image_tokens = 0; +#endif + void reset() { num_prompt_tokens = 0; generated_text = ""; @@ -375,6 +440,12 @@ struct llama_server_context std::string user_name = ""; // this should be the anti prompt std::string assistant_name = ""; // this is for generate the prompt +#ifdef SERVER_MULTIMODAL_SUPPORT + bool multimodal = false; + clip_ctx *clp_ctx = nullptr; + int n_embd; +#endif + llama_model *model = nullptr; llama_context *ctx = nullptr; llama_batch batch; @@ -407,12 +478,40 @@ struct llama_server_context bool loadModel(const gpt_params ¶ms_) { params = params_; +#ifdef SERVER_MULTIMODAL_SUPPORT + if(!params.mmproj.empty()) { + multimodal = true; + LOG_TEE("Multi Modal Mode Enabled"); + clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); + if(clp_ctx == nullptr) { + LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); + return false; + } + + if(params.n_ctx < 2048) { // request larger context for the image embedding + params.n_ctx = 2048; + } + } +#endif std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { - LOG_ERROR("unable to load model", {{"model", params_.model}}); + LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } + +#ifdef SERVER_MULTIMODAL_SUPPORT + if(multimodal) { + int n_img_embd = clip_n_mmproj_embd(clp_ctx); + n_embd = llama_n_embd(model); + if (n_img_embd != n_embd) { + LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_embd); + llama_free(ctx); + llama_free_model(model); + return false; + } + } +#endif n_ctx = llama_n_ctx(ctx); n_vocab = llama_n_vocab(model); candidates.reserve(n_vocab); @@ -729,6 +828,37 @@ struct llama_server_context std::this_thread::sleep_for(std::chrono::milliseconds(5)); } + // context shift takes effect only when there is a single slot + if(slots.size() == 1) { + llama_client_slot slot = slots[0]; + if (slot.isProcessing() && slot.cache_tokens.size() >= (size_t)n_ctx) + { + // Shift context + const int n_left = slot.n_past - params.n_keep - 1; + const int n_discard = n_left / 2; + + llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + + for (size_t i = params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + { + slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + } + + slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + + slot.n_past -= n_discard; + + slot.truncated = true; + + LOG_VERBOSE("input truncated", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + }); + } + } + // decode any currently ongoing sequences for (auto & slot : slots) { // release the slot @@ -764,6 +894,8 @@ struct llama_server_context batch.n_tokens += 1; } + // process in chunks of params.n_batch + int32_t n_batch = params.n_batch; // assign workload to the slots if (params.cont_batching || batch.n_tokens == 0) { @@ -772,9 +904,35 @@ struct llama_server_context if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) { slot.state = PROCESSING; slot.command = NONE; + std::vector prompt_tokens; +#ifdef SERVER_MULTIMODAL_SUPPORT + bool ingest_image = false; + if(slot.request_encode_image) { + ingest_image = true; + clip_image_f32 img_res; + if (!clip_image_preprocess(clp_ctx, &slot.img_data, &img_res, /*pad2square =*/ true)) { + LOG_TEE("Error processing the given image"); + clip_free(clp_ctx); + return false; + } + slot.image_tokens = clip_n_patches(clp_ctx); + slot.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); + if (!slot.image_embedding) { + LOG_TEE("Unable to allocate memory for image embeddings\n"); + clip_free(clp_ctx); + return false; + } + LOG_TEE("slot %i - encoding image\n", slot.id); + if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, slot.image_embedding)) { + LOG_TEE("Unable to encode image\n"); + return false; + } + slot.request_encode_image = false; + } +#endif slot.t_start_process_prompt = ggml_time_us(); slot.t_start_genereration = 0; - std::vector prompt_tokens; + if(slot.infill) { bool suff_rm_leading_spc = true; if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { @@ -809,7 +967,6 @@ struct llama_server_context slot.n_past--; } - slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; if(!slot.params.cache_prompt) { @@ -848,6 +1005,68 @@ struct llama_server_context {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); + +#ifdef SERVER_MULTIMODAL_SUPPORT + std::vector preffix_tokens = ingest_image ? tokenize(slot.params.input_prefix, true) : prompt_tokens; + for (; slot.n_past < preffix_tokens.size(); ++slot.n_past) { + printf(llama_token_to_piece(ctx, preffix_tokens[slot.n_past]).c_str()); + batch.token [batch.n_tokens] = preffix_tokens[slot.n_past]; + batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; + batch.seq_id[batch.n_tokens] = slot.id; + batch.logits[batch.n_tokens] = false; + batch.n_tokens += 1; + } + if(ingest_image) { + // process preffix prompt + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + if (llama_decode(ctx, batch_view)) { + LOG_TEE("%s : failed to eval\n", __func__); + return false; + } + } + + printf("\nEvaluated preffix prompt: %i\n", slot.n_past); + + // process image + for (int i = 0; i < slot.image_tokens; i += n_batch) { + int n_eval = slot.image_tokens - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + llama_batch batch = {int32_t(n_eval), nullptr, (slot.image_embedding + i * n_embd), nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + if (llama_decode(ctx, batch)) { + LOG_TEE("%s : failed to eval image\n", __func__); + return false; + } + slot.n_past += n_eval; + } + printf("Evaluated image embedding: %i\n", slot.n_past); + + // process suffix prompt + batch.n_tokens = 0; + std::vector suffix_tokens = tokenize(slot.params.input_suffix, true); + for (int i = 0; i < suffix_tokens.size(); ++i) { + printf(llama_token_to_piece(ctx, suffix_tokens[i]).c_str()); + batch.token [batch.n_tokens] = suffix_tokens[i]; + batch.pos [batch.n_tokens] = slot.n_past; + batch.seq_id[batch.n_tokens] = slot.id; + batch.logits[batch.n_tokens] = false; + slot.n_past += 1; + batch.n_tokens += 1; + } + printf("\nEvaluated suffix prompt: %i\n", slot.n_past); + } +#else for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; @@ -855,7 +1074,7 @@ struct llama_server_context batch.logits[batch.n_tokens] = false; batch.n_tokens += 1; } - +#endif // extract the logits only for the last token if (batch.n_tokens > 0) { batch.logits[batch.n_tokens - 1] = true; @@ -872,40 +1091,6 @@ struct llama_server_context return true; } - // context shift takes effect only when there is a single slot - if(slots.size() == 1) { - llama_client_slot slot = slots[0]; - if (slot.cache_tokens.size() >= (size_t)n_ctx) - { - // Shift context - const int n_left = slot.n_past - params.n_keep - 1; - const int n_discard = n_left / 2; - - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, slot.n_past, -n_discard); - - for (size_t i = params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) - { - slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; - } - - slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); - - slot.n_past -= n_discard; - - slot.truncated = true; - - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); - } - } - - // process in chunks of params.n_batch - int32_t n_batch = params.n_batch; - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { @@ -1045,6 +1230,11 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); + printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); + printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); +#ifdef SERVER_MULTIMODAL_SUPPORT + printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); +#endif printf("\n"); } @@ -1315,6 +1505,16 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_predict = std::stoi(argv[i]); } +#ifdef SERVER_MULTIMODAL_SUPPORT + else if(arg == "--mmproj") { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.mmproj = argv[i]; + } +#endif else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); @@ -1531,8 +1731,42 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, } } } - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); +#ifdef SERVER_MULTIMODAL_SUPPORT + if(!llama.multimodal) { + return; + } + + std::string data_b64 = json_value(body, "image_data", std::string("")); + if(!data_b64.empty()) { + if(!slot->prompt.is_array()) { + std::string prompt = slot->prompt.get(); + int pos = prompt.find("[(img)]"); + if(pos == std::string::npos) { + LOG_TEE("Missing image position in prompt\n"); + return; + } else { + // reuse infill prompt input + slot->params.input_prefix = prompt.substr(0, pos); + slot->params.input_suffix = prompt.substr(pos + 7); // ignore [(img)] + slot->params.cache_prompt = false; // multimodal doesn't support cache prompt + } + } + int width, height, channels; + std::vector image_buffer = base64_decode(data_b64); + data_b64.clear(); + // decode base64 + auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); + slot->img_data.nx = width; + slot->img_data.ny = height; + slot->img_data.size = width * height * 3; + slot->img_data.data = new uint8_t[slot->img_data.size](); + memcpy(slot->img_data.data, data, slot->img_data.size); + stbi_image_free(data); + LOG_TEE("slot %i - RGB image loaded (%i x %i)\n", slot->id, width, height); + slot->request_encode_image = true; + } +#endif } static void parse_options_infill(const json &body, llama_server_context &llama, llama_client_slot *slot) From de35b4790887c1799b0dde710231a9b6144a17c9 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 19:55:25 -0400 Subject: [PATCH 16/64] fixed tokens probs --- examples/server/server.cpp | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f3e6b6e39f385..11ee2b0167fdb 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,7 +3,7 @@ #include "build-info.h" #include "grammar-parser.h" -// #define SERVER_MULTIMODAL_SUPPORT +#define SERVER_MULTIMODAL_SUPPORT #ifdef SERVER_MULTIMODAL_SUPPORT #include "../llava/clip.h" @@ -746,10 +746,6 @@ struct llama_server_context } // add the token to slot queue and cache slot.addTokenString(result); - if (slot.sparams.n_probs > 0) - { - slot.generated_token_probs.push_back(result); - } if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -1009,13 +1005,13 @@ struct llama_server_context #ifdef SERVER_MULTIMODAL_SUPPORT std::vector preffix_tokens = ingest_image ? tokenize(slot.params.input_prefix, true) : prompt_tokens; for (; slot.n_past < preffix_tokens.size(); ++slot.n_past) { - printf(llama_token_to_piece(ctx, preffix_tokens[slot.n_past]).c_str()); batch.token [batch.n_tokens] = preffix_tokens[slot.n_past]; batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; batch.seq_id[batch.n_tokens] = slot.id; batch.logits[batch.n_tokens] = false; batch.n_tokens += 1; } + if(ingest_image) { // process preffix prompt for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { @@ -1035,8 +1031,6 @@ struct llama_server_context } } - printf("\nEvaluated preffix prompt: %i\n", slot.n_past); - // process image for (int i = 0; i < slot.image_tokens; i += n_batch) { int n_eval = slot.image_tokens - i; @@ -1050,13 +1044,11 @@ struct llama_server_context } slot.n_past += n_eval; } - printf("Evaluated image embedding: %i\n", slot.n_past); // process suffix prompt batch.n_tokens = 0; std::vector suffix_tokens = tokenize(slot.params.input_suffix, true); for (int i = 0; i < suffix_tokens.size(); ++i) { - printf(llama_token_to_piece(ctx, suffix_tokens[i]).c_str()); batch.token [batch.n_tokens] = suffix_tokens[i]; batch.pos [batch.n_tokens] = slot.n_past; batch.seq_id[batch.n_tokens] = slot.id; @@ -1064,7 +1056,6 @@ struct llama_server_context slot.n_past += 1; batch.n_tokens += 1; } - printf("\nEvaluated suffix prompt: %i\n", slot.n_past); } #else for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { From 9f72b446354e8aea15e859ebfaf8637bdbbf71ae Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 13 Oct 2023 23:36:32 -0400 Subject: [PATCH 17/64] add multimodal input - alfa --- examples/server/index.html.hpp | 4347 +++++++++++++++-------------- examples/server/public/index.html | 30 +- examples/server/server.cpp | 272 +- 3 files changed, 2421 insertions(+), 2228 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 2305fda77f662..8740b5f105716 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -307,2231 +307,2334 @@ unsigned char index_html[] = { 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x2d, 0x73, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x2d, 0x74, 0x6f, 0x2d, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x2e, 0x6d, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, - 0x61, 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, - 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x22, 0x54, 0x68, 0x69, 0x73, - 0x20, 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0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, + 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, + 0x3e, 0x0a }; -unsigned int index_html_len = 30401; +unsigned int index_html_len = 31634; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index b70be2035346a..8620454e5eaa7 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -194,6 +194,7 @@ import { llama } from '/completion.js'; import { SchemaConverter } from '/json-schema-to-grammar.mjs'; + let selected_image = false; const session = signal({ prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.", @@ -222,6 +223,7 @@ grammar: '', n_probs: 0, // no completion_probabilities, slot_id: -1, + image_data: [], cache_prompt: true }) @@ -424,7 +426,7 @@ transcriptUpdate([...session.value.transcript, ["{{user}}", msg]]) - const prompt = template(session.value.template, { + let prompt = template(session.value.template, { message: msg, history: session.value.transcript.flatMap( ([name, data]) => @@ -439,7 +441,9 @@ ) ).join("\n"), }); - + if(selected_image) { + prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER: [img-10]${msg}\nASSISTANT:`; + } await runLlama(prompt, { ...params.value, stop: ["", template("{{char}}:"), template("{{user}}:")], @@ -472,6 +476,24 @@ transcriptUpdate([]); } + const uploadImage = (e) => { + e.preventDefault(); + document.getElementById("fileInput").click(); + document.getElementById("fileInput").addEventListener("change", function (event) { + const selectedFile = event.target.files[0]; + if (selectedFile) { + const reader = new FileReader(); + reader.onload = function () { + const image_data = reader.result; + params.value = {...params.value, image_data: [ + { data: image_data.replace('data:image/png;base64,', ''), id: 10 }] } + }; + selected_image = true; + reader.readAsDataURL(selectedFile); + } + }); + } + function MessageInput() { const message = useSignal("") @@ -502,6 +524,7 @@
+
@@ -957,7 +980,8 @@

llama.cpp

-
+
+
diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 11ee2b0167fdb..2f8de939afcd1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -74,7 +74,6 @@ static const std::string base64_chars = "abcdefghijklmnopqrstuvwxyz" "0123456789+/"; - static inline bool is_base64(uint8_t c) { return (isalnum(c) || (c == '+') || (c == '/')); } @@ -273,6 +272,17 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector images; #endif void reset() { @@ -806,6 +813,91 @@ struct llama_server_context }); return has_next_token; // continue } +#ifdef SERVER_MULTIMODAL_SUPPORT + bool processImages(llama_client_slot &slot) { + for(slot_image &img : slot.images) { + if(!img.request_encode_image) { + continue; + } + clip_image_f32 img_res; + if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) { + LOG_TEE("Error processing the given image"); + clip_free(clp_ctx); + return false; + } + img.image_tokens = clip_n_patches(clp_ctx); + img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); + if (!img.image_embedding) { + LOG_TEE("Unable to allocate memory for image embeddings\n"); + clip_free(clp_ctx); + return false; + } + LOG_TEE("slot %i - encoding image %i\n", slot.id, img.id); + if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) { + LOG_TEE("Unable to encode image\n"); + return false; + } + img.request_encode_image = false; + } + return slot.images.size() > 0; + } + + // for multiple images processing + bool ingestImages(llama_client_slot &slot, int n_batch) { + int image_idx = 0; + while(image_idx < slot.images.size()) { + slot_image img = slot.images[image_idx]; + + // process prefix prompt + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + if (llama_decode(ctx, batch_view)) { + LOG_TEE("%s : failed to eval\n", __func__); + return false; + } + } + + // process image with llm + for (int i = 0; i < img.image_tokens; i += n_batch) { + int n_eval = img.image_tokens - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + llama_batch batch = {int32_t(n_eval), nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + if (llama_decode(ctx, batch)) { + LOG_TEE("%s : failed to eval image\n", __func__); + return false; + } + slot.n_past += n_eval; + } + image_idx++; + + // append prefix of next image + batch.n_tokens = 0; + std::vector append_tokens = tokenize( + image_idx >= slot.images.size() ? slot.params.input_suffix : // no more images, then process suffix prompt + slot.images[image_idx].prefix_prompt, true); // has next image + for (int i = 0; i < append_tokens.size(); ++i) { + batch.token [batch.n_tokens] = append_tokens[i]; + batch.pos [batch.n_tokens] = slot.n_past; + batch.seq_id[batch.n_tokens] = slot.id; + batch.logits[batch.n_tokens] = false; + slot.n_past += 1; + batch.n_tokens += 1; + } + } + return true; + } +#endif bool updateSlots() { // update the system prompt wait until all slots are idle state @@ -901,31 +993,6 @@ struct llama_server_context slot.state = PROCESSING; slot.command = NONE; std::vector prompt_tokens; -#ifdef SERVER_MULTIMODAL_SUPPORT - bool ingest_image = false; - if(slot.request_encode_image) { - ingest_image = true; - clip_image_f32 img_res; - if (!clip_image_preprocess(clp_ctx, &slot.img_data, &img_res, /*pad2square =*/ true)) { - LOG_TEE("Error processing the given image"); - clip_free(clp_ctx); - return false; - } - slot.image_tokens = clip_n_patches(clp_ctx); - slot.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); - if (!slot.image_embedding) { - LOG_TEE("Unable to allocate memory for image embeddings\n"); - clip_free(clp_ctx); - return false; - } - LOG_TEE("slot %i - encoding image\n", slot.id); - if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, slot.image_embedding)) { - LOG_TEE("Unable to encode image\n"); - return false; - } - slot.request_encode_image = false; - } -#endif slot.t_start_process_prompt = ggml_time_us(); slot.t_start_genereration = 0; @@ -1003,59 +1070,21 @@ struct llama_server_context }); #ifdef SERVER_MULTIMODAL_SUPPORT - std::vector preffix_tokens = ingest_image ? tokenize(slot.params.input_prefix, true) : prompt_tokens; - for (; slot.n_past < preffix_tokens.size(); ++slot.n_past) { - batch.token [batch.n_tokens] = preffix_tokens[slot.n_past]; + bool ingest_images = processImages(slot); // has images? + + // process the prefix of first image + std::vector prefix_tokens = ingest_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; + for (; slot.n_past < prefix_tokens.size(); ++slot.n_past) { + batch.token [batch.n_tokens] = prefix_tokens[slot.n_past]; batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; batch.seq_id[batch.n_tokens] = slot.id; batch.logits[batch.n_tokens] = false; batch.n_tokens += 1; } - if(ingest_image) { - // process preffix prompt - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); - llama_batch batch_view = { - n_tokens, - batch.token + i, - nullptr, - batch.pos + i, - batch.seq_id + i, - batch.logits + i, - 0, 0, 0, // unused - }; - if (llama_decode(ctx, batch_view)) { - LOG_TEE("%s : failed to eval\n", __func__); - return false; - } - } - - // process image - for (int i = 0; i < slot.image_tokens; i += n_batch) { - int n_eval = slot.image_tokens - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - llama_batch batch = {int32_t(n_eval), nullptr, (slot.image_embedding + i * n_embd), nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; - if (llama_decode(ctx, batch)) { - LOG_TEE("%s : failed to eval image\n", __func__); - return false; - } - slot.n_past += n_eval; - } - - // process suffix prompt - batch.n_tokens = 0; - std::vector suffix_tokens = tokenize(slot.params.input_suffix, true); - for (int i = 0; i < suffix_tokens.size(); ++i) { - batch.token [batch.n_tokens] = suffix_tokens[i]; - batch.pos [batch.n_tokens] = slot.n_past; - batch.seq_id[batch.n_tokens] = slot.id; - batch.logits[batch.n_tokens] = false; - slot.n_past += 1; - batch.n_tokens += 1; - } + if(ingest_images && !ingestImages(slot, n_batch)) { + LOG_TEE("failed processing images\n"); + return false; } #else for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { @@ -1111,7 +1140,6 @@ struct llama_server_context } for (auto & slot : slots) { - if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { continue; } @@ -1728,34 +1756,72 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, return; } - std::string data_b64 = json_value(body, "image_data", std::string("")); - if(!data_b64.empty()) { - if(!slot->prompt.is_array()) { - std::string prompt = slot->prompt.get(); - int pos = prompt.find("[(img)]"); - if(pos == std::string::npos) { - LOG_TEE("Missing image position in prompt\n"); + const auto &images_data = body.find("image_data"); + if (images_data != body.end() && images_data->is_array()) + { + slot->images.clear(); + for (const auto &img : *images_data) + { + slot_image img_sl; + std::string data_b64 = img["data"].get(); + img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); + int width, height, channels; + std::vector image_buffer = base64_decode(data_b64); + data_b64.clear(); + auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); + if(!data) { + LOG_TEE("slot %i - failed to load image\n", slot->id); return; - } else { - // reuse infill prompt input - slot->params.input_prefix = prompt.substr(0, pos); - slot->params.input_suffix = prompt.substr(pos + 7); // ignore [(img)] - slot->params.cache_prompt = false; // multimodal doesn't support cache prompt } + LOG_TEE("slot %i - RGB image %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); + img_sl.img_data.nx = width; + img_sl.img_data.ny = height; + img_sl.img_data.size = width * height * 3; + img_sl.img_data.data = new uint8_t[width * height * 3](); + memcpy(img_sl.img_data.data, data, width * height * 3); + stbi_image_free(data); + img_sl.request_encode_image = true; + slot->images.push_back(img_sl); + } + // process prompt + // example: system prompt user describe -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} + if(slot->images.size() > 0 && !slot->prompt.is_array()) { + std::string prompt = slot->prompt.get(); + size_t pos = 0, begin_prefix = 0; + std::string pattern = "[img-"; + while ((pos = prompt.find(pattern, pos)) != std::string::npos) { + size_t end_prefix = pos; + pos += pattern.length(); + size_t end_pos = prompt.find("]", pos); + if (end_pos != std::string::npos) { + std::string image_id = prompt.substr(pos, end_pos - pos); + try { + int img_id = std::stoi(image_id); + bool found = false; + for(slot_image &img : slot->images) { + if(img.id == img_id) { + found = true; + img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); + begin_prefix = end_pos + 1; + break; + } + } + if(!found) { + LOG_TEE("ERROR: Image with id %i not found.\n", img_id); + slot->images.clear(); + return; + } + } catch (const std::invalid_argument& e) { + LOG_TEE("Invalid image number id in prompt\n"); + slot->images.clear(); + return; + } + } + } + slot->prompt = ""; + slot->params.input_suffix = prompt.substr(begin_prefix); + slot->params.cache_prompt = false; // multimodal doesn't support cache prompt } - int width, height, channels; - std::vector image_buffer = base64_decode(data_b64); - data_b64.clear(); - // decode base64 - auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); - slot->img_data.nx = width; - slot->img_data.ny = height; - slot->img_data.size = width * height * 3; - slot->img_data.data = new uint8_t[slot->img_data.size](); - memcpy(slot->img_data.data, data, slot->img_data.size); - stbi_image_free(data); - LOG_TEE("slot %i - RGB image loaded (%i x %i)\n", slot->id, width, height); - slot->request_encode_image = true; } #endif } From 7e64bfe06036e6f06a2b94ce4879c68b9bebd4b0 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Sat, 14 Oct 2023 00:31:34 -0400 Subject: [PATCH 18/64] refactor code + remove unused comments + improved README.md --- examples/server/README.md | 28 +++++++- examples/server/server.cpp | 129 +++++++++++++++++++++---------------- 2 files changed, 102 insertions(+), 55 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index e28e5845b60af..c61fc83826f3d 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -26,6 +26,8 @@ Command line options: - `--embedding`: Enable embedding extraction, Default: disabled. - `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) - `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) +- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) +- ## Build @@ -164,7 +166,7 @@ node index.js `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) - `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. + `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) - **POST** `/tokenize`: Tokenize a given text. @@ -196,8 +198,32 @@ node index.js It also accepts all the options of `/completion` except `stream` and `prompt`. +- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. + ## More examples +### Change system prompt on runtime + +To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it. + +`prompt`: Specify a context that you want all connecting clients to respect. + +`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint. + +`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint. + +```json +{ + "system_prompt": { + "prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:", + "anti_prompt": "User:", + "assistant_name": "Assistant:" + } +} +``` + +**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`. + ### Interactive mode Check the sample in [chat.mjs](chat.mjs). diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2f8de939afcd1..5a69d034d7ea3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,7 +3,7 @@ #include "build-info.h" #include "grammar-parser.h" -#define SERVER_MULTIMODAL_SUPPORT +// #define SERVER_MULTIMODAL_SUPPORT #ifdef SERVER_MULTIMODAL_SUPPORT #include "../llava/clip.h" @@ -44,11 +44,6 @@ struct server_params int32_t write_timeout = 600; }; -// struct beam_search_callback_data { -// llama_server_context* ctx; -// llama_client_slot* slot; -// }; - static bool server_verbose = false; #if SERVER_VERBOSE != 1 @@ -660,6 +655,7 @@ struct llama_server_context } waitAllAreIdle(); all_slots_are_idle = true; + // wait until system prompt load update_system_prompt = true; while(update_system_prompt) { @@ -672,7 +668,11 @@ struct llama_server_context system_prompt = sys_props.value("prompt", ""); user_name = sys_props.value("anti_prompt", ""); assistant_name = sys_props.value("assistant_name", ""); - notifySystemPromptChanged(); + if(slots.size() > 0) { + notifySystemPromptChanged(); + } else { + update_system_prompt = true; + } } void waitAllAreIdle() { @@ -813,6 +813,7 @@ struct llama_server_context }); return has_next_token; // continue } + #ifdef SERVER_MULTIMODAL_SUPPORT bool processImages(llama_client_slot &slot) { for(slot_image &img : slot.images) { @@ -1204,6 +1205,11 @@ struct llama_server_context } }; +struct server_beam_search_callback_data { + llama_context * ctx; + llama_client_slot * slot; +}; + static void server_print_usage(const char *argv0, const gpt_params ¶ms, const server_params &sparams) { @@ -1251,6 +1257,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -spf FNAME, --system-prompt-file FNAME\n"); + printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); #ifdef SERVER_MULTIMODAL_SUPPORT printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); #endif @@ -1258,7 +1266,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, } static void server_params_parse(int argc, char **argv, server_params &sparams, - gpt_params ¶ms) + gpt_params ¶ms, llama_server_context& llama) { gpt_params default_params; server_params default_sparams; @@ -1523,6 +1531,26 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.n_predict = std::stoi(argv[i]); + } else if (arg == "-spf" || arg == "--system-prompt-file") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + break; + } + std::string systm_content = ""; + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(systm_content) + ); + llama.processSystemPromptData(json::parse(systm_content)); } #ifdef SERVER_MULTIMODAL_SUPPORT else if(arg == "--mmproj") { @@ -1864,8 +1892,8 @@ static void log_server_request(const Request &req, const Response &res) }); } -static bool is_at_eob(llama_server_context * server_context, const llama_token *tokens, const size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context->ctx); +static bool is_at_eob(const server_beam_search_callback_data & server_context, const llama_token *tokens, const size_t n_tokens) { + return n_tokens && tokens[n_tokens - 1] == llama_token_eos(server_context.ctx); } // Function matching type llama_beam_search_callback_fn_t. @@ -1875,34 +1903,34 @@ static bool is_at_eob(llama_server_context * server_context, const llama_token * // This is also called when the stop condition is met. // Collect tokens into std::vector response which is pointed to by callback_data. -// AVOID HEADACHES unnecessaries - -// static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { -// auto & llama = *static_cast(callback_data); -// // Mark beams as EOS as needed. -// for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { -// llama_beam_view& beam_view = beams_state.beam_views[i]; -// if (!beam_view.eob && is_at_eob(llama.ctx, beam_view.tokens, beam_view.n_tokens)) { -// beam_view.eob = true; -// } -// } -// printf(","); // Show progress -// if (const size_t n = beams_state.common_prefix_length) { -// llama.slot->generated_token_probs.resize(llama.slot->generated_token_probs.size() + n); -// assert(0u < beams_state.n_beams); -// const llama_token * tokens = beams_state.beam_views[0].tokens; -// const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; -// std::transform(tokens, tokens + n, llama.slot->generated_token_probs.end() - n, map); -// printf("%zu", n); -// } -// fflush(stdout); -// #if 0 // DEBUG: print current beams for this iteration -// std::cout << "\n\nCurrent beams:\n"; -// for (size_t i=0 ; i < beams_state.n_beams ; ++i) { -// std::cout << "beams["<(callback_data); + // Mark beams as EOS as needed. + for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { + llama_beam_view& beam_view = beams_state.beam_views[i]; + if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { + beam_view.eob = true; + } + } + printf(","); // Show progress + if (const size_t n = beams_state.common_prefix_length) { + llama.slot->generated_token_probs.resize(llama.slot->generated_token_probs.size() + n); + assert(0u < beams_state.n_beams); + const llama_token * tokens = beams_state.beam_views[0].tokens; + const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; + std::transform(tokens, tokens + n, llama.slot->generated_token_probs.end() - n, map); + printf("%zu", n); + } + fflush(stdout); +#if 0 // DEBUG: print current beams for this iteration + std::cout << "\n\nCurrent beams:\n"; + for (size_t i=0 ; i < beams_state.n_beams ; ++i) { + std::cout << "beams["<reset(); parse_options_completion(data, slot, llama); @@ -2030,14 +2056,14 @@ int main(int argc, char **argv) if (!slot->params.stream) { std::string completion_text = ""; if (llama.params.n_beams) { - // // Fill llama.generated_token_probs vector with final beam. - // beam_search_callback_data data_; - // data_.slot = slot; - // data_.ctx = &llama; - // llama_beam_search(llama.ctx, beam_search_callback, &data_, llama.params.n_beams, - // slot->n_past, llama.params.n_predict); - // // Translate llama.generated_token_probs to llama.generated_text. - // append_to_generated_text_from_generated_token_probs(llama, slot); + // Fill llama.generated_token_probs vector with final beam. + server_beam_search_callback_data data_beam; + data_beam.slot = slot; + data_beam.ctx = llama.ctx; + llama_beam_search(llama.ctx, beam_search_callback, &data_beam, llama.params.n_beams, + slot->n_past, llama.params.n_predict); + // Translate llama.generated_token_probs to llama.generated_text. + append_to_generated_text_from_generated_token_probs(llama, slot); } else { while (slot->isProcessing()) { if(slot->hasNewToken()) { @@ -2055,8 +2081,6 @@ int main(int argc, char **argv) } const json data = format_final_response(llama, slot, completion_text, probs); - - //llama_print_timings(llama.ctx); slot->release(); res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); @@ -2138,8 +2162,6 @@ int main(int argc, char **argv) llama.processSystemPromptData(data["system_prompt"]); } - // llama_reset_timings(llama.ctx); - slot->reset(); slot->infill = true; @@ -2167,7 +2189,6 @@ int main(int argc, char **argv) } const json data = format_final_response(llama, slot, completion_text, probs); - //llama_print_timings(llama.ctx); res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { From 299f6b54d8f8146c5e332bc93ce7656446fc3173 Mon Sep 17 00:00:00 2001 From: Damian Stewart Date: Sat, 14 Oct 2023 11:17:38 +0200 Subject: [PATCH 19/64] fix compilation errors with llvm --- examples/server/server.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5a69d034d7ea3..c057ade7ffe4c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,7 +3,7 @@ #include "build-info.h" #include "grammar-parser.h" -// #define SERVER_MULTIMODAL_SUPPORT +#define SERVER_MULTIMODAL_SUPPORT #ifdef SERVER_MULTIMODAL_SUPPORT #include "../llava/clip.h" @@ -78,7 +78,7 @@ std::vector base64_decode(std::string const& encoded_string) { int i = 0; int j = 0; int in_ = 0; - BYTE char_array_4[4], char_array_3[3]; + unsigned char char_array_4[4], char_array_3[3]; std::vector ret; while (in_len-- && ( encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { char_array_4[i++] = encoded_string[in_]; in_++; @@ -884,9 +884,10 @@ struct llama_server_context // append prefix of next image batch.n_tokens = 0; - std::vector append_tokens = tokenize( - image_idx >= slot.images.size() ? slot.params.input_suffix : // no more images, then process suffix prompt - slot.images[image_idx].prefix_prompt, true); // has next image + const auto json_prompt = (image_idx >= slot.images.size()) ? + slot.params.input_suffix : // no more images, then process suffix prompt + (json)(slot.images[image_idx].prefix_prompt); + std::vector append_tokens = tokenize(json_prompt, true); // has next image for (int i = 0; i < append_tokens.size(); ++i) { batch.token [batch.n_tokens] = append_tokens[i]; batch.pos [batch.n_tokens] = slot.n_past; From 4e5c5c451c1ffaa9e573695017567fcfe51ced47 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Sat, 14 Oct 2023 08:28:49 -0400 Subject: [PATCH 20/64] notify the user from server ui that multimodality is unavialable --- examples/server/index.html.hpp | 4452 +++++++++++++++-------------- examples/server/public/index.html | 6 +- examples/server/server.cpp | 7 +- 3 files changed, 2241 insertions(+), 2224 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 8740b5f105716..09ba4d74c3daf 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -306,2335 +306,2343 @@ unsigned char index_html[] = { 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x2d, 0x73, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x2d, 0x74, 0x6f, 0x2d, 0x67, 0x72, 0x61, - 0x6d, 0x6d, 0x61, 0x72, 0x2e, 0x6d, 0x6a, 0x73, 0x27, 0x3b, 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0x2a, 0x22, 0x20, 0x73, 0x74, 0x79, 0x6c, 0x65, + 0x3d, 0x22, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x6e, + 0x6f, 0x6e, 0x65, 0x3b, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, + 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, + 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a }; -unsigned int index_html_len = 31634; +unsigned int index_html_len = 31736; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 8620454e5eaa7..3f11ccfd9038a 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -406,6 +406,10 @@ } else { currentMessages.push(data); slot_id = data.slot_id; + if(selected_image && !data.multimodal) { + alert("The server was no compiled for multimodal or the model projector can't be loaded."); + return; + } transcriptUpdate([...history, [char, currentMessages]]) } @@ -524,7 +528,7 @@
- +
diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5a69d034d7ea3..d6196fb65aeea 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1669,7 +1669,12 @@ static json format_partial_response( json res = json{ {"content", content }, {"stop", false}, - { "slot_id", slot->id } + { "slot_id", slot->id }, +#ifdef SERVER_MULTIMODAL_SUPPORT + {"multimodal", llama.multimodal } +#else + {"multimodal", false } +#endif }; if (slot->sparams.n_probs > 0) From ce961a304b59579bd5285889ea99aa40d94406dd Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Sun, 15 Oct 2023 18:46:01 -0400 Subject: [PATCH 21/64] some ci fixes --- examples/server/server.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 493148b38e269..1a6254996034f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -78,7 +78,7 @@ std::vector base64_decode(std::string const& encoded_string) { int i = 0; int j = 0; int in_ = 0; - unsigned char char_array_4[4], char_array_3[3]; + uint8_t char_array_4[4], char_array_3[3]; std::vector ret; while (in_len-- && ( encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { char_array_4[i++] = encoded_string[in_]; in_++; @@ -884,7 +884,7 @@ struct llama_server_context // append prefix of next image batch.n_tokens = 0; - const auto json_prompt = (image_idx >= slot.images.size()) ? + const auto json_prompt = (image_idx >= slot.images.size()) ? slot.params.input_suffix : // no more images, then process suffix prompt (json)(slot.images[image_idx].prefix_prompt); std::vector append_tokens = tokenize(json_prompt, true); // has next image @@ -1818,7 +1818,7 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, slot->images.push_back(img_sl); } // process prompt - // example: system prompt user describe -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} + // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} if(slot->images.size() > 0 && !slot->prompt.is_array()) { std::string prompt = slot->prompt.get(); size_t pos = 0, begin_prefix = 0; From b727e022d6a5f9f664ada00719ed9573858eb7c7 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Sun, 15 Oct 2023 18:53:48 -0400 Subject: [PATCH 22/64] fix ci make build undefined ref errors --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1a6254996034f..d2abee864d68a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,7 +3,7 @@ #include "build-info.h" #include "grammar-parser.h" -#define SERVER_MULTIMODAL_SUPPORT +// #define SERVER_MULTIMODAL_SUPPORT #ifdef SERVER_MULTIMODAL_SUPPORT #include "../llava/clip.h" From fd64f04fc25e7d6b269e813843bfc47b26ce984d Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Sun, 15 Oct 2023 19:07:18 -0400 Subject: [PATCH 23/64] fix long prompt than ctx proposed in #3639 --- examples/server/server.cpp | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d2abee864d68a..66632a4ed5141 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -395,7 +395,7 @@ struct llama_client_slot } bool isProcessing() { - return (state == IDLE || state == SLEEPING) && command == LOAD_PROMPT || state == PROCESSING; + return ((state == IDLE || state == SLEEPING) && command == LOAD_PROMPT) || state == PROCESSING; } completion_token_output next() { @@ -1041,26 +1041,22 @@ struct llama_server_context //if input prompt is too big, truncate like normal if (slot.num_prompt_tokens >= (size_t)n_ctx) { - const int n_left = (n_ctx - params.n_keep) / 2; + const int n_left = n_ctx - params.n_keep; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (slot.num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), slot.last_n_tokens.begin()); - + // Use half the left-over space in the context for the prompt + new_tokens.insert(new_tokens.end(), prompt_tokens.end() - n_left / 2, prompt_tokens.end()); LOG_VERBOSE("input truncated", { {"n_ctx", n_ctx}, {"n_keep", params.n_keep}, {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, }); - slot.truncated = true; prompt_tokens = new_tokens; - } else { - const size_t ps = slot.num_prompt_tokens; - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps); } + const size_t ps = slot.num_prompt_tokens; + std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end() - ps, 0); + std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps); } llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); @@ -1925,7 +1921,7 @@ static void beam_search_callback(void *callback_data, llama_beams_state beams_st llama.slot->generated_token_probs.resize(llama.slot->generated_token_probs.size() + n); assert(0u < beams_state.n_beams); const llama_token * tokens = beams_state.beam_views[0].tokens; - const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; + const auto map = [](llama_token tok) { return completion_token_output{{},tok,""}; }; std::transform(tokens, tokens + n, llama.slot->generated_token_probs.end() - n, map); printf("%zu", n); } From 2d9f11db28f6e3c3cedf6905cef69c009cd94fd1 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Mon, 16 Oct 2023 12:36:05 -0400 Subject: [PATCH 24/64] fixed premature end due stop word --- examples/server/chat.mjs | 2 +- examples/server/server.cpp | 35 ++++++++++++++++++++--------------- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index 103116bf28999..219ebb51a1c9b 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -86,7 +86,7 @@ async function chat_completion(question) { n_predict: 256, cache_prompt: no_cached_prompt === "false", slot_id: slot_id, - stop: ["### Human:"], // stop completion after generating this + stop: ["\n### Human:"], // stop completion after generating this grammar, stream: true, }) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 66632a4ed5141..b8bff24433ad6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -316,6 +316,7 @@ struct llama_client_slot struct slot_params params; struct llama_sampling_params sparams; llama_sampling_context ctx_sampling; + bool has_next_token = true; // grammar props grammar_parser::parse_state parsed_grammar; @@ -710,9 +711,14 @@ struct llama_server_context if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { + if (type == STOP_FULL) + { + slot.stopped_word = true; + slot.stopping_word = word; + slot.has_next_token = false; + } stop_pos = pos; - slot.stopped_word = true; - slot.stopping_word = word; + } } return stop_pos; @@ -727,6 +733,8 @@ struct llama_server_context // search stop word and delete it slot.generated_text += token_str; + slot.has_next_token = true; + size_t pos = std::min(slot.sent_count, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; @@ -744,15 +752,13 @@ struct llama_server_context } // check if there is any token to predict - bool has_next_token = !is_stop_full && stop_pos > 0; - if(stop_pos == std::string::npos) { + if(stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.sent_count += result.text_to_send.size(); - has_next_token = true; + // add the token to slot queue and cache + slot.addTokenString(result); } - // add the token to slot queue and cache - slot.addTokenString(result); if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -781,29 +787,29 @@ struct llama_server_context } } - if (slot.multibyte_pending > 0 && !has_next_token) + if (slot.multibyte_pending > 0 && !slot.has_next_token) { - has_next_token = true; + slot.has_next_token = true; } // check the limits if ( - slot.n_decoded > 2 && has_next_token && !slot.hasBudget(params)) + slot.n_decoded > 2 && slot.has_next_token && !slot.hasBudget(params)) { slot.stopped_limit = true; - has_next_token = false; + slot.has_next_token = false; } if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)){ slot.stopped_eos = true; - has_next_token = false; + slot.has_next_token = false; LOG_VERBOSE("eos token found", {}); } LOG_VERBOSE("next token", { {"token", result.tok}, {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, - {"has_next_token", has_next_token}, + {"has_next_token", slot.has_next_token}, {"n_remain", slot.n_remaining}, {"num_tokens_predicted", slot.num_tokens_predicted}, {"stopped_eos", slot.stopped_eos}, @@ -811,7 +817,7 @@ struct llama_server_context {"stopped_limit", slot.stopped_limit}, {"stopping_word", slot.stopping_word}, }); - return has_next_token; // continue + return slot.has_next_token; // continue } #ifdef SERVER_MULTIMODAL_SUPPORT @@ -2293,7 +2299,6 @@ int main(int argc, char **argv) const json body = json::parse(req.body); llama_client_slot* slot = llama.getSlot(-1); slot->reset(); - //llama_reset_timings(llama.ctx); if (body.count("content") != 0) { slot->prompt = body["content"]; From d7eca255d78ef8d9e215399b15129aa017ee56f3 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Mon, 16 Oct 2023 14:43:10 -0400 Subject: [PATCH 25/64] context shift fixed --- examples/server/server.cpp | 59 +++++++++++++++++++++++++------------- 1 file changed, 39 insertions(+), 20 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b8bff24433ad6..5fdcde6da9f1d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -757,8 +757,8 @@ struct llama_server_context result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.sent_count += result.text_to_send.size(); // add the token to slot queue and cache - slot.addTokenString(result); } + slot.addTokenString(result); if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -925,8 +925,8 @@ struct llama_server_context } // context shift takes effect only when there is a single slot - if(slots.size() == 1) { - llama_client_slot slot = slots[0]; + if(params.n_parallel == 1) { + llama_client_slot &slot = slots[0]; if (slot.isProcessing() && slot.cache_tokens.size() >= (size_t)n_ctx) { // Shift context @@ -1028,22 +1028,16 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); - slot.n_past = slot.params.cache_prompt ? common_part(slot.cache_tokens, prompt_tokens) : 0; - - slot.cache_tokens = prompt_tokens; - - if (slot.n_past == slot.num_prompt_tokens) { - // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); - slot.n_past--; - } - - slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; - - if(!slot.params.cache_prompt) { + if(!slot.params.cache_prompt) { std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + slot.n_past = 0; + slot.num_prompt_tokens_processed = slot.num_prompt_tokens; } else { - LOG_TEE("slot %i - in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); + if (params.n_keep < 0 && params.n_parallel == 1) + { + params.n_keep = (int)slot.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 (slot.num_prompt_tokens >= (size_t)n_ctx) { @@ -1059,14 +1053,26 @@ struct llama_server_context }); slot.truncated = true; prompt_tokens = new_tokens; + slot.num_prompt_tokens = prompt_tokens.size(); } const size_t ps = slot.num_prompt_tokens; std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end() - ps, 0); std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps); + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + LOG_TEE("slot %i - in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); + slot.cache_tokens = prompt_tokens; + + if (slot.n_past == slot.num_prompt_tokens) { + // we have to evaluate at least 1 token to generate logits. + printf("we have to evaluate at least 1 token to generate logits\n"); + slot.n_past--; + } + LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, @@ -1185,7 +1191,7 @@ struct llama_server_context } } - if(kv_cache_free < 0) { + if(kv_cache_free < 0 && params.n_parallel > 1) { LOG_TEE("\nError: kv cache is full, increase context size."); return false; } @@ -1581,6 +1587,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } +static void slot_print_timings(struct llama_client_slot * slot) { + LOG_TEE("\n"); + LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, slot->t_prompt_processing, slot->num_prompt_tokens_processed, slot->t_prompt_processing / slot->num_prompt_tokens_processed, 1e3 / slot->t_prompt_processing * slot->num_prompt_tokens_processed); + LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, slot->t_token_generation, slot->n_decoded, slot->t_token_generation / slot->n_decoded, 1e3 / slot->t_token_generation * slot->n_decoded); + LOG_TEE("%s: total time = %10.2f ms\n", __func__, slot->t_prompt_processing + slot->t_token_generation); +} + static json format_generation_settings(llama_server_context &llama, llama_client_slot* slot) { const auto eos_bias = slot->sparams.logit_bias.find(llama_token_eos(llama.ctx)); @@ -1606,7 +1621,7 @@ static json format_generation_settings(llama_server_context &llama, llama_client {"penalize_nl", slot->sparams.penalize_nl}, {"stop", slot->params.antiprompt}, {"n_predict", slot->params.n_predict}, - // {"n_keep", slot.params.n_keep}, + {"n_keep", llama.params.n_keep}, {"ignore_eos", ignore_eos}, {"stream", slot->params.stream}, {"logit_bias", slot->sparams.logit_bias}, @@ -1730,7 +1745,7 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama.params.n_keep = json_value(body, "n_keep", -1); + llama.params.n_keep = json_value(body, "n_keep", 0); slot->params.seed = json_value(body, "seed", default_params.seed); slot->params.grammar = json_value(body, "grammar", default_params.grammar); slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); @@ -2089,6 +2104,7 @@ int main(int argc, char **argv) } const json data = format_final_response(llama, slot, completion_text, probs); + slot_print_timings(slot); slot->release(); res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); @@ -2131,6 +2147,7 @@ int main(int argc, char **argv) slot->generated_token_probs.begin(), slot->generated_token_probs.begin() + sent_token_probs_index) ); + slot_print_timings(slot); const std::string str = "data: " + data.dump(-1, ' ', false, json::error_handler_t::replace) + @@ -2197,6 +2214,7 @@ int main(int argc, char **argv) } const json data = format_final_response(llama, slot, completion_text, probs); + slot_print_timings(slot); res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { @@ -2238,6 +2256,7 @@ int main(int argc, char **argv) slot->generated_token_probs.begin(), slot->generated_token_probs.begin() + sent_token_probs_index) ); + slot_print_timings(slot); const std::string str = "data: " + data.dump(-1, ' ', false, json::error_handler_t::replace) + From 4d1804330e51e57068673694e9c9546e71d467df Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Mon, 16 Oct 2023 16:31:17 -0400 Subject: [PATCH 26/64] fix llava implementation --- examples/server/index.html.hpp | 2996 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0x79, 0x3a, 0x20, 0x6e, 0x6f, + 0x6e, 0x65, 0x3b, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, + 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, + 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, + 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a }; -unsigned int index_html_len = 31736; +unsigned int index_html_len = 31735; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 3f11ccfd9038a..5f46fa808df7f 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -446,7 +446,7 @@ ).join("\n"), }); if(selected_image) { - prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER: [img-10]${msg}\nASSISTANT:`; + prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`; } await runLlama(prompt, { ...params.value, diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5fdcde6da9f1d..628094f7e9b17 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -285,7 +285,7 @@ struct llama_client_slot int32_t n_past = 0; int32_t n_decoded = 0; int32_t i_batch = -1; - int32_t num_prompt_tokens = 0; + size_t num_prompt_tokens = 0; int32_t num_prompt_tokens_processed = 0; int32_t n_remaining = -1; @@ -347,6 +347,15 @@ struct llama_client_slot ctx_sampling.grammar = NULL; } +#ifdef SERVER_MULTIMODAL_SUPPORT + for(slot_image img : images) { + free(img.image_embedding); + delete[] img.img_data.data; + img.prefix_prompt = ""; + } + images.clear(); +#endif + // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } @@ -893,7 +902,7 @@ struct llama_server_context const auto json_prompt = (image_idx >= slot.images.size()) ? slot.params.input_suffix : // no more images, then process suffix prompt (json)(slot.images[image_idx].prefix_prompt); - std::vector append_tokens = tokenize(json_prompt, true); // has next image + std::vector append_tokens = tokenize(json_prompt, false); // has next image for (int i = 0; i < append_tokens.size(); ++i) { batch.token [batch.n_tokens] = append_tokens[i]; batch.pos [batch.n_tokens] = slot.n_past; @@ -1810,7 +1819,6 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, const auto &images_data = body.find("image_data"); if (images_data != body.end() && images_data->is_array()) { - slot->images.clear(); for (const auto &img : *images_data) { slot_image img_sl; @@ -1821,10 +1829,10 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, data_b64.clear(); auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); if(!data) { - LOG_TEE("slot %i - failed to load image\n", slot->id); + LOG_TEE("slot %i - failed to load image id= %i\n", slot->id, img_sl.id); return; } - LOG_TEE("slot %i - RGB image %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); + LOG_TEE("slot %i - image id= %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); img_sl.img_data.nx = width; img_sl.img_data.ny = height; img_sl.img_data.size = width * height * 3; From aa2268f4cdc2919fe92a0c1adfed32f5035875b6 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 16:21:05 -0400 Subject: [PATCH 27/64] sync README.md changes --- examples/server/README.md | 40 ++++++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index c61fc83826f3d..6d3ce8031c2ad 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -110,25 +110,25 @@ node index.js ## API Endpoints -- **POST** `/completion`: Given a prompt, it returns the predicted completion. +- **POST** `/completion`: Given a `prompt`, it returns the predicted completion. *Options:* + `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does. + `temperature`: Adjust the randomness of the generated text (default: 0.8). `top_k`: Limit the next token selection to the K most probable tokens (default: 40). `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). - `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity). + `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity). - `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. + `n_keep`: Specify the number of tokens from the prompt to retain when context size is exceeded and tokens need to be discarded. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt. `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. - `prompt`: Provide a prompt as a string, or as an array of strings and numbers representing tokens. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. If the prompt is a string, or an array with the first element given as a string, a space is inserted in the front like main.cpp does. - `stop`: Specify a JSON array of stopping strings. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []). @@ -162,6 +162,36 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + *Result JSON:* + + Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. + + `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. + + `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) + + `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model` + + `model`: The path to the model loaded with `-m` + + `prompt`: The provided `prompt` + + `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token + + `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered + + `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided + + `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word) + + `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second` + + `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`) + + `tokens_evaluated`: Number of tokens evaluated in total from the prompt + + `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) + `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) From 58f8ae9bfeb355d24e0351cff2f2d23b57e7172d Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 16:32:19 -0400 Subject: [PATCH 28/64] readme change --- examples/server/README.md | 42 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 40 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 31d65a57f1524..898e32bb6f024 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -114,6 +114,8 @@ node index.js *Options:* + `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does. + `temperature`: Adjust the randomness of the generated text (default: 0.8). `top_k`: Limit the next token selection to the K most probable tokens (default: 40). @@ -122,8 +124,8 @@ node index.js `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity). - `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. - By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt. + `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. + By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt. `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. @@ -160,6 +162,42 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + *Result JSON:* + + Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. + + `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. + + `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) + + `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model` + + `model`: The path to the model loaded with `-m` + + `prompt`: The provided `prompt` + + `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token + + `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered + + `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided + + `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word) + + `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second` + + `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`) + + `tokens_evaluated`: Number of tokens evaluated in total from the prompt + + `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) + + `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) + + `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) + + `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) + - **POST** `/tokenize`: Tokenize a given text. *Options:* From 6c277eaab5225484b74e40cd3406faca639ccfc1 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 16:53:38 -0400 Subject: [PATCH 29/64] update api like OpenAI --- examples/server/api_like_OAI.py | 5 ++++- examples/server/server.cpp | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py index 14d2dcf6580e4..313e1a9652d14 100755 --- a/examples/server/api_like_OAI.py +++ b/examples/server/api_like_OAI.py @@ -8,6 +8,7 @@ app = Flask(__name__) +slot_id = -1 parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n') @@ -77,7 +78,8 @@ def make_postData(body, chat=False, stream=False): if(is_present(body, "stop")): postData["stop"] += body["stop"] postData["n_keep"] = -1 postData["stream"] = stream - + postData["cache_prompt"] = True + postData["slot_id"] = slot_id return postData def make_resData(data, chat=False, promptToken=[]): @@ -128,6 +130,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False): } ] } + slot_id = data["slot_id"] if (chat): if (start): resData["choices"][0]["delta"] = { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 628094f7e9b17..7677147acfb2c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,7 +3,7 @@ #include "build-info.h" #include "grammar-parser.h" -// #define SERVER_MULTIMODAL_SUPPORT +//#define SERVER_MULTIMODAL_SUPPORT #ifdef SERVER_MULTIMODAL_SUPPORT #include "../llava/clip.h" From ed0c11cb835da1988b7acffdfd4b6e97012b60d4 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 16:58:20 -0400 Subject: [PATCH 30/64] multimodal support enabled by default --- examples/server/server.cpp | 44 +++----------------------------------- 1 file changed, 3 insertions(+), 41 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7677147acfb2c..6b74b2dba7157 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,12 +3,8 @@ #include "build-info.h" #include "grammar-parser.h" -//#define SERVER_MULTIMODAL_SUPPORT - -#ifdef SERVER_MULTIMODAL_SUPPORT #include "../llava/clip.h" #include "stb_image.h" -#endif #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -63,7 +59,6 @@ static bool server_verbose = false; #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -#ifdef SERVER_MULTIMODAL_SUPPORT static const std::string base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" "abcdefghijklmnopqrstuvwxyz" @@ -112,7 +107,6 @@ std::vector base64_decode(std::string const& encoded_string) { return ret; } -#endif // parallel enum slot_state @@ -267,7 +261,6 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector images; -#endif void reset() { num_prompt_tokens = 0; @@ -347,15 +338,12 @@ struct llama_client_slot ctx_sampling.grammar = NULL; } -#ifdef SERVER_MULTIMODAL_SUPPORT for(slot_image img : images) { free(img.image_embedding); delete[] img.img_data.data; img.prefix_prompt = ""; } images.clear(); -#endif - // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } @@ -452,11 +440,9 @@ struct llama_server_context std::string user_name = ""; // this should be the anti prompt std::string assistant_name = ""; // this is for generate the prompt -#ifdef SERVER_MULTIMODAL_SUPPORT bool multimodal = false; clip_ctx *clp_ctx = nullptr; int n_embd; -#endif llama_model *model = nullptr; llama_context *ctx = nullptr; @@ -490,7 +476,6 @@ struct llama_server_context bool loadModel(const gpt_params ¶ms_) { params = params_; -#ifdef SERVER_MULTIMODAL_SUPPORT if(!params.mmproj.empty()) { multimodal = true; LOG_TEE("Multi Modal Mode Enabled"); @@ -504,7 +489,6 @@ struct llama_server_context params.n_ctx = 2048; } } -#endif std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { @@ -512,7 +496,6 @@ struct llama_server_context return false; } -#ifdef SERVER_MULTIMODAL_SUPPORT if(multimodal) { int n_img_embd = clip_n_mmproj_embd(clp_ctx); n_embd = llama_n_embd(model); @@ -523,7 +506,6 @@ struct llama_server_context return false; } } -#endif n_ctx = llama_n_ctx(ctx); n_vocab = llama_n_vocab(model); candidates.reserve(n_vocab); @@ -829,7 +811,6 @@ struct llama_server_context return slot.has_next_token; // continue } -#ifdef SERVER_MULTIMODAL_SUPPORT bool processImages(llama_client_slot &slot) { for(slot_image &img : slot.images) { if(!img.request_encode_image) { @@ -914,7 +895,6 @@ struct llama_server_context } return true; } -#endif bool updateSlots() { // update the system prompt wait until all slots are idle state @@ -1088,7 +1068,6 @@ struct llama_server_context {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); -#ifdef SERVER_MULTIMODAL_SUPPORT bool ingest_images = processImages(slot); // has images? // process the prefix of first image @@ -1105,15 +1084,7 @@ struct llama_server_context LOG_TEE("failed processing images\n"); return false; } -#else - for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) { - batch.token [batch.n_tokens] = prompt_tokens[slot.n_past]; - batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; - batch.seq_id[batch.n_tokens] = slot.id; - batch.logits[batch.n_tokens] = false; - batch.n_tokens += 1; - } -#endif + // extract the logits only for the last token if (batch.n_tokens > 0) { batch.logits[batch.n_tokens - 1] = true; @@ -1277,9 +1248,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" -spf FNAME, --system-prompt-file FNAME\n"); printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); -#ifdef SERVER_MULTIMODAL_SUPPORT printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); -#endif printf("\n"); } @@ -1570,7 +1539,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, ); llama.processSystemPromptData(json::parse(systm_content)); } -#ifdef SERVER_MULTIMODAL_SUPPORT else if(arg == "--mmproj") { if (++i >= argc) { @@ -1579,7 +1547,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.mmproj = argv[i]; } -#endif else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); @@ -1697,11 +1664,7 @@ static json format_partial_response( {"content", content }, {"stop", false}, { "slot_id", slot->id }, -#ifdef SERVER_MULTIMODAL_SUPPORT {"multimodal", llama.multimodal } -#else - {"multimodal", false } -#endif }; if (slot->sparams.n_probs > 0) @@ -1810,8 +1773,8 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, } } } + LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); -#ifdef SERVER_MULTIMODAL_SUPPORT if(!llama.multimodal) { return; } @@ -1882,7 +1845,6 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, slot->params.cache_prompt = false; // multimodal doesn't support cache prompt } } -#endif } static void parse_options_infill(const json &body, llama_server_context &llama, llama_client_slot *slot) From d2b1fac6c7334e13b8b1784720f5392278d4927b Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 17:18:56 -0400 Subject: [PATCH 31/64] fix make bui;d errors --- Makefile | 2 +- examples/server/server.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 9a8faef456145..5db7128a75d95 100644 --- a/Makefile +++ b/Makefile @@ -605,7 +605,7 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml. save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) $(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6b74b2dba7157..4028a7b95a832 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -922,8 +922,8 @@ struct llama_server_context const int n_left = slot.n_past - params.n_keep - 1; const int n_discard = n_left / 2; - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, params.n_keep + 1 , params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, slot.id, params.n_keep + 1 + n_discard, slot.n_past, -n_discard); for (size_t i = params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) { From c02c52efb5789e6bdceb1893a2454c6c8a83b380 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 17:54:56 -0400 Subject: [PATCH 32/64] fix multiple clients --- examples/server/index.html.hpp | 4398 ++++++++++++++--------------- examples/server/public/index.html | 5 +- examples/server/server.cpp | 51 +- 3 files changed, 2233 insertions(+), 2221 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index f6f157b860318..2e1df2cacca52 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -309,2340 +309,2336 @@ unsigned char index_html[] = { 0x6d, 0x6d, 0x61, 0x72, 0x2e, 0x6d, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x5f, 0x69, 0x6d, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, - 0x66, 0x61, 0x6c, 0x73, 0x65, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, 0x73, 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0x76, 0x3e, + 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, + 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, + 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a }; -unsigned int index_html_len = 31735; +unsigned int index_html_len = 31688; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 5f46fa808df7f..f55b29da343bf 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -195,6 +195,7 @@ import { llama } from '/completion.js'; import { SchemaConverter } from '/json-schema-to-grammar.mjs'; let selected_image = false; + var slot_id = -1; const session = signal({ prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.", @@ -222,7 +223,6 @@ mirostat_eta: 0.1, // learning rate grammar: '', n_probs: 0, // no completion_probabilities, - slot_id: -1, image_data: [], cache_prompt: true }) @@ -389,7 +389,6 @@ throw new Error("already running"); } controller.value = new AbortController(); - let slot_id = -1; for await (const chunk of llama(prompt, llamaParams, {controller: controller.value})) { const data = chunk.data; @@ -401,7 +400,6 @@ currentMessages.pop(); } transcriptUpdate([...history, [char, currentMessages]]) - params.value = {...params.value, slot_id} console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data); } else { currentMessages.push(data); @@ -450,6 +448,7 @@ } await runLlama(prompt, { ...params.value, + slot_id: slot_id, stop: ["", template("{{char}}:"), template("{{user}}:")], }, "{{char}}"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 4028a7b95a832..97171b125dcea 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -125,6 +125,7 @@ enum slot_command { struct slot_params { bool stream = true; uint32_t seed = -1; // RNG seed + int n_keep = 0; // RNG seed int32_t n_predict = -1; // new tokens to predict std::string grammar = ""; // optional BNF-like grammar to constrain sampling bool cache_prompt = false; // remember a the prompt to avoid reprocessing all prompt @@ -452,6 +453,7 @@ struct llama_server_context gpt_params params; int n_ctx; int n_vocab; + int max_ctx_per_slot = -1; bool clean_kv_cache = true; ~llama_server_context() @@ -514,16 +516,23 @@ struct llama_server_context void initialize() { // create slots - LOG_TEE("Available slots:\n"); all_slots_are_idle = true; + if(max_ctx_per_slot == -1) { + max_ctx_per_slot = n_ctx / params.n_parallel; // split context + } + if(max_ctx_per_slot * params.n_parallel > n_ctx) { + printf("Error: The max context per slot is more greater than model context size"); + return; + } + LOG_TEE("Available slots:\n"); for (int i = 0; i < params.n_parallel; i++) { llama_client_slot slot; slot.id = i; - slot.last_n_tokens.resize(n_ctx); // a slot can fill context size + slot.last_n_tokens.resize(max_ctx_per_slot); std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); slot.reset(); - LOG_TEE(" -> Slot %i\n", slot.id); + LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot); slots.push_back(slot); } batch = llama_batch_init(n_ctx, 0); @@ -914,18 +923,17 @@ struct llama_server_context } // context shift takes effect only when there is a single slot - if(params.n_parallel == 1) { - llama_client_slot &slot = slots[0]; - if (slot.isProcessing() && slot.cache_tokens.size() >= (size_t)n_ctx) + for(llama_client_slot &slot : slots) { + if (slot.isProcessing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot) { // Shift context - const int n_left = slot.n_past - params.n_keep - 1; + const int n_left = slot.n_past - slot.params.n_keep - 1; const int n_discard = n_left / 2; - llama_kv_cache_seq_rm (ctx, slot.id, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, slot.id, params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); - for (size_t i = params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) { slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; } @@ -1022,16 +1030,16 @@ struct llama_server_context slot.n_past = 0; slot.num_prompt_tokens_processed = slot.num_prompt_tokens; } else { - if (params.n_keep < 0 && params.n_parallel == 1) + if (slot.params.n_keep < 0) { - params.n_keep = (int)slot.num_prompt_tokens; + slot.params.n_keep = (int)slot.num_prompt_tokens; } - params.n_keep = std::min(params.n_ctx - 4, params.n_keep); + slot.params.n_keep = std::min(max_ctx_per_slot - 4, slot.params.n_keep); //if input prompt is too big, truncate like normal - if (slot.num_prompt_tokens >= (size_t)n_ctx) + if (slot.num_prompt_tokens >= (size_t)max_ctx_per_slot) { - const int n_left = n_ctx - params.n_keep; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); + const int n_left = max_ctx_per_slot - slot.params.n_keep; + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); // Use half the left-over space in the context for the prompt new_tokens.insert(new_tokens.end(), prompt_tokens.end() - n_left / 2, prompt_tokens.end()); LOG_VERBOSE("input truncated", { @@ -1331,6 +1339,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.n_ctx = std::stoi(argv[i]); + } + else if (arg == "-cps" || arg == "--ctx-per-slot" || arg == "--ctx_per_slot") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + llama.max_ctx_per_slot = std::stoi(argv[i]); } else if (arg == "--rope-freq-base") { @@ -1717,7 +1734,7 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama.params.n_keep = json_value(body, "n_keep", 0); + slot->params.n_keep = json_value(body, "n_keep", slot->params.n_keep); slot->params.seed = json_value(body, "seed", default_params.seed); slot->params.grammar = json_value(body, "grammar", default_params.grammar); slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); From 35fd37430fb726f4f89cc352e1a560a802514c5d Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Tue, 17 Oct 2023 18:04:26 -0400 Subject: [PATCH 33/64] fix zig build --- build.zig | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/build.zig b/build.zig index 0b74cee485320..dcfa3dd6b9764 100644 --- a/build.zig +++ b/build.zig @@ -131,6 +131,7 @@ pub fn build(b: *std.build.Builder) !void { const sampling = make.obj("sampling", "common/sampling.cpp"); const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); const train = make.obj("train", "common/train.cpp"); + const clip = make.obj("clip", "examples/llava/clip.cpp"); _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, console, grammar_parser }); _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common }); @@ -139,7 +140,7 @@ pub fn build(b: *std.build.Builder) !void { _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } From 7196c4e08ae60608f60a12676e24034532aaed87 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Wed, 18 Oct 2023 16:50:09 -0400 Subject: [PATCH 34/64] new sampling API --- examples/server/server.cpp | 166 +++++++++++++++++-------------------- 1 file changed, 77 insertions(+), 89 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 97171b125dcea..8beb19983ace0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -125,7 +125,7 @@ enum slot_command { struct slot_params { bool stream = true; uint32_t seed = -1; // RNG seed - int n_keep = 0; // RNG seed + int n_keep = 0; // number of tokens to keep from initial prompt int32_t n_predict = -1; // new tokens to predict std::string grammar = ""; // optional BNF-like grammar to constrain sampling bool cache_prompt = false; // remember a the prompt to avoid reprocessing all prompt @@ -262,6 +262,34 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vectorparams = sparams; + result->grammar = nullptr; + + // if there is a grammar, parse it + if (!grammar.empty()) { + result->parsed_grammar = grammar_parser::parse(grammar.c_str()); + + // will be empty (default) if there are parse errors + if (result->parsed_grammar.rules.empty()) { + fprintf(stderr, "%s: failed to parse grammar\n", __func__); + return nullptr; + } + + std::vector grammar_rules(result->parsed_grammar.c_rules()); + + result->grammar = llama_grammar_init( + grammar_rules.data(), + grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root")); + } + + result->prev.resize(n_ctx); + + return result; +} + struct slot_image { clip_image_u8 img_data; bool request_encode_image = false; @@ -287,7 +315,6 @@ struct llama_client_slot int num_tokens_predicted = 0; llama_token sampled; std::vector cache_tokens; - std::vector last_n_tokens; std::vector generated_token_probs; int sent_tokens = 0; slot_state state = IDLE; @@ -307,13 +334,12 @@ struct llama_client_slot double t_token_generation; // ms struct slot_params params; + + // sampling struct llama_sampling_params sparams; - llama_sampling_context ctx_sampling; + llama_sampling_context* ctx_sampling = nullptr; bool has_next_token = true; - - // grammar props - grammar_parser::parse_state parsed_grammar; - llama_grammar *grammar = nullptr; + int max_context_size = 0; // multimodal std::vector images; @@ -332,47 +358,26 @@ struct llama_client_slot infill = false; clean_tokens(); - if (grammar != nullptr) { - llama_grammar_free(grammar); - grammar = nullptr; - ctx_sampling.params = sparams; - ctx_sampling.grammar = NULL; + if (ctx_sampling != nullptr) { + llama_sampling_free(ctx_sampling); } + ctx_sampling = llama_sampling_init_srv(sparams, params.grammar, max_context_size); + for(slot_image img : images) { free(img.image_embedding); delete[] img.img_data.data; img.prefix_prompt = ""; } + images.clear(); // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } bool loadGrammar(llama_token eos) { - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); - return false; - } - grammar_parser::print_grammar(stderr, parsed_grammar); - - { - auto it = sparams.logit_bias.find(eos); - if (it != sparams.logit_bias.end() && it->second == -INFINITY) { - LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); - } - } - - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - } - ctx_sampling.params = sparams; - ctx_sampling.grammar = grammar; - return true; + ctx_sampling = llama_sampling_init_srv(sparams, params.grammar, max_context_size); + return ctx_sampling != nullptr; } bool hasBudget(gpt_params &global_params) { @@ -448,7 +453,6 @@ struct llama_server_context llama_model *model = nullptr; llama_context *ctx = nullptr; llama_batch batch; - std::vector candidates; bool all_slots_are_idle = false; gpt_params params; int n_ctx; @@ -468,11 +472,6 @@ struct llama_server_context llama_free_model(model); model = nullptr; } - for(auto &slot : slots) { - if(slot.grammar) { - llama_grammar_free(slot.grammar); - } - } } bool loadModel(const gpt_params ¶ms_) @@ -510,7 +509,6 @@ struct llama_server_context } n_ctx = llama_n_ctx(ctx); n_vocab = llama_n_vocab(model); - candidates.reserve(n_vocab); return true; } @@ -529,13 +527,12 @@ struct llama_server_context { llama_client_slot slot; slot.id = i; - slot.last_n_tokens.resize(max_ctx_per_slot); - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + slot.max_context_size = max_ctx_per_slot; slot.reset(); LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot); slots.push_back(slot); } - batch = llama_batch_init(n_ctx, 0); + batch = llama_batch_init(n_ctx, 0, 1); // empty system prompt system_prompt = ""; num_tokens_system = 0; @@ -626,10 +623,7 @@ struct llama_server_context for (int32_t i = 0; i < batch.n_tokens; ++i) { - batch.token[i] = tokens_system[i]; - batch.pos[i] = i; - batch.seq_id[i] = 0; - batch.logits[i] = false; + llama_batch_add(batch, tokens_system[i], i, { 0 }, false); } if (llama_decode(ctx, batch) != 0) @@ -726,8 +720,6 @@ struct llama_server_context bool processToken(completion_token_output & result, llama_client_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling - slot.last_n_tokens.erase(slot.last_n_tokens.begin()); - slot.last_n_tokens.push_back(result.tok); const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; @@ -859,11 +851,12 @@ struct llama_server_context const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { n_tokens, - batch.token + i, + batch.token + i, nullptr, - batch.pos + i, - batch.seq_id + i, - batch.logits + i, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, 0, 0, 0, // unused }; if (llama_decode(ctx, batch_view)) { @@ -878,8 +871,8 @@ struct llama_server_context if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = {int32_t(n_eval), nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; - if (llama_decode(ctx, batch)) { + llama_batch batch_img = {int32_t(n_eval), nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + if (llama_decode(ctx, batch_img)) { LOG_TEE("%s : failed to eval image\n", __func__); return false; } @@ -894,10 +887,7 @@ struct llama_server_context (json)(slot.images[image_idx].prefix_prompt); std::vector append_tokens = tokenize(json_prompt, false); // has next image for (int i = 0; i < append_tokens.size(); ++i) { - batch.token [batch.n_tokens] = append_tokens[i]; - batch.pos [batch.n_tokens] = slot.n_past; - batch.seq_id[batch.n_tokens] = slot.id; - batch.logits[batch.n_tokens] = false; + llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); slot.n_past += 1; batch.n_tokens += 1; } @@ -922,7 +912,6 @@ struct llama_server_context std::this_thread::sleep_for(std::chrono::milliseconds(5)); } - // context shift takes effect only when there is a single slot for(llama_client_slot &slot : slots) { if (slot.isProcessing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot) { @@ -976,16 +965,12 @@ struct llama_server_context continue; } - batch.token [batch.n_tokens] = slot.sampled; - batch.pos [batch.n_tokens] = num_tokens_system + slot.n_past; - batch.seq_id[batch.n_tokens] = slot.id; - batch.logits[batch.n_tokens] = true; + slot.i_batch = batch.n_tokens; + + llama_batch_add(batch, slot.sampled, num_tokens_system + slot.n_past, { slot.id }, true); slot.n_decoded += 1; - slot.i_batch = batch.n_tokens; slot.n_past += 1; - - batch.n_tokens += 1; } // process in chunks of params.n_batch int32_t n_batch = params.n_batch; @@ -1026,7 +1011,7 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); if(!slot.params.cache_prompt) { - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0); + std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end(), 0); slot.n_past = 0; slot.num_prompt_tokens_processed = slot.num_prompt_tokens; } else { @@ -1038,23 +1023,27 @@ struct llama_server_context //if input prompt is too big, truncate like normal if (slot.num_prompt_tokens >= (size_t)max_ctx_per_slot) { + // applied bug of #3661 const int n_left = max_ctx_per_slot - slot.params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); // Use half the left-over space in the context for the prompt - new_tokens.insert(new_tokens.end(), prompt_tokens.end() - n_left / 2, prompt_tokens.end()); + new_tokens.insert(new_tokens.end(), prompt_tokens.end() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, + {"n_ctx", max_ctx_per_slot}, + {"n_keep", slot.params.n_keep}, {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, }); slot.truncated = true; prompt_tokens = new_tokens; slot.num_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.num_prompt_tokens < (size_t)max_ctx_per_slot); } const size_t ps = slot.num_prompt_tokens; - std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps); + std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end() - ps, 0); + std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.ctx_sampling->prev.end() - ps); slot.n_past = common_part(slot.cache_tokens, prompt_tokens); slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; LOG_TEE("slot %i - in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); @@ -1081,11 +1070,7 @@ struct llama_server_context // process the prefix of first image std::vector prefix_tokens = ingest_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; for (; slot.n_past < prefix_tokens.size(); ++slot.n_past) { - batch.token [batch.n_tokens] = prefix_tokens[slot.n_past]; - batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system; - batch.seq_id[batch.n_tokens] = slot.id; - batch.logits[batch.n_tokens] = false; - batch.n_tokens += 1; + llama_batch_add(batch, prefix_tokens[slot.n_past], num_tokens_system + slot.n_past, { slot.id }, false); } if(ingest_images && !ingestImages(slot, n_batch)) { @@ -1113,11 +1098,12 @@ struct llama_server_context const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { n_tokens, - batch.token + i, + batch.token + i, nullptr, - batch.pos + i, - batch.seq_id + i, - batch.logits + i, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, 0, 0, 0, // unused }; @@ -1150,25 +1136,27 @@ struct llama_server_context } completion_token_output result; - const llama_token id = llama_sampling_sample(ctx, NULL, slot.ctx_sampling, slot.last_n_tokens, candidates, slot.i_batch - i); + const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i); + + llama_sampling_accept(slot.ctx_sampling, ctx, id); if (slot.n_decoded == 1) { slot.t_start_genereration = ggml_time_us(); slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; result.tok = id; const int32_t n_probs = slot.sparams.n_probs; if (slot.sparams.temp <= 0 && n_probs > 0) { // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &candidates_p); + llama_sample_softmax(ctx, &cur_p); } - for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i) + for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) { - result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p}); + result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); } if (!processToken(result, slot)) { From ab2fc002248f9098b02a8a015f5b5f6f03836cc2 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Wed, 18 Oct 2023 16:57:48 -0400 Subject: [PATCH 35/64] latest changes of sampling API --- examples/server/server.cpp | 780 ++++++++++++++++++++++++------------- 1 file changed, 499 insertions(+), 281 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1e2ae5ea8f401..8beb19983ace0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3,6 +3,9 @@ #include "build-info.h" #include "grammar-parser.h" +#include "../llava/clip.h" +#include "stb_image.h" + #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 @@ -308,18 +311,14 @@ struct llama_client_slot int32_t n_remaining = -1; json prompt; - std::vector embd; - std::vector last_n_tokens; - - llama_model *model = nullptr; - llama_context *ctx = nullptr; - gpt_params params; - llama_sampling_context ctx_sampling; - int n_ctx; - - grammar_parser::parse_state parsed_grammar; - llama_grammar *grammar = nullptr; - + std::string generated_text = ""; + int num_tokens_predicted = 0; + llama_token sampled; + std::vector cache_tokens; + std::vector generated_token_probs; + int sent_tokens = 0; + slot_state state = IDLE; + slot_command command = NONE; bool truncated = false; bool stopped_eos = false; bool stopped_word = false; @@ -477,32 +476,20 @@ struct llama_server_context bool loadModel(const gpt_params ¶ms_) { - params.antiprompt.clear(); - params.grammar.clear(); - num_prompt_tokens = 0; - num_tokens_predicted = 0; - generated_text = ""; - generated_text.reserve(n_ctx); - generated_token_probs.clear(); - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - n_remain = 0; - n_past = 0; + params = params_; + if(!params.mmproj.empty()) { + multimodal = true; + LOG_TEE("Multi Modal Mode Enabled"); + clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); + if(clp_ctx == nullptr) { + LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); + return false; + } - if (grammar != nullptr) { - llama_grammar_free(grammar); - grammar = nullptr; - ctx_sampling = llama_sampling_context_init(params, NULL); + if(params.n_ctx < 2048) { // request larger context for the image embedding + params.n_ctx = 2048; + } } - } - - bool loadModel(const gpt_params ¶ms_) - { - params = params_; std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { @@ -521,8 +508,7 @@ struct llama_server_context } } n_ctx = llama_n_ctx(ctx); - last_n_tokens.resize(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); + n_vocab = llama_n_vocab(model); return true; } @@ -597,29 +583,24 @@ struct llama_server_context return prompt_tokens; } - bool loadGrammar() - { - if (!params.grammar.empty()) { - parsed_grammar = grammar_parser::parse(params.grammar.c_str()); - // will be empty (default) if there are parse errors - if (parsed_grammar.rules.empty()) { - LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); - return false; - } - grammar_parser::print_grammar(stderr, parsed_grammar); - + llama_client_slot* getSlot(int id) { + for (llama_client_slot & slot : slots) + { + if ((id == -1 && slot.available()) || slot.id == id) { - auto it = params.sampling_params.logit_bias.find(llama_token_eos(ctx)); - if (it != params.sampling_params.logit_bias.end() && it->second == -INFINITY) { - LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); - } + return &slot; } + } + return nullptr; + } - std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + bool launchSlot(llama_client_slot* &slot) { + if(!slot->loadGrammar(llama_token_eos(ctx))) { + return false; } - ctx_sampling = llama_sampling_context_init(params, grammar); + all_slots_are_idle = false; + slot->command = LOAD_PROMPT; + LOG_TEE("slot %i is processing\n", slot->id); return true; } @@ -665,119 +646,253 @@ struct llama_server_context // release all slots for (llama_client_slot &slot : slots) { - params.n_keep = (int)num_prompt_tokens; + slot.release(); } - params.n_keep = std::min(params.n_ctx - 4, params.n_keep); + waitAllAreIdle(); + all_slots_are_idle = true; - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)params.n_ctx) - { - printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens); - // todo we probably want to cut from both sides - const int n_left = (params.n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", params.n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; - } - 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); + // wait until system prompt load + update_system_prompt = true; + while(update_system_prompt) { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); } + // system prompt loaded, continue + } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - embd = prompt_tokens; - - if (n_past == num_prompt_tokens) - { - // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); - n_past--; + void processSystemPromptData(json sys_props) { + system_prompt = sys_props.value("prompt", ""); + user_name = sys_props.value("anti_prompt", ""); + assistant_name = sys_props.value("assistant_name", ""); + if(slots.size() > 0) { + notifySystemPromptChanged(); + } else { + update_system_prompt = true; } + } - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); - - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - - has_next_token = true; + void waitAllAreIdle() { + bool wait = true; + while(wait) { + wait = false; + for (auto &slot : slots) + { + if (!slot.available()) + { + wait = true; + break; + } + } + } } - void loadPrompt() + + size_t findStoppingStrings(const std::string &text, const size_t last_token_size, + const stop_type type, llama_client_slot &slot) { - auto prompt_tokens = tokenize(prompt, true); // always add BOS + size_t stop_pos = std::string::npos; + for (const std::string &word : slot.params.antiprompt) + { + size_t pos; + if (type == STOP_FULL) + { + const size_t tmp = word.size() + last_token_size; + const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; + pos = text.find(word, from_pos); + } + else + { + pos = find_partial_stop_string(word, text); + } + if (pos != std::string::npos && + (stop_pos == std::string::npos || pos < stop_pos)) + { + if (type == STOP_FULL) + { + slot.stopped_word = true; + slot.stopping_word = word; + slot.has_next_token = false; + } + stop_pos = pos; - num_prompt_tokens = prompt_tokens.size(); + } + } + return stop_pos; + } - if (params.n_keep < 0) - { - slot.stopped_limit = true; - slot.has_next_token = false; + bool processToken(completion_token_output & result, llama_client_slot & slot) { + // remember which tokens were sampled - used for repetition penalties during sampling + const std::string token_str = llama_token_to_piece(ctx, result.tok); + slot.sampled = result.tok; + + // search stop word and delete it + slot.generated_text += token_str; + slot.has_next_token = true; + + size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + const std::string str_test = slot.generated_text.substr(pos); + bool is_stop_full = false; + size_t stop_pos = findStoppingStrings(str_test, token_str.size(), STOP_FULL, slot); + if (stop_pos != std::string::npos) { + is_stop_full = true; + slot.generated_text.erase( + slot.generated_text.begin() + pos + stop_pos, + slot.generated_text.end()); + pos = std::min(slot.sent_count, slot.generated_text.size()); + } else { + is_stop_full = false; + stop_pos = findStoppingStrings(str_test, token_str.size(), + STOP_PARTIAL, slot); } - params.n_keep = std::min(n_ctx - 4, params.n_keep); - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t)n_ctx) + // check if there is any token to predict + if(stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { + // no send the stop word in the response + result.text_to_send = slot.generated_text.substr(pos, std::string::npos); + slot.sent_count += result.text_to_send.size(); + // add the token to slot queue and cache + } + slot.addTokenString(result); + if (slot.multibyte_pending > 0) { - const int n_left = (n_ctx - params.n_keep) / 2; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); - std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - - truncated = true; - prompt_tokens = new_tokens; + slot.multibyte_pending -= token_str.size(); } - else + else if (token_str.size() == 1) { - 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); + const char c = token_str[0]; + // 2-byte characters: 110xxxxx 10xxxxxx + if ((c & 0xE0) == 0xC0) + { + slot.multibyte_pending = 1; + // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx + } + else if ((c & 0xF0) == 0xE0) + { + slot.multibyte_pending = 2; + // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx + } + else if ((c & 0xF8) == 0xF0) + { + slot.multibyte_pending = 3; + } + else + { + slot.multibyte_pending = 0; + } } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); + if (slot.multibyte_pending > 0 && !slot.has_next_token) + { + slot.has_next_token = true; + } - embd = prompt_tokens; - if (n_past == num_prompt_tokens) + // check the limits + if ( + slot.n_decoded > 2 && slot.has_next_token && !slot.hasBudget(params)) { - // we have to evaluate at least 1 token to generate logits. - n_past--; + slot.stopped_limit = true; + slot.has_next_token = false; } - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)){ + slot.stopped_eos = true; + slot.has_next_token = false; + LOG_VERBOSE("eos token found", {}); + } + + LOG_VERBOSE("next token", { + {"token", result.tok}, + {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"num_tokens_predicted", slot.num_tokens_predicted}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }); + return slot.has_next_token; // continue + } - LOG_VERBOSE("prompt ingested", { - {"n_past", n_past}, - {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, - {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); + bool processImages(llama_client_slot &slot) { + for(slot_image &img : slot.images) { + if(!img.request_encode_image) { + continue; + } + clip_image_f32 img_res; + if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) { + LOG_TEE("Error processing the given image"); + clip_free(clp_ctx); + return false; + } + img.image_tokens = clip_n_patches(clp_ctx); + img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); + if (!img.image_embedding) { + LOG_TEE("Unable to allocate memory for image embeddings\n"); + clip_free(clp_ctx); + return false; + } + LOG_TEE("slot %i - encoding image %i\n", slot.id, img.id); + if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) { + LOG_TEE("Unable to encode image\n"); + return false; + } + img.request_encode_image = false; + } + return slot.images.size() > 0; + } + + // for multiple images processing + bool ingestImages(llama_client_slot &slot, int n_batch) { + int image_idx = 0; + while(image_idx < slot.images.size()) { + slot_image img = slot.images[image_idx]; + + // process prefix prompt + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + if (llama_decode(ctx, batch_view)) { + LOG_TEE("%s : failed to eval\n", __func__); + return false; + } + } - has_next_token = true; + // process image with llm + for (int i = 0; i < img.image_tokens; i += n_batch) { + int n_eval = img.image_tokens - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + llama_batch batch_img = {int32_t(n_eval), nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + if (llama_decode(ctx, batch_img)) { + LOG_TEE("%s : failed to eval image\n", __func__); + return false; + } + slot.n_past += n_eval; + } + image_idx++; + + // append prefix of next image + batch.n_tokens = 0; + const auto json_prompt = (image_idx >= slot.images.size()) ? + slot.params.input_suffix : // no more images, then process suffix prompt + (json)(slot.images[image_idx].prefix_prompt); + std::vector append_tokens = tokenize(json_prompt, false); // has next image + for (int i = 0; i < append_tokens.size(); ++i) { + llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); + slot.n_past += 1; + batch.n_tokens += 1; + } + } + return true; } bool updateSlots() { @@ -814,159 +929,198 @@ struct llama_server_context slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); - truncated = true; - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); + slot.n_past -= n_discard; + + slot.truncated = true; + + LOG_VERBOSE("input truncated", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + }); + } } - bool tg = true; - while (n_past < embd.size()) - { - int n_eval = (int)embd.size() - n_past; - tg = n_eval == 1; - if (n_eval > params.n_batch) + // decode any currently ongoing sequences + for (auto & slot : slots) { + // release the slot + if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken()) { - n_eval = params.n_batch; + slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; + if(slot.state == SLEEPING) { + LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, slot.cache_tokens.size()); + } else { + LOG_TEE("slot %i released\n", slot.id); + } + slot.command = NONE; + continue; } - if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0))) - { - LOG_ERROR("failed to eval", { - {"n_eval", n_eval}, - {"n_past", n_past}, - {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - has_next_token = false; - return result; + kv_cache_free -= slot.num_prompt_tokens; + + if ( + slot.state == IDLE || + slot.state == SLEEPING || + slot.command == RELEASE) { + continue; } - n_past += n_eval; - } - if (params.n_predict == 0) - { - has_next_token = false; - result.tok = llama_token_eos(ctx); - return result; + slot.i_batch = batch.n_tokens; + + llama_batch_add(batch, slot.sampled, num_tokens_system + slot.n_past, { slot.id }, true); + + slot.n_decoded += 1; + slot.n_past += 1; } + // process in chunks of params.n_batch + int32_t n_batch = params.n_batch; - { - // out of user input, sample next token - std::vector candidates; - candidates.reserve(llama_n_vocab(model)); + // assign workload to the slots + if (params.cont_batching || batch.n_tokens == 0) { + for (auto & slot : slots) { + // need process the prompt + if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) { + slot.state = PROCESSING; + slot.command = NONE; + std::vector prompt_tokens; + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_genereration = 0; + + if(slot.infill) { + bool suff_rm_leading_spc = true; + if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + params.input_suffix.erase(0, 1); + suff_rm_leading_spc = false; + } + auto prefix_tokens = tokenize(slot.params.input_prefix, false); + auto suffix_tokens = tokenize(slot.params.input_suffix, false); + const int space_token = 29871; + if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { + suffix_tokens.erase(suffix_tokens.begin()); + } + prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); + prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.push_back(llama_token_middle(ctx)); + prompt_tokens = prefix_tokens; + } else { + prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt + } - result.tok = llama_sampling_sample(ctx, NULL, ctx_sampling, last_n_tokens, candidates); + slot.num_prompt_tokens = prompt_tokens.size(); - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + if(!slot.params.cache_prompt) { + std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end(), 0); + slot.n_past = 0; + slot.num_prompt_tokens_processed = slot.num_prompt_tokens; + } else { + if (slot.params.n_keep < 0) + { + slot.params.n_keep = (int)slot.num_prompt_tokens; + } + slot.params.n_keep = std::min(max_ctx_per_slot - 4, slot.params.n_keep); + //if input prompt is too big, truncate like normal + if (slot.num_prompt_tokens >= (size_t)max_ctx_per_slot) + { + // applied bug of #3661 + const int n_left = max_ctx_per_slot - slot.params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); + // Use half the left-over space in the context for the prompt + new_tokens.insert(new_tokens.end(), prompt_tokens.end() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + LOG_VERBOSE("input truncated", { + {"n_ctx", max_ctx_per_slot}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); + slot.truncated = true; + prompt_tokens = new_tokens; + slot.num_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.num_prompt_tokens < (size_t)max_ctx_per_slot); + } + const size_t ps = slot.num_prompt_tokens; + std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end() - ps, 0); + std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.ctx_sampling->prev.end() - ps); + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + LOG_TEE("slot %i - in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); + } - const int32_t n_probs = params.sampling_params.n_probs; - if (params.sampling_params.temp <= 0 && n_probs > 0) - { - // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &candidates_p); - } + llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); - 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}); - } + slot.cache_tokens = prompt_tokens; - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(result.tok); - if (tg) { - num_tokens_predicted++; - } - } + if (slot.n_past == slot.num_prompt_tokens) { + // we have to evaluate at least 1 token to generate logits. + printf("we have to evaluate at least 1 token to generate logits\n"); + slot.n_past--; + } - // add it to the context - embd.push_back(result.tok); - // decrement remaining sampling budget - --n_remain; + LOG_VERBOSE("prompt ingested", { + {"n_past", slot.n_past}, + {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, + }); - if (!embd.empty() && embd.back() == llama_token_eos(ctx)) - { - // stopping_word = llama_token_to_piece(ctx, embd.back()); - has_next_token = false; - stopped_eos = true; - LOG_VERBOSE("eos token found", {}); - return result; - } + bool ingest_images = processImages(slot); // has images? - has_next_token = params.n_predict == -1 || n_remain != 0; - return result; - } + // process the prefix of first image + std::vector prefix_tokens = ingest_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; + for (; slot.n_past < prefix_tokens.size(); ++slot.n_past) { + llama_batch_add(batch, prefix_tokens[slot.n_past], num_tokens_system + slot.n_past, { slot.id }, false); + } - size_t findStoppingStrings(const std::string &text, const size_t last_token_size, - const stop_type type) - { - size_t stop_pos = std::string::npos; - for (const std::string &word : params.antiprompt) - { - size_t pos; - if (type == STOP_FULL) - { - const size_t tmp = word.size() + last_token_size; - const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; - pos = text.find(word, from_pos); - } - else - { - pos = find_partial_stop_string(word, text); - } - if (pos != std::string::npos && - (stop_pos == std::string::npos || pos < stop_pos)) - { - if (type == STOP_FULL) - { - stopping_word = word; - stopped_word = true; - has_next_token = false; + if(ingest_images && !ingestImages(slot, n_batch)) { + LOG_TEE("failed processing images\n"); + return false; + } + + // extract the logits only for the last token + if (batch.n_tokens > 0) { + batch.logits[batch.n_tokens - 1] = true; + } + + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; } - stop_pos = pos; } } - return stop_pos; - } - completion_token_output doCompletion() - { - auto token_with_probs = nextToken(); + if (batch.n_tokens == 0) { + all_slots_are_idle = true; + return true; + } - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); - generated_text += token_text; + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; - if (params.sampling_params.n_probs > 0) - { - generated_token_probs.push_back(token_with_probs); - } + const int ret = llama_decode(ctx, batch_view); + if (ret != 0) { + if (n_batch == 1 || ret < 0) { + // if you get here, it means the KV cache is full - try increasing it via the context size + LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); + return false; + } - if (multibyte_pending > 0) - { - multibyte_pending -= token_text.size(); - } - else if (token_text.size() == 1) - { - const char c = token_text[0]; - // 2-byte characters: 110xxxxx 10xxxxxx - if ((c & 0xE0) == 0xC0) - { - multibyte_pending = 1; - // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx - } - else if ((c & 0xF0) == 0xE0) - { - multibyte_pending = 2; - // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx - } - else if ((c & 0xF8) == 0xF0) - { - multibyte_pending = 3; - } - else - { - multibyte_pending = 0; + LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2); + + // retry with half the batch size to try to find a free slot in the KV cache + n_batch /= 2; + i -= n_batch; + continue; } for (auto & slot : slots) { @@ -1625,9 +1779,77 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, } } - llama.ctx_sampling = llama_sampling_context_init(llama.params, llama.grammar); + LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); + if(!llama.multimodal) { + return; + } - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); + const auto &images_data = body.find("image_data"); + if (images_data != body.end() && images_data->is_array()) + { + for (const auto &img : *images_data) + { + slot_image img_sl; + std::string data_b64 = img["data"].get(); + img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); + int width, height, channels; + std::vector image_buffer = base64_decode(data_b64); + data_b64.clear(); + auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); + if(!data) { + LOG_TEE("slot %i - failed to load image id= %i\n", slot->id, img_sl.id); + return; + } + LOG_TEE("slot %i - image id= %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); + img_sl.img_data.nx = width; + img_sl.img_data.ny = height; + img_sl.img_data.size = width * height * 3; + img_sl.img_data.data = new uint8_t[width * height * 3](); + memcpy(img_sl.img_data.data, data, width * height * 3); + stbi_image_free(data); + img_sl.request_encode_image = true; + slot->images.push_back(img_sl); + } + // process prompt + // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} + if(slot->images.size() > 0 && !slot->prompt.is_array()) { + std::string prompt = slot->prompt.get(); + size_t pos = 0, begin_prefix = 0; + std::string pattern = "[img-"; + while ((pos = prompt.find(pattern, pos)) != std::string::npos) { + size_t end_prefix = pos; + pos += pattern.length(); + size_t end_pos = prompt.find("]", pos); + if (end_pos != std::string::npos) { + std::string image_id = prompt.substr(pos, end_pos - pos); + try { + int img_id = std::stoi(image_id); + bool found = false; + for(slot_image &img : slot->images) { + if(img.id == img_id) { + found = true; + img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); + begin_prefix = end_pos + 1; + break; + } + } + if(!found) { + LOG_TEE("ERROR: Image with id %i not found.\n", img_id); + slot->images.clear(); + return; + } + } catch (const std::invalid_argument& e) { + LOG_TEE("Invalid image number id in prompt\n"); + slot->images.clear(); + return; + } + } + } + slot->prompt = ""; + slot->params.input_suffix = prompt.substr(begin_prefix); + slot->params.cache_prompt = false; // multimodal doesn't support cache prompt + } + } } static void parse_options_infill(const json &body, llama_server_context &llama, llama_client_slot *slot) @@ -2144,10 +2366,6 @@ int main(int argc, char **argv) { return 1; } - - if (llama.grammar != nullptr) { - llama_grammar_free(llama.grammar); - } llama_backend_free(); return 0; } From e44ed60187c30b1cab16a7e685ac0cf3e86f8a15 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 13:37:39 +0300 Subject: [PATCH 36/64] server : coding-style normalization --- examples/llava/clip.cpp | 4 +- examples/server/server.cpp | 596 ++++++++++++++++++++++--------------- 2 files changed, 362 insertions(+), 238 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f4258b34d7f16..3e91ffd68c3a9 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -608,8 +608,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); for (int i = 0; i < 3; ++i) { - new_clip->image_mean[i] = *((float *)gguf_get_arr_data(ctx, idx_mean)); - new_clip->image_std[i] = *((float *)gguf_get_arr_data(ctx, idx_std)); + new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean)); + new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); } if (verbosity >= 2) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8beb19983ace0..933f040e86ea1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -4,6 +4,7 @@ #include "grammar-parser.h" #include "../llava/clip.h" + #include "stb_image.h" #ifndef NDEBUG @@ -28,7 +29,6 @@ #define SERVER_VERBOSE 1 #endif -using namespace httplib; using json = nlohmann::json; struct server_params @@ -55,9 +55,13 @@ static bool server_verbose = false; } while (0) #endif -#define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) + +// +// base64 utils (TODO: move to common in the future) +// static const std::string base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" @@ -65,73 +69,112 @@ static const std::string base64_chars = "0123456789+/"; static inline bool is_base64(uint8_t c) { - return (isalnum(c) || (c == '+') || (c == '/')); + return (isalnum(c) || (c == '+') || (c == '/')); } -std::vector base64_decode(std::string const& encoded_string) { - int in_len = encoded_string.size(); - int i = 0; - int j = 0; - int in_ = 0; - uint8_t char_array_4[4], char_array_3[3]; - std::vector ret; - while (in_len-- && ( encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { - char_array_4[i++] = encoded_string[in_]; in_++; - if (i ==4) { - for (i = 0; i <4; i++) - char_array_4[i] = base64_chars.find(char_array_4[i]); - - char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); - char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); - char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - - for (i = 0; (i < 3); i++) - ret.push_back(char_array_3[i]); - i = 0; +static std::vector base64_decode(std::string const &encoded_string) +{ + int i = 0; + int j = 0; + int in_ = 0; + + int in_len = encoded_string.size(); + + uint8_t char_array_4[4]; + uint8_t char_array_3[3]; + + std::vector ret; + + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) + { + char_array_4[i++] = encoded_string[in_]; in_++; + if (i == 4) + { + for (i = 0; i <4; i++) + { + char_array_4[i] = base64_chars.find(char_array_4[i]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) + { + ret.push_back(char_array_3[i]); + } + i = 0; + } } - } - if (i) { - for (j = i; j <4; j++) - char_array_4[j] = 0; + if (i) + { + for (j = i; j <4; j++) + { + char_array_4[j] = 0; + } - for (j = 0; j <4; j++) - char_array_4[j] = base64_chars.find(char_array_4[j]); + for (j = 0; j <4; j++) + { + char_array_4[j] = base64_chars.find(char_array_4[j]); + } - char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); - char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); - char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - for (j = 0; (j < i - 1); j++) ret.push_back(char_array_3[j]); - } + for (j = 0; (j < i - 1); j++) + { + ret.push_back(char_array_3[j]); + } + } - return ret; + return ret; } +// // parallel +// + enum slot_state { IDLE, SLEEPING, - PROCESSING + PROCESSING, }; -enum slot_command { +enum slot_command +{ NONE, LOAD_PROMPT, - RELEASE + RELEASE, }; -struct slot_params { - bool stream = true; - uint32_t seed = -1; // RNG seed - int n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_predict = -1; // new tokens to predict - std::string grammar = ""; // optional BNF-like grammar to constrain sampling - bool cache_prompt = false; // remember a the prompt to avoid reprocessing all prompt +struct slot_params +{ + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + + uint32_t seed = -1; // RNG seed + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_predict = -1; // new tokens to predict + + std::string grammar; // optional BNF-like grammar to constrain sampling + std::vector antiprompt; - json input_prefix; - json input_suffix; + + json input_prefix; + json input_suffix; +}; + +struct slot_image +{ + clip_image_u8 img_data; + bool request_encode_image = false; + float* image_embedding = nullptr; + int image_tokens = 0; + int id; + std::string prefix_prompt = ""; // before of this image }; // completion token output with probabilities @@ -190,6 +233,7 @@ static size_t find_partial_stop_string(const std::string &stop, return std::string::npos; } +// TODO: reuse llama_detokenize template static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) { @@ -204,12 +248,13 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) static void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { - nlohmann::ordered_json log{ + nlohmann::ordered_json log + { {"timestamp", time(nullptr)}, - {"level", level}, - {"function", function}, - {"line", line}, - {"message", message}, + {"level", level}, + {"function", function}, + {"line", line}, + {"message", message}, }; if (!extra.empty()) @@ -239,7 +284,7 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c } // convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector & probs) +static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) { json out = json::array(); for (const auto &prob : probs) @@ -248,21 +293,24 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vectorparams = sparams; @@ -290,15 +338,6 @@ struct llama_sampling_context * llama_sampling_init_srv(const struct llama_sampl return result; } -struct slot_image { - clip_image_u8 img_data; - bool request_encode_image = false; - float* image_embedding = nullptr; - int image_tokens = 0; - int id; - std::string prefix_prompt = ""; // before of this image -}; - struct llama_client_slot { int id; @@ -358,13 +397,15 @@ struct llama_client_slot infill = false; clean_tokens(); - if (ctx_sampling != nullptr) { + if (ctx_sampling != nullptr) + { llama_sampling_free(ctx_sampling); } ctx_sampling = llama_sampling_init_srv(sparams, params.grammar, max_context_size); - for(slot_image img : images) { + for (slot_image img : images) + { free(img.image_embedding); delete[] img.img_data.data; img.prefix_prompt = ""; @@ -374,31 +415,39 @@ struct llama_client_slot // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } - bool loadGrammar(llama_token eos) + bool load_grammar() { + if (ctx_sampling != nullptr) + { + llama_sampling_free(ctx_sampling); + } + ctx_sampling = llama_sampling_init_srv(sparams, params.grammar, max_context_size); return ctx_sampling != nullptr; } - bool hasBudget(gpt_params &global_params) { + bool has_budget(gpt_params &global_params) { n_remaining = -1; - if(params.n_predict != -1) { + if(params.n_predict != -1) + { n_remaining = params.n_predict - n_decoded; - } else if(global_params.n_predict != -1) { + } + else if (global_params.n_predict != -1) + { n_remaining = global_params.n_predict - n_decoded; } return n_remaining > 0 || n_remaining == -1; // no budget || limitless } - bool hasNewToken() { + bool has_new_token() const { return num_tokens_predicted > sent_tokens; } - bool available() { + bool available() const { return state == IDLE && command == NONE; } - bool isProcessing() { + bool is_processing() const { return ((state == IDLE || state == SLEEPING) && command == LOAD_PROMPT) || state == PROCESSING; } @@ -408,8 +457,9 @@ struct llama_client_slot return tkn; } - void addTokenString(completion_token_output token) { - if(command == RELEASE) { + void add_token_string(const completion_token_output &token) { + if (command == RELEASE) + { num_tokens_predicted = 0; return; } @@ -419,13 +469,15 @@ struct llama_client_slot } void release() { - if(state == PROCESSING) { + if (state == PROCESSING) + { t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; command = RELEASE; } } - void clean_tokens() { + void clean_tokens() + { sent_tokens = 0; generated_token_probs.clear(); num_tokens_predicted = 0; @@ -438,7 +490,7 @@ struct llama_server_context // system prompt std::string system_prompt = ""; - bool update_system_prompt = false; + bool need_update_system_prompt = false; std::vector tokens_system; int32_t num_tokens_system; @@ -474,7 +526,7 @@ struct llama_server_context } } - bool loadModel(const gpt_params ¶ms_) + bool load_model(const gpt_params ¶ms_) { params = params_; if(!params.mmproj.empty()) { @@ -583,7 +635,7 @@ struct llama_server_context return prompt_tokens; } - llama_client_slot* getSlot(int id) { + llama_client_slot* get_slot(int id) { for (llama_client_slot & slot : slots) { if ((id == -1 && slot.available()) || slot.id == id) @@ -594,8 +646,9 @@ struct llama_server_context return nullptr; } - bool launchSlot(llama_client_slot* &slot) { - if(!slot->loadGrammar(llama_token_eos(ctx))) { + bool launch_slot(llama_client_slot* &slot) { + if (!slot->load_grammar()) + { return false; } all_slots_are_idle = false; @@ -604,7 +657,7 @@ struct llama_server_context return true; } - void cleanKVCache() { + void kv_cache_clear() { // clear the entire KV cache for (int i = 0; i < params.n_parallel; ++i) { @@ -613,13 +666,13 @@ struct llama_server_context clean_kv_cache = false; } - void updateSystemPrompt() { + void update_system_prompt() { tokens_system = ::llama_tokenize(ctx, system_prompt, true); num_tokens_system = tokens_system.size(); batch.n_tokens = num_tokens_system; - cleanKVCache(); + kv_cache_clear(); for (int32_t i = 0; i < batch.n_tokens; ++i) { @@ -639,40 +692,45 @@ struct llama_server_context } LOG_TEE("system prompt updated\n"); - update_system_prompt = false; + need_update_system_prompt = false; } - void notifySystemPromptChanged() { + void notify_system_prompt_changed() { // release all slots for (llama_client_slot &slot : slots) { slot.release(); } - waitAllAreIdle(); + wait_all_are_idle(); all_slots_are_idle = true; // wait until system prompt load - update_system_prompt = true; - while(update_system_prompt) { + need_update_system_prompt = true; + while(need_update_system_prompt) { std::this_thread::sleep_for(std::chrono::milliseconds(5)); } // system prompt loaded, continue } - void processSystemPromptData(json sys_props) { - system_prompt = sys_props.value("prompt", ""); - user_name = sys_props.value("anti_prompt", ""); + void process_system_prompt_data(const json &sys_props) { + system_prompt = sys_props.value("prompt", ""); + user_name = sys_props.value("anti_prompt", ""); assistant_name = sys_props.value("assistant_name", ""); - if(slots.size() > 0) { - notifySystemPromptChanged(); - } else { - update_system_prompt = true; + + if (slots.size() > 0) + { + notify_system_prompt_changed(); + } + else + { + need_update_system_prompt = true; } } - void waitAllAreIdle() { + void wait_all_are_idle() { bool wait = true; - while(wait) { + while (wait) + { wait = false; for (auto &slot : slots) { @@ -685,8 +743,8 @@ struct llama_server_context } } - size_t findStoppingStrings(const std::string &text, const size_t last_token_size, - const stop_type type, llama_client_slot &slot) + static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, + const stop_type type, llama_client_slot &slot) { size_t stop_pos = std::string::npos; for (const std::string &word : slot.params.antiprompt) @@ -718,7 +776,7 @@ struct llama_server_context return stop_pos; } - bool processToken(completion_token_output & result, llama_client_slot & slot) { + bool process_token(completion_token_output &result, llama_client_slot &slot) { // remember which tokens were sampled - used for repetition penalties during sampling const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; @@ -730,7 +788,7 @@ struct llama_server_context size_t pos = std::min(slot.sent_count, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; - size_t stop_pos = findStoppingStrings(str_test, token_str.size(), STOP_FULL, slot); + size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); if (stop_pos != std::string::npos) { is_stop_full = true; slot.generated_text.erase( @@ -739,7 +797,7 @@ struct llama_server_context pos = std::min(slot.sent_count, slot.generated_text.size()); } else { is_stop_full = false; - stop_pos = findStoppingStrings(str_test, token_str.size(), + stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); } @@ -750,7 +808,7 @@ struct llama_server_context slot.sent_count += result.text_to_send.size(); // add the token to slot queue and cache } - slot.addTokenString(result); + slot.add_token_string(result); if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -786,7 +844,7 @@ struct llama_server_context // check the limits if ( - slot.n_decoded > 2 && slot.has_next_token && !slot.hasBudget(params)) + slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params)) { slot.stopped_limit = true; slot.has_next_token = false; @@ -812,26 +870,32 @@ struct llama_server_context return slot.has_next_token; // continue } - bool processImages(llama_client_slot &slot) { - for(slot_image &img : slot.images) { - if(!img.request_encode_image) { + bool processImages(llama_client_slot &slot) + { + for (slot_image &img : slot.images) + { + if (!img.request_encode_image) + { continue; } clip_image_f32 img_res; - if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) { + if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) + { LOG_TEE("Error processing the given image"); clip_free(clp_ctx); return false; } img.image_tokens = clip_n_patches(clp_ctx); img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); - if (!img.image_embedding) { + if (!img.image_embedding) + { LOG_TEE("Unable to allocate memory for image embeddings\n"); clip_free(clp_ctx); return false; } LOG_TEE("slot %i - encoding image %i\n", slot.id, img.id); - if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) { + if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) + { LOG_TEE("Unable to encode image\n"); return false; } @@ -841,13 +905,16 @@ struct llama_server_context } // for multiple images processing - bool ingestImages(llama_client_slot &slot, int n_batch) { + bool ingest_images(llama_client_slot &slot, int n_batch) + { int image_idx = 0; - while(image_idx < slot.images.size()) { + while (image_idx < (int) slot.images.size()) + { slot_image img = slot.images[image_idx]; // process prefix prompt - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) + { const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { n_tokens, @@ -859,20 +926,25 @@ struct llama_server_context batch.logits + i, 0, 0, 0, // unused }; - if (llama_decode(ctx, batch_view)) { + if (llama_decode(ctx, batch_view)) + { LOG_TEE("%s : failed to eval\n", __func__); return false; } } // process image with llm - for (int i = 0; i < img.image_tokens; i += n_batch) { + for (int i = 0; i < img.image_tokens; i += n_batch) + { int n_eval = img.image_tokens - i; - if (n_eval > n_batch) { + if (n_eval > n_batch) + { n_eval = n_batch; } + llama_batch batch_img = {int32_t(n_eval), nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; - if (llama_decode(ctx, batch_img)) { + if (llama_decode(ctx, batch_img)) + { LOG_TEE("%s : failed to eval image\n", __func__); return false; } @@ -882,11 +954,13 @@ struct llama_server_context // append prefix of next image batch.n_tokens = 0; - const auto json_prompt = (image_idx >= slot.images.size()) ? + const auto json_prompt = (image_idx >= (int) slot.images.size()) ? slot.params.input_suffix : // no more images, then process suffix prompt (json)(slot.images[image_idx].prefix_prompt); + std::vector append_tokens = tokenize(json_prompt, false); // has next image - for (int i = 0; i < append_tokens.size(); ++i) { + for (int i = 0; i < (int) append_tokens.size(); ++i) + { llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); slot.n_past += 1; batch.n_tokens += 1; @@ -895,25 +969,29 @@ struct llama_server_context return true; } - bool updateSlots() { + bool update_slots() { // update the system prompt wait until all slots are idle state - if(update_system_prompt) { - updateSystemPrompt(); + if (need_update_system_prompt) + { + update_system_prompt(); } batch.n_tokens = 0; int kv_cache_free = (n_ctx - num_tokens_system); - if(all_slots_are_idle) { - if(system_prompt.empty() && clean_kv_cache) { - cleanKVCache(); + if (all_slots_are_idle) + { + if (system_prompt.empty() && clean_kv_cache) + { + kv_cache_clear(); } // avoid 100% usage of cpu all time std::this_thread::sleep_for(std::chrono::milliseconds(5)); } - for(llama_client_slot &slot : slots) { - if (slot.isProcessing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot) + for (llama_client_slot &slot : slots) + { + if (slot.is_processing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot) { // Shift context const int n_left = slot.n_past - slot.params.n_keep - 1; @@ -942,14 +1020,17 @@ struct llama_server_context } // decode any currently ongoing sequences - for (auto & slot : slots) { + for (auto & slot : slots) + { // release the slot - if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken()) + if (slot.state == PROCESSING && slot.command == RELEASE && !slot.has_new_token()) { slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; if(slot.state == SLEEPING) { - LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, slot.cache_tokens.size()); - } else { + LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, (int) slot.cache_tokens.size()); + } + else + { LOG_TEE("slot %i released\n", slot.id); } slot.command = NONE; @@ -961,7 +1042,8 @@ struct llama_server_context if ( slot.state == IDLE || slot.state == SLEEPING || - slot.command == RELEASE) { + slot.command == RELEASE) + { continue; } @@ -976,19 +1058,24 @@ struct llama_server_context int32_t n_batch = params.n_batch; // assign workload to the slots - if (params.cont_batching || batch.n_tokens == 0) { - for (auto & slot : slots) { + if (params.cont_batching || batch.n_tokens == 0) + { + for (auto & slot : slots) + { // need process the prompt - if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) { + if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) + { slot.state = PROCESSING; slot.command = NONE; std::vector prompt_tokens; slot.t_start_process_prompt = ggml_time_us(); slot.t_start_genereration = 0; - if(slot.infill) { + if (slot.infill) + { bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) + { params.input_suffix.erase(0, 1); suff_rm_leading_spc = false; } @@ -1010,11 +1097,14 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); - if(!slot.params.cache_prompt) { + if(!slot.params.cache_prompt) + { std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end(), 0); slot.n_past = 0; slot.num_prompt_tokens_processed = slot.num_prompt_tokens; - } else { + } + else + { if (slot.params.n_keep < 0) { slot.params.n_keep = (int)slot.num_prompt_tokens; @@ -1053,7 +1143,8 @@ struct llama_server_context slot.cache_tokens = prompt_tokens; - if (slot.n_past == slot.num_prompt_tokens) { + if (slot.n_past == (int) slot.num_prompt_tokens) + { // we have to evaluate at least 1 token to generate logits. printf("we have to evaluate at least 1 token to generate logits\n"); slot.n_past--; @@ -1065,21 +1156,24 @@ struct llama_server_context {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); - bool ingest_images = processImages(slot); // has images? + const bool has_images = processImages(slot); // has images? // process the prefix of first image - std::vector prefix_tokens = ingest_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; - for (; slot.n_past < prefix_tokens.size(); ++slot.n_past) { + std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) + { llama_batch_add(batch, prefix_tokens[slot.n_past], num_tokens_system + slot.n_past, { slot.id }, false); } - if(ingest_images && !ingestImages(slot, n_batch)) { + if (has_images && !ingest_images(slot, n_batch)) + { LOG_TEE("failed processing images\n"); return false; } // extract the logits only for the last token - if (batch.n_tokens > 0) { + if (batch.n_tokens > 0) + { batch.logits[batch.n_tokens - 1] = true; } @@ -1089,14 +1183,17 @@ struct llama_server_context } } - if (batch.n_tokens == 0) { + if (batch.n_tokens == 0) + { all_slots_are_idle = true; return true; } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) + { const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); - llama_batch batch_view = { + llama_batch batch_view = + { n_tokens, batch.token + i, nullptr, @@ -1108,8 +1205,10 @@ struct llama_server_context }; const int ret = llama_decode(ctx, batch_view); - if (ret != 0) { - if (n_batch == 1 || ret < 0) { + if (ret != 0) + { + if (n_batch == 1 || ret < 0) + { // if you get here, it means the KV cache is full - try increasing it via the context size LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); return false; @@ -1123,13 +1222,16 @@ struct llama_server_context continue; } - for (auto & slot : slots) { - if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { + for (auto & slot : slots) + { + if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) + { continue; } // prompt evaluated for embedding - if(params.embedding) { + if (params.embedding) + { slot.release(); slot.i_batch = -1; return true; @@ -1140,13 +1242,15 @@ struct llama_server_context llama_sampling_accept(slot.ctx_sampling, ctx, id); - if (slot.n_decoded == 1) { + if (slot.n_decoded == 1) + { slot.t_start_genereration = ggml_time_us(); slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; } llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; result.tok = id; + const int32_t n_probs = slot.sparams.n_probs; if (slot.sparams.temp <= 0 && n_probs > 0) { @@ -1159,9 +1263,11 @@ struct llama_server_context result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); } - if (!processToken(result, slot)) { + if (!process_token(result, slot)) + { slot.release(); } + kv_cache_free -= slot.num_tokens_predicted; slot.i_batch = -1; } @@ -1174,7 +1280,7 @@ struct llama_server_context return true; } - std::vector getEmbedding() + std::vector get_embedding() { static const int n_embd = llama_n_embd(model); if (!params.embedding) @@ -1190,7 +1296,8 @@ struct llama_server_context } }; -struct server_beam_search_callback_data { +struct server_beam_search_callback_data +{ llama_context * ctx; llama_client_slot * slot; }; @@ -1536,13 +1643,13 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - std::string systm_content = ""; + std::string systm_content; std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), std::back_inserter(systm_content) ); - llama.processSystemPromptData(json::parse(systm_content)); + llama.process_system_prompt_data(json::parse(systm_content)); } else if(arg == "--mmproj") { if (++i >= argc) @@ -1613,14 +1720,16 @@ static json format_generation_settings(llama_server_context &llama, llama_client static json format_embedding_response(llama_server_context &llama) { - return json{ - {"embedding", llama.getEmbedding()}, + return json + { + {"embedding", llama.get_embedding()}, }; } static json format_timings(llama_client_slot* slot) { - return json{ + return json + { {"prompt_n", slot->num_prompt_tokens_processed}, {"prompt_ms", slot->t_prompt_processing}, {"prompt_per_token_ms",slot->t_prompt_processing / slot->num_prompt_tokens_processed}, @@ -1636,7 +1745,8 @@ static json format_timings(llama_client_slot* slot) static json format_final_response(llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs) { - json res = json{ + json res = json + { {"content", content}, {"slot_id", slot->id}, {"stop", true}, @@ -1665,7 +1775,8 @@ static json format_final_response(llama_server_context &llama, llama_client_slot static json format_partial_response( llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs ) { - json res = json{ + json res = json + { {"content", content }, {"stop", false}, { "slot_id", slot->id }, @@ -1706,26 +1817,26 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, slot_params default_params; llama_sampling_params default_sparams; - slot->params.stream = json_value(body, "stream", false); - slot->params.cache_prompt = json_value(body, "cache_prompt", false); - slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict); - slot->sparams.top_k = json_value(body, "top_k", default_sparams.top_k); - slot->sparams.top_p = json_value(body, "top_p", default_sparams.top_p); - slot->sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); - slot->sparams.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n); - slot->sparams.temp = json_value(body, "temperature", default_sparams.temp); - slot->sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty); - slot->sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty); + slot->params.stream = json_value(body, "stream", false); + slot->params.cache_prompt = json_value(body, "cache_prompt", false); + slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(body, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(body, "top_p", default_sparams.top_p); + slot->sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); + slot->sparams.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n); + slot->sparams.temp = json_value(body, "temperature", default_sparams.temp); + slot->sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty); + slot->sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty); slot->sparams.frequency_penalty = json_value(body, "frequency_penalty", default_sparams.frequency_penalty); - slot->sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); - slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - slot->params.n_keep = json_value(body, "n_keep", slot->params.n_keep); - slot->params.seed = json_value(body, "seed", default_params.seed); - slot->params.grammar = json_value(body, "grammar", default_params.grammar); - slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); + slot->sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); + slot->params.n_keep = json_value(body, "n_keep", slot->params.n_keep); + slot->params.seed = json_value(body, "seed", default_params.seed); + slot->params.grammar = json_value(body, "grammar", default_params.grammar); + slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); if (body.count("prompt") != 0) { @@ -1780,7 +1891,8 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, } LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); - if(!llama.multimodal) { + if(!llama.multimodal) + { return; } @@ -1812,7 +1924,8 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, } // process prompt // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} - if(slot->images.size() > 0 && !slot->prompt.is_array()) { + if (slot->images.size() > 0 && !slot->prompt.is_array()) + { std::string prompt = slot->prompt.get(); size_t pos = 0, begin_prefix = 0; std::string pattern = "[img-"; @@ -1873,7 +1986,7 @@ static void parse_options_infill(const json &body, llama_server_context &llama, parse_options_completion(body, slot, llama); } -static void log_server_request(const Request &req, const Response &res) +static void log_server_request(const httplib::Request &req, const httplib::Response &res) { LOG_INFO("request", { {"remote_addr", req.remote_addr}, @@ -1978,44 +2091,44 @@ int main(int argc, char **argv) }); // load the model - if (!llama.loadModel(params)) + if (!llama.load_model(params)) { return 1; } llama.initialize(); - Server svr; + httplib::Server svr; svr.set_default_headers({{"Server", "llama.cpp"}, {"Access-Control-Allow-Origin", "*"}, {"Access-Control-Allow-Headers", "content-type"}}); // this is only called if no index.html is found in the public --path - svr.Get("/", [](const Request &, Response &res) + svr.Get("/", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); return false; }); // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const Request &, Response &res) + svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); return false; }); // this is only called if no index.html is found in the public --path - svr.Get("/completion.js", [](const Request &, Response &res) + svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); return false; }); // this is only called if no index.html is found in the public --path - svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res) + svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); return false; }); - svr.Get("/props", [&llama](const Request & /*req*/, Response &res) + svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) { res.set_header("Access-Control-Allow-Origin", "*"); json data = { @@ -2024,11 +2137,11 @@ int main(int argc, char **argv) }; res.set_content(data.dump(), "application/json"); }); - svr.Post("/completion", [&llama](const Request &req, Response &res) + svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res) { json data = json::parse(req.body); - llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1)); + llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); if(slot == nullptr) { LOG_TEE("slot unavailable\n"); @@ -2038,14 +2151,14 @@ int main(int argc, char **argv) } if(data.contains("system_prompt")) { - llama.processSystemPromptData(data["system_prompt"]); + llama.process_system_prompt_data(data["system_prompt"]); } slot->reset(); parse_options_completion(data, slot, llama); - if (!llama.launchSlot(slot)) + if (!llama.launch_slot(slot)) { res.status = 400; return; @@ -2063,8 +2176,8 @@ int main(int argc, char **argv) // Translate llama.generated_token_probs to llama.generated_text. append_to_generated_text_from_generated_token_probs(llama, slot); } else { - while (slot->isProcessing()) { - if(slot->hasNewToken()) { + while (slot->is_processing()) { + if(slot->has_new_token()) { completion_text += slot->next().text_to_send; } else { std::this_thread::sleep_for(std::chrono::microseconds(5)); @@ -2084,10 +2197,10 @@ int main(int argc, char **argv) res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { - const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { + const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { size_t sent_token_probs_index = 0; - while(slot->isProcessing()) { - if(slot->hasNewToken()) { // new token notification + while(slot->is_processing()) { + if(slot->has_new_token()) { // new token notification const completion_token_output token = slot->next(); std::vector probs_output = {}; if (slot->sparams.n_probs > 0) { @@ -2137,19 +2250,19 @@ int main(int argc, char **argv) sink.done(); return true; }; - auto on_complete = [slot, &llama] (bool) { + auto on_complete = [slot] (bool) { slot->release(); slot->clean_tokens(); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); - svr.Post("/infill", [&llama](const Request &req, Response &res) + svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) { json data = json::parse(req.body); - llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1)); + llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); if(slot == nullptr) { LOG_TEE("slot unavailable\n"); @@ -2159,7 +2272,7 @@ int main(int argc, char **argv) } if(data.contains("system_prompt")) { - llama.processSystemPromptData(data["system_prompt"]); + llama.process_system_prompt_data(data["system_prompt"]); } slot->reset(); @@ -2167,23 +2280,28 @@ int main(int argc, char **argv) parse_options_infill(data, llama, slot); - if (!llama.launchSlot(slot)) + if (!llama.launch_slot(slot)) { res.status = 400; return; } - if(!slot->params.stream) { + if (!slot->params.stream) + { std::string completion_text = ""; - while (slot->isProcessing()) { - if(slot->hasNewToken()) { + while (slot->is_processing()) + { + if(slot->has_new_token()) + { completion_text += slot->next().text_to_send; } else { std::this_thread::sleep_for(std::chrono::microseconds(5)); } } + auto probs = slot->generated_token_probs; - if (slot->sparams.n_probs > 0 && slot->stopped_word) { + if (slot->sparams.n_probs > 0 && slot->stopped_word) + { const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); } @@ -2192,11 +2310,13 @@ int main(int argc, char **argv) slot_print_timings(slot); res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); - } else { - const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) { + } + else + { + const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { size_t sent_token_probs_index = 0; - while(slot->isProcessing()) { - if(slot->hasNewToken()) { // new token notification + while(slot->is_processing()) { + if(slot->has_new_token()) { // new token notification const completion_token_output token = slot->next(); std::vector probs_output = {}; if (slot->sparams.n_probs > 0) { @@ -2246,7 +2366,8 @@ int main(int argc, char **argv) sink.done(); return true; }; - auto on_complete = [slot, &llama] (bool) { + auto on_complete = [slot] (bool) + { slot->clean_tokens(); slot->release(); }; @@ -2254,15 +2375,15 @@ int main(int argc, char **argv) } }); - svr.Get("/model.json", [&llama](const Request &, Response &res) + svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) { - const json data = format_generation_settings(llama, llama.getSlot(0)); + const json data = format_generation_settings(llama, llama.get_slot(0)); return res.set_content(data.dump(), "application/json"); }); - svr.Options(R"(/.*)", [](const Request &, Response &res) + svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json"); }); - svr.Post("/tokenize", [&llama](const Request &req, Response &res) + svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) { const json body = json::parse(req.body); @@ -2274,7 +2395,7 @@ int main(int argc, char **argv) const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json"); }); - svr.Post("/detokenize", [&llama](const Request &req, Response &res) + svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) { const json body = json::parse(req.body); @@ -2288,10 +2409,10 @@ int main(int argc, char **argv) const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json"); }); - svr.Post("/embedding", [&llama](const Request &req, Response &res) + svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) { const json body = json::parse(req.body); - llama_client_slot* slot = llama.getSlot(-1); + llama_client_slot* slot = llama.get_slot(-1); slot->reset(); if (body.count("content") != 0) { @@ -2302,8 +2423,8 @@ int main(int argc, char **argv) slot->prompt = ""; } llama.params.n_predict = 0; - llama.launchSlot(slot); - while(slot->isProcessing()) { + llama.launch_slot(slot); + while (slot->is_processing()) { std::this_thread::sleep_for(std::chrono::microseconds(10)); } const json data = format_embedding_response(llama); @@ -2311,7 +2432,7 @@ int main(int argc, char **argv) svr.set_logger(log_server_request); - svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep) + svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) { const char fmt[] = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; @@ -2325,7 +2446,7 @@ int main(int argc, char **argv) res.set_content(buf, "text/plain"); res.status = 500; }); - svr.set_error_handler([](const Request &, Response &res) + svr.set_error_handler([](const httplib::Request &, httplib::Response &res) { if (res.status == 400) { res.set_content("Invalid request", "text/plain"); @@ -2335,7 +2456,7 @@ int main(int argc, char **argv) } }); // set timeouts and change hostname and port - svr.set_read_timeout(sparams.read_timeout); + svr.set_read_timeout (sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); if (!svr.bind_to_port(sparams.hostname, sparams.port)) @@ -2355,17 +2476,20 @@ int main(int argc, char **argv) {"port", sparams.port}, }); std::thread t([&llama]() - { + { bool running = true; while (running) { - running = llama.updateSlots(); - } }); + running = llama.update_slots(); + } + } + ); if (!svr.listen_after_bind()) { return 1; } + llama_backend_free(); return 0; } From 654e0a1fe07dfd720682ea09d8e1cbb4077d2cab Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 14:09:45 +0300 Subject: [PATCH 37/64] server : coding-style normalization (part 2) --- examples/server/server.cpp | 727 ++++++++++++++++++++----------------- 1 file changed, 390 insertions(+), 337 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 933f040e86ea1..ab1b5fbd3f63e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1435,7 +1435,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_ctx = std::stoi(argv[i]); } - else if (arg == "-cps" || arg == "--ctx-per-slot" || arg == "--ctx_per_slot") + else if (arg == "-cps" || arg == "--ctx-per-slot" || arg == "--ctx_per_slot") { if (++i >= argc) { @@ -1691,30 +1691,30 @@ static json format_generation_settings(llama_server_context &llama, llama_client eos_bias->second < 0.0f && std::isinf(eos_bias->second); return json{ - {"n_ctx", llama.n_ctx}, - {"model", llama.params.model_alias}, - {"seed", slot->params.seed}, - {"temp", slot->sparams.temp}, - {"top_k", slot->sparams.top_k}, - {"top_p", slot->sparams.top_p}, - {"tfs_z", slot->sparams.tfs_z}, - {"typical_p", slot->sparams.typical_p}, - {"repeat_last_n", slot->sparams.repeat_last_n}, - {"repeat_penalty", slot->sparams.repeat_penalty}, - {"presence_penalty",slot->sparams.presence_penalty}, + {"n_ctx", llama.n_ctx}, + {"model", llama.params.model_alias}, + {"seed", slot->params.seed}, + {"temp", slot->sparams.temp}, + {"top_k", slot->sparams.top_k}, + {"top_p", slot->sparams.top_p}, + {"tfs_z", slot->sparams.tfs_z}, + {"typical_p", slot->sparams.typical_p}, + {"repeat_last_n", slot->sparams.repeat_last_n}, + {"repeat_penalty", slot->sparams.repeat_penalty}, + {"presence_penalty", slot->sparams.presence_penalty}, {"frequency_penalty", slot->sparams.frequency_penalty}, - {"mirostat", slot->sparams.mirostat}, - {"mirostat_tau", slot->sparams.mirostat_tau}, - {"mirostat_eta", slot->sparams.mirostat_eta}, - {"penalize_nl", slot->sparams.penalize_nl}, - {"stop", slot->params.antiprompt}, - {"n_predict", slot->params.n_predict}, - {"n_keep", llama.params.n_keep}, - {"ignore_eos", ignore_eos}, - {"stream", slot->params.stream}, - {"logit_bias", slot->sparams.logit_bias}, - {"n_probs", slot->sparams.n_probs}, - {"grammar", slot->params.grammar}, + {"mirostat", slot->sparams.mirostat}, + {"mirostat_tau", slot->sparams.mirostat_tau}, + {"mirostat_eta", slot->sparams.mirostat_eta}, + {"penalize_nl", slot->sparams.penalize_nl}, + {"stop", slot->params.antiprompt}, + {"n_predict", slot->params.n_predict}, + {"n_keep", llama.params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", slot->params.stream}, + {"logit_bias", slot->sparams.logit_bias}, + {"n_probs", slot->sparams.n_probs}, + {"grammar", slot->params.grammar}, }; } @@ -1730,15 +1730,15 @@ static json format_timings(llama_client_slot* slot) { return json { - {"prompt_n", slot->num_prompt_tokens_processed}, - {"prompt_ms", slot->t_prompt_processing}, - {"prompt_per_token_ms",slot->t_prompt_processing / slot->num_prompt_tokens_processed}, - {"prompt_per_second", 1e3 / slot->t_prompt_processing * slot->num_prompt_tokens_processed}, - - {"predicted_n", slot->n_decoded}, - {"predicted_ms", slot->t_token_generation}, - {"predicted_per_token_ms",slot->t_token_generation / slot->n_decoded}, - {"predicted_per_second", 1e3 / slot->t_token_generation * slot->n_decoded}, + {"prompt_n", slot->num_prompt_tokens_processed}, + {"prompt_ms", slot->t_prompt_processing}, + {"prompt_per_token_ms", slot->t_prompt_processing / slot->num_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / slot->t_prompt_processing * slot->num_prompt_tokens_processed}, + + {"predicted_n", slot->n_decoded}, + {"predicted_ms", slot->t_token_generation}, + {"predicted_per_token_ms", slot->t_token_generation / slot->n_decoded}, + {"predicted_per_second", 1e3 / slot->t_token_generation * slot->n_decoded}, }; } @@ -1747,21 +1747,21 @@ static json format_final_response(llama_server_context &llama, llama_client_slot json res = json { - {"content", content}, - {"slot_id", slot->id}, - {"stop", true}, - {"model", llama.params.model_alias}, - {"tokens_predicted", slot->n_decoded}, - {"tokens_evaluated", slot->num_prompt_tokens}, + {"content", content}, + {"slot_id", slot->id}, + {"stop", true}, + {"model", llama.params.model_alias}, + {"tokens_predicted", slot->n_decoded}, + {"tokens_evaluated", slot->num_prompt_tokens}, {"generation_settings", format_generation_settings(llama, slot)}, - {"prompt", slot->prompt}, - {"truncated", slot->truncated}, - {"stopped_eos", slot->stopped_eos}, - {"stopped_word", slot->stopped_word}, - {"stopped_limit", slot->stopped_limit}, - {"stopping_word", slot->stopping_word}, - {"tokens_cached", slot->n_past}, - {"timings", format_timings(slot)} + {"prompt", slot->prompt}, + {"truncated", slot->truncated}, + {"stopped_eos", slot->stopped_eos}, + {"stopped_word", slot->stopped_word}, + {"stopped_limit", slot->stopped_limit}, + {"stopping_word", slot->stopping_word}, + {"tokens_cached", slot->n_past}, + {"timings", format_timings(slot)} }; if (slot->sparams.n_probs > 0) @@ -1777,9 +1777,9 @@ static json format_partial_response( ) { json res = json { - {"content", content }, - {"stop", false}, - { "slot_id", slot->id }, + {"content", content }, + {"stop", false}, + {"slot_id", slot->id }, {"multimodal", llama.multimodal } }; @@ -1908,7 +1908,7 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, std::vector image_buffer = base64_decode(data_b64); data_b64.clear(); auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); - if(!data) { + if (!data) { LOG_TEE("slot %i - failed to load image id= %i\n", slot->id, img_sl.id); return; } @@ -1933,20 +1933,23 @@ static void parse_options_completion(const json &body, llama_client_slot* slot, size_t end_prefix = pos; pos += pattern.length(); size_t end_pos = prompt.find("]", pos); - if (end_pos != std::string::npos) { + if (end_pos != std::string::npos) + { std::string image_id = prompt.substr(pos, end_pos - pos); - try { + try + { int img_id = std::stoi(image_id); bool found = false; - for(slot_image &img : slot->images) { - if(img.id == img_id) { + for (slot_image &img : slot->images) + { + if (img.id == img_id) { found = true; img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); begin_prefix = end_pos + 1; break; } } - if(!found) { + if (!found) { LOG_TEE("ERROR: Image with id %i not found.\n", img_id); slot->images.clear(); return; @@ -2017,7 +2020,7 @@ static bool is_at_eob(const server_beam_search_callback_data & server_context, c // NO TESTED after PR #3589 static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { - auto & llama = *static_cast(callback_data); + auto & llama = *static_cast(callback_data); // Mark beams as EOS as needed. for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { llama_beam_view& beam_view = beams_state.beam_views[i]; @@ -2043,22 +2046,25 @@ static void beam_search_callback(void *callback_data, llama_beams_state beams_st #endif } -struct token_translator { +struct token_translator +{ llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } - std::string operator()(const completion_token_output & cto) const { return (*this)(cto.tok); } + std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } + std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } }; -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot* slot) +static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot) { auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); - if (slot->generated_text.capacity() < slot->generated_text.size() + len) { + if (slot->generated_text.capacity() < slot->generated_text.size() + len) + { slot->generated_text.reserve(slot->generated_text.size() + len); } - for (const completion_token_output & cto : gtps) { + for (const completion_token_output & cto : gtps) + { slot->generated_text += translator(cto); } } @@ -2107,353 +2113,400 @@ int main(int argc, char **argv) // this is only called if no index.html is found in the public --path svr.Get("/", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); - return false; }); + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); + return false; + }); // this is only called if no index.js is found in the public --path svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); - return false; }); + res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); + return false; + }); // this is only called if no index.html is found in the public --path svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); - return false; }); + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); + return false; + }); // this is only called if no index.html is found in the public --path svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); - return false; }); + res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); + return false; + }); - svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) + svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) { res.set_header("Access-Control-Allow-Origin", "*"); json data = { { "user_name", llama.user_name.c_str() }, { "assistant_name", llama.assistant_name.c_str() } }; - res.set_content(data.dump(), "application/json"); }); + res.set_content(data.dump(), "application/json"); + }); svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res) - { - json data = json::parse(req.body); + { + json data = json::parse(req.body); - llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); + llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); - if(slot == nullptr) { - LOG_TEE("slot unavailable\n"); - res.status = 404; - res.set_content("slot_error", "text/plain"); - return; - } + if(slot == nullptr) { + LOG_TEE("slot unavailable\n"); + res.status = 404; + res.set_content("slot_error", "text/plain"); + return; + } - if(data.contains("system_prompt")) { - llama.process_system_prompt_data(data["system_prompt"]); - } + if(data.contains("system_prompt")) { + llama.process_system_prompt_data(data["system_prompt"]); + } - slot->reset(); + slot->reset(); - parse_options_completion(data, slot, llama); + parse_options_completion(data, slot, llama); - if (!llama.launch_slot(slot)) - { - res.status = 400; - return; - } + if (!llama.launch_slot(slot)) + { + res.status = 400; + return; + } - if (!slot->params.stream) { - std::string completion_text = ""; - if (llama.params.n_beams) { - // Fill llama.generated_token_probs vector with final beam. - server_beam_search_callback_data data_beam; - data_beam.slot = slot; - data_beam.ctx = llama.ctx; - llama_beam_search(llama.ctx, beam_search_callback, &data_beam, llama.params.n_beams, - slot->n_past, llama.params.n_predict); - // Translate llama.generated_token_probs to llama.generated_text. - append_to_generated_text_from_generated_token_probs(llama, slot); - } else { - while (slot->is_processing()) { - if(slot->has_new_token()) { - completion_text += slot->next().text_to_send; - } else { - std::this_thread::sleep_for(std::chrono::microseconds(5)); + if (!slot->params.stream) { + std::string completion_text = ""; + if (llama.params.n_beams) + { + // Fill llama.generated_token_probs vector with final beam. + server_beam_search_callback_data data_beam; + data_beam.slot = slot; + data_beam.ctx = llama.ctx; + llama_beam_search(llama.ctx, beam_search_callback, &data_beam, llama.params.n_beams, + slot->n_past, llama.params.n_predict); + // Translate llama.generated_token_probs to llama.generated_text. + append_to_generated_text_from_generated_token_probs(llama, slot); + } + else + { + while (slot->is_processing()) + { + if (slot->has_new_token()) + { + completion_text += slot->next().text_to_send; + } + else + { + std::this_thread::sleep_for(std::chrono::microseconds(5)); + } + } } - } - } - auto probs = slot->generated_token_probs; - if (slot->sparams.n_probs > 0 && slot->stopped_word) { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); - probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); - } + auto probs = slot->generated_token_probs; + if (slot->sparams.n_probs > 0 && slot->stopped_word) + { + const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); + probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); + } - const json data = format_final_response(llama, slot, completion_text, probs); - slot_print_timings(slot); - slot->release(); - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), - "application/json"); - } else { - const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { - size_t sent_token_probs_index = 0; - while(slot->is_processing()) { - if(slot->has_new_token()) { // new token notification - const completion_token_output token = slot->next(); - std::vector probs_output = {}; - if (slot->sparams.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); + const json data = format_final_response(llama, slot, completion_text, probs); + slot_print_timings(slot); + slot->release(); + res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); + } else { + const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { + size_t sent_token_probs_index = 0; + while (slot->is_processing()) + { + if (slot->has_new_token()) + { // new token notification + const completion_token_output token = slot->next(); + std::vector probs_output = {}; + if (slot->sparams.n_probs > 0) + { + const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); + size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); + size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); + if (probs_pos < probs_stop_pos) + { + probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); + } + sent_token_probs_index = probs_stop_pos; + } + const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.c_str(), str.size())) + { + slot->release(); + return false; } - sent_token_probs_index = probs_stop_pos; } - const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if(!sink.write(str.c_str(), str.size())) { - slot->release(); - return false; + else + { + std::this_thread::sleep_for(std::chrono::microseconds(5)); } - } else { - std::this_thread::sleep_for(std::chrono::microseconds(5)); } - } - const json data = format_final_response( - llama, slot, - "", - std::vector( - slot->generated_token_probs.begin(), - slot->generated_token_probs.begin() + sent_token_probs_index) - ); - slot_print_timings(slot); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.data(), str.size())) { + const json data = format_final_response( + llama, slot, + "", + std::vector( + slot->generated_token_probs.begin(), + slot->generated_token_probs.begin() + sent_token_probs_index) + ); + slot_print_timings(slot); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + + if (!sink.write(str.data(), str.size())) + { + slot->release(); + return false; + } + sink.done(); + return true; + }; + auto on_complete = [slot] (bool) { slot->release(); - return false; - } - sink.done(); - return true; - }; - auto on_complete = [slot] (bool) { - slot->release(); - slot->clean_tokens(); - }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } }); + slot->clean_tokens(); + }; + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) - { - - json data = json::parse(req.body); - - llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); + { + json data = json::parse(req.body); - if(slot == nullptr) { - LOG_TEE("slot unavailable\n"); - res.status = 404; - res.set_content("slot_error", "text/plain"); - return; - } + llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); - if(data.contains("system_prompt")) { - llama.process_system_prompt_data(data["system_prompt"]); - } + if (slot == nullptr) + { + LOG_TEE("slot unavailable\n"); + res.status = 404; + res.set_content("slot_error", "text/plain"); + return; + } - slot->reset(); - slot->infill = true; + if (data.contains("system_prompt")) + { + llama.process_system_prompt_data(data["system_prompt"]); + } - parse_options_infill(data, llama, slot); + slot->reset(); + slot->infill = true; - if (!llama.launch_slot(slot)) - { - res.status = 400; - return; - } + parse_options_infill(data, llama, slot); - if (!slot->params.stream) - { - std::string completion_text = ""; - while (slot->is_processing()) - { - if(slot->has_new_token()) + if (!llama.launch_slot(slot)) { - completion_text += slot->next().text_to_send; - } else { - std::this_thread::sleep_for(std::chrono::microseconds(5)); + res.status = 400; + return; } - } - auto probs = slot->generated_token_probs; - if (slot->sparams.n_probs > 0 && slot->stopped_word) - { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); - probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); - } + if (!slot->params.stream) + { + std::string completion_text = ""; + while (slot->is_processing()) + { + if(slot->has_new_token()) + { + completion_text += slot->next().text_to_send; + } + else + { + std::this_thread::sleep_for(std::chrono::microseconds(5)); + } + } - const json data = format_final_response(llama, slot, completion_text, probs); - slot_print_timings(slot); - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), + auto probs = slot->generated_token_probs; + if (slot->sparams.n_probs > 0 && slot->stopped_word) + { + const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); + probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); + } + + const json data = format_final_response(llama, slot, completion_text, probs); + slot_print_timings(slot); + res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); - } - else - { - const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { - size_t sent_token_probs_index = 0; - while(slot->is_processing()) { - if(slot->has_new_token()) { // new token notification - const completion_token_output token = slot->next(); - std::vector probs_output = {}; - if (slot->sparams.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); + } + else + { + const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { + size_t sent_token_probs_index = 0; + while (slot->is_processing()) + { + if (slot->has_new_token()) + { + // new token notification + const completion_token_output token = slot->next(); + std::vector probs_output = {}; + if (slot->sparams.n_probs > 0) + { + const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); + size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); + size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); + if (probs_pos < probs_stop_pos) + { + probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); + } + sent_token_probs_index = probs_stop_pos; + } + const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.c_str(), str.size())) + { + slot->release(); + return false; } - sent_token_probs_index = probs_stop_pos; } - const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if(!sink.write(str.c_str(), str.size())) { - slot->release(); - return false; + else + { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); } - } else { - std::this_thread::sleep_for(std::chrono::milliseconds(5)); } - } - const json data = format_final_response( - llama, slot, - "", - std::vector( - slot->generated_token_probs.begin(), - slot->generated_token_probs.begin() + sent_token_probs_index) - ); - slot_print_timings(slot); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.data(), str.size())) { + const json data = format_final_response( + llama, slot, + "", + std::vector( + slot->generated_token_probs.begin(), + slot->generated_token_probs.begin() + sent_token_probs_index) + ); + slot_print_timings(slot); + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.data(), str.size())) + { + slot->release(); + return false; + } + sink.done(); + return true; + }; + auto on_complete = [slot] (bool) + { + slot->clean_tokens(); slot->release(); - return false; - } - sink.done(); - return true; - }; - auto on_complete = [slot] (bool) - { - slot->clean_tokens(); - slot->release(); - }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); + }; + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) { - const json data = format_generation_settings(llama, llama.get_slot(0)); - return res.set_content(data.dump(), "application/json"); }); + const json data = format_generation_settings(llama, llama.get_slot(0)); + return res.set_content(data.dump(), "application/json"); + }); svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json"); }); svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - - const json body = json::parse(req.body); - std::vector tokens; - if (body.count("content") != 0) - { - tokens = llama.tokenize(body["content"], false); - } - const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json"); }); + { + const json body = json::parse(req.body); + std::vector tokens; + if (body.count("content") != 0) + { + tokens = llama.tokenize(body["content"], false); + } + const json data = format_tokenizer_response(tokens); + return res.set_content(data.dump(), "application/json"); + }); svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - - const json body = json::parse(req.body); - std::string content; - if (body.count("tokens") != 0) - { - const std::vector tokens = body["tokens"]; - content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); - } + { + const json body = json::parse(req.body); + std::string content; + if (body.count("tokens") != 0) + { + const std::vector tokens = body["tokens"]; + content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); + } - const json data = format_detokenized_response(content); - return res.set_content(data.dump(), "application/json"); }); + const json data = format_detokenized_response(content); + return res.set_content(data.dump(), "application/json"); + }); svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) - { - const json body = json::parse(req.body); - llama_client_slot* slot = llama.get_slot(-1); - slot->reset(); - if (body.count("content") != 0) - { - slot->prompt = body["content"]; - } - else - { - slot->prompt = ""; - } - llama.params.n_predict = 0; - llama.launch_slot(slot); - while (slot->is_processing()) { - std::this_thread::sleep_for(std::chrono::microseconds(10)); - } - const json data = format_embedding_response(llama); - return res.set_content(data.dump(), "application/json"); }); + { + const json body = json::parse(req.body); + llama_client_slot* slot = llama.get_slot(-1); + slot->reset(); + if (body.count("content") != 0) + { + slot->prompt = body["content"]; + } + else + { + slot->prompt = ""; + } + llama.params.n_predict = 0; + llama.launch_slot(slot); + while (slot->is_processing()) { + std::this_thread::sleep_for(std::chrono::microseconds(10)); + } + const json data = format_embedding_response(llama); + return res.set_content(data.dump(), "application/json"); + }); svr.set_logger(log_server_request); svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) - { - const char fmt[] = "500 Internal Server Error\n%s"; - char buf[BUFSIZ]; - try { - std::rethrow_exception(std::move(ep)); - } catch (std::exception & e) { - snprintf(buf, sizeof(buf), fmt, e.what()); - } catch (...) { - snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); - } - res.set_content(buf, "text/plain"); - res.status = 500; }); + { + const char fmt[] = "500 Internal Server Error\n%s"; + char buf[BUFSIZ]; + try + { + std::rethrow_exception(std::move(ep)); + } + catch (std::exception &e) + { + snprintf(buf, sizeof(buf), fmt, e.what()); + } + catch (...) + { + snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); + } + res.set_content(buf, "text/plain"); + res.status = 500; + }); svr.set_error_handler([](const httplib::Request &, httplib::Response &res) - { - if (res.status == 400) { - res.set_content("Invalid request", "text/plain"); - } else if (res.status != 500) { - res.set_content("File Not Found", "text/plain"); - res.status = 404; - } }); + { + if (res.status == 400) + { + res.set_content("Invalid request", "text/plain"); + } + else if (res.status != 500) + { + res.set_content("File Not Found", "text/plain"); + res.status = 404; + } + }); // set timeouts and change hostname and port svr.set_read_timeout (sparams.read_timeout); From a8c981b7340f4a9ce62d9086f35c70af5406913b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 14:10:37 +0300 Subject: [PATCH 38/64] server : remove beam-search functionality --- examples/server/server.cpp | 77 +++++--------------------------------- 1 file changed, 9 insertions(+), 68 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ab1b5fbd3f63e..7469715208b6e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1296,12 +1296,6 @@ struct llama_server_context } }; -struct server_beam_search_callback_data -{ - llama_context * ctx; - llama_client_slot * slot; -}; - static void server_print_usage(const char *argv0, const gpt_params ¶ms, const server_params &sparams) { @@ -2006,46 +2000,6 @@ static void log_server_request(const httplib::Request &req, const httplib::Respo }); } -static bool is_at_eob(const server_beam_search_callback_data & server_context, const llama_token *tokens, const size_t n_tokens) { - return n_tokens && tokens[n_tokens - 1] == llama_token_eos(server_context.ctx); -} - -// Function matching type llama_beam_search_callback_fn_t. -// Custom callback example is called each time the beams lengths increase: -// * Show progress by printing ',' following by number of convergent beam tokens if any. -// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. -// This is also called when the stop condition is met. -// Collect tokens into std::vector response which is pointed to by callback_data. - -// NO TESTED after PR #3589 - -static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { - auto & llama = *static_cast(callback_data); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - llama.slot->generated_token_probs.resize(llama.slot->generated_token_probs.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - const auto map = [](llama_token tok) { return completion_token_output{{},tok,""}; }; - std::transform(tokens, tokens + n, llama.slot->generated_token_probs.end() - n, map); - printf("%zu", n); - } - fflush(stdout); -#if 0 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams:\n"; - for (size_t i=0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["<params.stream) { - std::string completion_text = ""; - if (llama.params.n_beams) - { - // Fill llama.generated_token_probs vector with final beam. - server_beam_search_callback_data data_beam; - data_beam.slot = slot; - data_beam.ctx = llama.ctx; - llama_beam_search(llama.ctx, beam_search_callback, &data_beam, llama.params.n_beams, - slot->n_past, llama.params.n_predict); - // Translate llama.generated_token_probs to llama.generated_text. - append_to_generated_text_from_generated_token_probs(llama, slot); - } - else + std::string completion_text; + + while (slot->is_processing()) { - while (slot->is_processing()) + if (slot->has_new_token()) { - if (slot->has_new_token()) - { - completion_text += slot->next().text_to_send; - } - else - { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - } + completion_text += slot->next().text_to_send; + } + else + { + std::this_thread::sleep_for(std::chrono::microseconds(5)); } } From 3d5929e8eea7bc7f36763ec0cd0596ad67692656 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 14:43:19 +0300 Subject: [PATCH 39/64] server : bug fix in ingest_images n_tokens is incremented internally by llama_batch_add --- examples/server/server.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7469715208b6e..9137ab32ec5ec 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -963,7 +963,6 @@ struct llama_server_context { llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); slot.n_past += 1; - batch.n_tokens += 1; } } return true; From e3a2c3fe3217d60d5327cae9b22334da546361cb Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 14:44:04 +0300 Subject: [PATCH 40/64] server : use refs + use llama_batch_clear() --- examples/server/server.cpp | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9137ab32ec5ec..5d2e165b860d1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -68,7 +68,8 @@ static const std::string base64_chars = "abcdefghijklmnopqrstuvwxyz" "0123456789+/"; -static inline bool is_base64(uint8_t c) { +static inline bool is_base64(uint8_t c) +{ return (isalnum(c) || (c == '+') || (c == '/')); } @@ -174,7 +175,7 @@ struct slot_image float* image_embedding = nullptr; int image_tokens = 0; int id; - std::string prefix_prompt = ""; // before of this image + std::string prefix_prompt; // before of this image }; // completion token output with probabilities @@ -350,7 +351,7 @@ struct llama_client_slot int32_t n_remaining = -1; json prompt; - std::string generated_text = ""; + std::string generated_text; int num_tokens_predicted = 0; llama_token sampled; std::vector cache_tokens; @@ -404,7 +405,7 @@ struct llama_client_slot ctx_sampling = llama_sampling_init_srv(sparams, params.grammar, max_context_size); - for (slot_image img : images) + for (slot_image &img : images) { free(img.image_embedding); delete[] img.img_data.data; @@ -489,14 +490,14 @@ struct llama_server_context std::vector slots; // system prompt - std::string system_prompt = ""; + std::string system_prompt; bool need_update_system_prompt = false; std::vector tokens_system; int32_t num_tokens_system; // broadcast to all clients to keep the same prompt format - std::string user_name = ""; // this should be the anti prompt - std::string assistant_name = ""; // this is for generate the prompt + std::string user_name; // this should be the anti prompt + std::string assistant_name; // this is for generate the prompt bool multimodal = false; clip_ctx *clp_ctx = nullptr; @@ -870,7 +871,7 @@ struct llama_server_context return slot.has_next_token; // continue } - bool processImages(llama_client_slot &slot) + bool processImages(llama_client_slot &slot) const { for (slot_image &img : slot.images) { @@ -901,6 +902,7 @@ struct llama_server_context } img.request_encode_image = false; } + return slot.images.size() > 0; } @@ -908,9 +910,10 @@ struct llama_server_context bool ingest_images(llama_client_slot &slot, int n_batch) { int image_idx = 0; + while (image_idx < (int) slot.images.size()) { - slot_image img = slot.images[image_idx]; + slot_image &img = slot.images[image_idx]; // process prefix prompt for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) @@ -942,7 +945,7 @@ struct llama_server_context n_eval = n_batch; } - llama_batch batch_img = {int32_t(n_eval), nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; if (llama_decode(ctx, batch_img)) { LOG_TEE("%s : failed to eval image\n", __func__); @@ -952,8 +955,9 @@ struct llama_server_context } image_idx++; + llama_batch_clear(batch); + // append prefix of next image - batch.n_tokens = 0; const auto json_prompt = (image_idx >= (int) slot.images.size()) ? slot.params.input_suffix : // no more images, then process suffix prompt (json)(slot.images[image_idx].prefix_prompt); @@ -975,7 +979,8 @@ struct llama_server_context update_system_prompt(); } - batch.n_tokens = 0; + llama_batch_clear(batch); + int kv_cache_free = (n_ctx - num_tokens_system); if (all_slots_are_idle) From 9740824ba500fd6103dbaa38f906966d28038027 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 14:44:37 +0300 Subject: [PATCH 41/64] server : snake case --- examples/server/server.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5d2e165b860d1..ed7d1f2dcf527 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -871,7 +871,7 @@ struct llama_server_context return slot.has_next_token; // continue } - bool processImages(llama_client_slot &slot) const + bool process_images(llama_client_slot &slot) const { for (slot_image &img : slot.images) { @@ -969,6 +969,7 @@ struct llama_server_context slot.n_past += 1; } } + return true; } @@ -1160,7 +1161,7 @@ struct llama_server_context {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); - const bool has_images = processImages(slot); // has images? + const bool has_images = process_images(slot); // has images? // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; From 325d1793f7bbe7650b37a15b6db10c83bf75bdff Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Oct 2023 15:03:24 +0300 Subject: [PATCH 42/64] server : minor sync --- examples/server/server.cpp | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ed7d1f2dcf527..851a3d314db41 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -707,7 +707,7 @@ struct llama_server_context // wait until system prompt load need_update_system_prompt = true; - while(need_update_system_prompt) { + while (need_update_system_prompt) { std::this_thread::sleep_for(std::chrono::milliseconds(5)); } // system prompt loaded, continue @@ -748,6 +748,7 @@ struct llama_server_context const stop_type type, llama_client_slot &slot) { size_t stop_pos = std::string::npos; + for (const std::string &word : slot.params.antiprompt) { size_t pos; @@ -774,6 +775,7 @@ struct llama_server_context } } + return stop_pos; } @@ -798,8 +800,7 @@ struct llama_server_context pos = std::min(slot.sent_count, slot.generated_text.size()); } else { is_stop_full = false; - stop_pos = find_stopping_strings(str_test, token_str.size(), - STOP_PARTIAL, slot); + stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); } // check if there is any token to predict @@ -2113,14 +2114,14 @@ int main(int argc, char **argv) llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); - if(slot == nullptr) { + if (slot == nullptr) { LOG_TEE("slot unavailable\n"); res.status = 404; res.set_content("slot_error", "text/plain"); return; } - if(data.contains("system_prompt")) { + if (data.contains("system_prompt")) { llama.process_system_prompt_data(data["system_prompt"]); } @@ -2286,8 +2287,7 @@ int main(int argc, char **argv) const json data = format_final_response(llama, slot, completion_text, probs); slot_print_timings(slot); - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), - "application/json"); + res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { @@ -2342,9 +2342,11 @@ int main(int argc, char **argv) "data: " + data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; + LOG_VERBOSE("data stream", { { "to_send", str } }); + if (!sink.write(str.data(), str.size())) { slot->release(); From 6b2437e32d146ae2012bf81730b35ff85a2e6347 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Fri, 20 Oct 2023 12:07:32 -0400 Subject: [PATCH 43/64] added thread safe pipeline --- examples/server/server.cpp | 1081 ++++++++++++++++++------------------ 1 file changed, 532 insertions(+), 549 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 851a3d314db41..3fa60e0b9ac32 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -23,6 +23,8 @@ #include #include +#include +#include #include #ifndef SERVER_VERBOSE @@ -137,6 +139,26 @@ static std::vector base64_decode(std::string const &encoded_string) // parallel // +enum task_type { + COMPLETION_TASK, + CANCEL_TASK +}; + +struct task_server { + int id; + int target_id; + task_type type; + json data; + bool infill_mode = false; +}; + +struct task_result { + int id; + bool stop; + bool error; + json result_json; +}; + enum slot_state { IDLE, @@ -309,6 +331,15 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector +static T json_value(const json &body, const std::string &key, const T &default_value) +{ + // Fallback null to default value + return body.contains(key) && !body.at(key).is_null() + ? body.value(key, default_value) + : default_value; +} + // TODO: this is not needed, should reuse llama_sampling_init from common/sampling.h static struct llama_sampling_context * llama_sampling_init_srv(const struct llama_sampling_params &sparams, const std::string &grammar, int n_ctx) { @@ -342,6 +373,7 @@ static struct llama_sampling_context * llama_sampling_init_srv(const struct llam struct llama_client_slot { int id; + int task_id = -1; // generation props int32_t n_past = 0; int32_t n_decoded = 0; @@ -352,11 +384,9 @@ struct llama_client_slot json prompt; std::string generated_text; - int num_tokens_predicted = 0; llama_token sampled; std::vector cache_tokens; std::vector generated_token_probs; - int sent_tokens = 0; slot_state state = IDLE; slot_command command = NONE; bool truncated = false; @@ -366,6 +396,8 @@ struct llama_client_slot std::string stopping_word; int32_t multibyte_pending = 0; size_t sent_count = 0; + size_t sent_token_probs_index = 0; + bool infill = false; int64_t t_start_process_prompt; int64_t t_start_genereration; @@ -395,8 +427,9 @@ struct llama_client_slot multibyte_pending = 0; n_past = 0; sent_count = 0; + sent_token_probs_index = 0; infill = false; - clean_tokens(); + generated_token_probs.clear(); if (ctx_sampling != nullptr) { @@ -440,10 +473,6 @@ struct llama_client_slot return n_remaining > 0 || n_remaining == -1; // no budget || limitless } - bool has_new_token() const { - return num_tokens_predicted > sent_tokens; - } - bool available() const { return state == IDLE && command == NONE; } @@ -452,21 +481,13 @@ struct llama_client_slot return ((state == IDLE || state == SLEEPING) && command == LOAD_PROMPT) || state == PROCESSING; } - completion_token_output next() { - completion_token_output tkn = generated_token_probs.at(sent_tokens); - sent_tokens++; - return tkn; - } - void add_token_string(const completion_token_output &token) { if (command == RELEASE) { - num_tokens_predicted = 0; return; } cache_tokens.push_back(token.tok); generated_token_probs.push_back(token); - num_tokens_predicted++; } void release() { @@ -477,11 +498,28 @@ struct llama_client_slot } } - void clean_tokens() - { - sent_tokens = 0; - generated_token_probs.clear(); - num_tokens_predicted = 0; + json get_formated_timings() { + return json + { + {"prompt_n", num_prompt_tokens_processed}, + {"prompt_ms", t_prompt_processing}, + {"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed}, + + {"predicted_n", n_decoded}, + {"predicted_ms", t_token_generation}, + {"predicted_per_token_ms", t_token_generation / n_decoded}, + {"predicted_per_second", 1e3 / t_token_generation * n_decoded}, + }; + } + + void print_timings() { + LOG_TEE("\n"); + LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed); + LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded); + LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation); } }; @@ -513,6 +551,13 @@ struct llama_server_context int max_ctx_per_slot = -1; bool clean_kv_cache = true; + std::atomic id_gen; + + std::vector queue_tasks; + std::vector queue_results; + std::mutex mutex_tasks; + std::mutex mutex_results; + ~llama_server_context() { if (ctx) @@ -566,6 +611,7 @@ struct llama_server_context } void initialize() { + id_gen.store(0); // reset ids to 0 // create slots all_slots_are_idle = true; if(max_ctx_per_slot == -1) { @@ -647,14 +693,179 @@ struct llama_server_context return nullptr; } - bool launch_slot(llama_client_slot* &slot) { + bool launch_slot_with_data(llama_client_slot* &slot, json data) { + slot_params default_params; + llama_sampling_params default_sparams; + slot->params.stream = json_value(data, "stream", false); + slot->params.cache_prompt = json_value(data, "cache_prompt", false); + slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); + slot->sparams.repeat_last_n = json_value(data, "repeat_last_n", default_sparams.repeat_last_n); + slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot->sparams.repeat_penalty = json_value(data, "repeat_penalty", default_sparams.repeat_penalty); + slot->sparams.presence_penalty = json_value(data, "presence_penalty", default_sparams.presence_penalty); + slot->sparams.frequency_penalty = json_value(data, "frequency_penalty", default_sparams.frequency_penalty); + slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); + slot->params.seed = json_value(data, "seed", default_params.seed); + slot->params.grammar = json_value(data, "grammar", default_params.grammar); + slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + + // infill + if (data.count("input_prefix") != 0) + { + slot->params.input_prefix = data["input_prefix"]; + } + else + { + slot->params.input_prefix = ""; + } + if (data.count("input_suffix") != 0) + { + slot->params.input_suffix = data["input_suffix"]; + } + + // common params + else + { + slot->params.input_suffix = ""; + } + if (data.count("prompt") != 0) + { + slot->prompt = data["prompt"]; + } + else + { + slot->prompt = ""; + } + slot->sparams.logit_bias.clear(); + if (json_value(data, "ignore_eos", false)) + { + slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY; + } + const auto &logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) + { + const int n_vocab = llama_n_vocab(model); + for (const auto &el : *logit_bias) + { + if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) + { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) + { + if (el[1].is_number()) + { + slot->sparams.logit_bias[tok] = el[1].get(); + } + else if (el[1].is_boolean() && !el[1].get()) + { + slot->sparams.logit_bias[tok] = -INFINITY; + } + } + } + } + } + + slot->params.antiprompt.clear(); + const auto &stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) + { + for (const auto &word : *stop) + { + if (!word.empty()) + { + slot->params.antiprompt.push_back(word); + } + } + } + + if(multimodal) + { + const auto &images_data = data.find("image_data"); + if (images_data != data.end() && images_data->is_array()) + { + for (const auto &img : *images_data) + { + slot_image img_sl; + std::string data_b64 = img["data"].get(); + img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); + int width, height, channels; + std::vector image_buffer = base64_decode(data_b64); + data_b64.clear(); + auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); + if (!data) { + LOG_TEE("slot %i - failed to load image id= %i\n", slot->id, img_sl.id); + return false; + } + LOG_TEE("slot %i - image id= %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); + img_sl.img_data.nx = width; + img_sl.img_data.ny = height; + img_sl.img_data.size = width * height * 3; + img_sl.img_data.data = new uint8_t[width * height * 3](); + memcpy(img_sl.img_data.data, data, width * height * 3); + stbi_image_free(data); + img_sl.request_encode_image = true; + slot->images.push_back(img_sl); + } + // process prompt + // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} + if (slot->images.size() > 0 && !slot->prompt.is_array()) + { + std::string prompt = slot->prompt.get(); + size_t pos = 0, begin_prefix = 0; + std::string pattern = "[img-"; + while ((pos = prompt.find(pattern, pos)) != std::string::npos) { + size_t end_prefix = pos; + pos += pattern.length(); + size_t end_pos = prompt.find("]", pos); + if (end_pos != std::string::npos) + { + std::string image_id = prompt.substr(pos, end_pos - pos); + try + { + int img_id = std::stoi(image_id); + bool found = false; + for (slot_image &img : slot->images) + { + if (img.id == img_id) { + found = true; + img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); + begin_prefix = end_pos + 1; + break; + } + } + if (!found) { + LOG_TEE("ERROR: Image with id %i not found.\n", img_id); + slot->images.clear(); + return false; + } + } catch (const std::invalid_argument& e) { + LOG_TEE("Invalid image number id in prompt\n"); + slot->images.clear(); + return false; + } + } + } + slot->prompt = ""; + slot->params.input_suffix = prompt.substr(begin_prefix); + slot->params.cache_prompt = false; // multimodal doesn't support cache prompt + } + } + } if (!slot->load_grammar()) { return false; } all_slots_are_idle = false; slot->command = LOAD_PROMPT; - LOG_TEE("slot %i is processing\n", slot->id); + LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); return true; } @@ -811,6 +1022,9 @@ struct llama_server_context // add the token to slot queue and cache } slot.add_token_string(result); + if(slot.params.stream) { + send_partial_response(slot, result); + } if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -863,7 +1077,7 @@ struct llama_server_context {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, {"has_next_token", slot.has_next_token}, {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.num_tokens_predicted}, + {"num_tokens_predicted", slot.n_decoded}, {"stopped_eos", slot.stopped_eos}, {"stopped_word", slot.stopped_word}, {"stopped_limit", slot.stopped_limit}, @@ -907,6 +1121,179 @@ struct llama_server_context return slot.images.size() > 0; } + void send_error(int id, std::string error) { + std::lock_guard lock(mutex_results); + task_result res; + res.id = id; + res.error = true; + res.result_json = { { "content", error } }; + queue_results.push_back(res); + } + + json get_model_props() { + return get_formated_generation(slots[0]); + } + + json get_formated_generation(llama_client_slot & slot) { + const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx)); + const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && + eos_bias->second < 0.0f && std::isinf(eos_bias->second); + return json{ + {"n_ctx", max_ctx_per_slot}, + {"model", params.model_alias}, + {"seed", slot.params.seed}, + {"temp", slot.sparams.temp}, + {"top_k", slot.sparams.top_k}, + {"top_p", slot.sparams.top_p}, + {"tfs_z", slot.sparams.tfs_z}, + {"typical_p", slot.sparams.typical_p}, + {"repeat_last_n", slot.sparams.repeat_last_n}, + {"repeat_penalty", slot.sparams.repeat_penalty}, + {"presence_penalty", slot.sparams.presence_penalty}, + {"frequency_penalty", slot.sparams.frequency_penalty}, + {"mirostat", slot.sparams.mirostat}, + {"mirostat_tau", slot.sparams.mirostat_tau}, + {"mirostat_eta", slot.sparams.mirostat_eta}, + {"penalize_nl", slot.sparams.penalize_nl}, + {"stop", slot.params.antiprompt}, + {"n_predict", slot.params.n_predict}, + {"n_keep", params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", slot.params.stream}, + {"logit_bias", slot.sparams.logit_bias}, + {"n_probs", slot.sparams.n_probs}, + {"grammar", slot.params.grammar}, + }; + } + + void send_partial_response(llama_client_slot & slot, completion_token_output tkn) { + std::lock_guard lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = false; + res.result_json = json + { + {"content", tkn.text_to_send }, + {"stop", false}, + {"slot_id", slot.id }, + {"multimodal", multimodal } + }; + if (slot.sparams.n_probs > 0) + { + std::vector probs_output = {}; + const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); + size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); + size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); + if (probs_pos < probs_stop_pos) + { + probs_output = std::vector(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); + } + slot.sent_token_probs_index = probs_stop_pos; + res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); + } + queue_results.push_back(res); + } + + void send_final_response(llama_client_slot & slot) { + std::lock_guard lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = true; + res.result_json = json + { + {"content", !slot.params.stream ? slot.generated_text : ""}, + {"slot_id", slot.id}, + {"stop", true}, + {"model", params.model_alias}, + {"tokens_predicted", slot.n_decoded}, + {"tokens_evaluated", slot.num_prompt_tokens}, + {"generation_settings", get_formated_generation(slot)}, + {"prompt", slot.prompt}, + {"truncated", slot.truncated}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + {"tokens_cached", slot.n_past}, + {"timings", slot.get_formated_timings()} + }; + + if (slot.sparams.n_probs > 0) + { + std::vector probs = {}; + if(!slot.params.stream && slot.stopped_word) { + const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); + probs = std::vector(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); + } else { + probs = std::vector( + slot.generated_token_probs.begin(), + slot.generated_token_probs.begin() + slot.sent_token_probs_index); + } + res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); + } + queue_results.push_back(res); + } + + void send_embedding(llama_client_slot & slot) { + std::lock_guard lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = true; + static const int n_embd = llama_n_embd(model); + if (!params.embedding) + { + LOG_WARNING("embedding disabled", { + {"params.embedding", params.embedding}, + }); + res.result_json = json + { + {"embedding", std::vector(n_embd, 0.0f)}, + }; + } else { + const float *data = llama_get_embeddings(ctx); + std::vector embedding(data, data + n_embd); + res.result_json = json + { + {"embedding", embedding }, + }; + } + queue_results.push_back(res); + } + + int request_completion(json data, bool infill) { + std::lock_guard lock(mutex_tasks); + task_server task; + task.id = id_gen.load(); + id_gen.fetch_add(1); // increment id generator + task.data = data; + task.infill_mode = infill; + task.type = COMPLETION_TASK; + queue_tasks.push_back(task); + return task.id; + } + + task_result next_result(int task_id) { + while(true) { + std::this_thread::sleep_for(std::chrono::microseconds(5)); + std::lock_guard lock(mutex_results); + if(queue_results.empty()) { + continue; + } + + for(int i = 0; i < queue_results.size(); i++) { + if(queue_results[i].id == task_id) { + task_result res = queue_results[i]; + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + return task_result{-1, false, false, {}}; + } + // for multiple images processing bool ingest_images(llama_client_slot &slot, int n_batch) { @@ -974,7 +1361,68 @@ struct llama_server_context return true; } + void request_cancel(int task_id) { + std::lock_guard lock(mutex_tasks); + task_server task; + task.id = id_gen.load(); + id_gen.fetch_add(1); // increment id generator + task.type = CANCEL_TASK; + task.target_id = task_id; + queue_tasks.push_back(task); + } + + void process_tasks() { + std::lock_guard lock(mutex_tasks); + while(!queue_tasks.empty()) { + task_server task = queue_tasks.front(); + queue_tasks.erase(queue_tasks.begin()); + switch (task.type) + { + case COMPLETION_TASK: { // perform completion task + llama_client_slot* slot = get_slot(json_value(task.data, "slot_id", -1)); + if (slot == nullptr) { + LOG_TEE("slot unavailable\n"); + // send error result + send_error(task.id, "slot unavaliable"); + return; + } + + if (task.data.contains("system_prompt")) { + process_system_prompt_data(task.data["system_prompt"]); + } + + slot->reset(); + + slot->infill = task.infill_mode; + slot->task_id = task.id; + + if (!launch_slot_with_data(slot, task.data)) + { + // send error result + send_error(task.id, "internal_error"); + break; + } + } + case CANCEL_TASK: { // release slot linked with the task id + for(auto & slot : slots) { + if(slot.task_id == task.target_id) { + slot.release(); + break; + } + } + } + break; + + default: + break; + } + } + } + bool update_slots() { + // attend tasks + process_tasks(); + // update the system prompt wait until all slots are idle state if (need_update_system_prompt) { @@ -983,8 +1431,6 @@ struct llama_server_context llama_batch_clear(batch); - int kv_cache_free = (n_ctx - num_tokens_system); - if (all_slots_are_idle) { if (system_prompt.empty() && clean_kv_cache) @@ -1029,7 +1475,7 @@ struct llama_server_context for (auto & slot : slots) { // release the slot - if (slot.state == PROCESSING && slot.command == RELEASE && !slot.has_new_token()) + if (slot.state == PROCESSING && slot.command == RELEASE) { slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; if(slot.state == SLEEPING) { @@ -1043,8 +1489,6 @@ struct llama_server_context continue; } - kv_cache_free -= slot.num_prompt_tokens; - if ( slot.state == IDLE || slot.state == SLEEPING || @@ -1238,6 +1682,7 @@ struct llama_server_context // prompt evaluated for embedding if (params.embedding) { + send_embedding(slot); slot.release(); slot.i_batch = -1; return true; @@ -1272,34 +1717,15 @@ struct llama_server_context if (!process_token(result, slot)) { slot.release(); + send_final_response(slot); + slot.print_timings(); } - kv_cache_free -= slot.num_tokens_predicted; slot.i_batch = -1; } } - - if(kv_cache_free < 0 && params.n_parallel > 1) { - LOG_TEE("\nError: kv cache is full, increase context size."); - return false; - } return true; } - - std::vector get_embedding() - { - static const int n_embd = llama_n_embd(model); - if (!params.embedding) - { - LOG_WARNING("embedding disabled", { - {"params.embedding", params.embedding}, - }); - return std::vector(n_embd, 0.0f); - } - const float *data = llama_get_embeddings(ctx); - std::vector embedding(data, data + n_embd); - return embedding; - } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, @@ -1675,103 +2101,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } -static void slot_print_timings(struct llama_client_slot * slot) { - LOG_TEE("\n"); - LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", - __func__, slot->t_prompt_processing, slot->num_prompt_tokens_processed, slot->t_prompt_processing / slot->num_prompt_tokens_processed, 1e3 / slot->t_prompt_processing * slot->num_prompt_tokens_processed); - LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", - __func__, slot->t_token_generation, slot->n_decoded, slot->t_token_generation / slot->n_decoded, 1e3 / slot->t_token_generation * slot->n_decoded); - LOG_TEE("%s: total time = %10.2f ms\n", __func__, slot->t_prompt_processing + slot->t_token_generation); -} - -static json format_generation_settings(llama_server_context &llama, llama_client_slot* slot) -{ - const auto eos_bias = slot->sparams.logit_bias.find(llama_token_eos(llama.ctx)); - const bool ignore_eos = eos_bias != slot->sparams.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); - - return json{ - {"n_ctx", llama.n_ctx}, - {"model", llama.params.model_alias}, - {"seed", slot->params.seed}, - {"temp", slot->sparams.temp}, - {"top_k", slot->sparams.top_k}, - {"top_p", slot->sparams.top_p}, - {"tfs_z", slot->sparams.tfs_z}, - {"typical_p", slot->sparams.typical_p}, - {"repeat_last_n", slot->sparams.repeat_last_n}, - {"repeat_penalty", slot->sparams.repeat_penalty}, - {"presence_penalty", slot->sparams.presence_penalty}, - {"frequency_penalty", slot->sparams.frequency_penalty}, - {"mirostat", slot->sparams.mirostat}, - {"mirostat_tau", slot->sparams.mirostat_tau}, - {"mirostat_eta", slot->sparams.mirostat_eta}, - {"penalize_nl", slot->sparams.penalize_nl}, - {"stop", slot->params.antiprompt}, - {"n_predict", slot->params.n_predict}, - {"n_keep", llama.params.n_keep}, - {"ignore_eos", ignore_eos}, - {"stream", slot->params.stream}, - {"logit_bias", slot->sparams.logit_bias}, - {"n_probs", slot->sparams.n_probs}, - {"grammar", slot->params.grammar}, - }; -} - -static json format_embedding_response(llama_server_context &llama) -{ - return json - { - {"embedding", llama.get_embedding()}, - }; -} - -static json format_timings(llama_client_slot* slot) -{ - return json - { - {"prompt_n", slot->num_prompt_tokens_processed}, - {"prompt_ms", slot->t_prompt_processing}, - {"prompt_per_token_ms", slot->t_prompt_processing / slot->num_prompt_tokens_processed}, - {"prompt_per_second", 1e3 / slot->t_prompt_processing * slot->num_prompt_tokens_processed}, - - {"predicted_n", slot->n_decoded}, - {"predicted_ms", slot->t_token_generation}, - {"predicted_per_token_ms", slot->t_token_generation / slot->n_decoded}, - {"predicted_per_second", 1e3 / slot->t_token_generation * slot->n_decoded}, - }; -} - -static json format_final_response(llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs) -{ - - json res = json - { - {"content", content}, - {"slot_id", slot->id}, - {"stop", true}, - {"model", llama.params.model_alias}, - {"tokens_predicted", slot->n_decoded}, - {"tokens_evaluated", slot->num_prompt_tokens}, - {"generation_settings", format_generation_settings(llama, slot)}, - {"prompt", slot->prompt}, - {"truncated", slot->truncated}, - {"stopped_eos", slot->stopped_eos}, - {"stopped_word", slot->stopped_word}, - {"stopped_limit", slot->stopped_limit}, - {"stopping_word", slot->stopping_word}, - {"tokens_cached", slot->n_past}, - {"timings", format_timings(slot)} - }; - - if (slot->sparams.n_probs > 0) - { - res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); - } - - return res; -} - static json format_partial_response( llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs ) { @@ -1803,191 +2132,6 @@ static json format_detokenized_response(std::string content) {"content", content}}; } -template -static T json_value(const json &body, const std::string &key, const T &default_value) -{ - // Fallback null to default value - return body.contains(key) && !body.at(key).is_null() - ? body.value(key, default_value) - : default_value; -} - -static void parse_options_completion(const json &body, llama_client_slot* slot, llama_server_context &llama) -{ - slot_params default_params; - llama_sampling_params default_sparams; - - slot->params.stream = json_value(body, "stream", false); - slot->params.cache_prompt = json_value(body, "cache_prompt", false); - slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict); - slot->sparams.top_k = json_value(body, "top_k", default_sparams.top_k); - slot->sparams.top_p = json_value(body, "top_p", default_sparams.top_p); - slot->sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); - slot->sparams.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n); - slot->sparams.temp = json_value(body, "temperature", default_sparams.temp); - slot->sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty); - slot->sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty); - slot->sparams.frequency_penalty = json_value(body, "frequency_penalty", default_sparams.frequency_penalty); - slot->sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); - slot->sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - slot->sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - slot->sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - slot->params.n_keep = json_value(body, "n_keep", slot->params.n_keep); - slot->params.seed = json_value(body, "seed", default_params.seed); - slot->params.grammar = json_value(body, "grammar", default_params.grammar); - slot->sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); - - if (body.count("prompt") != 0) - { - slot->prompt = body["prompt"]; - } - else - { - slot->prompt = ""; - } - - slot->sparams.logit_bias.clear(); - if (json_value(body, "ignore_eos", false)) - { - slot->sparams.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; - } - - const auto &logit_bias = body.find("logit_bias"); - if (logit_bias != body.end() && logit_bias->is_array()) - { - const int n_vocab = llama_n_vocab(llama.model); - for (const auto &el : *logit_bias) - { - if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) - { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) - { - if (el[1].is_number()) - { - slot->sparams.logit_bias[tok] = el[1].get(); - } - else if (el[1].is_boolean() && !el[1].get()) - { - slot->sparams.logit_bias[tok] = -INFINITY; - } - } - } - } - } - - slot->params.antiprompt.clear(); - const auto &stop = body.find("stop"); - if (stop != body.end() && stop->is_array()) - { - for (const auto &word : *stop) - { - if (!word.empty()) - { - slot->params.antiprompt.push_back(word); - } - } - } - - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot)); - if(!llama.multimodal) - { - return; - } - - const auto &images_data = body.find("image_data"); - if (images_data != body.end() && images_data->is_array()) - { - for (const auto &img : *images_data) - { - slot_image img_sl; - std::string data_b64 = img["data"].get(); - img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); - int width, height, channels; - std::vector image_buffer = base64_decode(data_b64); - data_b64.clear(); - auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); - if (!data) { - LOG_TEE("slot %i - failed to load image id= %i\n", slot->id, img_sl.id); - return; - } - LOG_TEE("slot %i - image id= %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); - img_sl.img_data.nx = width; - img_sl.img_data.ny = height; - img_sl.img_data.size = width * height * 3; - img_sl.img_data.data = new uint8_t[width * height * 3](); - memcpy(img_sl.img_data.data, data, width * height * 3); - stbi_image_free(data); - img_sl.request_encode_image = true; - slot->images.push_back(img_sl); - } - // process prompt - // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} - if (slot->images.size() > 0 && !slot->prompt.is_array()) - { - std::string prompt = slot->prompt.get(); - size_t pos = 0, begin_prefix = 0; - std::string pattern = "[img-"; - while ((pos = prompt.find(pattern, pos)) != std::string::npos) { - size_t end_prefix = pos; - pos += pattern.length(); - size_t end_pos = prompt.find("]", pos); - if (end_pos != std::string::npos) - { - std::string image_id = prompt.substr(pos, end_pos - pos); - try - { - int img_id = std::stoi(image_id); - bool found = false; - for (slot_image &img : slot->images) - { - if (img.id == img_id) { - found = true; - img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); - begin_prefix = end_pos + 1; - break; - } - } - if (!found) { - LOG_TEE("ERROR: Image with id %i not found.\n", img_id); - slot->images.clear(); - return; - } - } catch (const std::invalid_argument& e) { - LOG_TEE("Invalid image number id in prompt\n"); - slot->images.clear(); - return; - } - } - } - slot->prompt = ""; - slot->params.input_suffix = prompt.substr(begin_prefix); - slot->params.cache_prompt = false; // multimodal doesn't support cache prompt - } - } -} - -static void parse_options_infill(const json &body, llama_server_context &llama, llama_client_slot *slot) -{ - if (body.count("input_prefix") != 0) - { - slot->params.input_prefix = body["input_prefix"]; - } - else - { - slot->params.input_prefix = ""; - } - if (body.count("input_suffix") != 0) - { - slot->params.input_suffix = body["input_suffix"]; - } - else - { - slot->params.input_suffix = ""; - } - parse_options_completion(body, slot, llama); -} static void log_server_request(const httplib::Request &req, const httplib::Response &res) { @@ -2111,123 +2255,48 @@ int main(int argc, char **argv) svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res) { json data = json::parse(req.body); - - llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); - - if (slot == nullptr) { - LOG_TEE("slot unavailable\n"); - res.status = 404; - res.set_content("slot_error", "text/plain"); - return; - } - - if (data.contains("system_prompt")) { - llama.process_system_prompt_data(data["system_prompt"]); - } - - slot->reset(); - - parse_options_completion(data, slot, llama); - - if (!llama.launch_slot(slot)) - { - res.status = 400; - return; - } - - if (!slot->params.stream) { + const int task_id = llama.request_completion(data, false); + if (!json_value(data, "stream", false)) { std::string completion_text; - - while (slot->is_processing()) - { - if (slot->has_new_token()) - { - completion_text += slot->next().text_to_send; - } - else - { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - } + task_result result = llama.next_result(task_id); + if(!result.error && result.stop) { + res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } - - auto probs = slot->generated_token_probs; - if (slot->sparams.n_probs > 0 && slot->stopped_word) + else { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); - probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); + res.status = 404; + res.set_content(result.result_json["content"], "text/plain"); + return; } - - const json data = format_final_response(llama, slot, completion_text, probs); - slot_print_timings(slot); - slot->release(); - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else { - const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { - size_t sent_token_probs_index = 0; - while (slot->is_processing()) - { - if (slot->has_new_token()) - { // new token notification - const completion_token_output token = slot->next(); - std::vector probs_output = {}; - if (slot->sparams.n_probs > 0) - { - const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); - if (probs_pos < probs_stop_pos) - { - probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); - } - sent_token_probs_index = probs_stop_pos; - } - const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { + while(true) { + task_result result = llama.next_result(task_id); + if(!result.error) { const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; + "data: " + + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; LOG_VERBOSE("data stream", { { "to_send", str } }); if (!sink.write(str.c_str(), str.size())) { - slot->release(); return false; } + if(result.stop) { + break; + } + } else { + break; } - else - { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - } - } - const json data = format_final_response( - llama, slot, - "", - std::vector( - slot->generated_token_probs.begin(), - slot->generated_token_probs.begin() + sent_token_probs_index) - ); - slot_print_timings(slot); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) - { - slot->release(); - return false; } sink.done(); return true; }; - auto on_complete = [slot] (bool) { - slot->release(); - slot->clean_tokens(); + auto on_complete = [task_id, &llama] (bool) { + // cancel + llama.request_cancel(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } @@ -2236,129 +2305,48 @@ int main(int argc, char **argv) svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) { json data = json::parse(req.body); - - llama_client_slot* slot = llama.get_slot(json_value(data, "slot_id", -1)); - - if (slot == nullptr) - { - LOG_TEE("slot unavailable\n"); - res.status = 404; - res.set_content("slot_error", "text/plain"); - return; - } - - if (data.contains("system_prompt")) - { - llama.process_system_prompt_data(data["system_prompt"]); - } - - slot->reset(); - slot->infill = true; - - parse_options_infill(data, llama, slot); - - if (!llama.launch_slot(slot)) - { - res.status = 400; - return; - } - - if (!slot->params.stream) - { - std::string completion_text = ""; - while (slot->is_processing()) - { - if(slot->has_new_token()) - { - completion_text += slot->next().text_to_send; - } - else - { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - } + const int task_id = llama.request_completion(data, true); + if (!json_value(data, "stream", false)) { + std::string completion_text; + task_result result = llama.next_result(task_id); + if(!result.error && result.stop) { + res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } - - auto probs = slot->generated_token_probs; - if (slot->sparams.n_probs > 0 && slot->stopped_word) + else { - const std::vector stop_word_toks = llama_tokenize(llama.ctx, slot->stopping_word, false); - probs = std::vector(slot->generated_token_probs.begin(), slot->generated_token_probs.end() - stop_word_toks.size()); + res.status = 404; + res.set_content(result.result_json["content"], "text/plain"); + return; } - - const json data = format_final_response(llama, slot, completion_text, probs); - slot_print_timings(slot); - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); - } - else - { - const auto chunked_content_provider = [slot, &llama](size_t, httplib::DataSink & sink) { - size_t sent_token_probs_index = 0; - while (slot->is_processing()) - { - if (slot->has_new_token()) - { - // new token notification - const completion_token_output token = slot->next(); - std::vector probs_output = {}; - if (slot->sparams.n_probs > 0) - { - const std::vector to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size()); - if (probs_pos < probs_stop_pos) - { - probs_output = std::vector(slot->generated_token_probs.begin() + probs_pos, slot->generated_token_probs.begin() + probs_stop_pos); - } - sent_token_probs_index = probs_stop_pos; - } - const json data = format_partial_response(llama, slot, token.text_to_send, probs_output); + } else { + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { + while(true) { + task_result result = llama.next_result(task_id); + if(!result.error) { const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; + "data: " + + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; LOG_VERBOSE("data stream", { { "to_send", str } }); if (!sink.write(str.c_str(), str.size())) { - slot->release(); return false; } + if(result.stop) { + break; + } + } else { + break; } - else - { - std::this_thread::sleep_for(std::chrono::milliseconds(5)); - } - } - const json data = format_final_response( - llama, slot, - "", - std::vector( - slot->generated_token_probs.begin(), - slot->generated_token_probs.begin() + sent_token_probs_index) - ); - slot_print_timings(slot); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) - { - slot->release(); - return false; } sink.done(); return true; }; - auto on_complete = [slot] (bool) - { - slot->clean_tokens(); - slot->release(); + auto on_complete = [task_id, &llama] (bool) { + // cancel + llama.request_cancel(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } @@ -2366,7 +2354,7 @@ int main(int argc, char **argv) svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) { - const json data = format_generation_settings(llama, llama.get_slot(0)); + const json data = llama.get_model_props(); return res.set_content(data.dump(), "application/json"); }); @@ -2402,23 +2390,18 @@ int main(int argc, char **argv) svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) { const json body = json::parse(req.body); - llama_client_slot* slot = llama.get_slot(-1); - slot->reset(); + json prompt; if (body.count("content") != 0) { - slot->prompt = body["content"]; + prompt = body["content"]; } else { - slot->prompt = ""; + prompt = ""; } - llama.params.n_predict = 0; - llama.launch_slot(slot); - while (slot->is_processing()) { - std::this_thread::sleep_for(std::chrono::microseconds(10)); - } - const json data = format_embedding_response(llama); - return res.set_content(data.dump(), "application/json"); + const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false); + task_result result = llama.next_result(task_id); + return res.set_content(result.result_json.dump(), "application/json"); }); svr.set_logger(log_server_request); From 113dd60005f57dbd418e1e0854444b0834841b13 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 20 Oct 2023 20:42:45 +0300 Subject: [PATCH 44/64] server : bach has to be allocated for n_parallel sequences --- examples/server/server.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3fa60e0b9ac32..b3add3c402fa7 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -631,7 +631,9 @@ struct llama_server_context LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot); slots.push_back(slot); } - batch = llama_batch_init(n_ctx, 0, 1); + + batch = llama_batch_init(n_ctx, 0, params.n_parallel); + // empty system prompt system_prompt = ""; num_tokens_system = 0; From 5d540e80d1f97cceca08574a26b665ec06b8ab8f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 20 Oct 2023 20:44:29 +0300 Subject: [PATCH 45/64] server : no need for atomic int - already using mutex --- examples/server/server.cpp | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b3add3c402fa7..552f1c5123410 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -23,7 +23,6 @@ #include #include -#include #include #include @@ -551,7 +550,7 @@ struct llama_server_context int max_ctx_per_slot = -1; bool clean_kv_cache = true; - std::atomic id_gen; + int id_gen; std::vector queue_tasks; std::vector queue_results; @@ -611,7 +610,8 @@ struct llama_server_context } void initialize() { - id_gen.store(0); // reset ids to 0 + id_gen = 0; + // create slots all_slots_are_idle = true; if(max_ctx_per_slot == -1) { @@ -1268,8 +1268,7 @@ struct llama_server_context int request_completion(json data, bool infill) { std::lock_guard lock(mutex_tasks); task_server task; - task.id = id_gen.load(); - id_gen.fetch_add(1); // increment id generator + task.id = id_gen++; task.data = data; task.infill_mode = infill; task.type = COMPLETION_TASK; @@ -1366,8 +1365,7 @@ struct llama_server_context void request_cancel(int task_id) { std::lock_guard lock(mutex_tasks); task_server task; - task.id = id_gen.load(); - id_gen.fetch_add(1); // increment id generator + task.id = id_gen++; task.type = CANCEL_TASK; task.target_id = task_id; queue_tasks.push_back(task); From 778c070d1b8a67f4387946e424dfadbd58748334 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 20 Oct 2023 20:44:51 +0300 Subject: [PATCH 46/64] server : logs + minor code style --- examples/server/server.cpp | 112 ++++++++++++++++++++----------------- 1 file changed, 62 insertions(+), 50 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 552f1c5123410..fb900d299d632 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -614,13 +614,16 @@ struct llama_server_context // create slots all_slots_are_idle = true; - if(max_ctx_per_slot == -1) { + if (max_ctx_per_slot == -1) + { max_ctx_per_slot = n_ctx / params.n_parallel; // split context } - if(max_ctx_per_slot * params.n_parallel > n_ctx) { + if (max_ctx_per_slot * params.n_parallel > n_ctx) + { printf("Error: The max context per slot is more greater than model context size"); return; } + LOG_TEE("Available slots:\n"); for (int i = 0; i < params.n_parallel; i++) { @@ -628,6 +631,7 @@ struct llama_server_context slot.id = i; slot.max_context_size = max_ctx_per_slot; slot.reset(); + LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot); slots.push_back(slot); } @@ -788,7 +792,7 @@ struct llama_server_context } } - if(multimodal) + if (multimodal) { const auto &images_data = data.find("image_data"); if (images_data != data.end() && images_data->is_array()) @@ -1068,10 +1072,10 @@ struct llama_server_context slot.has_next_token = false; } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)){ - slot.stopped_eos = true; - slot.has_next_token = false; - LOG_VERBOSE("eos token found", {}); + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) { + slot.stopped_eos = true; + slot.has_next_token = false; + LOG_VERBOSE("eos token found", {}); } LOG_VERBOSE("next token", { @@ -1277,22 +1281,25 @@ struct llama_server_context } task_result next_result(int task_id) { - while(true) { + while (true) { std::this_thread::sleep_for(std::chrono::microseconds(5)); std::lock_guard lock(mutex_results); - if(queue_results.empty()) { + + if (queue_results.empty()) { continue; } - for(int i = 0; i < queue_results.size(); i++) { - if(queue_results[i].id == task_id) { + for (int i = 0; i < (int) queue_results.size(); i++) { + if (queue_results[i].id == task_id) { task_result res = queue_results[i]; queue_results.erase(queue_results.begin() + i); return res; } } } - return task_result{-1, false, false, {}}; + + // never reached + //return task_result{-1, false, false, {}}; } // for multiple images processing @@ -1373,48 +1380,48 @@ struct llama_server_context void process_tasks() { std::lock_guard lock(mutex_tasks); - while(!queue_tasks.empty()) { + while (!queue_tasks.empty()) { task_server task = queue_tasks.front(); queue_tasks.erase(queue_tasks.begin()); switch (task.type) { - case COMPLETION_TASK: { // perform completion task - llama_client_slot* slot = get_slot(json_value(task.data, "slot_id", -1)); - if (slot == nullptr) { - LOG_TEE("slot unavailable\n"); - // send error result - send_error(task.id, "slot unavaliable"); - return; - } + case COMPLETION_TASK: { + llama_client_slot* slot = get_slot(json_value(task.data, "slot_id", -1)); + if (slot == nullptr) { + LOG_TEE("slot unavailable\n"); + // send error result + send_error(task.id, "slot unavaliable"); + return; + } - if (task.data.contains("system_prompt")) { - process_system_prompt_data(task.data["system_prompt"]); - } + if (task.data.contains("system_prompt")) { + process_system_prompt_data(task.data["system_prompt"]); + } - slot->reset(); + slot->reset(); - slot->infill = task.infill_mode; - slot->task_id = task.id; + slot->infill = task.infill_mode; + slot->task_id = task.id; - if (!launch_slot_with_data(slot, task.data)) - { - // send error result - send_error(task.id, "internal_error"); - break; - } - } - case CANCEL_TASK: { // release slot linked with the task id - for(auto & slot : slots) { - if(slot.task_id == task.target_id) { - slot.release(); + if (!launch_slot_with_data(slot, task.data)) + { + // send error result + send_error(task.id, "internal_error"); break; } } - } - break; + case CANCEL_TASK: { // release slot linked with the task id + for (auto & slot : slots) { + if (slot.task_id == task.target_id) { + slot.release(); + break; + } + } + } + break; - default: - break; + default: + break; } } } @@ -1426,6 +1433,7 @@ struct llama_server_context // update the system prompt wait until all slots are idle state if (need_update_system_prompt) { + LOG_TEE("updating system prompt\n"); update_system_prompt(); } @@ -1435,6 +1443,7 @@ struct llama_server_context { if (system_prompt.empty() && clean_kv_cache) { + LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); kv_cache_clear(); } // avoid 100% usage of cpu all time @@ -1449,6 +1458,7 @@ struct llama_server_context const int n_left = slot.n_past - slot.params.n_keep - 1; const int n_discard = n_left / 2; + LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); @@ -1463,7 +1473,7 @@ struct llama_server_context slot.truncated = true; - LOG_VERBOSE("input truncated", { + LOG_VERBOSE("context shift", { {"n_ctx", n_ctx}, {"n_keep", params.n_keep}, {"n_left", n_left}, @@ -1478,7 +1488,7 @@ struct llama_server_context if (slot.state == PROCESSING && slot.command == RELEASE) { slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; - if(slot.state == SLEEPING) { + if (slot.state == SLEEPING) { LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, (int) slot.cache_tokens.size()); } else @@ -1504,6 +1514,7 @@ struct llama_server_context slot.n_decoded += 1; slot.n_past += 1; } + // process in chunks of params.n_batch int32_t n_batch = params.n_batch; @@ -1547,7 +1558,7 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); - if(!slot.params.cache_prompt) + if (!slot.params.cache_prompt) { std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end(), 0); slot.n_past = 0; @@ -1586,9 +1597,10 @@ struct llama_server_context std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.ctx_sampling->prev.end() - ps); slot.n_past = common_part(slot.cache_tokens, prompt_tokens); slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; - LOG_TEE("slot %i - in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); + LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } + LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, num_tokens_system + slot.n_past); llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); slot.cache_tokens = prompt_tokens; @@ -1596,7 +1608,7 @@ struct llama_server_context if (slot.n_past == (int) slot.num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); + LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); slot.n_past--; } @@ -1606,7 +1618,7 @@ struct llama_server_context {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); - const bool has_images = process_images(slot); // has images? + const bool has_images = process_images(slot); // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; @@ -1664,7 +1676,7 @@ struct llama_server_context return false; } - LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2); + LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; @@ -1705,7 +1717,7 @@ struct llama_server_context const int32_t n_probs = slot.sparams.n_probs; if (slot.sparams.temp <= 0 && n_probs > 0) { - // For llama_sample_token_greedy we need to sort candidates + // for llama_sample_token_greedy we need to sort candidates llama_sample_softmax(ctx, &cur_p); } From 17b23eb9cb06a1462d5490263bd029488306567d Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 21 Oct 2023 19:58:03 +0800 Subject: [PATCH 47/64] server : fix multibyte handle in partial response (#3706) --- examples/server/server.cpp | 56 ++++++++++++++++++++------------------ 1 file changed, 30 insertions(+), 26 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fb900d299d632..2fb0779ad080a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1005,32 +1005,6 @@ struct llama_server_context slot.generated_text += token_str; slot.has_next_token = true; - size_t pos = std::min(slot.sent_count, slot.generated_text.size()); - const std::string str_test = slot.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); - if (stop_pos != std::string::npos) { - is_stop_full = true; - slot.generated_text.erase( - slot.generated_text.begin() + pos + stop_pos, - slot.generated_text.end()); - pos = std::min(slot.sent_count, slot.generated_text.size()); - } else { - is_stop_full = false; - stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); - } - - // check if there is any token to predict - if(stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { - // no send the stop word in the response - result.text_to_send = slot.generated_text.substr(pos, std::string::npos); - slot.sent_count += result.text_to_send.size(); - // add the token to slot queue and cache - } - slot.add_token_string(result); - if(slot.params.stream) { - send_partial_response(slot, result); - } if (slot.multibyte_pending > 0) { slot.multibyte_pending -= token_str.size(); @@ -1059,6 +1033,36 @@ struct llama_server_context } } + if (slot.multibyte_pending == 0) + { + size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + const std::string str_test = slot.generated_text.substr(pos); + bool is_stop_full = false; + size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); + if (stop_pos != std::string::npos) { + is_stop_full = true; + slot.generated_text.erase( + slot.generated_text.begin() + pos + stop_pos, + slot.generated_text.end()); + pos = std::min(slot.sent_count, slot.generated_text.size()); + } else { + is_stop_full = false; + stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); + } + + // check if there is any token to predict + if(stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { + // no send the stop word in the response + result.text_to_send = slot.generated_text.substr(pos, std::string::npos); + slot.sent_count += result.text_to_send.size(); + // add the token to slot queue and cache + } + slot.add_token_string(result); + if (slot.params.stream) { + send_partial_response(slot, result); + } + } + if (slot.multibyte_pending > 0 && !slot.has_next_token) { slot.has_next_token = true; From 2eb4c11ec5bb115831402048144dc6652f00a645 Mon Sep 17 00:00:00 2001 From: FSSRepo Date: Sat, 21 Oct 2023 14:34:19 -0400 Subject: [PATCH 48/64] fix image load + view image in chat --- examples/server/completion.js.hpp | 798 +++--- examples/server/index.html.hpp | 4395 +++++++++++++++-------------- examples/server/public/index.html | 6 +- examples/server/server.cpp | 8 +- 4 files changed, 2626 insertions(+), 2581 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index f0a071a69bca1..2a0f81b3efeae 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -1,335 +1,347 @@ unsigned char completion_js[] = { 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, - 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72, - 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, - 0x69, 0x63, 0x74, 0x3a, 0x20, 0x35, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, - 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, - 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, - 0x3a, 0x20, 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a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 2e1df2cacca52..1b73cc022bbfa 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -349,1198 +349,1194 @@ unsigned char index_html[] = { 0x72, 0x3a, 0x20, 0x22, 0x4c, 0x6c, 0x61, 0x6d, 0x61, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x3a, 0x20, 0x22, 0x55, 0x73, 0x65, 0x72, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, - 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x73, - 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x3a, - 0x20, 0x34, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, - 0x20, 0x30, 0x2e, 0x37, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x72, 0x65, 0x70, 0x65, 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user: "User", + image_selected: '' }) const params = signal({ @@ -298,6 +299,7 @@ function userTemplateApply(t) { session.value = t.data.session; + session.value = {...session.value, image_selected: ''}; params.value = t.data.params; } @@ -488,8 +490,9 @@ const reader = new FileReader(); reader.onload = function () { const image_data = reader.result; + session.value = {...session.value, image_selected: image_data}; params.value = {...params.value, image_data: [ - { data: image_data.replace('data:image/png;base64,', ''), id: 10 }] } + { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] } }; selected_image = true; reader.readAsDataURL(selectedFile); @@ -580,6 +583,7 @@ return html`
+ ${messages.flatMap(chatLine)}
`; }; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2fb0779ad080a..cedc43ccc37bc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -807,10 +807,10 @@ struct llama_server_context data_b64.clear(); auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); if (!data) { - LOG_TEE("slot %i - failed to load image id= %i\n", slot->id, img_sl.id); + LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id); return false; } - LOG_TEE("slot %i - image id= %i loaded (%i x %i)\n", slot->id, img_sl.id, width, height); + LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height); img_sl.img_data.nx = width; img_sl.img_data.ny = height; img_sl.img_data.size = width * height * 3; @@ -848,7 +848,7 @@ struct llama_server_context } } if (!found) { - LOG_TEE("ERROR: Image with id %i not found.\n", img_id); + LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id); slot->images.clear(); return false; } @@ -1119,7 +1119,7 @@ struct llama_server_context clip_free(clp_ctx); return false; } - LOG_TEE("slot %i - encoding image %i\n", slot.id, img.id); + LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id); if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) { LOG_TEE("Unable to encode image\n"); From 4b4ab722ab96249f88433db94a0225bc9faa9cbe Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 16:51:59 +0300 Subject: [PATCH 49/64] make : silence stb warnings --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 7385e64eccc1e..80179631f95a5 100644 --- a/Makefile +++ b/Makefile @@ -606,7 +606,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml. $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) + $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) From 715f384a6b9847f2a807b782e61684737a8a98d0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 16:52:12 +0300 Subject: [PATCH 50/64] clip : link to ggml, not to llama --- examples/llava/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index d02e6ab461ff2..2d7979ecd8952 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -1,7 +1,7 @@ set(TARGET clip) add_library(${TARGET} clip.cpp clip.h) install(TARGETS ${TARGET} LIBRARY) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if (NOT MSVC) target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h From 197a0a9e23340944f8760c71ec3dfee11c495497 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 16:55:05 +0300 Subject: [PATCH 51/64] server : fix switch fallthrough --- examples/server/server.cpp | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 032ac89900a23..cdc643ffef16c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1358,14 +1358,16 @@ struct llama_server_context { case COMPLETION_TASK: { llama_client_slot* slot = get_slot(json_value(task.data, "slot_id", -1)); - if (slot == nullptr) { + if (slot == nullptr) + { LOG_TEE("slot unavailable\n"); // send error result send_error(task.id, "slot unavaliable"); return; } - if (task.data.contains("system_prompt")) { + if (task.data.contains("system_prompt")) + { process_system_prompt_data(task.data["system_prompt"]); } @@ -1380,7 +1382,7 @@ struct llama_server_context send_error(task.id, "internal_error"); break; } - } + } break; case CANCEL_TASK: { // release slot linked with the task id for (auto & slot : slots) { if (slot.task_id == task.target_id) { @@ -1388,11 +1390,7 @@ struct llama_server_context break; } } - } - break; - - default: - break; + } break; } } } From ef18f4d579b90344eff045dcc29d15048310e6bc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 16:55:40 +0300 Subject: [PATCH 52/64] server : fix crash in Debug on macOS (I have no idea why this fixes it!?) --- examples/server/server.cpp | 31 +++++++++++++++++++++---------- 1 file changed, 21 insertions(+), 10 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index cdc643ffef16c..105b6d92ecd25 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2440,20 +2440,31 @@ int main(int argc, char **argv) {"hostname", sparams.hostname}, {"port", sparams.port}, }); - std::thread t([&llama]() - { - bool running = true; - while (running) + + // run the HTTP server in a thread - see comment below + std::thread t([&]() { - running = llama.update_slots(); - } - } - ); + if (!svr.listen_after_bind()) + { + return 1; + } - if (!svr.listen_after_bind()) + return 0; + }); + + // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? + // "Bus error: 10" - this is on macOS, it does not crash on Linux + //std::thread t2([&]() { - return 1; + bool running = true; + while (running) + { + running = llama.update_slots(); + } } + //); + + t.join(); llama_backend_free(); return 0; From 569ebf11cfd99ffbacc94ca4e52e2d3925c0ec0b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 16:57:05 +0300 Subject: [PATCH 53/64] server : refactor ctx_sampling init + n_ctx + names --- examples/server/server.cpp | 250 +++++++++++++++++-------------------- 1 file changed, 118 insertions(+), 132 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 105b6d92ecd25..5a6c434de629c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -341,47 +341,55 @@ struct llama_client_slot { int id; int task_id = -1; + + struct slot_params params; + + slot_state state = IDLE; + slot_command command = NONE; + // generation props - int32_t n_past = 0; - int32_t n_decoded = 0; - int32_t i_batch = -1; - size_t num_prompt_tokens = 0; - int32_t num_prompt_tokens_processed = 0; + int32_t n_ctx = 0; // context size per slot + int32_t n_past = 0; + int32_t n_decoded = 0; int32_t n_remaining = -1; + int32_t i_batch = -1; + + int32_t num_prompt_tokens = 0; + int32_t num_prompt_tokens_processed = 0; + int32_t multibyte_pending = 0; json prompt; std::string generated_text; llama_token sampled; std::vector cache_tokens; std::vector generated_token_probs; - slot_state state = IDLE; - slot_command command = NONE; + + bool infill = false; + bool has_next_token = true; bool truncated = false; bool stopped_eos = false; bool stopped_word = false; bool stopped_limit = false; + std::string stopping_word; - int32_t multibyte_pending = 0; + + // sampling + struct llama_sampling_params sparams; + llama_sampling_context *ctx_sampling = nullptr; + + // multimodal + std::vector images; + + // stats size_t sent_count = 0; size_t sent_token_probs_index = 0; - bool infill = false; int64_t t_start_process_prompt; int64_t t_start_genereration; double t_prompt_processing; // ms double t_token_generation; // ms - struct slot_params params; - - // sampling - struct llama_sampling_params sparams; - llama_sampling_context* ctx_sampling = nullptr; - bool has_next_token = true; - - // multimodal - std::vector images; - void reset() { num_prompt_tokens = 0; generated_text = ""; @@ -397,13 +405,6 @@ struct llama_client_slot infill = false; generated_token_probs.clear(); - if (ctx_sampling != nullptr) - { - llama_sampling_free(ctx_sampling); - } - - ctx_sampling = llama_sampling_init(sparams); - for (slot_image &img : images) { free(img.image_embedding); @@ -415,17 +416,6 @@ struct llama_client_slot // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } - bool load_grammar() - { - if (ctx_sampling != nullptr) - { - llama_sampling_free(ctx_sampling); - } - - ctx_sampling = llama_sampling_init(sparams); - return ctx_sampling != nullptr; - } - bool has_budget(gpt_params &global_params) { n_remaining = -1; if(params.n_predict != -1) @@ -491,33 +481,33 @@ struct llama_client_slot struct llama_server_context { - std::vector slots; - - // system prompt - std::string system_prompt; - bool need_update_system_prompt = false; - std::vector tokens_system; - int32_t num_tokens_system; - - // broadcast to all clients to keep the same prompt format - std::string user_name; // this should be the anti prompt - std::string assistant_name; // this is for generate the prompt + llama_model *model = nullptr; + llama_context *ctx = nullptr; - bool multimodal = false; clip_ctx *clp_ctx = nullptr; - int n_embd; - llama_model *model = nullptr; - llama_context *ctx = nullptr; + gpt_params params; + llama_batch batch; + + bool multimodal = false; + bool clean_kv_cache = true; bool all_slots_are_idle = false; - gpt_params params; - int n_ctx; - int n_vocab; - int max_ctx_per_slot = -1; - bool clean_kv_cache = true; - int id_gen; + int32_t id_gen; + int32_t n_ctx; // total context for all clients / slots + + // system prompt + bool system_need_update = false; + + std::string system_prompt; + std::vector system_tokens; + + std::string name_user; // this should be the antiprompt + std::string name_assistant; + + // slots / clients + std::vector slots; std::vector queue_tasks; std::vector queue_results; @@ -541,7 +531,7 @@ struct llama_server_context bool load_model(const gpt_params ¶ms_) { params = params_; - if(!params.mmproj.empty()) { + if (!params.mmproj.empty()) { multimodal = true; LOG_TEE("Multi Modal Mode Enabled"); clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); @@ -550,10 +540,11 @@ struct llama_server_context return false; } - if(params.n_ctx < 2048) { // request larger context for the image embedding + if (params.n_ctx < 2048) { // request larger context for the image embedding params.n_ctx = 2048; } } + std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { @@ -561,18 +552,19 @@ struct llama_server_context return false; } - if(multimodal) { - int n_img_embd = clip_n_mmproj_embd(clp_ctx); - n_embd = llama_n_embd(model); - if (n_img_embd != n_embd) { - LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_embd); + if (multimodal) { + const int n_embd_clip = clip_n_mmproj_embd(clp_ctx); + const int n_embd_llm = llama_n_embd(model); + if (n_embd_clip != n_embd_llm) { + LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm); llama_free(ctx); llama_free_model(model); return false; } } + n_ctx = llama_n_ctx(ctx); - n_vocab = llama_n_vocab(model); + return true; } @@ -581,25 +573,19 @@ struct llama_server_context // create slots all_slots_are_idle = true; - if (max_ctx_per_slot == -1) - { - max_ctx_per_slot = n_ctx / params.n_parallel; // split context - } - if (max_ctx_per_slot * params.n_parallel > n_ctx) - { - printf("Error: The max context per slot is more greater than model context size"); - return; - } + + const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_TEE("Available slots:\n"); for (int i = 0; i < params.n_parallel; i++) { llama_client_slot slot; + slot.id = i; - slot.sparams.n_prev = max_ctx_per_slot; + slot.n_ctx = n_ctx_slot; slot.reset(); - LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot); + LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); slots.push_back(slot); } @@ -607,7 +593,7 @@ struct llama_server_context // empty system prompt system_prompt = ""; - num_tokens_system = 0; + system_tokens.clear(); } std::vector tokenize(const json & json_prompt, bool add_bos) const @@ -699,16 +685,16 @@ struct llama_server_context { slot->params.input_prefix = ""; } + if (data.count("input_suffix") != 0) { slot->params.input_suffix = data["input_suffix"]; } - - // common params else { slot->params.input_suffix = ""; } + if (data.count("prompt") != 0) { slot->prompt = data["prompt"]; @@ -717,11 +703,14 @@ struct llama_server_context { slot->prompt = ""; } + slot->sparams.logit_bias.clear(); + if (json_value(data, "ignore_eos", false)) { slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY; } + const auto &logit_bias = data.find("logit_bias"); if (logit_bias != data.end() && logit_bias->is_array()) { @@ -832,36 +821,37 @@ struct llama_server_context } } } - if (!slot->load_grammar()) + + if (slot->ctx_sampling != nullptr) { - return false; + llama_sampling_free(slot->ctx_sampling); } - all_slots_are_idle = false; + slot->ctx_sampling = llama_sampling_init(slot->sparams); slot->command = LOAD_PROMPT; + + all_slots_are_idle = false; + LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); + return true; } void kv_cache_clear() { // clear the entire KV cache - for (int i = 0; i < params.n_parallel; ++i) - { - llama_kv_cache_seq_rm(ctx, i, 0, -1); - } + llama_kv_cache_tokens_rm(ctx, -1, -1); clean_kv_cache = false; } void update_system_prompt() { - tokens_system = ::llama_tokenize(ctx, system_prompt, true); - num_tokens_system = tokens_system.size(); + system_tokens = ::llama_tokenize(ctx, system_prompt, true); - batch.n_tokens = num_tokens_system; + llama_batch_clear(batch); kv_cache_clear(); for (int32_t i = 0; i < batch.n_tokens; ++i) { - llama_batch_add(batch, tokens_system[i], i, { 0 }, false); + llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } if (llama_decode(ctx, batch) != 0) @@ -873,11 +863,11 @@ struct llama_server_context // assign the system KV cache to all parallel sequences for (int32_t i = 1; i < params.n_parallel; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, num_tokens_system); + llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); } LOG_TEE("system prompt updated\n"); - need_update_system_prompt = false; + system_need_update = false; } void notify_system_prompt_changed() { @@ -890,8 +880,8 @@ struct llama_server_context all_slots_are_idle = true; // wait until system prompt load - need_update_system_prompt = true; - while (need_update_system_prompt) { + system_need_update = true; + while (system_need_update) { std::this_thread::sleep_for(std::chrono::milliseconds(5)); } // system prompt loaded, continue @@ -899,8 +889,8 @@ struct llama_server_context void process_system_prompt_data(const json &sys_props) { system_prompt = sys_props.value("prompt", ""); - user_name = sys_props.value("anti_prompt", ""); - assistant_name = sys_props.value("assistant_name", ""); + name_user = sys_props.value("anti_prompt", ""); + name_assistant = sys_props.value("assistant_name", ""); if (slots.size() > 0) { @@ -908,7 +898,7 @@ struct llama_server_context } else { - need_update_system_prompt = true; + system_need_update = true; } } @@ -1036,14 +1026,14 @@ struct llama_server_context } // check the limits - if ( - slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params)) + if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params)) { slot.stopped_limit = true; slot.has_next_token = false; } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) { + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) + { slot.stopped_eos = true; slot.has_next_token = false; LOG_VERBOSE("eos token found", {}); @@ -1111,12 +1101,12 @@ struct llama_server_context return get_formated_generation(slots[0]); } - json get_formated_generation(llama_client_slot & slot) { + json get_formated_generation(llama_client_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); - return json{ - {"n_ctx", max_ctx_per_slot}, + return json { + {"n_ctx", slot.n_ctx}, {"model", params.model_alias}, {"seed", slot.params.seed}, {"temp", slot.sparams.temp}, @@ -1219,7 +1209,8 @@ struct llama_server_context res.id = slot.task_id; res.error = false; res.stop = true; - static const int n_embd = llama_n_embd(model); + + const int n_embd = llama_n_embd(model); if (!params.embedding) { LOG_WARNING("embedding disabled", { @@ -1229,7 +1220,9 @@ struct llama_server_context { {"embedding", std::vector(n_embd, 0.0f)}, }; - } else { + } + else + { const float *data = llama_get_embeddings(ctx); std::vector embedding(data, data + n_embd); res.result_json = json @@ -1312,6 +1305,7 @@ struct llama_server_context n_eval = n_batch; } + const int n_embd = llama_n_embd(model); llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; if (llama_decode(ctx, batch_img)) { @@ -1400,7 +1394,7 @@ struct llama_server_context process_tasks(); // update the system prompt wait until all slots are idle state - if (need_update_system_prompt) + if (system_need_update) { LOG_TEE("updating system prompt\n"); update_system_prompt(); @@ -1421,7 +1415,7 @@ struct llama_server_context for (llama_client_slot &slot : slots) { - if (slot.is_processing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot) + if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) { // Shift context const int n_left = slot.n_past - slot.params.n_keep - 1; @@ -1443,7 +1437,7 @@ struct llama_server_context slot.truncated = true; LOG_VERBOSE("context shift", { - {"n_ctx", n_ctx}, + {"n_ctx", n_ctx}, {"n_keep", params.n_keep}, {"n_left", n_left}, }); @@ -1478,7 +1472,7 @@ struct llama_server_context slot.i_batch = batch.n_tokens; - llama_batch_add(batch, slot.sampled, num_tokens_system + slot.n_past, { slot.id }, true); + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); slot.n_decoded += 1; slot.n_past += 1; @@ -1537,21 +1531,21 @@ struct llama_server_context { if (slot.params.n_keep < 0) { - slot.params.n_keep = (int)slot.num_prompt_tokens; + slot.params.n_keep = slot.num_prompt_tokens; } - slot.params.n_keep = std::min(max_ctx_per_slot - 4, slot.params.n_keep); + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); //if input prompt is too big, truncate like normal - if (slot.num_prompt_tokens >= (size_t)max_ctx_per_slot) + if (slot.num_prompt_tokens >= slot.n_ctx) { // applied bug of #3661 - const int n_left = max_ctx_per_slot - slot.params.n_keep; + const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); // Use half the left-over space in the context for the prompt new_tokens.insert(new_tokens.end(), prompt_tokens.end() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); LOG_VERBOSE("input truncated", { - {"n_ctx", max_ctx_per_slot}, + {"n_ctx", slot.n_ctx}, {"n_keep", slot.params.n_keep}, {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, @@ -1559,7 +1553,7 @@ struct llama_server_context slot.truncated = true; prompt_tokens = new_tokens; slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < (size_t)max_ctx_per_slot); + GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); } const size_t ps = slot.num_prompt_tokens; std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end() - ps, 0); @@ -1569,12 +1563,13 @@ struct llama_server_context LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } - LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, num_tokens_system + slot.n_past); - llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1); + LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); + + llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); slot.cache_tokens = prompt_tokens; - if (slot.n_past == (int) slot.num_prompt_tokens) + if (slot.n_past == slot.num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); @@ -1593,7 +1588,7 @@ struct llama_server_context std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { - llama_batch_add(batch, prefix_tokens[slot.n_past], num_tokens_system + slot.n_past, { slot.id }, false); + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false); } if (has_images && !ingest_images(slot, n_batch)) @@ -1842,15 +1837,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_ctx = std::stoi(argv[i]); } - else if (arg == "-cps" || arg == "--ctx-per-slot" || arg == "--ctx_per_slot") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - llama.max_ctx_per_slot = std::stoi(argv[i]); - } else if (arg == "--rope-freq-base") { if (++i >= argc) @@ -2227,8 +2213,8 @@ int main(int argc, char **argv) { res.set_header("Access-Control-Allow-Origin", "*"); json data = { - { "user_name", llama.user_name.c_str() }, - { "assistant_name", llama.assistant_name.c_str() } + { "user_name", llama.name_user.c_str() }, + { "assistant_name", llama.name_assistant.c_str() } }; res.set_content(data.dump(), "application/json"); }); @@ -2434,7 +2420,7 @@ int main(int argc, char **argv) svr.set_base_dir(sparams.public_path); // to make it ctrl+clickable: - printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); + LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); LOG_INFO("HTTP server listening", { {"hostname", sparams.hostname}, From f67d971344232a4ffc434ece9b3cf4dda8549117 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 17:52:59 +0300 Subject: [PATCH 54/64] server : bug fix for prompt caching --- examples/server/completion.js.hpp | 798 +++++++++--------- .../server/json-schema-to-grammar.mjs.hpp | 580 +++++++------ examples/server/server.cpp | 2 +- 3 files changed, 677 insertions(+), 703 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 2a0f81b3efeae..f0a071a69bca1 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -1,347 +1,335 @@ 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, 0x0d, - 0x0a, 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, - 0x72, 0x75, 0x65, 0x2c, 0x0d, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, - 0x65, 0x64, 0x69, 0x63, 0x74, 0x3a, 0x20, 0x35, 0x30, 0x30, 0x2c, 0x0d, - 0x0a, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, - 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0d, 0x0a, 0x20, 0x20, - 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, - 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b/examples/server/json-schema-to-grammar.mjs.hpp index 27934b17c7437..0a05c369d77ba 100644 --- a/examples/server/json-schema-to-grammar.mjs.hpp +++ b/examples/server/json-schema-to-grammar.mjs.hpp @@ -1,198 +1,192 @@ unsigned char json_schema_to_grammar_mjs[] = { 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x53, 0x50, 0x41, 0x43, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, 0x20, 0x3d, 0x20, 0x27, 0x22, 0x20, 0x22, 0x3f, - 0x27, 0x3b, 0x0d, 0x0a, 0x0d, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x50, 0x52, 0x49, 0x4d, 0x49, 0x54, 0x49, 0x56, 0x45, 0x5f, 0x52, 0x55, - 0x4c, 0x45, 0x53, 0x20, 0x3d, 0x20, 0x7b, 0x0d, 0x0a, 0x20, 0x20, 0x62, - 0x6f, 0x6f, 0x6c, 0x65, 0x61, 0x6e, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x74, - 0x72, 0x75, 0x65, 0x22, 0x20, 0x7c, 0x20, 0x22, 0x66, 0x61, 0x6c, 0x73, - 0x65, 0x22, 0x29, 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x27, 0x2c, 0x0d, - 0x0a, 0x20, 0x20, 0x6e, 0x75, 0x6d, 0x62, 0x65, 0x72, 0x3a, 0x20, 0x27, - 0x28, 0x22, 0x2d, 0x22, 0x3f, 0x20, 0x28, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 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a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1543,7 +1543,7 @@ struct llama_server_context const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); // Use half the left-over space in the context for the prompt - new_tokens.insert(new_tokens.end(), prompt_tokens.end() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); LOG_VERBOSE("input truncated", { {"n_ctx", slot.n_ctx}, {"n_keep", slot.params.n_keep}, From 5359fb9267a62ca9d4debaed9410307151f87e55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sun, 22 Oct 2023 19:08:09 +0300 Subject: [PATCH 55/64] Do not save/load image_data to localStorage --- examples/server/index.html.hpp | 4099 +++++++++++++++-------------- examples/server/public/index.html | 105 +- 2 files changed, 2110 insertions(+), 2094 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 1b73cc022bbfa..4318d0f297461 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -186,270 +186,290 @@ unsigned char index_html[] = { 0x64, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x32, 0x32, 0x32, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x64, 0x64, 0x64, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x64, 0x65, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, - 0x74, 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, 0x79, 0x3a, 0x20, 0x6d, 0x6f, - 0x6e, 0x6f, 0x73, 0x70, 0x61, 0x63, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, - 0x30, 0x2e, 0x31, 0x65, 0x6d, 0x20, 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0x6c, 0x65, 0x3d, 0x22, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, + 0x20, 0x6e, 0x6f, 0x6e, 0x65, 0x3b, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x3c, + 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, + 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, + 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, + 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e }; -unsigned int index_html_len = 31992; +unsigned int index_html_len = 32076; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 3b46a2e2954c6..feea7e1f5d900 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -125,6 +125,7 @@ background-color: #222; color: #ddd; } + code { font-family: monospace; padding: 0.1em 0.3em; @@ -141,7 +142,8 @@ display: inline; } - header, footer { + header, + footer { text-align: center; } @@ -163,6 +165,7 @@ 0% { background-position: 0%; } + 100% { background-position: 100%; } @@ -181,6 +184,7 @@ --loading-color-1: #22222200; --loading-color-2: #222222ff; } + .popover-content { background-color: black; } @@ -275,6 +279,7 @@ // saved templates were successfuly imported. console.log('Processing saved templates and updating default template') + params.value = { ...params.value, image_data: [] }; //console.log(importedTemplates); savedUserTemplates.value = importedTemplates; @@ -299,8 +304,9 @@ function userTemplateApply(t) { session.value = t.data.session; - session.value = {...session.value, image_selected: ''}; + session.value = { ...session.value, image_selected: '' }; params.value = t.data.params; + params.value = { ...params.value, image_data: [] }; } function userTemplateResetToDefaultAndApply() { @@ -391,14 +397,14 @@ throw new Error("already running"); } controller.value = new AbortController(); - for await (const chunk of llama(prompt, llamaParams, {controller: controller.value})) { + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) { const data = chunk.data; if (data.stop) { while ( currentMessages.length > 0 && currentMessages[currentMessages.length - 1].content.match(/\n$/) != null - ) { + ) { currentMessages.pop(); } transcriptUpdate([...history, [char, currentMessages]]) @@ -406,7 +412,7 @@ } else { currentMessages.push(data); slot_id = data.slot_id; - if(selected_image && !data.multimodal) { + if (selected_image && !data.multimodal) { alert("The server was no compiled for multimodal or the model projector can't be loaded."); return; } @@ -445,7 +451,7 @@ ) ).join("\n"), }); - if(selected_image) { + if (selected_image) { prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`; } await runLlama(prompt, { @@ -460,7 +466,7 @@ console.log('already running...'); return; } - const {prompt} = session.value; + const { prompt } = session.value; transcriptUpdate([...session.value.transcript, ["", prompt]]); await runLlama(prompt, { ...params.value, @@ -485,19 +491,21 @@ e.preventDefault(); document.getElementById("fileInput").click(); document.getElementById("fileInput").addEventListener("change", function (event) { - const selectedFile = event.target.files[0]; - if (selectedFile) { - const reader = new FileReader(); - reader.onload = function () { - const image_data = reader.result; - session.value = {...session.value, image_selected: image_data}; - params.value = {...params.value, image_data: [ - { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] } - }; - selected_image = true; - reader.readAsDataURL(selectedFile); + const selectedFile = event.target.files[0]; + if (selectedFile) { + const reader = new FileReader(); + reader.onload = function () { + const image_data = reader.result; + session.value = { ...session.value, image_selected: image_data }; + params.value = { + ...params.value, image_data: [ + { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] } - }); + }; + selected_image = true; + reader.readAsDataURL(selectedFile); + } + }); } function MessageInput() { @@ -574,7 +582,7 @@ data; message = html`<${Markdownish} text=${template(text)} />` } - if(user) { + if (user) { return html`

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

` } else { return html`

${message}

` @@ -583,7 +591,7 @@ return html`
- + ${messages.flatMap(chatLine)}
`; }; @@ -602,7 +610,7 @@ const converter = new SchemaConverter( grammarJsonSchemaPropOrder.value .split(',') - .reduce((acc, cur, i) => ({...acc, [cur.trim()]: i}), {}) + .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}) ) converter.visit(schema, '') params.value = { @@ -614,7 +622,7 @@ } } - const FloatField = ({label, max, min, name, step, value}) => { + const FloatField = ({ label, max, min, name, step, value }) => { return html`
@@ -624,7 +632,7 @@ ` }; - const IntField = ({label, max, min, name, value}) => { + const IntField = ({ label, max, min, name, value }) => { return html`
@@ -707,7 +715,7 @@ ${GrammarControl()} ` - ); + ); const CompletionConfigForm = () => ( html` @@ -729,20 +737,20 @@ ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
- ${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})} - ${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})} - ${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})} - ${IntField({label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n})} - ${IntField({label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k})} - ${FloatField({label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p})} + ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })} + ${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} + ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} + ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} + ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} + ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
More options
- ${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})} - ${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})} - ${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})} - ${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})} + ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} + ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} + ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
@@ -751,11 +759,11 @@
- ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})} - ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})} + ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })} + ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
- ${IntField({label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs})} + ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
@@ -794,20 +802,20 @@ const popoverChildren = html`
${probs.map((p, index) => { - return html` + return html`
${p.tok_str}: ${Math.floor(p.prob * 100)}%
` - })} + })}
` @@ -886,9 +894,9 @@ ref=${popoverRef} class="popover-content" style=${{ - top: position.value.top, - left: position.value.left, - }} + top: position.value.top, + left: position.value.left, + }} > ${props.popoverChildren}
@@ -988,8 +996,9 @@

llama.cpp

-
+ +
- + \ No newline at end of file From f305d6434f4844634812038d8180339181816939 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 19:10:30 +0300 Subject: [PATCH 56/64] editorconfig : new line in index.html --- examples/server/public/index.html | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/server/public/index.html b/examples/server/public/index.html index feea7e1f5d900..443181465c313 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -1001,4 +1001,5 @@

llama.cpp

- \ No newline at end of file + + From a8063171bdb2b5529c483f1d854a10b9d8584952 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 19:34:48 +0300 Subject: [PATCH 57/64] server : completion requests remember slot_id --- examples/server/index.html.hpp | 3277 +++++++++++++++-------------- examples/server/public/index.html | 3 +- examples/server/server.cpp | 15 +- 3 files changed, 1651 insertions(+), 1644 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 4318d0f297461..5d3bdfbdd7da3 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -947,1730 +947,1733 @@ unsigned char index_html[] = { 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x6c, 0x65, 0x72, 0x74, 0x28, 0x22, 0x54, 0x68, 0x65, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x20, 0x77, 0x61, - 0x73, 0x20, 0x6e, 0x6f, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x69, 0x6c, 0x65, - 0x64, 0x20, 0x66, 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-unsigned int index_html_len = 32076; +unsigned int index_html_len = 32105; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 443181465c313..39d7bb93d9c4c 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -413,7 +413,7 @@ currentMessages.push(data); slot_id = data.slot_id; if (selected_image && !data.multimodal) { - alert("The server was no compiled for multimodal or the model projector can't be loaded."); + alert("The server was not compiled for multimodal or the model projector can't be loaded."); return; } transcriptUpdate([...history, [char, currentMessages]]) @@ -470,6 +470,7 @@ transcriptUpdate([...session.value.transcript, ["", prompt]]); await runLlama(prompt, { ...params.value, + slot_id: slot_id, stop: [], }, ""); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f28e9e0c5ac76..14edd3dd17c90 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -655,6 +655,7 @@ struct llama_server_context bool launch_slot_with_data(llama_client_slot* &slot, json data) { slot_params default_params; llama_sampling_params default_sparams; + slot->params.stream = json_value(data, "stream", false); slot->params.cache_prompt = json_value(data, "cache_prompt", false); slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); @@ -1141,10 +1142,10 @@ struct llama_server_context res.stop = false; res.result_json = json { - {"content", tkn.text_to_send }, + {"content", tkn.text_to_send}, {"stop", false}, - {"slot_id", slot.id }, - {"multimodal", multimodal } + {"slot_id", slot.id}, + {"multimodal", multimodal} }; if (slot.sparams.n_probs > 0) { @@ -1351,7 +1352,7 @@ struct llama_server_context switch (task.type) { case COMPLETION_TASK: { - llama_client_slot* slot = get_slot(json_value(task.data, "slot_id", -1)); + llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); if (slot == nullptr) { LOG_TEE("slot unavailable\n"); @@ -1515,7 +1516,9 @@ struct llama_server_context prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(ctx)); prompt_tokens = prefix_tokens; - } else { + } + else + { prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt } @@ -2069,7 +2072,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } static json format_partial_response( - llama_server_context &llama, llama_client_slot* slot, const std::string &content, const std::vector &probs + llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector &probs ) { json res = json { From 2679c432d54e496c048fcb34bb1521a8f5f0630d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sun, 22 Oct 2023 19:49:33 +0300 Subject: [PATCH 58/64] Update readme to document multimodal in server --- examples/server/README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 898e32bb6f024..bd369444b70b7 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -26,8 +26,8 @@ Command line options: - `--embedding`: Enable embedding extraction, Default: disabled. - `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) - `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) -- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) -- +- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) +- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. ## Build @@ -162,6 +162,8 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": ["data": "", "id": 12]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + *Result JSON:* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. From dd1af2ed35dfcf1e6842a57c4e69478e93622d89 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 19:52:38 +0300 Subject: [PATCH 59/64] server : minor style --- examples/server/server.cpp | 145 +++++++++++++++++++++++++------------ 1 file changed, 98 insertions(+), 47 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 14edd3dd17c90..686a8c7c3eecc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -391,18 +391,19 @@ struct llama_client_slot double t_token_generation; // ms void reset() { - num_prompt_tokens = 0; - generated_text = ""; - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - n_past = 0; - sent_count = 0; + num_prompt_tokens = 0; + generated_text = ""; + truncated = false; + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + multibyte_pending = 0; + n_past = 0; + sent_count = 0; sent_token_probs_index = 0; - infill = false; + infill = false; + generated_token_probs.clear(); for (slot_image &img : images) @@ -882,7 +883,8 @@ struct llama_server_context // wait until system prompt load system_need_update = true; - while (system_need_update) { + while (system_need_update) + { std::this_thread::sleep_for(std::chrono::milliseconds(5)); } // system prompt loaded, continue @@ -997,26 +999,31 @@ struct llama_server_context const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); - if (stop_pos != std::string::npos) { + if (stop_pos != std::string::npos) + { is_stop_full = true; slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.sent_count, slot.generated_text.size()); - } else { + } + else + { is_stop_full = false; stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); } // check if there is any token to predict - if(stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { + if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) + { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.sent_count += result.text_to_send.size(); // add the token to slot queue and cache } slot.add_token_string(result); - if (slot.params.stream) { + if (slot.params.stream) + { send_partial_response(slot, result); } } @@ -1051,6 +1058,7 @@ struct llama_server_context {"stopped_limit", slot.stopped_limit}, {"stopping_word", slot.stopping_word}, }); + return slot.has_next_token; // continue } @@ -1089,7 +1097,8 @@ struct llama_server_context return slot.images.size() > 0; } - void send_error(int id, std::string error) { + void send_error(int id, std::string error) + { std::lock_guard lock(mutex_results); task_result res; res.id = id; @@ -1098,11 +1107,13 @@ struct llama_server_context queue_results.push_back(res); } - json get_model_props() { + json get_model_props() + { return get_formated_generation(slots[0]); } - json get_formated_generation(llama_client_slot &slot) { + json get_formated_generation(llama_client_slot &slot) + { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); @@ -1134,12 +1145,14 @@ struct llama_server_context }; } - void send_partial_response(llama_client_slot & slot, completion_token_output tkn) { + void send_partial_response(llama_client_slot &slot, completion_token_output tkn) + { std::lock_guard lock(mutex_results); task_result res; res.id = slot.task_id; res.error = false; res.stop = false; + res.result_json = json { {"content", tkn.text_to_send}, @@ -1147,6 +1160,7 @@ struct llama_server_context {"slot_id", slot.id}, {"multimodal", multimodal} }; + if (slot.sparams.n_probs > 0) { std::vector probs_output = {}; @@ -1160,15 +1174,18 @@ struct llama_server_context slot.sent_token_probs_index = probs_stop_pos; res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } + queue_results.push_back(res); } - void send_final_response(llama_client_slot & slot) { + void send_final_response(llama_client_slot &slot) + { std::lock_guard lock(mutex_results); task_result res; res.id = slot.task_id; res.error = false; res.stop = true; + res.result_json = json { {"content", !slot.params.stream ? slot.generated_text : ""}, @@ -1191,20 +1208,25 @@ struct llama_server_context if (slot.sparams.n_probs > 0) { std::vector probs = {}; - if(!slot.params.stream && slot.stopped_word) { + if (!slot.params.stream && slot.stopped_word) + { const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); probs = std::vector(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); - } else { + } + else + { probs = std::vector( slot.generated_token_probs.begin(), slot.generated_token_probs.begin() + slot.sent_token_probs_index); } res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); } + queue_results.push_back(res); } - void send_embedding(llama_client_slot & slot) { + void send_embedding(llama_client_slot &slot) + { std::lock_guard lock(mutex_results); task_result res; res.id = slot.task_id; @@ -1234,7 +1256,8 @@ struct llama_server_context queue_results.push_back(res); } - int request_completion(json data, bool infill) { + int request_completion(json data, bool infill) + { std::lock_guard lock(mutex_tasks); task_server task; task.id = id_gen++; @@ -1245,17 +1268,22 @@ struct llama_server_context return task.id; } - task_result next_result(int task_id) { - while (true) { + task_result next_result(int task_id) + { + while (true) + { std::this_thread::sleep_for(std::chrono::microseconds(5)); std::lock_guard lock(mutex_results); - if (queue_results.empty()) { + if (queue_results.empty()) + { continue; } - for (int i = 0; i < (int) queue_results.size(); i++) { - if (queue_results[i].id == task_id) { + for (int i = 0; i < (int) queue_results.size(); i++) + { + if (queue_results[i].id == task_id) + { task_result res = queue_results[i]; queue_results.erase(queue_results.begin() + i); return res; @@ -1335,7 +1363,8 @@ struct llama_server_context return true; } - void request_cancel(int task_id) { + void request_cancel(int task_id) + { std::lock_guard lock(mutex_tasks); task_server task; task.id = id_gen++; @@ -1344,9 +1373,11 @@ struct llama_server_context queue_tasks.push_back(task); } - void process_tasks() { + void process_tasks() + { std::lock_guard lock(mutex_tasks); - while (!queue_tasks.empty()) { + while (!queue_tasks.empty()) + { task_server task = queue_tasks.front(); queue_tasks.erase(queue_tasks.begin()); switch (task.type) @@ -1379,8 +1410,10 @@ struct llama_server_context } } break; case CANCEL_TASK: { // release slot linked with the task id - for (auto & slot : slots) { - if (slot.task_id == task.target_id) { + for (auto & slot : slots) + { + if (slot.task_id == task.target_id) + { slot.release(); break; } @@ -2006,7 +2039,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, else if (arg == "--embedding") { params.embedding = true; - } else if (arg == "-cb" || arg == "--cont-batching") + } + else if (arg == "-cb" || arg == "--cont-batching") { params.cont_batching = true; } @@ -2047,7 +2081,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, ); llama.process_system_prompt_data(json::parse(systm_content)); } - else if(arg == "--mmproj") { + else if(arg == "--mmproj") + { if (++i >= argc) { invalid_param = true; @@ -2163,6 +2198,7 @@ int main(int argc, char **argv) LOG_INFO("build info", {{"build", BUILD_NUMBER}, {"commit", BUILD_COMMIT}}); + LOG_INFO("system info", { {"n_threads", params.n_threads}, {"n_threads_batch", params.n_threads_batch}, @@ -2239,10 +2275,12 @@ int main(int argc, char **argv) return; } } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { - while(true) { + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) + { + while (true) + { task_result result = llama.next_result(task_id); - if(!result.error) { + if (!result.error) { const std::string str = "data: " + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + @@ -2264,10 +2302,13 @@ int main(int argc, char **argv) sink.done(); return true; }; - auto on_complete = [task_id, &llama] (bool) { + + auto on_complete = [task_id, &llama] (bool) + { // cancel llama.request_cancel(task_id); }; + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); @@ -2279,7 +2320,8 @@ int main(int argc, char **argv) if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); - if(!result.error && result.stop) { + if (!result.error && result.stop) + { res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } else @@ -2290,9 +2332,10 @@ int main(int argc, char **argv) } } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { - while(true) { + while (true) + { task_result result = llama.next_result(task_id); - if(!result.error) { + if (!result.error) { const std::string str = "data: " + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + @@ -2304,20 +2347,28 @@ int main(int argc, char **argv) { return false; } - if(result.stop) { + if (result.stop) + { break; } - } else { + } + else + { break; } } + sink.done(); + return true; }; - auto on_complete = [task_id, &llama] (bool) { + + auto on_complete = [task_id, &llama] (bool) + { // cancel llama.request_cancel(task_id); }; + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); From 3d6a687f1de15980c04021043853d4c668cb7bd0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sun, 22 Oct 2023 20:03:35 +0300 Subject: [PATCH 60/64] Update readme to document multimodal in server --- examples/server/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index bd369444b70b7..715007735c122 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -162,7 +162,7 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": ["data": "", "id": 12]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. *Result JSON:* From 00ae55b3883450f7cce89fd6e24109cf130fc269 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 20:09:01 +0300 Subject: [PATCH 61/64] server : hide ctx_sampling->prev behind API (#3696) --- examples/server/server.cpp | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 686a8c7c3eecc..bd16100f3d17e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1559,7 +1559,8 @@ struct llama_server_context if (!slot.params.cache_prompt) { - std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end(), 0); + llama_sampling_reset(slot.ctx_sampling); + slot.n_past = 0; slot.num_prompt_tokens_processed = slot.num_prompt_tokens; } @@ -1570,16 +1571,17 @@ struct llama_server_context slot.params.n_keep = slot.num_prompt_tokens; } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - //if input prompt is too big, truncate like normal + + // if input prompt is too big, truncate it if (slot.num_prompt_tokens >= slot.n_ctx) { - // applied bug of #3661 const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); - // Use half the left-over space in the context for the prompt new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + LOG_VERBOSE("input truncated", { {"n_ctx", slot.n_ctx}, {"n_keep", slot.params.n_keep}, @@ -1588,14 +1590,20 @@ struct llama_server_context }); slot.truncated = true; prompt_tokens = new_tokens; + slot.num_prompt_tokens = prompt_tokens.size(); GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); } - const size_t ps = slot.num_prompt_tokens; - std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end() - ps, 0); - std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.ctx_sampling->prev.end() - ps); + + // push the prompt into the sampling context (do not apply grammar) + for (auto &token : prompt_tokens) + { + llama_sampling_accept(slot.ctx_sampling, ctx, token, false); + } + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } From 8fe7ca4875e9f432b05db0c752bb929976363ead Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 21:05:45 +0300 Subject: [PATCH 62/64] server : apply fix from #3722 --- examples/server/server.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index bd16100f3d17e..daf8d42434bdd 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1539,10 +1539,12 @@ struct llama_server_context } auto prefix_tokens = tokenize(slot.params.input_prefix, false); auto suffix_tokens = tokenize(slot.params.input_suffix, false); - const int space_token = 29871; - if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { + + const int space_token = 29871; // TODO: this should not be hardcoded + if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { suffix_tokens.erase(suffix_tokens.begin()); } + prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); From 83e14901870af123d53101aecd66b07b1035195d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 21:57:23 +0300 Subject: [PATCH 63/64] server : fix slot reuse --- examples/server/server.cpp | 40 ++++++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 17 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index daf8d42434bdd..9aef21dd3e08a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -161,7 +161,6 @@ struct task_result { enum slot_state { IDLE, - SLEEPING, PROCESSING, }; @@ -347,6 +346,9 @@ struct llama_client_slot slot_state state = IDLE; slot_command command = NONE; + // used to determine the slot that has been used the longest + int64_t t_last_used = -1; + // generation props int32_t n_ctx = 0; // context size per slot int32_t n_past = 0; @@ -435,7 +437,7 @@ struct llama_client_slot } bool is_processing() const { - return ((state == IDLE || state == SLEEPING) && command == LOAD_PROMPT) || state == PROCESSING; + return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; } void add_token_string(const completion_token_output &token) { @@ -643,14 +645,24 @@ struct llama_server_context } llama_client_slot* get_slot(int id) { + int64_t t_last = ggml_time_us(); + llama_client_slot *last_used = nullptr; + for (llama_client_slot & slot : slots) { - if ((id == -1 && slot.available()) || slot.id == id) + if (slot.id == id && slot.available()) { return &slot; } + + if (slot.available() && slot.t_last_used < t_last) + { + last_used = &slot; + t_last = slot.t_last_used; + } } - return nullptr; + + return last_used; } bool launch_slot_with_data(llama_client_slot* &slot, json data) { @@ -1484,22 +1496,16 @@ struct llama_server_context // release the slot if (slot.state == PROCESSING && slot.command == RELEASE) { - slot.state = slot.params.cache_prompt ? SLEEPING : IDLE; - if (slot.state == SLEEPING) { - LOG_TEE("slot %i has %i tokens in cache.\n", slot.id, (int) slot.cache_tokens.size()); - } - else - { - LOG_TEE("slot %i released\n", slot.id); - } + slot.state = IDLE; slot.command = NONE; + slot.t_last_used = ggml_time_us(); + + LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size()); + continue; } - if ( - slot.state == IDLE || - slot.state == SLEEPING || - slot.command == RELEASE) + if (slot.state == IDLE || slot.command == RELEASE) { continue; } @@ -1521,7 +1527,7 @@ struct llama_server_context for (auto & slot : slots) { // need process the prompt - if ((slot.state == IDLE || slot.state == SLEEPING) && slot.command == LOAD_PROMPT) + if (slot.state == IDLE && slot.command == LOAD_PROMPT) { slot.state = PROCESSING; slot.command = NONE; From c0f4d54870568c2977db9327223c5c2546bda018 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 22 Oct 2023 22:24:39 +0300 Subject: [PATCH 64/64] server : add comment about changing slot_state to bool --- examples/server/server.cpp | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9aef21dd3e08a..c3279dbc9c456 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -158,6 +158,7 @@ struct task_result { json result_json; }; +// TODO: can become bool if we can't find use of more states enum slot_state { IDLE, @@ -188,11 +189,14 @@ struct slot_params struct slot_image { - clip_image_u8 img_data; + int32_t id; + bool request_encode_image = false; float* image_embedding = nullptr; - int image_tokens = 0; - int id; + int32_t image_tokens = 0; + + clip_image_u8 img_data; + std::string prefix_prompt; // before of this image }; @@ -769,8 +773,8 @@ struct llama_server_context { for (const auto &img : *images_data) { - slot_image img_sl; std::string data_b64 = img["data"].get(); + slot_image img_sl; img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); int width, height, channels; std::vector image_buffer = base64_decode(data_b64);