From 052051d8ae4639a1c3c61e7da3237bcc572469d4 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 29 Feb 2024 21:42:11 +0100 Subject: [PATCH 01/86] Server: normalize naming (#5779) * server: normalize naming * fix spacing --- examples/server/server.cpp | 370 ++++++++++++++++--------------------- examples/server/utils.hpp | 186 ++++++++++++------- 2 files changed, 277 insertions(+), 279 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 080fa9bd5..bf20e0cf1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -33,8 +33,7 @@ using json = nlohmann::json; -struct server_params -{ +struct server_params { std::string hostname = "127.0.0.1"; std::vector api_keys; std::string public_path = "examples/server/public"; @@ -49,103 +48,50 @@ struct server_params bool server_verbose = false; bool server_log_json = true; -static size_t common_part(const std::vector &a, const std::vector &b) -{ - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) - { - } - return i; -} - -enum stop_type -{ +enum stop_type { STOP_FULL, STOP_PARTIAL, }; -static bool ends_with(const std::string &str, const std::string &suffix) -{ - return str.size() >= suffix.size() && - 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); -} +// TODO: can become bool if we can't find use of more states +enum slot_state { + IDLE, + PROCESSING, +}; -static size_t find_partial_stop_string(const std::string &stop, - const std::string &text) -{ - if (!text.empty() && !stop.empty()) - { - const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) - { - if (stop[char_index] == text_last_char) - { - const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) - { - return text.size() - char_index - 1; - } - } - } - } - return std::string::npos; -} +enum slot_command { + NONE, + LOAD_PROMPT, + RELEASE, +}; -// TODO: reuse llama_detokenize -template -static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) -{ - std::string ret; - for (; begin != end; ++begin) - { - ret += llama_token_to_piece(ctx, *begin); - } - return ret; -} +struct slot_params { + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt -// format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) -{ - std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); - // if the size is 1 and first bit is 1, meaning it's a partial character - // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) - { - std::stringstream ss; - ss << std::hex << (out[0] & 0xff); - std::string res(ss.str()); - out = "byte: \\x" + res; - } - return out; -} + 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 -// convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) -{ - json out = json::array(); - for (const auto &prob : probs) - { - json probs_for_token = json::array(); - for (const auto &p : prob.probs) - { - std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json - { - {"tok_str", tok_str}, - {"prob", p.prob}, - }); - } - std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json{ - {"content", tok_str}, - {"probs", probs_for_token}, - }); - } - return out; -} + std::vector antiprompt; -struct llama_client_slot -{ + json input_prefix; + json input_suffix; +}; + +struct slot_image { + int32_t id; + + bool request_encode_image = false; + float * image_embedding = nullptr; + int32_t image_tokens = 0; + + clip_image_u8 * img_data; + + std::string prefix_prompt; // before of this image +}; + +struct server_slot { int id; int task_id = -1; @@ -165,8 +111,8 @@ struct llama_client_slot int32_t i_batch = -1; int32_t n_predict = -1; - int32_t num_prompt_tokens = 0; - int32_t num_prompt_tokens_processed = 0; + int32_t n_prompt_tokens = 0; + int32_t n_prompt_tokens_processed = 0; json prompt; std::string generated_text; @@ -201,8 +147,8 @@ struct llama_client_slot std::vector images; // stats - size_t sent_count = 0; - size_t sent_token_probs_index = 0; + size_t n_sent_text = 0; // number of sent text character + size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_genereration; @@ -214,7 +160,7 @@ struct llama_client_slot int multitask_id = -1; void reset() { - num_prompt_tokens = 0; + n_prompt_tokens = 0; generated_text = ""; truncated = false; stopped_eos = false; @@ -222,16 +168,15 @@ struct llama_client_slot stopped_limit = false; stopping_word = ""; n_past = 0; - sent_count = 0; - sent_token_probs_index = 0; + n_sent_text = 0; + n_sent_token_probs = 0; infill = false; ga_i = 0; n_past_se = 0; generated_token_probs.clear(); - for (slot_image & img : images) - { + for (slot_image & img : images) { free(img.image_embedding); if (img.img_data) { clip_image_u8_free(img.img_data); @@ -243,19 +188,15 @@ struct llama_client_slot } bool has_budget(gpt_params &global_params) { - if (params.n_predict == -1 && global_params.n_predict == -1) - { + if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } 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; } @@ -271,8 +212,7 @@ struct llama_client_slot } void add_token_string(const completion_token_output &token) { - if (command == RELEASE) - { + if (command == RELEASE) { return; } cache_tokens.push_back(token.tok); @@ -290,10 +230,10 @@ struct llama_client_slot json get_formated_timings() { return json { - {"prompt_n", num_prompt_tokens_processed}, + {"prompt_n", n_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}, + {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed}, {"predicted_n", n_decoded}, {"predicted_ms", t_token_generation}, @@ -304,18 +244,18 @@ struct llama_client_slot void print_timings() const { char buffer[512]; - double t_token = t_prompt_processing / num_prompt_tokens_processed; - double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed; + double t_token = t_prompt_processing / n_prompt_tokens_processed; + double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", - t_prompt_processing, num_prompt_tokens_processed, + t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second); LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, - {"t_prompt_processing", t_prompt_processing}, - {"num_prompt_tokens_processed", num_prompt_tokens_processed}, - {"t_token", t_token}, - {"n_tokens_second", n_tokens_second}, + {"slot_id", id}, + {"task_id", task_id}, + {"t_prompt_processing", t_prompt_processing}, + {"n_prompt_tokens_processed", n_prompt_tokens_processed}, + {"t_token", t_token}, + {"n_tokens_second", n_tokens_second}, }); t_token = t_token_generation / n_decoded; @@ -343,7 +283,7 @@ struct llama_client_slot } }; -struct llama_metrics { +struct server_metrics { uint64_t n_prompt_tokens_processed_total = 0; uint64_t n_tokens_predicted_total = 0; @@ -354,18 +294,16 @@ struct llama_metrics { uint64_t t_tokens_generation = 0; - void on_prompt_eval(const llama_client_slot &slot) { - n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed; - - n_prompt_tokens_processed += slot.num_prompt_tokens_processed; - t_prompt_processing += slot.t_prompt_processing; + void on_prompt_eval(const server_slot &slot) { + n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; + n_prompt_tokens_processed += slot.n_prompt_tokens_processed; + t_prompt_processing += slot.t_prompt_processing; } - void on_prediction(const llama_client_slot &slot) { + void on_prediction(const server_slot &slot) { n_tokens_predicted_total += slot.n_decoded; - - n_tokens_predicted += slot.n_decoded; - t_tokens_generation += slot.t_token_generation; + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; } void reset_bucket() { @@ -404,13 +342,13 @@ struct llama_server_context std::string name_assistant; // slots / clients - std::vector slots; + std::vector slots; json default_generation_settings_for_props; - llama_server_queue queue_tasks; + llama_server_queue queue_tasks; llama_server_response queue_results; - llama_metrics metrics; + server_metrics metrics; ~llama_server_context() { @@ -487,7 +425,7 @@ struct llama_server_context LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); for (int i = 0; i < params.n_parallel; i++) { - llama_client_slot slot; + server_slot slot; slot.id = i; slot.n_ctx = n_ctx_slot; @@ -579,11 +517,11 @@ struct llama_server_context return prompt_tokens; } - llama_client_slot* get_slot(int id) { + server_slot* get_slot(int id) { int64_t t_last = ggml_time_us(); - llama_client_slot *last_used = nullptr; + server_slot *last_used = nullptr; - for (llama_client_slot & slot : slots) + for (server_slot & slot : slots) { if (slot.id == id && slot.available()) { @@ -600,7 +538,7 @@ struct llama_server_context return last_used; } - bool launch_slot_with_data(llama_client_slot* &slot, json data) { + bool launch_slot_with_data(server_slot* &slot, json data) { slot_params default_params; llama_sampling_params default_sparams; @@ -888,7 +826,7 @@ struct llama_server_context clean_kv_cache = false; } - void update_system_prompt() { + void system_prompt_update() { kv_cache_clear(); system_tokens.clear(); @@ -933,9 +871,9 @@ struct llama_server_context system_need_update = false; } - void notify_system_prompt_changed() { + void system_prompt_notify() { // release all slots - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { slot.release(); } @@ -943,17 +881,17 @@ struct llama_server_context system_need_update = true; } - void process_system_prompt_data(const json &sys_props) { + void system_prompt_process(const json &sys_props) { system_prompt = sys_props.value("prompt", ""); name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); - notify_system_prompt_changed(); + system_prompt_notify(); } static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, - const stop_type type, llama_client_slot &slot) + const stop_type type, server_slot &slot) { size_t stop_pos = std::string::npos; @@ -975,8 +913,8 @@ struct llama_server_context { if (type == STOP_FULL) { - slot.stopped_word = true; - slot.stopping_word = word; + slot.stopped_word = true; + slot.stopping_word = word; slot.has_next_token = false; } stop_pos = pos; @@ -986,7 +924,7 @@ struct llama_server_context return stop_pos; } - bool process_token(completion_token_output &result, llama_client_slot &slot) { + bool process_token(completion_token_output &result, server_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; @@ -1032,7 +970,7 @@ struct llama_server_context if (!incomplete) { - size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + size_t pos = std::min(slot.n_sent_text, 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); @@ -1042,7 +980,7 @@ struct llama_server_context 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()); + pos = std::min(slot.n_sent_text, slot.generated_text.size()); } else { @@ -1055,7 +993,7 @@ struct llama_server_context { // 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(); + slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache } slot.add_token_string(result); @@ -1099,7 +1037,7 @@ struct llama_server_context return slot.has_next_token; // continue } - bool process_images(llama_client_slot &slot) const + bool process_images(server_slot &slot) const { for (slot_image &img : slot.images) { @@ -1132,7 +1070,7 @@ struct llama_server_context queue_results.send(res); } - json get_formated_generation(llama_client_slot &slot) + json get_formated_generation(server_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && @@ -1179,7 +1117,7 @@ struct llama_server_context }; } - void send_partial_response(llama_client_slot &slot, completion_token_output tkn) + void send_partial_response(server_slot &slot, completion_token_output tkn) { task_result res; res.id = slot.task_id; @@ -1199,13 +1137,13 @@ struct llama_server_context { 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()); + size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); + size_t probs_stop_pos = std::min(slot.n_sent_token_probs + 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; + slot.n_sent_token_probs = probs_stop_pos; res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } @@ -1218,7 +1156,7 @@ struct llama_server_context queue_results.send(res); } - void send_final_response(llama_client_slot &slot) + void send_final_response(server_slot &slot) { task_result res; res.id = slot.task_id; @@ -1233,7 +1171,7 @@ struct llama_server_context {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, - {"tokens_evaluated", slot.num_prompt_tokens}, + {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, {"prompt", slot.prompt}, {"truncated", slot.truncated}, @@ -1271,7 +1209,7 @@ struct llama_server_context queue_results.send(res); } - void send_embedding(llama_client_slot &slot) + void send_embedding(server_slot &slot) { task_result res; res.id = slot.task_id; @@ -1282,9 +1220,7 @@ struct llama_server_context const int n_embd = llama_n_embd(model); if (!params.embedding) { - LOG_WARNING("embedding disabled", { - {"params.embedding", params.embedding}, - }); + LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); res.result_json = json { {"embedding", std::vector(n_embd, 0.0f)}, @@ -1296,7 +1232,7 @@ struct llama_server_context std::vector embedding(data, data + n_embd); res.result_json = json { - {"embedding", embedding }, + {"embedding", embedding}, }; } queue_results.send(res); @@ -1345,7 +1281,7 @@ struct llama_server_context } // for multiple images processing - bool ingest_images(llama_client_slot &slot, int n_batch) + bool ingest_images(server_slot &slot, int n_batch) { int image_idx = 0; @@ -1384,7 +1320,17 @@ struct llama_server_context } 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, }; + 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__); @@ -1454,7 +1400,7 @@ struct llama_server_context switch (task.type) { case TASK_TYPE_COMPLETION: { - llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); + server_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); if (slot == nullptr) { // if no slot is available, we defer this task for processing later @@ -1469,10 +1415,10 @@ struct llama_server_context send_error(task, "system prompt can only be updated when all slots are idle"); break; } - process_system_prompt_data(task.data["system_prompt"]); + system_prompt_process(task.data["system_prompt"]); // reset cache_tokens for all slots - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { slot.cache_tokens.clear(); slot.n_past = 0; @@ -1512,20 +1458,20 @@ struct llama_server_context int n_idle_slots = 0; int n_processing_slots = 0; - for (llama_client_slot &slot: slots) { + for (server_slot &slot: slots) { json slot_data = get_formated_generation(slot); slot_data["id"] = slot.id; slot_data["task_id"] = slot.task_id; slot_data["state"] = slot.state; slot_data["prompt"] = slot.prompt; slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, }; if (slot_data["state"] == IDLE) { n_idle_slots++; @@ -1563,10 +1509,10 @@ struct llama_server_context { "n_tokens_predicted", metrics.n_tokens_predicted}, { "t_tokens_generation", metrics.t_tokens_generation}, - { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, - { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, + { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, + { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, - { "slots", slots_data }, + { "slots", slots_data }, }; metrics.reset_bucket(); queue_results.send(res); @@ -1597,7 +1543,7 @@ struct llama_server_context if (system_need_update) { LOG_INFO("updating system prompt", {}); - update_system_prompt(); + system_prompt_update(); } llama_batch_clear(batch); @@ -1618,7 +1564,7 @@ struct llama_server_context task.target_id = -1; queue_tasks.post(task); - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { if (slot.ga_n == 1) { @@ -1754,45 +1700,50 @@ struct llama_server_context prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt } - slot.num_prompt_tokens = prompt_tokens.size(); + slot.n_prompt_tokens = prompt_tokens.size(); if (slot.params.n_keep < 0) { - slot.params.n_keep = slot.num_prompt_tokens; + slot.params.n_keep = slot.n_prompt_tokens; } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); // if input prompt is too big, truncate it - if (slot.num_prompt_tokens >= slot.n_ctx) + if (slot.n_prompt_tokens >= slot.n_ctx) { 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; + const int erased_blocks = (slot.n_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); - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + std::vector new_tokens( + prompt_tokens.begin(), + prompt_tokens.begin() + slot.params.n_keep); + 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}, - {"n_left", n_left}, + {"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())}, }); slot.truncated = true; prompt_tokens = new_tokens; - slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + slot.n_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); } if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); - slot.n_past = 0; + slot.n_past = 0; slot.n_past_se = 0; - slot.ga_i = 0; - slot.num_prompt_tokens_processed = slot.num_prompt_tokens; + slot.ga_i = 0; + slot.n_prompt_tokens_processed = slot.n_prompt_tokens; } else { @@ -1811,7 +1762,7 @@ struct llama_server_context slot.n_past -= 1; } - slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past; if (slot.ga_n != 1) { @@ -1836,13 +1787,13 @@ struct llama_server_context { "slot_id", slot.id }, { "task_id", slot.task_id }, { "n_past", slot.n_past }, - { "num_prompt_tokens_processed", slot.num_prompt_tokens_processed } + { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } }); } slot.cache_tokens = prompt_tokens; - if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) + if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. LOG_INFO("we have to evaluate at least 1 token to generate logits", { @@ -1898,8 +1849,8 @@ struct llama_server_context if (has_images && !ingest_images(slot, n_batch)) { LOG_ERROR("failed processing images", { - "slot_id", slot.id, - "task_id", slot.task_id, + {"slot_id", slot.id}, + {"task_id", slot.task_id}, }); // FIXME @phymbert: to be properly tested // early returning without changing the slot state will block the slot for ever @@ -2049,10 +2000,6 @@ struct llama_server_context LOG_VERBOSE("slots updated", {}); return true; } - - void run_on_all_tasks_finished() { - update_slots(); - } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, @@ -2561,7 +2508,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, std::istreambuf_iterator(), std::back_inserter(systm_content) ); - llama.process_system_prompt_data(json::parse(systm_content)); + llama.system_prompt_process(json::parse(systm_content)); } else if (arg == "-ctk" || arg == "--cache-type-k") { params.cache_type_k = argv[++i]; @@ -2692,7 +2639,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, /* llama.cpp completion api semantics */ 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, server_slot *slot, const std::string &content, const std::vector &probs ) { json res = json { @@ -2748,14 +2695,7 @@ static void log_server_request(const httplib::Request &req, const httplib::Respo }); } -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); } -}; - -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, server_slot *slot) { auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; @@ -3526,8 +3466,8 @@ int main(int argc, char **argv) &llama_server_context::process_single_task, &llama, std::placeholders::_1)); llama.queue_tasks.on_finish_multitask(std::bind( &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); - llama.queue_tasks.on_all_tasks_finished(std::bind( - &llama_server_context::run_on_all_tasks_finished, &llama)); + llama.queue_tasks.on_run_slots(std::bind( + &llama_server_context::update_slots, &llama)); llama.queue_results.on_multitask_update(std::bind( &llama_server_queue::update_multitask, &llama.queue_tasks, diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d7abd7cbb..d98541f26 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -37,10 +37,6 @@ extern bool server_log_json; #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -// -// parallel -// - enum server_state { SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet SERVER_STATE_READY, // Server is ready and model is loaded @@ -78,51 +74,8 @@ struct task_multi { std::vector results{}; }; -// TODO: can become bool if we can't find use of more states -enum slot_state -{ - IDLE, - PROCESSING, -}; - -enum slot_command -{ - NONE, - LOAD_PROMPT, - RELEASE, -}; - -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::vector antiprompt; - - json input_prefix; - json input_suffix; -}; - -struct slot_image -{ - int32_t id; - - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; - - clip_image_u8 * img_data; - - std::string prefix_prompt; // before of this image -}; - // completion token output with probabilities -struct completion_token_output -{ +struct completion_token_output { struct token_prob { llama_token tok; @@ -134,8 +87,13 @@ struct completion_token_output std::string text_to_send; }; -static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) -{ +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); } +}; + +static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { std::stringstream ss_tid; ss_tid << std::this_thread::get_id(); json log = nlohmann::ordered_json{ @@ -183,8 +141,7 @@ static inline void server_log(const char *level, const char *function, int line, // template -static T json_value(const json &body, const std::string &key, const T &default_value) -{ +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) @@ -200,8 +157,7 @@ inline bool verify_custom_template(const std::string & tmpl) { } // Format given chat. If tmpl is empty, we take the template from model metadata -inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) -{ +inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { size_t alloc_size = 0; // vector holding all allocated string to be passed to llama_chat_apply_template std::vector str(messages.size() * 2); @@ -250,7 +206,7 @@ struct llama_server_queue { // callback functions std::function callback_new_task; std::function callback_finish_multitask; - std::function callback_all_task_finished; + std::function callback_run_slots; // Add a new task to the end of the queue int post(task_server task) { @@ -283,14 +239,14 @@ struct llama_server_queue { callback_new_task = callback; } - // Register function to process a multitask + // Register function to process a multitask when it is finished void on_finish_multitask(std::function callback) { callback_finish_multitask = callback; } - // Register the function to be called when the batch of tasks is finished - void on_all_tasks_finished(std::function callback) { - callback_all_task_finished = callback; + // Register the function to be called when all slots data is ready to be processed + void on_run_slots(std::function callback) { + callback_run_slots = callback; } // Call when the state of one slot is changed @@ -312,7 +268,13 @@ struct llama_server_queue { condition_tasks.notify_all(); } - // Start the main loop. + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Run all slots + */ void start_loop() { running = true; while (true) { @@ -331,8 +293,8 @@ struct llama_server_queue { LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); callback_new_task(task); } - LOG_VERBOSE("callback_all_task_finished", {}); - // process and update all the multitasks + LOG_VERBOSE("update_multitasks", {}); + // check if we have any finished multitasks auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { @@ -349,8 +311,9 @@ struct llama_server_queue { ++queue_iterator; } } - // all tasks in the current loop is finished - callback_all_task_finished(); + // all tasks in the current loop is processed, slots data is now ready + LOG_VERBOSE("callback_run_slots", {}); + callback_run_slots(); } LOG_VERBOSE("wait for new task", {}); // wait for new task @@ -408,12 +371,14 @@ struct llama_server_response { std::mutex mutex_results; std::condition_variable condition_results; + // add the task_id to the list of tasks waiting for response void add_waiting_task_id(int task_id) { LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); waiting_task_ids.insert(task_id); } + // when the request is finished, we can remove task associated with it void remove_waiting_task_id(int task_id) { LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); @@ -574,3 +539,96 @@ static std::string gen_chatcmplid() chatcmplid << "chatcmpl-" << random_string(); return chatcmplid.str(); } + +// +// other common utils +// + +static size_t common_part(const std::vector &a, const std::vector &b) +{ + size_t i; + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) + { + } + return i; +} + +static bool ends_with(const std::string &str, const std::string &suffix) +{ + return str.size() >= suffix.size() && + 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); +} + +static size_t find_partial_stop_string(const std::string &stop, + const std::string &text) +{ + if (!text.empty() && !stop.empty()) + { + const char text_last_char = text.back(); + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) + { + if (stop[char_index] == text_last_char) + { + const std::string current_partial = stop.substr(0, char_index + 1); + if (ends_with(text, current_partial)) + { + return text.size() - char_index - 1; + } + } + } + } + return std::string::npos; +} + +// TODO: reuse llama_detokenize +template +static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) +{ + std::string ret; + for (; begin != end; ++begin) + { + ret += llama_token_to_piece(ctx, *begin); + } + return ret; +} + +// format incomplete utf-8 multibyte character for output +static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) +{ + std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); + // if the size is 1 and first bit is 1, meaning it's a partial character + // (size > 1 meaning it's already a known token) + if (out.size() == 1 && (out[0] & 0x80) == 0x80) + { + std::stringstream ss; + ss << std::hex << (out[0] & 0xff); + std::string res(ss.str()); + out = "byte: \\x" + res; + } + return out; +} + +// convert a vector of completion_token_output to json +static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) +{ + json out = json::array(); + for (const auto &prob : probs) + { + json probs_for_token = json::array(); + for (const auto &p : prob.probs) + { + std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json + { + {"tok_str", tok_str}, + {"prob", p.prob}, + }); + } + std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json{ + {"content", tok_str}, + {"probs", probs_for_token}, + }); + } + return out; +} From 38d152160898b0173ffe4dc7df5daadcbd2eceb0 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 1 Mar 2024 07:36:47 +0000 Subject: [PATCH 02/86] [SYCL] Use batched mul_mat pathway (#5591) * Use batched mul_mat pathway * rm extra line * Explicitly state scaled data type --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 107 +++++++++++++++++++++----------------------------- 1 file changed, 44 insertions(+), 63 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a054ec8b9..6f391b0c6 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -12726,6 +12726,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -13269,31 +13270,23 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, int64_t i03 = i13 / r3; int64_t i02 = i12 / r2; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } -static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { +static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_BINARY_OP_LOCALS - GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb); - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); - - GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); - - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; @@ -13312,11 +13305,16 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; // convert src1 to fp16 - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - GGML_ASSERT(to_fp16_sycl != nullptr); - - sycl_pool_alloc src1_as_f16(ne1); - to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream); + sycl_pool_alloc src1_f16_alloc; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + } + sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf + : src1_f16_alloc.get(); sycl_pool_alloc dst_f16; char * dst_t; @@ -13337,20 +13335,12 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, const void * alpha = &alpha_f16; const void * beta = &beta_f16; - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne); + // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway + // once oneMKL open source supports half, half, float, float: datatypes + dst_t = (char *) dst_f16.alloc(ne_dst); - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); - } else { - dst_t = (char *) dst_ddf; - - cu_compute_type = dpct::library_data_t::real_float; - cu_data_type = dpct::library_data_t::real_float; - - alpha = &alpha_f32; - beta = &beta_f32; - } + nbd2 /= sizeof(float) / sizeof(sycl::half); + nbd3 /= sizeof(float) / sizeof(sycl::half); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13386,10 +13376,10 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, - nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half), - (const char *)src1_as_f16.get(), dpct::library_data_t::real_half, - nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta, - (char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float), + nb01 / nb00, nb02 / nb00, + (const char *)src1_f16, dpct::library_data_t::real_half, + nb11 / nb10, nb12 / nb10, beta, + (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); } else { // use syclGemmBatchedEx @@ -13409,44 +13399,35 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, {sycl::aspect::fp16}); main_stream->submit([&](sycl::handler &cgh) { - const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get(); - const void **ptrs_src_get_ct3 = ptrs_src.get(); - void **ptrs_dst_get_ct4 = ptrs_dst.get(); - + const void **ptrs_src_get = ptrs_src.get(); + void **ptrs_dst_get = ptrs_dst.get(); + size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2; + size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2; cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_compute_batched_ptrs( - src0_as_f16, src1_as_f16_get_ct1, - dst_t, ptrs_src_get_ct3, - ptrs_dst_get_ct4, ne12, ne13, ne23, - nb02, nb03, nb12, nb13, nbd2, nbd3, r2, - r3, item_ct1); + src0_as_f16, src1_f16, + dst_t, ptrs_src_get, + ptrs_dst_get, ne12, ne13, ne23, + nb02, nb03, nb12_scaled, nb13_scaled, + nbd2, nbd3, r2, r3, item_ct1); }); }); } - /* - DPCT1010:95: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this - code. - */ - SYCL_CHECK(0); - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), - dpct::library_data_t::real_half, nb01 / sizeof(sycl::half), + dpct::library_data_t::real_half, nb01 / nb00, (const void **)(ptrs_src.get() + 1 * ne23), - dpct::library_data_t::real_half, nb11 / sizeof(float), beta, + dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); } #endif - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream); - } + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -13491,10 +13472,10 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 // KQV single-batch // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); ggml_sycl_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n"); - ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst); + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); + ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); From f105471ef6aa4727afac8240da398590d7277f45 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Mar 2024 09:59:43 +0200 Subject: [PATCH 03/86] server : fix newlines in help (#5785) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index bf20e0cf1..45c4aec4d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2080,8 +2080,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); From 6ea0f010ff6967034528d9e0b8330b9b0f0b7c13 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Fri, 1 Mar 2024 08:54:53 +0000 Subject: [PATCH 04/86] ci : add Ubuntu 22 Vulkan CI run (#5789) --- .github/workflows/build.yml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 66ad85938..9144f9266 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -145,6 +145,28 @@ jobs: cd build ctest -L main --verbose + ubuntu-22-cmake-vulkan: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libvulkan-dev + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake -DLLAMA_VULKAN=ON .. + cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 From 5cb02b4a012bb16c6c699c0c62c05ffa653eee0f Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 1 Mar 2024 10:08:08 +0100 Subject: [PATCH 05/86] server: allow to override threads server pool with --threads-http (#5794) --- examples/server/README.md | 1 + examples/server/server.cpp | 16 ++++++++++++++++ 2 files changed, 17 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index 0e9bd7fd4..ad35306c6 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -18,6 +18,7 @@ The project is under active development, and we are [looking for feedback and co - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. +- `--threads-http N`: number of threads in the http server pool to process requests (default: `std::thread::hardware_concurrency()`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 45c4aec4d..eea987966 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -43,6 +43,7 @@ struct server_params { int32_t write_timeout = 600; bool slots_endpoint = true; bool metrics_endpoint = false; + int n_threads_http = -1; }; bool server_verbose = false; @@ -2012,6 +2013,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, 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(" --threads-http N number of threads in the http server pool to process requests (default: hardware concurrency)\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); @@ -2298,6 +2300,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads_batch = std::stoi(argv[i]); } + else if (arg == "--threads-http") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + sparams.n_threads_http = std::stoi(argv[i]); + } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -3449,6 +3460,11 @@ int main(int argc, char **argv) }*/ //); + if (sparams.n_threads_http > 0) { + log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); + svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + } + LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() From 9600d59e010c18f5872580a21734ea1bf1968d04 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Fri, 1 Mar 2024 03:15:36 -0600 Subject: [PATCH 06/86] unicode : switch to multimap based nfd_map (#5799) * switch to multimap based nfd_map due to compile time issues * simplify multimap keys * dont construct new locale every time --- llama.cpp | 11 +- unicode.h | 566 +++++++++++++++++++++++++++++------------------------- 2 files changed, 312 insertions(+), 265 deletions(-) diff --git a/llama.cpp b/llama.cpp index 62699ce52..a35f07aa4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8947,10 +8947,10 @@ struct llm_tokenizer_wpm { std::vector codepoints = codepoints_from_utf8(text); std::vector nfd_codepoints; for (uint32_t code : codepoints) { - auto it = nfd_map.find(code); - if (it != nfd_map.end()) { - for (uint32_t c : it->second) { - nfd_codepoints.push_back(c); + auto it = nfd_map.equal_range(code); + if (it.first != it.second) { + for (auto jt = it.first; jt != it.second; jt++) { + nfd_codepoints.push_back(jt->second); } } else { nfd_codepoints.push_back(code); @@ -9001,12 +9001,13 @@ struct llm_tokenizer_wpm { } uint32_t to_lower(uint32_t code) { + static const std::locale locale("en_US.UTF-8"); #if defined(_WIN32) if (code > 0xFFFF) { return code; } #endif - return std::tolower(wchar_t(code), std::locale("en_US.UTF-8")); + return std::tolower(wchar_t(code), locale); } bool is_ascii_punct(uint32_t code) { diff --git a/unicode.h b/unicode.h index 620e2b580..f6be4549b 100644 --- a/unicode.h +++ b/unicode.h @@ -1,6 +1,7 @@ #pragma once #include +#include #include #include #include @@ -223,266 +224,311 @@ static const std::vector> control_ranges = { {0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF}, }; -static const std::unordered_map> nfd_map = { -{0xC0, {0x41, 0x300}}, {0xC1, {0x41, 0x301}}, {0xC2, {0x41, 0x302}}, {0xC3, {0x41, 0x303}}, {0xC4, {0x41, 0x308}}, {0xC5, {0x41, 0x30A}}, {0xC7, {0x43, 0x327}}, {0xC8, {0x45, 0x300}}, -{0xC9, {0x45, 0x301}}, {0xCA, {0x45, 0x302}}, {0xCB, {0x45, 0x308}}, {0xCC, {0x49, 0x300}}, {0xCD, {0x49, 0x301}}, {0xCE, {0x49, 0x302}}, {0xCF, {0x49, 0x308}}, {0xD1, {0x4E, 0x303}}, -{0xD2, {0x4F, 0x300}}, {0xD3, {0x4F, 0x301}}, {0xD4, {0x4F, 0x302}}, {0xD5, {0x4F, 0x303}}, {0xD6, {0x4F, 0x308}}, {0xD9, {0x55, 0x300}}, {0xDA, {0x55, 0x301}}, {0xDB, {0x55, 0x302}}, -{0xDC, {0x55, 0x308}}, {0xDD, {0x59, 0x301}}, {0xE0, {0x61, 0x300}}, {0xE1, {0x61, 0x301}}, {0xE2, {0x61, 0x302}}, {0xE3, {0x61, 0x303}}, {0xE4, {0x61, 0x308}}, {0xE5, {0x61, 0x30A}}, -{0xE7, {0x63, 0x327}}, {0xE8, {0x65, 0x300}}, {0xE9, {0x65, 0x301}}, {0xEA, {0x65, 0x302}}, {0xEB, {0x65, 0x308}}, {0xEC, {0x69, 0x300}}, {0xED, {0x69, 0x301}}, {0xEE, {0x69, 0x302}}, -{0xEF, {0x69, 0x308}}, {0xF1, {0x6E, 0x303}}, {0xF2, {0x6F, 0x300}}, {0xF3, {0x6F, 0x301}}, {0xF4, {0x6F, 0x302}}, {0xF5, {0x6F, 0x303}}, {0xF6, {0x6F, 0x308}}, {0xF9, {0x75, 0x300}}, -{0xFA, {0x75, 0x301}}, {0xFB, {0x75, 0x302}}, {0xFC, {0x75, 0x308}}, {0xFD, {0x79, 0x301}}, {0xFF, {0x79, 0x308}}, {0x100, {0x41, 0x304}}, {0x101, {0x61, 0x304}}, {0x102, {0x41, 0x306}}, -{0x103, {0x61, 0x306}}, {0x104, {0x41, 0x328}}, {0x105, {0x61, 0x328}}, {0x106, {0x43, 0x301}}, {0x107, {0x63, 0x301}}, {0x108, {0x43, 0x302}}, {0x109, {0x63, 0x302}}, {0x10A, {0x43, 0x307}}, -{0x10B, {0x63, 0x307}}, {0x10C, {0x43, 0x30C}}, {0x10D, {0x63, 0x30C}}, {0x10E, {0x44, 0x30C}}, {0x10F, {0x64, 0x30C}}, {0x112, {0x45, 0x304}}, {0x113, {0x65, 0x304}}, {0x114, {0x45, 0x306}}, -{0x115, {0x65, 0x306}}, {0x116, {0x45, 0x307}}, {0x117, {0x65, 0x307}}, {0x118, {0x45, 0x328}}, {0x119, {0x65, 0x328}}, {0x11A, {0x45, 0x30C}}, {0x11B, {0x65, 0x30C}}, {0x11C, {0x47, 0x302}}, -{0x11D, {0x67, 0x302}}, {0x11E, {0x47, 0x306}}, {0x11F, {0x67, 0x306}}, {0x120, {0x47, 0x307}}, {0x121, {0x67, 0x307}}, {0x122, {0x47, 0x327}}, {0x123, {0x67, 0x327}}, {0x124, {0x48, 0x302}}, -{0x125, {0x68, 0x302}}, {0x128, {0x49, 0x303}}, {0x129, {0x69, 0x303}}, {0x12A, {0x49, 0x304}}, {0x12B, {0x69, 0x304}}, {0x12C, {0x49, 0x306}}, {0x12D, {0x69, 0x306}}, {0x12E, {0x49, 0x328}}, -{0x12F, {0x69, 0x328}}, {0x130, {0x49, 0x307}}, {0x134, {0x4A, 0x302}}, {0x135, {0x6A, 0x302}}, {0x136, {0x4B, 0x327}}, {0x137, {0x6B, 0x327}}, {0x139, {0x4C, 0x301}}, {0x13A, {0x6C, 0x301}}, -{0x13B, {0x4C, 0x327}}, {0x13C, {0x6C, 0x327}}, {0x13D, {0x4C, 0x30C}}, {0x13E, {0x6C, 0x30C}}, {0x143, {0x4E, 0x301}}, {0x144, {0x6E, 0x301}}, {0x145, {0x4E, 0x327}}, {0x146, {0x6E, 0x327}}, -{0x147, {0x4E, 0x30C}}, {0x148, {0x6E, 0x30C}}, {0x14C, {0x4F, 0x304}}, {0x14D, {0x6F, 0x304}}, {0x14E, {0x4F, 0x306}}, {0x14F, {0x6F, 0x306}}, {0x150, {0x4F, 0x30B}}, {0x151, {0x6F, 0x30B}}, -{0x154, {0x52, 0x301}}, {0x155, {0x72, 0x301}}, {0x156, {0x52, 0x327}}, {0x157, {0x72, 0x327}}, {0x158, {0x52, 0x30C}}, {0x159, {0x72, 0x30C}}, {0x15A, {0x53, 0x301}}, {0x15B, {0x73, 0x301}}, -{0x15C, {0x53, 0x302}}, {0x15D, {0x73, 0x302}}, {0x15E, {0x53, 0x327}}, {0x15F, {0x73, 0x327}}, {0x160, {0x53, 0x30C}}, {0x161, {0x73, 0x30C}}, {0x162, {0x54, 0x327}}, {0x163, {0x74, 0x327}}, -{0x164, {0x54, 0x30C}}, {0x165, {0x74, 0x30C}}, {0x168, {0x55, 0x303}}, {0x169, {0x75, 0x303}}, {0x16A, {0x55, 0x304}}, {0x16B, {0x75, 0x304}}, {0x16C, {0x55, 0x306}}, {0x16D, {0x75, 0x306}}, -{0x16E, {0x55, 0x30A}}, {0x16F, {0x75, 0x30A}}, {0x170, {0x55, 0x30B}}, {0x171, {0x75, 0x30B}}, {0x172, {0x55, 0x328}}, {0x173, {0x75, 0x328}}, {0x174, {0x57, 0x302}}, {0x175, {0x77, 0x302}}, -{0x176, {0x59, 0x302}}, {0x177, {0x79, 0x302}}, {0x178, {0x59, 0x308}}, {0x179, {0x5A, 0x301}}, {0x17A, {0x7A, 0x301}}, {0x17B, {0x5A, 0x307}}, {0x17C, {0x7A, 0x307}}, {0x17D, {0x5A, 0x30C}}, -{0x17E, {0x7A, 0x30C}}, {0x1A0, {0x4F, 0x31B}}, {0x1A1, {0x6F, 0x31B}}, {0x1AF, {0x55, 0x31B}}, {0x1B0, {0x75, 0x31B}}, {0x1CD, {0x41, 0x30C}}, {0x1CE, {0x61, 0x30C}}, {0x1CF, {0x49, 0x30C}}, -{0x1D0, {0x69, 0x30C}}, {0x1D1, {0x4F, 0x30C}}, {0x1D2, {0x6F, 0x30C}}, {0x1D3, {0x55, 0x30C}}, {0x1D4, {0x75, 0x30C}}, {0x1D5, {0x55, 0x308, 0x304}}, {0x1D6, {0x75, 0x308, 0x304}}, -{0x1D7, {0x55, 0x308, 0x301}}, {0x1D8, {0x75, 0x308, 0x301}}, {0x1D9, {0x55, 0x308, 0x30C}}, {0x1DA, {0x75, 0x308, 0x30C}}, {0x1DB, {0x55, 0x308, 0x300}}, {0x1DC, {0x75, 0x308, 0x300}}, -{0x1DE, {0x41, 0x308, 0x304}}, {0x1DF, {0x61, 0x308, 0x304}}, {0x1E0, {0x41, 0x307, 0x304}}, {0x1E1, {0x61, 0x307, 0x304}}, {0x1E2, {0xC6, 0x304}}, {0x1E3, {0xE6, 0x304}}, {0x1E6, {0x47, 0x30C}}, -{0x1E7, {0x67, 0x30C}}, {0x1E8, {0x4B, 0x30C}}, {0x1E9, {0x6B, 0x30C}}, {0x1EA, {0x4F, 0x328}}, {0x1EB, {0x6F, 0x328}}, {0x1EC, {0x4F, 0x328, 0x304}}, {0x1ED, {0x6F, 0x328, 0x304}}, -{0x1EE, {0x1B7, 0x30C}}, {0x1EF, {0x292, 0x30C}}, {0x1F0, {0x6A, 0x30C}}, {0x1F4, {0x47, 0x301}}, {0x1F5, {0x67, 0x301}}, {0x1F8, {0x4E, 0x300}}, {0x1F9, {0x6E, 0x300}}, {0x1FA, {0x41, 0x30A, 0x301}}, -{0x1FB, {0x61, 0x30A, 0x301}}, {0x1FC, {0xC6, 0x301}}, {0x1FD, {0xE6, 0x301}}, {0x1FE, {0xD8, 0x301}}, {0x1FF, {0xF8, 0x301}}, {0x200, {0x41, 0x30F}}, {0x201, {0x61, 0x30F}}, {0x202, {0x41, 0x311}}, -{0x203, {0x61, 0x311}}, {0x204, {0x45, 0x30F}}, {0x205, {0x65, 0x30F}}, {0x206, {0x45, 0x311}}, {0x207, {0x65, 0x311}}, {0x208, {0x49, 0x30F}}, {0x209, {0x69, 0x30F}}, {0x20A, {0x49, 0x311}}, -{0x20B, {0x69, 0x311}}, {0x20C, {0x4F, 0x30F}}, {0x20D, {0x6F, 0x30F}}, {0x20E, {0x4F, 0x311}}, {0x20F, {0x6F, 0x311}}, {0x210, {0x52, 0x30F}}, {0x211, {0x72, 0x30F}}, {0x212, {0x52, 0x311}}, -{0x213, {0x72, 0x311}}, {0x214, {0x55, 0x30F}}, {0x215, {0x75, 0x30F}}, {0x216, {0x55, 0x311}}, {0x217, {0x75, 0x311}}, {0x218, {0x53, 0x326}}, {0x219, {0x73, 0x326}}, {0x21A, {0x54, 0x326}}, -{0x21B, {0x74, 0x326}}, 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{0x2F987, {0x267A7}}, {0x2F988, {0x267B5}}, {0x2F989, {0x23393}}, {0x2F98A, {0x2339C}}, {0x2F98B, {0x8201}}, -{0x2F98C, {0x8204}}, {0x2F98D, {0x8F9E}}, {0x2F98E, {0x446B}}, {0x2F98F, {0x8291}}, {0x2F990, {0x828B}}, {0x2F991, {0x829D}}, {0x2F992, {0x52B3}}, {0x2F993, {0x82B1}}, {0x2F994, {0x82B3}}, -{0x2F995, {0x82BD}}, {0x2F996, {0x82E6}}, {0x2F997, {0x26B3C}}, {0x2F998, {0x82E5}}, {0x2F999, {0x831D}}, {0x2F99A, {0x8363}}, {0x2F99B, {0x83AD}}, {0x2F99C, {0x8323}}, {0x2F99D, {0x83BD}}, -{0x2F99E, {0x83E7}}, {0x2F99F, {0x8457}}, {0x2F9A0, {0x8353}}, {0x2F9A1, {0x83CA}}, {0x2F9A2, {0x83CC}}, {0x2F9A3, {0x83DC}}, {0x2F9A4, {0x26C36}}, {0x2F9A5, {0x26D6B}}, {0x2F9A6, {0x26CD5}}, -{0x2F9A7, {0x452B}}, {0x2F9A8, {0x84F1}}, {0x2F9A9, {0x84F3}}, {0x2F9AA, {0x8516}}, {0x2F9AB, {0x273CA}}, {0x2F9AC, {0x8564}}, {0x2F9AD, {0x26F2C}}, {0x2F9AE, {0x455D}}, {0x2F9AF, {0x4561}}, -{0x2F9B0, {0x26FB1}}, {0x2F9B1, {0x270D2}}, {0x2F9B2, {0x456B}}, {0x2F9B3, {0x8650}}, {0x2F9B4, {0x865C}}, {0x2F9B5, {0x8667}}, {0x2F9B6, {0x8669}}, {0x2F9B7, {0x86A9}}, {0x2F9B8, {0x8688}}, -{0x2F9B9, {0x870E}}, {0x2F9BA, {0x86E2}}, {0x2F9BB, {0x8779}}, {0x2F9BC, {0x8728}}, {0x2F9BD, {0x876B}}, {0x2F9BE, {0x8786}}, {0x2F9BF, {0x45D7}}, {0x2F9C0, {0x87E1}}, {0x2F9C1, {0x8801}}, -{0x2F9C2, {0x45F9}}, {0x2F9C3, {0x8860}}, {0x2F9C4, {0x8863}}, {0x2F9C5, {0x27667}}, {0x2F9C6, {0x88D7}}, {0x2F9C7, {0x88DE}}, {0x2F9C8, {0x4635}}, {0x2F9C9, {0x88FA}}, {0x2F9CA, {0x34BB}}, -{0x2F9CB, {0x278AE}}, {0x2F9CC, {0x27966}}, {0x2F9CD, {0x46BE}}, {0x2F9CE, {0x46C7}}, {0x2F9CF, {0x8AA0}}, {0x2F9D0, {0x8AED}}, {0x2F9D1, {0x8B8A}}, {0x2F9D2, {0x8C55}}, {0x2F9D3, {0x27CA8}}, -{0x2F9D4, {0x8CAB}}, {0x2F9D5, {0x8CC1}}, {0x2F9D6, {0x8D1B}}, {0x2F9D7, {0x8D77}}, {0x2F9D8, {0x27F2F}}, {0x2F9D9, {0x20804}}, {0x2F9DA, {0x8DCB}}, {0x2F9DB, {0x8DBC}}, {0x2F9DC, {0x8DF0}}, -{0x2F9DD, {0x208DE}}, {0x2F9DE, {0x8ED4}}, {0x2F9DF, {0x8F38}}, {0x2F9E0, {0x285D2}}, {0x2F9E1, {0x285ED}}, {0x2F9E2, {0x9094}}, {0x2F9E3, {0x90F1}}, {0x2F9E4, {0x9111}}, {0x2F9E5, {0x2872E}}, -{0x2F9E6, {0x911B}}, {0x2F9E7, {0x9238}}, {0x2F9E8, {0x92D7}}, {0x2F9E9, {0x92D8}}, {0x2F9EA, {0x927C}}, {0x2F9EB, {0x93F9}}, {0x2F9EC, {0x9415}}, {0x2F9ED, {0x28BFA}}, {0x2F9EE, {0x958B}}, -{0x2F9EF, {0x4995}}, {0x2F9F0, {0x95B7}}, {0x2F9F1, {0x28D77}}, {0x2F9F2, {0x49E6}}, {0x2F9F3, {0x96C3}}, {0x2F9F4, {0x5DB2}}, {0x2F9F5, {0x9723}}, {0x2F9F6, {0x29145}}, {0x2F9F7, {0x2921A}}, -{0x2F9F8, {0x4A6E}}, {0x2F9F9, {0x4A76}}, {0x2F9FA, {0x97E0}}, {0x2F9FB, {0x2940A}}, {0x2F9FC, {0x4AB2}}, {0x2F9FD, {0x29496}}, {0x2F9FE, {0x980B}}, {0x2F9FF, {0x980B}}, {0x2FA00, {0x9829}}, -{0x2FA01, {0x295B6}}, {0x2FA02, {0x98E2}}, {0x2FA03, {0x4B33}}, {0x2FA04, {0x9929}}, {0x2FA05, {0x99A7}}, {0x2FA06, {0x99C2}}, {0x2FA07, {0x99FE}}, {0x2FA08, {0x4BCE}}, {0x2FA09, {0x29B30}}, -{0x2FA0A, {0x9B12}}, {0x2FA0B, {0x9C40}}, {0x2FA0C, {0x9CFD}}, {0x2FA0D, {0x4CCE}}, {0x2FA0E, {0x4CED}}, {0x2FA0F, {0x9D67}}, {0x2FA10, {0x2A0CE}}, {0x2FA11, {0x4CF8}}, {0x2FA12, {0x2A105}}, -{0x2FA13, {0x2A20E}}, {0x2FA14, {0x2A291}}, {0x2FA15, {0x9EBB}}, {0x2FA16, {0x4D56}}, {0x2FA17, {0x9EF9}}, {0x2FA18, {0x9EFE}}, {0x2FA19, {0x9F05}}, {0x2FA1A, {0x9F0F}}, {0x2FA1B, {0x9F16}}, -{0x2FA1D, {0x2A600}}, +static const std::multimap nfd_map = { +{0xC0, 0x41}, {0xC0, 0x300}, {0xC1, 0x41}, {0xC1, 0x301}, {0xC2, 0x41}, {0xC2, 0x302}, {0xC3, 0x41}, {0xC3, 0x303}, {0xC4, 0x41}, {0xC4, 0x308}, {0xC5, 0x41}, {0xC5, 0x30A}, {0xC7, 0x43}, +{0xC7, 0x327}, {0xC8, 0x45}, {0xC8, 0x300}, {0xC9, 0x45}, {0xC9, 0x301}, {0xCA, 0x45}, {0xCA, 0x302}, {0xCB, 0x45}, {0xCB, 0x308}, {0xCC, 0x49}, {0xCC, 0x300}, {0xCD, 0x49}, {0xCD, 0x301}, +{0xCE, 0x49}, {0xCE, 0x302}, {0xCF, 0x49}, {0xCF, 0x308}, {0xD1, 0x4E}, {0xD1, 0x303}, {0xD2, 0x4F}, {0xD2, 0x300}, {0xD3, 0x4F}, {0xD3, 0x301}, {0xD4, 0x4F}, {0xD4, 0x302}, {0xD5, 0x4F}, +{0xD5, 0x303}, {0xD6, 0x4F}, {0xD6, 0x308}, {0xD9, 0x55}, {0xD9, 0x300}, {0xDA, 0x55}, {0xDA, 0x301}, {0xDB, 0x55}, {0xDB, 0x302}, {0xDC, 0x55}, {0xDC, 0x308}, {0xDD, 0x59}, {0xDD, 0x301}, +{0xE0, 0x61}, {0xE0, 0x300}, {0xE1, 0x61}, {0xE1, 0x301}, {0xE2, 0x61}, {0xE2, 0x302}, {0xE3, 0x61}, {0xE3, 0x303}, {0xE4, 0x61}, {0xE4, 0x308}, {0xE5, 0x61}, {0xE5, 0x30A}, {0xE7, 0x63}, +{0xE7, 0x327}, {0xE8, 0x65}, {0xE8, 0x300}, {0xE9, 0x65}, {0xE9, 0x301}, {0xEA, 0x65}, {0xEA, 0x302}, {0xEB, 0x65}, {0xEB, 0x308}, {0xEC, 0x69}, {0xEC, 0x300}, {0xED, 0x69}, {0xED, 0x301}, +{0xEE, 0x69}, {0xEE, 0x302}, {0xEF, 0x69}, {0xEF, 0x308}, {0xF1, 0x6E}, {0xF1, 0x303}, {0xF2, 0x6F}, {0xF2, 0x300}, {0xF3, 0x6F}, {0xF3, 0x301}, {0xF4, 0x6F}, {0xF4, 0x302}, {0xF5, 0x6F}, +{0xF5, 0x303}, {0xF6, 0x6F}, {0xF6, 0x308}, {0xF9, 0x75}, {0xF9, 0x300}, {0xFA, 0x75}, {0xFA, 0x301}, {0xFB, 0x75}, {0xFB, 0x302}, {0xFC, 0x75}, {0xFC, 0x308}, {0xFD, 0x79}, {0xFD, 0x301}, +{0xFF, 0x79}, {0xFF, 0x308}, {0x100, 0x41}, {0x100, 0x304}, {0x101, 0x61}, {0x101, 0x304}, {0x102, 0x41}, {0x102, 0x306}, {0x103, 0x61}, {0x103, 0x306}, {0x104, 0x41}, {0x104, 0x328}, {0x105, 0x61}, +{0x105, 0x328}, {0x106, 0x43}, {0x106, 0x301}, {0x107, 0x63}, {0x107, 0x301}, {0x108, 0x43}, {0x108, 0x302}, {0x109, 0x63}, {0x109, 0x302}, {0x10A, 0x43}, {0x10A, 0x307}, {0x10B, 0x63}, +{0x10B, 0x307}, {0x10C, 0x43}, {0x10C, 0x30C}, {0x10D, 0x63}, {0x10D, 0x30C}, {0x10E, 0x44}, {0x10E, 0x30C}, {0x10F, 0x64}, {0x10F, 0x30C}, {0x112, 0x45}, {0x112, 0x304}, {0x113, 0x65}, +{0x113, 0x304}, {0x114, 0x45}, {0x114, 0x306}, {0x115, 0x65}, {0x115, 0x306}, {0x116, 0x45}, {0x116, 0x307}, {0x117, 0x65}, {0x117, 0x307}, {0x118, 0x45}, {0x118, 0x328}, {0x119, 0x65}, +{0x119, 0x328}, {0x11A, 0x45}, {0x11A, 0x30C}, {0x11B, 0x65}, {0x11B, 0x30C}, {0x11C, 0x47}, {0x11C, 0x302}, {0x11D, 0x67}, {0x11D, 0x302}, {0x11E, 0x47}, {0x11E, 0x306}, {0x11F, 0x67}, +{0x11F, 0x306}, {0x120, 0x47}, {0x120, 0x307}, {0x121, 0x67}, {0x121, 0x307}, {0x122, 0x47}, {0x122, 0x327}, {0x123, 0x67}, {0x123, 0x327}, {0x124, 0x48}, {0x124, 0x302}, {0x125, 0x68}, +{0x125, 0x302}, {0x128, 0x49}, {0x128, 0x303}, {0x129, 0x69}, {0x129, 0x303}, {0x12A, 0x49}, {0x12A, 0x304}, {0x12B, 0x69}, {0x12B, 0x304}, {0x12C, 0x49}, {0x12C, 0x306}, {0x12D, 0x69}, +{0x12D, 0x306}, {0x12E, 0x49}, {0x12E, 0x328}, {0x12F, 0x69}, {0x12F, 0x328}, {0x130, 0x49}, {0x130, 0x307}, {0x134, 0x4A}, {0x134, 0x302}, {0x135, 0x6A}, {0x135, 0x302}, {0x136, 0x4B}, +{0x136, 0x327}, {0x137, 0x6B}, {0x137, 0x327}, {0x139, 0x4C}, {0x139, 0x301}, {0x13A, 0x6C}, {0x13A, 0x301}, {0x13B, 0x4C}, {0x13B, 0x327}, {0x13C, 0x6C}, {0x13C, 0x327}, {0x13D, 0x4C}, +{0x13D, 0x30C}, {0x13E, 0x6C}, {0x13E, 0x30C}, {0x143, 0x4E}, {0x143, 0x301}, {0x144, 0x6E}, {0x144, 0x301}, {0x145, 0x4E}, {0x145, 0x327}, {0x146, 0x6E}, {0x146, 0x327}, {0x147, 0x4E}, +{0x147, 0x30C}, {0x148, 0x6E}, {0x148, 0x30C}, {0x14C, 0x4F}, {0x14C, 0x304}, {0x14D, 0x6F}, {0x14D, 0x304}, {0x14E, 0x4F}, {0x14E, 0x306}, {0x14F, 0x6F}, {0x14F, 0x306}, {0x150, 0x4F}, +{0x150, 0x30B}, {0x151, 0x6F}, {0x151, 0x30B}, {0x154, 0x52}, {0x154, 0x301}, {0x155, 0x72}, {0x155, 0x301}, {0x156, 0x52}, {0x156, 0x327}, {0x157, 0x72}, {0x157, 0x327}, {0x158, 0x52}, +{0x158, 0x30C}, {0x159, 0x72}, {0x159, 0x30C}, {0x15A, 0x53}, {0x15A, 0x301}, {0x15B, 0x73}, {0x15B, 0x301}, {0x15C, 0x53}, {0x15C, 0x302}, {0x15D, 0x73}, {0x15D, 0x302}, {0x15E, 0x53}, +{0x15E, 0x327}, {0x15F, 0x73}, {0x15F, 0x327}, {0x160, 0x53}, {0x160, 0x30C}, {0x161, 0x73}, {0x161, 0x30C}, {0x162, 0x54}, {0x162, 0x327}, {0x163, 0x74}, {0x163, 0x327}, {0x164, 0x54}, +{0x164, 0x30C}, {0x165, 0x74}, {0x165, 0x30C}, {0x168, 0x55}, {0x168, 0x303}, {0x169, 0x75}, {0x169, 0x303}, {0x16A, 0x55}, {0x16A, 0x304}, {0x16B, 0x75}, {0x16B, 0x304}, {0x16C, 0x55}, +{0x16C, 0x306}, {0x16D, 0x75}, {0x16D, 0x306}, {0x16E, 0x55}, {0x16E, 0x30A}, {0x16F, 0x75}, {0x16F, 0x30A}, {0x170, 0x55}, {0x170, 0x30B}, {0x171, 0x75}, {0x171, 0x30B}, {0x172, 0x55}, +{0x172, 0x328}, {0x173, 0x75}, {0x173, 0x328}, {0x174, 0x57}, {0x174, 0x302}, {0x175, 0x77}, {0x175, 0x302}, {0x176, 0x59}, {0x176, 0x302}, {0x177, 0x79}, {0x177, 0x302}, {0x178, 0x59}, +{0x178, 0x308}, {0x179, 0x5A}, {0x179, 0x301}, {0x17A, 0x7A}, {0x17A, 0x301}, {0x17B, 0x5A}, {0x17B, 0x307}, {0x17C, 0x7A}, {0x17C, 0x307}, {0x17D, 0x5A}, {0x17D, 0x30C}, {0x17E, 0x7A}, +{0x17E, 0x30C}, {0x1A0, 0x4F}, {0x1A0, 0x31B}, {0x1A1, 0x6F}, {0x1A1, 0x31B}, {0x1AF, 0x55}, {0x1AF, 0x31B}, {0x1B0, 0x75}, {0x1B0, 0x31B}, {0x1CD, 0x41}, {0x1CD, 0x30C}, {0x1CE, 0x61}, +{0x1CE, 0x30C}, {0x1CF, 0x49}, {0x1CF, 0x30C}, {0x1D0, 0x69}, {0x1D0, 0x30C}, {0x1D1, 0x4F}, {0x1D1, 0x30C}, {0x1D2, 0x6F}, {0x1D2, 0x30C}, {0x1D3, 0x55}, {0x1D3, 0x30C}, {0x1D4, 0x75}, +{0x1D4, 0x30C}, {0x1D5, 0x55}, {0x1D5, 0x308}, {0x1D5, 0x304}, {0x1D6, 0x75}, {0x1D6, 0x308}, {0x1D6, 0x304}, {0x1D7, 0x55}, {0x1D7, 0x308}, {0x1D7, 0x301}, {0x1D8, 0x75}, {0x1D8, 0x308}, +{0x1D8, 0x301}, {0x1D9, 0x55}, {0x1D9, 0x308}, {0x1D9, 0x30C}, {0x1DA, 0x75}, {0x1DA, 0x308}, {0x1DA, 0x30C}, {0x1DB, 0x55}, {0x1DB, 0x308}, {0x1DB, 0x300}, {0x1DC, 0x75}, {0x1DC, 0x308}, +{0x1DC, 0x300}, {0x1DE, 0x41}, {0x1DE, 0x308}, {0x1DE, 0x304}, {0x1DF, 0x61}, {0x1DF, 0x308}, {0x1DF, 0x304}, {0x1E0, 0x41}, {0x1E0, 0x307}, {0x1E0, 0x304}, {0x1E1, 0x61}, {0x1E1, 0x307}, +{0x1E1, 0x304}, {0x1E2, 0xC6}, {0x1E2, 0x304}, {0x1E3, 0xE6}, {0x1E3, 0x304}, {0x1E6, 0x47}, {0x1E6, 0x30C}, {0x1E7, 0x67}, {0x1E7, 0x30C}, {0x1E8, 0x4B}, {0x1E8, 0x30C}, {0x1E9, 0x6B}, +{0x1E9, 0x30C}, {0x1EA, 0x4F}, {0x1EA, 0x328}, {0x1EB, 0x6F}, {0x1EB, 0x328}, {0x1EC, 0x4F}, {0x1EC, 0x328}, {0x1EC, 0x304}, {0x1ED, 0x6F}, {0x1ED, 0x328}, {0x1ED, 0x304}, {0x1EE, 0x1B7}, +{0x1EE, 0x30C}, {0x1EF, 0x292}, {0x1EF, 0x30C}, {0x1F0, 0x6A}, {0x1F0, 0x30C}, {0x1F4, 0x47}, {0x1F4, 0x301}, {0x1F5, 0x67}, {0x1F5, 0x301}, {0x1F8, 0x4E}, {0x1F8, 0x300}, {0x1F9, 0x6E}, +{0x1F9, 0x300}, {0x1FA, 0x41}, {0x1FA, 0x30A}, {0x1FA, 0x301}, {0x1FB, 0x61}, {0x1FB, 0x30A}, {0x1FB, 0x301}, {0x1FC, 0xC6}, {0x1FC, 0x301}, {0x1FD, 0xE6}, {0x1FD, 0x301}, {0x1FE, 0xD8}, +{0x1FE, 0x301}, {0x1FF, 0xF8}, {0x1FF, 0x301}, {0x200, 0x41}, {0x200, 0x30F}, {0x201, 0x61}, {0x201, 0x30F}, {0x202, 0x41}, {0x202, 0x311}, {0x203, 0x61}, {0x203, 0x311}, {0x204, 0x45}, +{0x204, 0x30F}, {0x205, 0x65}, {0x205, 0x30F}, {0x206, 0x45}, {0x206, 0x311}, {0x207, 0x65}, {0x207, 0x311}, {0x208, 0x49}, {0x208, 0x30F}, {0x209, 0x69}, {0x209, 0x30F}, {0x20A, 0x49}, +{0x20A, 0x311}, {0x20B, 0x69}, {0x20B, 0x311}, {0x20C, 0x4F}, {0x20C, 0x30F}, {0x20D, 0x6F}, {0x20D, 0x30F}, {0x20E, 0x4F}, {0x20E, 0x311}, {0x20F, 0x6F}, {0x20F, 0x311}, {0x210, 0x52}, +{0x210, 0x30F}, {0x211, 0x72}, {0x211, 0x30F}, {0x212, 0x52}, {0x212, 0x311}, {0x213, 0x72}, {0x213, 0x311}, {0x214, 0x55}, {0x214, 0x30F}, {0x215, 0x75}, {0x215, 0x30F}, {0x216, 0x55}, +{0x216, 0x311}, {0x217, 0x75}, {0x217, 0x311}, {0x218, 0x53}, {0x218, 0x326}, {0x219, 0x73}, {0x219, 0x326}, {0x21A, 0x54}, {0x21A, 0x326}, {0x21B, 0x74}, {0x21B, 0x326}, {0x21E, 0x48}, +{0x21E, 0x30C}, {0x21F, 0x68}, {0x21F, 0x30C}, {0x226, 0x41}, {0x226, 0x307}, {0x227, 0x61}, {0x227, 0x307}, {0x228, 0x45}, {0x228, 0x327}, {0x229, 0x65}, {0x229, 0x327}, {0x22A, 0x4F}, +{0x22A, 0x308}, 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{0x2F97B, 0x264DA}, {0x2F97C, 0x26523}, {0x2F97D, 0x8060}, {0x2F97E, 0x265A8}, {0x2F97F, 0x8070}, {0x2F980, 0x2335F}, {0x2F981, 0x43D5}, +{0x2F982, 0x80B2}, {0x2F983, 0x8103}, {0x2F984, 0x440B}, {0x2F985, 0x813E}, {0x2F986, 0x5AB5}, {0x2F987, 0x267A7}, {0x2F988, 0x267B5}, {0x2F989, 0x23393}, {0x2F98A, 0x2339C}, {0x2F98B, 0x8201}, +{0x2F98C, 0x8204}, {0x2F98D, 0x8F9E}, {0x2F98E, 0x446B}, {0x2F98F, 0x8291}, {0x2F990, 0x828B}, {0x2F991, 0x829D}, {0x2F992, 0x52B3}, {0x2F993, 0x82B1}, {0x2F994, 0x82B3}, {0x2F995, 0x82BD}, +{0x2F996, 0x82E6}, {0x2F997, 0x26B3C}, {0x2F998, 0x82E5}, {0x2F999, 0x831D}, {0x2F99A, 0x8363}, {0x2F99B, 0x83AD}, {0x2F99C, 0x8323}, {0x2F99D, 0x83BD}, {0x2F99E, 0x83E7}, {0x2F99F, 0x8457}, +{0x2F9A0, 0x8353}, {0x2F9A1, 0x83CA}, {0x2F9A2, 0x83CC}, {0x2F9A3, 0x83DC}, {0x2F9A4, 0x26C36}, {0x2F9A5, 0x26D6B}, {0x2F9A6, 0x26CD5}, {0x2F9A7, 0x452B}, {0x2F9A8, 0x84F1}, {0x2F9A9, 0x84F3}, +{0x2F9AA, 0x8516}, {0x2F9AB, 0x273CA}, {0x2F9AC, 0x8564}, {0x2F9AD, 0x26F2C}, {0x2F9AE, 0x455D}, {0x2F9AF, 0x4561}, {0x2F9B0, 0x26FB1}, {0x2F9B1, 0x270D2}, {0x2F9B2, 0x456B}, {0x2F9B3, 0x8650}, +{0x2F9B4, 0x865C}, {0x2F9B5, 0x8667}, {0x2F9B6, 0x8669}, {0x2F9B7, 0x86A9}, {0x2F9B8, 0x8688}, {0x2F9B9, 0x870E}, {0x2F9BA, 0x86E2}, {0x2F9BB, 0x8779}, {0x2F9BC, 0x8728}, {0x2F9BD, 0x876B}, +{0x2F9BE, 0x8786}, {0x2F9BF, 0x45D7}, {0x2F9C0, 0x87E1}, {0x2F9C1, 0x8801}, {0x2F9C2, 0x45F9}, {0x2F9C3, 0x8860}, {0x2F9C4, 0x8863}, {0x2F9C5, 0x27667}, {0x2F9C6, 0x88D7}, {0x2F9C7, 0x88DE}, +{0x2F9C8, 0x4635}, {0x2F9C9, 0x88FA}, {0x2F9CA, 0x34BB}, {0x2F9CB, 0x278AE}, {0x2F9CC, 0x27966}, {0x2F9CD, 0x46BE}, {0x2F9CE, 0x46C7}, {0x2F9CF, 0x8AA0}, {0x2F9D0, 0x8AED}, {0x2F9D1, 0x8B8A}, +{0x2F9D2, 0x8C55}, {0x2F9D3, 0x27CA8}, {0x2F9D4, 0x8CAB}, {0x2F9D5, 0x8CC1}, {0x2F9D6, 0x8D1B}, {0x2F9D7, 0x8D77}, {0x2F9D8, 0x27F2F}, {0x2F9D9, 0x20804}, {0x2F9DA, 0x8DCB}, {0x2F9DB, 0x8DBC}, +{0x2F9DC, 0x8DF0}, {0x2F9DD, 0x208DE}, {0x2F9DE, 0x8ED4}, {0x2F9DF, 0x8F38}, {0x2F9E0, 0x285D2}, {0x2F9E1, 0x285ED}, {0x2F9E2, 0x9094}, {0x2F9E3, 0x90F1}, {0x2F9E4, 0x9111}, {0x2F9E5, 0x2872E}, +{0x2F9E6, 0x911B}, {0x2F9E7, 0x9238}, {0x2F9E8, 0x92D7}, {0x2F9E9, 0x92D8}, {0x2F9EA, 0x927C}, {0x2F9EB, 0x93F9}, {0x2F9EC, 0x9415}, {0x2F9ED, 0x28BFA}, {0x2F9EE, 0x958B}, {0x2F9EF, 0x4995}, +{0x2F9F0, 0x95B7}, {0x2F9F1, 0x28D77}, {0x2F9F2, 0x49E6}, {0x2F9F3, 0x96C3}, {0x2F9F4, 0x5DB2}, {0x2F9F5, 0x9723}, {0x2F9F6, 0x29145}, {0x2F9F7, 0x2921A}, {0x2F9F8, 0x4A6E}, {0x2F9F9, 0x4A76}, +{0x2F9FA, 0x97E0}, {0x2F9FB, 0x2940A}, {0x2F9FC, 0x4AB2}, {0x2F9FD, 0x29496}, {0x2F9FE, 0x980B}, {0x2F9FF, 0x980B}, {0x2FA00, 0x9829}, {0x2FA01, 0x295B6}, {0x2FA02, 0x98E2}, {0x2FA03, 0x4B33}, +{0x2FA04, 0x9929}, {0x2FA05, 0x99A7}, {0x2FA06, 0x99C2}, {0x2FA07, 0x99FE}, {0x2FA08, 0x4BCE}, {0x2FA09, 0x29B30}, {0x2FA0A, 0x9B12}, {0x2FA0B, 0x9C40}, {0x2FA0C, 0x9CFD}, {0x2FA0D, 0x4CCE}, +{0x2FA0E, 0x4CED}, {0x2FA0F, 0x9D67}, {0x2FA10, 0x2A0CE}, {0x2FA11, 0x4CF8}, {0x2FA12, 0x2A105}, {0x2FA13, 0x2A20E}, {0x2FA14, 0x2A291}, {0x2FA15, 0x9EBB}, {0x2FA16, 0x4D56}, {0x2FA17, 0x9EF9}, +{0x2FA18, 0x9EFE}, {0x2FA19, 0x9F05}, {0x2FA1A, 0x9F0F}, {0x2FA1B, 0x9F16}, {0x2FA1D, 0x2A600}, }; static std::string codepoint_to_utf8(uint32_t cp) { From 3ab8b3a92ede46df88bc5a2dfca3777de4a2b2b6 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 1 Mar 2024 12:39:06 +0100 Subject: [PATCH 07/86] llama : cleanup unused mmq flags (#5772) * cleanup unused --no-mul-mat-q,-nommq, -mmq, --mul-mat-q, mul_mat_q * remove: mul_mat_q in compare llama bench and usage * update llama-bench --------- Co-authored-by: slaren --- common/common.cpp | 2 -- common/common.h | 1 - examples/batched-bench/batched-bench.cpp | 18 +++++--------- examples/llama-bench/README.md | 1 - examples/llama-bench/llama-bench.cpp | 30 +++--------------------- examples/server/server.cpp | 8 ------- llama.cpp | 3 --- llama.h | 1 - scripts/compare-llama-bench.py | 2 +- 9 files changed, 10 insertions(+), 56 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 18289755c..bf1ed8a66 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1281,7 +1281,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_batch = params.n_batch; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; cparams.seed = params.seed; cparams.logits_all = params.logits_all; cparams.embedding = params.embedding; @@ -1725,7 +1724,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false"); fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false"); fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); diff --git a/common/common.h b/common/common.h index 25003df26..ab62bdb82 100644 --- a/common/common.h +++ b/common/common.h @@ -115,7 +115,6 @@ struct gpt_params { bool kl_divergence = false; // compute KL-divergence - bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index b4b8a38e1..19aff18ae 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -32,16 +32,15 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \n" , argv[0]); + printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] \n" , argv[0]); printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); - printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); + printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); return 1 ; } int n_kv_max = 2048; int is_pp_shared = 0; int n_gpu_layers = 0; - int mmq = 0; std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; std::vector n_tg = { 128, 256, }; @@ -65,19 +64,15 @@ int main(int argc, char ** argv) { } if (argc >= 6) { - mmq = std::atoi(argv[5]); + n_pp = parse_list(argv[5]); } if (argc >= 7) { - n_pp = parse_list(argv[6]); + n_tg = parse_list(argv[6]); } if (argc >= 8) { - n_tg = parse_list(argv[7]); - } - - if (argc >= 9) { - n_pl = parse_list(argv[8]); + n_pl = parse_list(argv[7]); } // init LLM @@ -106,7 +101,6 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_max; ctx_params.n_batch = 512; - ctx_params.mul_mat_q = mmq; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -159,7 +153,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 374e40a7d..10f37b441 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -35,7 +35,6 @@ options: -mg, --main-gpu (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - -mmq, --mul-mat-q <0|1> (default: 1) -ts, --tensor_split (default: 0) -r, --repetitions (default: 5) -o, --output (default: md) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 8fec3d43d..c2155b2ac 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -176,7 +176,6 @@ struct cmd_params { std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; - std::vector mul_mat_q; std::vector> tensor_split; std::vector use_mmap; int reps; @@ -196,7 +195,6 @@ static const cmd_params cmd_params_defaults = { /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, - /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, /* reps */ 5, @@ -221,7 +219,6 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); @@ -383,13 +380,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); - } else if (arg == "-mmq" || arg == "--mul-mat-q") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = split(argv[i], split_delim); - params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; @@ -466,7 +456,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } - if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } @@ -486,7 +475,6 @@ struct cmd_params_instance { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; @@ -518,7 +506,6 @@ struct cmd_params_instance { cparams.n_batch = n_batch; cparams.type_k = type_k; cparams.type_v = type_v; - cparams.mul_mat_q = mul_mat_q; cparams.offload_kqv = !no_kv_offload; return cparams; @@ -538,7 +525,6 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) - for (const auto & mmq : params.mul_mat_q) for (const auto & nkvo : params.no_kv_offload) for (const auto & nt : params.n_threads) { for (const auto & n_prompt : params.n_prompt) { @@ -557,7 +543,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -580,7 +565,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -616,7 +600,6 @@ struct test { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; int n_prompt; @@ -639,7 +622,6 @@ struct test { split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; - mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; @@ -713,7 +695,7 @@ struct test { "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", "use_mmap", + "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -733,7 +715,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q" || field == "use_mmap") { + field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -767,7 +749,7 @@ struct test { std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), + tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -931,9 +913,6 @@ struct markdown_printer : public printer { if (field == "n_threads") { return "threads"; } - if (field == "mul_mat_q") { - return "mmq"; - } if (field == "no_kv_offload") { return "nkvo"; } @@ -974,9 +953,6 @@ struct markdown_printer : public printer { if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { fields.emplace_back("split_mode"); } - if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.emplace_back("mul_mat_q"); - } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { fields.emplace_back("no_kv_offload"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index eea987966..2b2f4a0f4 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2390,14 +2390,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUBLAS - } - else if (arg == "--no-mul-mat-q" || arg == "-nommq") - { -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) - params.mul_mat_q = false; -#else - LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") diff --git a/llama.cpp b/llama.cpp index a35f07aa4..073fd3b70 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1645,7 +1645,6 @@ struct llama_cparams { float yarn_beta_slow; float defrag_thold; - bool mul_mat_q; bool offload_kqv; bool do_pooling; @@ -11633,7 +11632,6 @@ struct llama_context_params llama_context_default_params() { /*.cb_eval_user_data =*/ nullptr, /*.type_k =*/ GGML_TYPE_F16, /*.type_v =*/ GGML_TYPE_F16, - /*.mul_mat_q =*/ true, /*.logits_all =*/ false, /*.embedding =*/ false, /*.offload_kqv =*/ true, @@ -11785,7 +11783,6 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; - cparams.mul_mat_q = params.mul_mat_q; cparams.offload_kqv = params.offload_kqv; cparams.do_pooling = params.do_pooling; diff --git a/llama.h b/llama.h index 4d0ebe37d..ed51f478a 100644 --- a/llama.h +++ b/llama.h @@ -255,7 +255,6 @@ extern "C" { enum ggml_type type_v; // data type for V cache // Keep the booleans together to avoid misalignment during copy-by-value. - bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 70737f976..39c3e52e5 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -31,7 +31,7 @@ PRETTY_NAMES = { "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters", "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO", - "mul_mat_q": "MMQ", "tensor_split": "Tensor split" + "tensor_split": "Tensor split" } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. From f49a5356865ced0eca1df9f9d84631dfef71b9dc Mon Sep 17 00:00:00 2001 From: Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com> Date: Fri, 1 Mar 2024 22:48:56 +0900 Subject: [PATCH 08/86] common : fix flag `--logits-all` to `--all-logits` (#5805) --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index bf1ed8a66..938c428cf 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1015,7 +1015,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n"); + printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); From e7433867288d2f142cffe596f3751bda5d7ee2c7 Mon Sep 17 00:00:00 2001 From: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Date: Fri, 1 Mar 2024 06:08:08 -0800 Subject: [PATCH 09/86] gemma : fix bfloat16 -> float16 conversion issue (#5810) --- convert-hf-to-gguf.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ae30b2a76..d3e8ec1f6 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1811,16 +1811,15 @@ class GemmaModel(Model): tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): - # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 - if name.endswith("norm.weight"): - data_torch = data_torch + 1 - old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 data = data_torch.squeeze().numpy() # map tensor names From c2224f003bf9cf558b1a3c57033563e11a4de9a5 Mon Sep 17 00:00:00 2001 From: ddpasa <112642920+ddpasa@users.noreply.github.com> Date: Fri, 1 Mar 2024 18:00:00 +0100 Subject: [PATCH 10/86] ggml-vulkan: fix VULKAN_CHECK_RESULTS flag, which was previously broken (#5813) --- ggml-vulkan.cpp | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 896c290b2..ae9cb3c1c 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5428,7 +5428,8 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -5540,7 +5541,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5550,10 +5552,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu->size) { - src0_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src0_size >= buffer_gpu->size) { + src0_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5583,7 +5586,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5593,10 +5597,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu->size) { - src1_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src1_size >= buffer_gpu->size) { + src1_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5643,11 +5648,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { - if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); - } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); - } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { @@ -5753,11 +5754,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu->size) { - tensor_size = extra->buffer_gpu->size - (extra->offset); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (extra->offset + tensor_size >= buffer_gpu->size) { + tensor_size = buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; From 38d16b142624bdd7c41d9955752b7f7b59c5e048 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Mar 2024 20:00:58 +0200 Subject: [PATCH 11/86] server : remove api_like_OAI.py proxy script (#5808) --- README.md | 1 + examples/server/README.md | 17 +-- examples/server/api_like_OAI.py | 228 -------------------------------- 3 files changed, 3 insertions(+), 243 deletions(-) delete mode 100755 examples/server/api_like_OAI.py diff --git a/README.md b/README.md index 5401e197f..67717c1e3 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics +- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) - Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 - Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 diff --git a/examples/server/README.md b/examples/server/README.md index ad35306c6..397ee8252 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -326,7 +326,7 @@ Notice that each `probs` is an array of length `n_probs`. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - `total_slots` - the total number of slots for process requests (defined by `--parallel` option) -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. +- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. *Options:* @@ -528,20 +528,7 @@ bash chat.sh ### API like OAI -API example using Python Flask: [api_like_OAI.py](api_like_OAI.py) -This example must be used with server.cpp - -```sh -python api_like_OAI.py -``` - -After running the API server, you can use it in Python by setting the API base URL. - -```python -openai.api_base = "http://:port" -``` - -Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API +The HTTP server supports OAI-like API ### Extending or building alternative Web Front End diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py deleted file mode 100755 index 607fe49d3..000000000 --- a/examples/server/api_like_OAI.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -import argparse -from flask import Flask, jsonify, request, Response -import urllib.parse -import requests -import time -import json - - -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.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.') -parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ") -parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ") -parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ") -parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '')", default="") -parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080') -parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="") -parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1') -parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081) - -args = parser.parse_args() - -def is_present(json, key): - try: - buf = json[key] - except KeyError: - return False - if json[key] == None: - return False - return True - -#convert chat to prompt -def convert_chat(messages): - - system_n = args.system_name - user_n = args.user_name - ai_n = args.ai_name - stop = args.stop - - prompt = "" + args.chat_prompt + stop - - for line in messages: - if (line["role"] == "system"): - prompt += f"{system_n}{line['content']}{stop}" - if (line["role"] == "user"): - prompt += f"{user_n}{line['content']}{stop}" - if (line["role"] == "assistant"): - prompt += f"{ai_n}{line['content']}{stop}" - prompt += ai_n.rstrip() - - return prompt - -def make_postData(body, chat=False, stream=False): - postData = {} - if (chat): - postData["prompt"] = convert_chat(body["messages"]) - else: - postData["prompt"] = body["prompt"] - if(is_present(body, "temperature")): postData["temperature"] = body["temperature"] - if(is_present(body, "top_k")): postData["top_k"] = body["top_k"] - if(is_present(body, "top_p")): postData["top_p"] = body["top_p"] - if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"] - if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"] - if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"] - if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"] - if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"] - if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"] - if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"] - if(is_present(body, "seed")): postData["seed"] = body["seed"] - if(is_present(body, "grammar")): postData["grammar"] = body["grammar"] - if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()] - if (args.stop != ""): - postData["stop"] = [args.stop] - else: - postData["stop"] = [] - if(is_present(body, "stop")): postData["stop"] += body["stop"] - postData["n_keep"] = -1 - postData["stream"] = stream - postData["cache_prompt"] = True - postData["slot_id"] = slot_id - return postData - -def make_resData(data, chat=False, promptToken=[]): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion" if (chat) else "text_completion", - "created": int(time.time()), - "truncated": data["truncated"], - "model": "LLaMA_CPP", - "usage": { - "prompt_tokens": data["tokens_evaluated"], - "completion_tokens": data["tokens_predicted"], - "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"] - } - } - if (len(promptToken) != 0): - resData["promptToken"] = promptToken - if (chat): - #only one choice is supported - resData["choices"] = [{ - "index": 0, - "message": { - "role": "assistant", - "content": data["content"], - }, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - else: - #only one choice is supported - resData["choices"] = [{ - "text": data["content"], - "index": 0, - "logprobs": None, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - return resData - -def make_resData_stream(data, chat=False, time_now = 0, start=False): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion.chunk" if (chat) else "text_completion.chunk", - "created": time_now, - "model": "LLaMA_CPP", - "choices": [ - { - "finish_reason": None, - "index": 0 - } - ] - } - slot_id = data.get("slot_id") - if (chat): - if (start): - resData["choices"][0]["delta"] = { - "role": "assistant" - } - else: - resData["choices"][0]["delta"] = { - "content": data["content"] - } - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - else: - resData["choices"][0]["text"] = data["content"] - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - - return resData - - -@app.route('/chat/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS']) -def chat_completions(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=True, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=True, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - resData = make_resData_stream({}, chat=True, time_now=time_now, start=True) - yield 'data: {}\n\n'.format(json.dumps(resData)) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - - -@app.route('/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/completions', methods=['POST', 'OPTIONS']) -def completion(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=False, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=False, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - -if __name__ == '__main__': - app.run(args.host, port=args.port) From c29af7e2252d288f2ea58a7d437c1cb7c0abf160 Mon Sep 17 00:00:00 2001 From: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Date: Sat, 2 Mar 2024 01:00:46 +0530 Subject: [PATCH 12/86] llama : add StarCoder2 support (#5795) * Add support for starcoder2 * handle rope type * skip rope freq and rotary embeddings from being serialized * resolve comments * Update llama.cpp * remove redundant changes * handle `rope-theta` * llama : change starcoder2 rope type * address comment --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 8 +- gguf-py/gguf/constants.py | 21 ++++ gguf-py/gguf/tensor_mapping.py | 2 + llama.cpp | 199 +++++++++++++++++++++++++++++++++ 4 files changed, 229 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index d3e8ec1f6..28b92ac38 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -96,9 +96,11 @@ class Model: if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) - if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None: + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: self.gguf_writer.add_layer_norm_eps(f_norm_eps) if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) @@ -220,6 +222,8 @@ class Model: return NomicBertModel if model_architecture == "GemmaForCausalLM": return GemmaModel + if model_architecture == "Starcoder2ForCausalLM": + return Model return Model def _is_model_safetensors(self) -> bool: @@ -281,6 +285,8 @@ class Model: return gguf.MODEL_ARCH.NOMIC_BERT if arch == "GemmaForCausalLM": return gguf.MODEL_ARCH.GEMMA + if arch == "Starcoder2ForCausalLM": + return gguf.MODEL_ARCH.STARCODER2 raise NotImplementedError(f'Architecture "{arch}" not supported!') diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8f9139d1b..5db760cb1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -112,6 +112,7 @@ class MODEL_ARCH(IntEnum): INTERNLM2 = auto() MINICPM = auto() GEMMA = auto() + STARCODER2 = auto() class MODEL_TENSOR(IntEnum): @@ -169,6 +170,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.INTERNLM2: "internlm2", MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.STARCODER2: "starcoder2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -526,6 +528,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_NORM, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -554,6 +571,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 861003776..db2ec9704 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -210,6 +210,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert + "model.layers.{bid}.mlp.c_fc", # starcoder2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -256,6 +257,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert + "model.layers.{bid}.mlp.c_proj", # starcoder2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 073fd3b70..b1db5b179 100644 --- a/llama.cpp +++ b/llama.cpp @@ -211,6 +211,7 @@ enum llm_arch { LLM_ARCH_INTERNLM2, LLM_ARCH_MINICPM, LLM_ARCH_GEMMA, + LLM_ARCH_STARCODER2, LLM_ARCH_UNKNOWN, }; @@ -238,6 +239,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_INTERNLM2, "internlm2" }, { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, + { LLM_ARCH_STARCODER2, "starcoder2" }, }; enum llm_kv { @@ -779,6 +781,24 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_STARCODER2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -3320,6 +3340,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_STARCODER2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 30: model.type = e_model::MODEL_3B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_15B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4490,6 +4520,56 @@ static bool llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); } } break; + case LLM_ARCH_STARCODER2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + // optional bias tensors + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + + // optional bias tensors + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -7559,6 +7639,120 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_starcoder2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -7705,6 +7899,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_gemma(); } break; + case LLM_ARCH_STARCODER2: + { + result = llm.build_starcoder2(); + } break; default: GGML_ASSERT(false); } @@ -12084,6 +12282,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_QWEN2: case LLM_ARCH_PHI2: case LLM_ARCH_GEMMA: + case LLM_ARCH_STARCODER2: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here From da3b9ba2b710c0f8b44398a0eb9e5a7ae2ad967a Mon Sep 17 00:00:00 2001 From: nold Date: Fri, 1 Mar 2024 22:51:12 +0100 Subject: [PATCH 13/86] convert-hf-to-gguf : require einops for InternLM2ForCausalLM (#5792) --- requirements/requirements-convert-hf-to-gguf.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt index 6ac402610..6ce840d73 100644 --- a/requirements/requirements-convert-hf-to-gguf.txt +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -1,2 +1,3 @@ -r ./requirements-convert.txt torch~=2.1.1 +einops~=0.7.0 From cb5e8f7fc4ee57d4bcccafbe04a82cededd35486 Mon Sep 17 00:00:00 2001 From: Tushar Date: Sat, 2 Mar 2024 04:48:26 +0530 Subject: [PATCH 14/86] build(nix): Introduce flake.formatter for `nix fmt` (#5687) * build(nix): Introduce flake.formatter for `nix fmt` * chore: Switch to pkgs.nixfmt-rfc-style --- .devops/nix/sif.nix | 2 +- flake.nix | 10 +++++++--- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/.devops/nix/sif.nix b/.devops/nix/sif.nix index 7535ca0f3..7a5e1dd0f 100644 --- a/.devops/nix/sif.nix +++ b/.devops/nix/sif.nix @@ -7,7 +7,7 @@ }: let - optionalInt = cond: x: if cond then x else 0; + optionalInt = cond: x: if cond then x else 0; in singularity-tools.buildImage rec { inherit (llama-cpp) name; diff --git a/flake.nix b/flake.nix index dc4e503c3..45f9deda0 100644 --- a/flake.nix +++ b/flake.nix @@ -107,11 +107,12 @@ # ``` # # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format - flake.overlays.default = - (final: prev: { + flake.overlays.default = ( + final: prev: { llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; inherit (final.llamaPackages) llama-cpp; - }); + } + ); systems = [ "aarch64-darwin" @@ -131,6 +132,9 @@ ... }: { + # For standardised reproducible formatting with `nix fmt` + formatter = pkgs.nixfmt-rfc-style; + # Unlike `.#packages`, legacyPackages may contain values of # arbitrary types (including nested attrsets) and may even throw # exceptions. This attribute isn't recursed into by `nix flake From 9bf297a02bfbd474e51912409a470dd797e2fe13 Mon Sep 17 00:00:00 2001 From: crasm Date: Sat, 2 Mar 2024 00:11:06 -0500 Subject: [PATCH 15/86] workflows : remove nocleanup arg for check-requirements.sh (#5826) Reduces peak tmpfs usage and should prevent the check from failing from running out of space. Fixes the 'No space left on device' issue mentioned in #5703. --- .github/workflows/python-check-requirements.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml index 92e1108b3..b82205992 100644 --- a/.github/workflows/python-check-requirements.yml +++ b/.github/workflows/python-check-requirements.yml @@ -3,12 +3,14 @@ name: Python check requirements.txt on: push: paths: + - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - 'requirements.txt' - 'requirements/*.txt' pull_request: paths: + - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - 'requirements.txt' @@ -26,4 +28,4 @@ jobs: with: python-version: "3.11" - name: Run check-requirements.sh script - run: bash scripts/check-requirements.sh nocleanup + run: bash scripts/check-requirements.sh From 715641391dda1ff9762dc5d99d9a30acce99f2c6 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Sat, 2 Mar 2024 19:49:30 +0800 Subject: [PATCH 16/86] Support multiple GPUs (split mode) on SYCL backend (#5806) * suport multiple cards: split-mode - layer|row * rm warning * rebase with master, support tow new OPs, close feature for -sm=row, fix for unit test * update news * fix merge error * update according to review comments --- README-sycl.md | 21 + common/common.cpp | 4 + examples/llama-bench/llama-bench.cpp | 17 +- examples/sycl/ls-sycl-device.cpp | 2 +- examples/sycl/run-llama2.sh | 17 +- ggml-sycl.cpp | 2205 +++++++++++++++++--------- ggml-sycl.h | 5 + llama.cpp | 49 +- 8 files changed, 1506 insertions(+), 814 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index dd5bf9dea..85eb16f2b 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -1,6 +1,7 @@ # llama.cpp for SYCL - [Background](#background) +- [News](#news) - [OS](#os) - [Intel GPU](#intel-gpu) - [Docker](#docker) @@ -25,6 +26,21 @@ The llama.cpp for SYCL is used to support Intel GPUs. For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). +## News + +- 2024.3 + - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. + - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. + - Support detecting all GPUs with level-zero and same top **Max compute units**. + - Support OPs + - hardsigmoid + - hardswish + - pool2d + +- 2024.1 + - Create SYCL backend for Intel GPU. + - Support Windows build + ## OS |OS|Status|Verified| @@ -449,6 +465,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device |-|-|-| |GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output| |GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| +|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer| ## Known Issue @@ -458,6 +475,10 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Solution: add **--no-mmap** or **--mmap 0**. +- Split-mode: [row] is not supported + + It's on developing. + ## Q&A - Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. diff --git a/common/common.cpp b/common/common.cpp index 938c428cf..1c0b7c403 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -640,6 +640,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { +#ifdef GGML_USE_SYCL + fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); + exit(1); +#endif // GGML_USE_SYCL params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c2155b2ac..aa79d002a 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -123,20 +123,15 @@ static std::string get_gpu_info() { } #endif #ifdef GGML_USE_SYCL - int device_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); - - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { - if (device_list[i] >0 ){ - char buf[128]; - ggml_sycl_get_device_description(i, buf, sizeof(buf)); - id += buf; + int count = ggml_backend_sycl_get_device_count(); + for (int i = 0; i < count; i++) { + char buf[128]; + ggml_sycl_get_device_description(i, buf, sizeof(buf)); + id += buf; + if (i < count - 1) { id += "/"; } } - if (id.length() >2 ) { - id.pop_back(); - } #endif // TODO: other backends return id; diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 52442e4ca..74a8b7fd8 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -7,7 +7,7 @@ #include "ggml-sycl.h" -int main(int argc, char ** argv) { +int main() { ggml_backend_sycl_print_sycl_devices(); return 0; } diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index f5f4c1e98..52f7c01a4 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -8,12 +8,19 @@ INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" source /opt/intel/oneapi/setvars.sh if [ $# -gt 0 ]; then - export GGML_SYCL_DEVICE=$1 + GGML_SYCL_DEVICE=$1 else - export GGML_SYCL_DEVICE=0 + GGML_SYCL_DEVICE=0 fi -echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE +echo "use $GGML_SYCL_DEVICE as main GPU" #export GGML_SYCL_DEBUG=1 -./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0 + + +#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer. + +#use all GPUs with same max compute units +ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 + +#use main GPU only +#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 6f391b0c6..cad08d610 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -661,26 +661,29 @@ namespace dpct /// \param [out] total_memory The number of bytes of total memory on the SYCL device. void get_memory_info(size_t &free_memory, size_t &total_memory) { + total_memory = get_device_info().get_global_mem_size(); + const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not " + "supported (export/set ZES_ENABLE_SYSMAN=1 to support), " + "use total memory as free memory"; #if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105) if (!has(sycl::aspect::ext_intel_free_memory)) { - std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; - free_memory = 0; + std::cerr << warning_info << std::endl; + free_memory = total_memory; } else { free_memory = get_info(); } #else - std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; - free_memory = 0; + std::cerr << warning_info << std::endl; + free_memory = total_memory; #if defined(_MSC_VER) && !defined(__clang__) #pragma message("Querying the number of bytes of free memory is not supported") #else #warning "Querying the number of bytes of free memory is not supported" #endif #endif - total_memory = get_device_info().get_global_mem_size(); } void get_device_info(device_info &out) const @@ -738,15 +741,25 @@ namespace dpct #endif // DPCT_USM_LEVEL_NONE } - sycl::queue *create_in_order_queue(bool enable_exception_handler = false) - { - std::lock_guard lock(m_mutex); - return create_queue_impl(enable_exception_handler, - sycl::property::queue::in_order()); + sycl::queue *create_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + return create_in_order_queue(context, device, enable_exception_handler); } - sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) - { + sycl::queue *create_in_order_queue(bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_in_order_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(context, device, enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) { std::lock_guard lock(m_mutex); return create_queue_impl(enable_exception_handler); } @@ -809,6 +822,25 @@ namespace dpct return _queues.back().get(); } + template + sycl::queue *create_queue_impl(sycl::context context, sycl::device device, + bool enable_exception_handler, + Properties... properties) { + sycl::async_handler eh = {}; + if (enable_exception_handler) { + eh = exception_handler; + } + _queues.push_back(std::make_shared( + context, device, eh, + sycl::property_list( + #ifdef DPCT_PROFILING_ENABLED + sycl::property::queue::enable_profiling(), + #endif + properties...))); + + return _queues.back().get(); + } + void get_version(int &major, int &minor) const { detail::get_version(*this, major, minor); @@ -2943,14 +2975,11 @@ bool ggml_sycl_loaded(void); void * ggml_sycl_host_malloc(size_t size); void ggml_sycl_host_free(void * ptr); bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -void ggml_sycl_set_tensor_split(const float * tensor_split); -void ggml_sycl_transform_tensor(void * data, struct ggml_tensor * tensor); void ggml_sycl_free_data(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor); -void ggml_sycl_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset); void ggml_sycl_copy_to_device(struct ggml_tensor * tensor); void ggml_sycl_set_main_device(int main_device); void ggml_sycl_set_mul_mat_q(bool mul_mat_q); @@ -2963,6 +2992,14 @@ int get_main_device(); void print_ggml_tensor(const char*name, struct ggml_tensor *src); void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); +void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, + const void *ptr_src, size_t size) { + char *host_buf = (char *)malloc(size); + q_src.memcpy(host_buf, (const char *)ptr_src, size).wait(); + q_dst.memcpy((char *)ptr_dst, host_buf, size).wait(); + free(host_buf); +} + static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) { const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment @@ -3180,6 +3217,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define SYCL_SILU_BLOCK_SIZE 256 #define SYCL_TANH_BLOCK_SIZE 256 #define SYCL_RELU_BLOCK_SIZE 256 +#define SYCL_HARDSIGMOID_BLOCK_SIZE 256 +#define SYCL_HARDSWISH_BLOCK_SIZE 256 #define SYCL_SQR_BLOCK_SIZE 256 #define SYCL_CPY_BLOCK_SIZE 32 #define SYCL_SCALE_BLOCK_SIZE 256 @@ -3196,6 +3235,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define SYCL_PAD_BLOCK_SIZE 256 #define SYCL_ACC_BLOCK_SIZE 256 #define SYCL_IM2COL_BLOCK_SIZE 256 +#define SYCL_POOL2D_BLOCK_SIZE 256 // dmmv = dequantize_mul_mat_vec #ifndef GGML_SYCL_DMMV_X @@ -3218,8 +3258,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MUL_MAT_SRC1_COL_STRIDE 128 #define MAX_STREAMS 8 -static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = { - {0}}; +static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}}; struct ggml_tensor_extra_gpu { void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -3228,30 +3267,108 @@ struct ggml_tensor_extra_gpu { [MAX_STREAMS]; // events for synchronizing multiple GPUs }; -inline dpct::err0 ggml_sycl_set_device(const int device) try { - int current_device; +class sycl_gpu_mgr { + public: + std::vector gpus; + std::vector devices; + sycl::queue *first_queue; + sycl::context co_ctx; + int max_compute_units = 0; + int work_group_size = 0; + std::string gpus_list = ""; - SYCL_CHECK(CHECK_TRY_ERROR( - current_device = dpct::dev_mgr::instance().current_device_id())); + sycl_gpu_mgr() { + detect_sycl_gpu_list_with_max_cu(); + get_allow_gpus(); + create_context_with_gpus(); + } - // GGML_SYCL_DEBUG("ggml_sycl_set_device device=%d, current_device=%d\n", device, current_device); - if (device == current_device) { - return 0; - } + void create_context_with_gpus() { + sycl::context ctx = sycl::context(devices); + assert(gpus.size() > 0); + first_queue = dpct::get_current_device().create_queue(ctx, devices[0]); + co_ctx = first_queue->get_context(); + } - return CHECK_TRY_ERROR(dpct::select_device(device)); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - crash(); - std::exit(1); -} + sycl::context &get_co_ctx() { return co_ctx; } + void get_allow_gpus() { + gpus_list = ""; + for (size_t i = 0; i < gpus.size(); ++i) { + gpus_list += std::to_string(gpus[i]); + gpus_list += ","; + } + if (gpus_list.length() > 2) { + gpus_list.pop_back(); + } + } + + bool is_allowed_gpu(int device_id) { + return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end(); + } + + void detect_sycl_gpu_list_with_max_cu() try { + int device_count = dpct::dev_mgr::instance().device_count(); + + for (int id = 0; id < device_count; id++) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + if (!device.is_gpu()) + continue; + dpct::device_info prop; + dpct::get_device_info(prop, device); + if (max_compute_units < prop.get_max_compute_units()) + max_compute_units = prop.get_max_compute_units(); + } + + for (int id = 0; id < device_count; id++) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + if (!device.is_gpu()) + continue; + dpct::device_info prop; + dpct::get_device_info(prop, device); + if (max_compute_units == prop.get_max_compute_units() && + prop.get_major_version() == 1) { + gpus.push_back(id); + devices.push_back(device); + work_group_size = prop.get_max_work_group_size(); + } + } + return; + } catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + int get_gpu_count() { return (int)gpus.size(); } + + int get_index(int id) { + for (int i = 0; i < (int)gpus.size(); i++) { + if (gpus[i] == id) + return i; + } + assert(false); + return -1; + } + + int get_next_index(int id) { + int cur_index = get_index(id); + for (int i = cur_index + 1; i < (int)gpus.size(); i++) { + if (gpus[i] == id) + return i; + } + assert(false); + return -1; + } +}; + +static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL; static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; -static int g_main_device_index = -1; +static int g_main_device_id = -1; + +static std::array g_default_tensor_split = {}; static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0}; @@ -3268,8 +3385,6 @@ struct sycl_device_id2index { int index; }; -static sycl_device_id2index g_sycl_device_id2index[GGML_SYCL_MAX_DEVICES] = { {-1} }; - static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default static size_t g_scratch_offset = 0; @@ -3290,6 +3405,63 @@ static void bad_arch(const sycl::stream &stream_ct1) { (void) bad_arch; // suppress unused function warning } +/* +device_index: device index from 0 to n (continue numbers). + It is used for device select/set in SYCL backend internal data structure. +*/ +void check_allow_gpu_index(const int device_index) { + if (device_index >= g_device_count) { + char error_buf[256]; + snprintf(error_buf, sizeof(error_buf), + "%s error: device_index:%d is out of range: [0-%d]", __func__, + device_index, g_device_count - 1); + fprintf(stderr, "%s\n", error_buf); + assert(false); + } +} + +/* +device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices(). + It is only used to set current working device. +*/ +void check_allow_gpu_id(const int device_id) { + if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) { + char error_buf[256]; + snprintf(error_buf, sizeof(error_buf), + "error: cannot set device=%d, which is not allowed. Please " + "set GPU ID in: [%s]", + device_id, g_sycl_gpu_mgr->gpus_list.c_str()); + fprintf(stderr, "%s\n", error_buf); + throw std::invalid_argument(error_buf); + } +} + +int get_current_device_id() { + return dpct::dev_mgr::instance().current_device_id(); +} + +inline dpct::err0 ggml_sycl_set_device(const int device) try { + + int device_id = g_sycl_gpu_mgr->gpus[device]; + check_allow_gpu_id(device_id); + + int current_device_id; + SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id())); + + // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d, + // current_device_id=%d\n", device, current_device); + if (device_id == current_device_id) { + return 0; + } + + return CHECK_TRY_ERROR(dpct::select_device(device_id)); +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + crash(); + std::exit(1); +} + void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){ if(!g_ggml_sycl_debug) return; if(!src){ @@ -3302,22 +3474,18 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo size_t total_size = total_elements*sizeof(float); float *local_buf = NULL; - // printf("total_size %d2, src_on_device %d\n", total_size, src_on_device); if(src_on_device) { local_buf = (float *) ggml_sycl_host_malloc(total_size); - // printf("local buf %p size %d bytes\n", local_buf, total_size); ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; main_stream->memcpy(local_buf, src, total_size); } else { local_buf = (float *)src; - // printf("local buf from src-> data %p\n", local_buf); } std::ofstream logfile; logfile.open(filename); - // printf("local buf element %d\n", total_elements); for(size_t i=0; iextra; - src_data = (float*)src_extra->data_device[g_main_device_index]; + src_data = (float*)src_extra->data_device[g_main_device]; } else { src_data = (float *)src->data; @@ -3359,10 +3527,6 @@ void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cn sprintf(filename, "%s_%07d", name, log_file_name_idx); log_file_name_idx++; print_ggml_tensor(filename, src); - // print_ggml_tensor("ggml_sycl_rms_norm_src0", (ggml_tensor *)src0); - // print_ggml_tensor("ggml_sycl_rms_norm_src1", (ggml_tensor *)src1); - // int *ptr = NULL; - // *ptr = 0; } static __dpct_inline__ float warp_reduce_sum(float x, @@ -3583,6 +3747,28 @@ static void relu_f32(const float * x, float * dst, const int k, dst[i] = sycl::fmax((float)(x[i]), (float)0); } +static void hardsigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +static void hardswish_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + @@ -4964,8 +5150,8 @@ static void k_get_rows_float( template static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - 2 * item_ct1.get_local_id(2); + const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2)); if (i >= k) { return; @@ -7695,7 +7881,7 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) { static void cpy_1_f16_f32(const char * cxi, char * cdsti) { const sycl::half *xi = (const sycl::half *)cxi; - float *dsti = (float *)cdsti; + float * dsti = (float *) cdsti; *dsti = *xi; } @@ -8297,6 +8483,62 @@ static void im2col_kernel(const float *x, T *dst, int offset_delta, } } +template +static void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op, + const sycl::nd_item<3> &item_ct1) { + int idx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (idx >= parallel_elements) { + return; + } + + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = sycl::max(0, start_h); + const int eh = sycl::min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = sycl::max(0, start_w); + const int ew = sycl::min(iw, start_w + kw); + + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { +#if DPCT_COMPATIBILITY_TEMP >= 350 + /* + DPCT1098:106: The '*' expression is used instead of the __ldg + call. These two expressions do not provide the exact same + functionality. Check the generated code for potential precision + and/or performance issues. + */ + Ti cur = *(i_ptr + i * iw + j); +#else + Ti cur = i_ptr[i * iw + j]; +#endif + switch (op) { + case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break; + case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break; + } + } + } + o_ptr[cur_oh * ow + cur_ow] = res; +} + template static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const void *src0_dd, @@ -8585,6 +8827,30 @@ static void relu_f32_sycl(const float *x, float *dst, const int k, }); } +static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardsigmoid_f32(x, dst, k, item_ct1); + }); +} + +static void hardswish_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardswish_f32(x, dst, k, item_ct1); + }); +} + static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, const float negative_slope, dpct::queue_ptr stream) { @@ -8811,11 +9077,10 @@ template static void dequantize_block_sycl(const void *__restrict__ vx, dst_t *__restrict__ y, const int k, dpct::queue_ptr stream) { - const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; + const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE); { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( sycl::nd_range<3>( sycl::range<3>(1, 1, num_blocks) * @@ -9208,24 +9473,6 @@ static void mul_mat_vec_q_sycl_submitter(const void *vx, const void *vy, }); } -int get_device_index_by_id(int id){ - int res = g_sycl_device_id2index[id].index; - // GGML_SYCL_DEBUG("get_device_index_by_id id=%d device_index=%d\n", id, res); - GGML_ASSERT(res>=0); - return res; -} - -int get_device_id_by_index(int index){ - int res = g_device_caps[index].device_id; - GGML_ASSERT(res>=0); - return res; -} - - -int get_current_device_index(){ - return get_device_index_by_id(dpct::dev_mgr::instance().current_device_id()); -} - static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, @@ -9234,7 +9481,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9349,7 +9596,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9464,7 +9711,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9579,7 +9826,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9694,7 +9941,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9809,7 +10056,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9932,7 +10179,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10060,7 +10307,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10181,7 +10428,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10302,7 +10549,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10458,6 +10705,31 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( } } +static void +ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, + const int ne01, const int ne02, const int nb00, + const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, + const int nb10, const int nb11, const int nb12, + const int nb13, dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, + nb01, nb02, nb03, ne10, ne11, ne12, + nb10, nb11, nb12, nb13, item_ct1); + }); + } +} + static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, @@ -11014,12 +11286,9 @@ struct sycl_buffer { static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS]; static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0}; -static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { +static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg index %d\n", id); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size); #ifdef DEBUG_SYCL_MALLOC int nnz = 0; size_t max_size = 0; @@ -11027,7 +11296,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { size_t best_diff = 1ull << 36; int ibest = -1; for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[id][i]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; if (b.ptr != nullptr) { #ifdef DEBUG_SYCL_MALLOC ++nnz; @@ -11043,7 +11312,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { *actual_size = b.size; b.ptr = nullptr; b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr); return ptr; } } @@ -11051,30 +11320,30 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { } } if (ibest >= 0) { - sycl_buffer& b = g_sycl_buffer_pool[id][ibest]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest]; void * ptr = b.ptr; *actual_size = b.size; b.ptr = nullptr; b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr); return ptr; } void * ptr; size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); - const dpct::queue_ptr stream = g_syclStreams[id][0]; + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; SYCL_CHECK( CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( look_ahead_size, *stream))); *actual_size = look_ahead_size; - g_sycl_pool_size[id] += look_ahead_size; + g_sycl_pool_size[device_index] += look_ahead_size; #ifdef DEBUG_SYCL_MALLOC fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); #endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); return ptr; } catch (sycl::exception const &exc) { @@ -11083,15 +11352,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { +static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - - const dpct::queue_ptr stream = g_syclStreams[id][0]; + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[id][i]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; if (b.ptr == nullptr) { b.ptr = ptr; b.size = size; @@ -11100,7 +11365,7 @@ static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { } fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n"); SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream))); - g_sycl_pool_size[id] -= size; + g_sycl_pool_size[device_index] -= size; } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -11117,7 +11382,8 @@ DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported. static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0}; static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0}; -static void *ggml_sycl_pool_malloc_vmm(size_t size, size_t *actual_size) try { +static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try { + GGML_UNUSED(device_index); GGML_UNUSED(size); GGML_UNUSED(actual_size); return NULL; @@ -11128,20 +11394,16 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free_vmm(void *ptr, size_t size) try { +static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = dpct::dev_mgr::instance().current_device_id())); - #ifdef DEBUG_SYCL_MALLOC - printf("sycl pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); + printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr); #endif - g_sycl_pool_used[id] -= size; + g_sycl_pool_used[device_index] -= size; // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[id] + g_sycl_pool_used[id])); + GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index])); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -11149,14 +11411,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void *ggml_sycl_pool_malloc(size_t size, size_t *actual_size) try { - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - if (g_device_caps[id].vmm) { - return ggml_sycl_pool_malloc_vmm(size, actual_size); +static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try { + if (g_device_caps[device_index].vmm) { + return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size); } else { - return ggml_sycl_pool_malloc_leg(size, actual_size); + return ggml_sycl_pool_malloc_leg(device_index, size, actual_size); } } catch (sycl::exception const &exc) { @@ -11165,14 +11424,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free(void *ptr, size_t size) try { - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - if (g_device_caps[id].vmm) { - ggml_sycl_pool_free_vmm(ptr, size); +static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try { + if (g_device_caps[device_index].vmm) { + ggml_sycl_pool_free_vmm(device_index, ptr, size); } else { - ggml_sycl_pool_free_leg(ptr, size); + ggml_sycl_pool_free_leg(device_index, ptr, size); } } catch (sycl::exception const &exc) { @@ -11184,13 +11440,17 @@ catch (sycl::exception const &exc) { template struct sycl_pool_alloc { + int device_index = -1; + int device_id = -1; T * ptr = nullptr; size_t actual_size = 0; // size is in number of elements T * alloc(size_t size) { GGML_ASSERT(ptr == nullptr); - ptr = (T *) ggml_sycl_pool_malloc(size * sizeof(T), &this->actual_size); + device_id = get_current_device_id(); + device_index = g_sycl_gpu_mgr->get_index(device_id); + ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size); // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); return ptr; } @@ -11201,7 +11461,7 @@ struct sycl_pool_alloc { ~sycl_pool_alloc() { if (ptr != nullptr) { - ggml_sycl_pool_free(ptr, actual_size); + ggml_sycl_pool_free(device_index, ptr, actual_size); } } @@ -11222,44 +11482,57 @@ bool ggml_sycl_loaded(void) { return g_sycl_loaded; } -void ggml_backend_sycl_print_sycl_devices(){ - int device_count = dpct::dev_mgr::instance().device_count(); - fprintf(stderr, "found %d SYCL devices:\n", device_count); - for (int id = 0; id < device_count; ++id) { - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(id)))); - sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); - fprintf(stderr, " Device %d: %s,\tcompute capability %d.%d,\n\tmax compute_units %d,\tmax work group size %d,\tmax sub group size %d,\tglobal mem size %lu\n", id, - prop.get_name(), prop.get_major_version(), - prop.get_minor_version(), - prop.get_max_compute_units(), - prop.get_max_work_group_size(), - prop.get_max_sub_group_size(), - prop.get_global_mem_size() - ); - } - // fprintf(stderr, "\n"); +void print_device_detail(int id) { + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_device_info(prop, dpct::dev_mgr::instance().get_device(id)))); + sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); + std::string version; + version += std::to_string(prop.get_major_version()); + version += "."; + version += std::to_string(prop.get_minor_version()); + + fprintf(stderr, "|%2d|%45s|%18s|%17d|%14d|%13d|%15lu|\n", id, + prop.get_name(), version.c_str(), prop.get_max_compute_units(), + prop.get_max_work_group_size(), prop.get_max_sub_group_size(), + prop.get_global_mem_size()); } -int get_sycl_env(const char* env_name, int default_val){ - char * user_device_string = getenv(env_name); +void ggml_backend_sycl_print_sycl_devices() { + int device_count = dpct::dev_mgr::instance().device_count(); + fprintf(stderr, "found %d SYCL devices:\n", device_count); + fprintf(stderr, "|ID| Name |compute capability|Max compute units|Max work group|Max sub group|Global mem size|\n"); + fprintf(stderr, "|--|---------------------------------------------|------------------|-----------------|--------------|-------------|---------------|\n"); + for (int id = 0; id < device_count; ++id) { + print_device_detail(id); + } +} + +void print_gpu_device_list() { + fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", + g_sycl_gpu_mgr->get_gpu_count(), + g_sycl_gpu_mgr->gpus_list.c_str(), + g_sycl_gpu_mgr->max_compute_units); +} + +int get_sycl_env(const char *env_name, int default_val) { + char *user_device_string = getenv(env_name); int user_number = default_val; unsigned n; - if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1) { - user_number = (int)n; - } else { - user_number=default_val; - } + if (user_device_string != NULL && + sscanf(user_device_string, " %u", &n) == 1) { + user_number = (int)n; + } else { + user_number = default_val; + } return user_number; } -int get_work_group_size(int user_device_id){ +int get_work_group_size(int user_device_id) { dpct::device_info prop; - dpct::get_device_info( - prop, - dpct::dev_mgr::instance().get_device(user_device_id)); + dpct::get_device_info(prop, + dpct::dev_mgr::instance().get_device(user_device_id)); return prop.get_max_work_group_size(); } @@ -11268,113 +11541,81 @@ void ggml_init_sycl() try { if (!initialized) { g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); + fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); - printf("GGML_SYCL_DEBUG=%d\n", g_ggml_sycl_debug); - - int user_device_id = get_sycl_env("GGML_SYCL_DEVICE", 0); - +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); +#endif if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != - 0) { + dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; g_sycl_loaded = false; return; } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); + ggml_backend_sycl_print_sycl_devices(); + + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; + + print_gpu_device_list(); + int64_t total_vram = 0; -#if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); -#endif - - +/* NOT REMOVE, keep it for next optimize for XMX. #if defined(SYCL_USE_XMX) fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); #else fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); #endif - ggml_backend_sycl_print_sycl_devices(); +*/ for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_sycl_device_id2index[id].index = -1; g_device_caps[id].vmm = 0; g_device_caps[id].device_id = -1; g_device_caps[id].cc = 0; g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; } - int device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - - device_inx++; - - g_device_caps[device_inx].vmm = 0; - g_device_caps[device_inx].device_id = id; - g_sycl_device_id2index[id].index = device_inx; + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(id)))); + prop, dpct::dev_mgr::instance().get_device(device_id)))); - g_tensor_split[device_inx] = total_vram; + g_default_tensor_split[i] = total_vram; total_vram += prop.get_global_mem_size(); - g_device_caps[device_inx].cc = + g_device_caps[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - - } - device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - device_inx++; - g_tensor_split[device_inx] /= total_vram; } - device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - device_inx++; - SYCL_CHECK(ggml_sycl_set_device(id)); + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; + } + + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); // create sycl streams for (int is = 0; is < MAX_STREAMS; ++is) { - /* - DPCT1025:88: The SYCL queue is created ignoring the flag and - priority options. - */ SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[device_inx][is] = - dpct::get_current_device().create_queue())); + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); } - const dpct::queue_ptr stream = g_syclStreams[device_inx][0]; + const dpct::queue_ptr stream = g_syclStreams[i][0]; // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[device_inx] = - stream)); - /* - DPCT1027:89: The call to syclSetMathMode was replaced with 0 - because this functionality is redundant in SYCL. - */ - SYCL_CHECK(0); + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); } - // configure logging to stdout - // SYCL_CHECK(syclLoggerConfigure(1, 1, 0, nullptr)); - - //hardcode, force set to 1 device - g_device_count = 1; - ggml_sycl_set_main_device(user_device_id); - ggml_sycl_set_device(user_device_id); - g_work_group_size = get_work_group_size(user_device_id); - // fprintf(stderr, "Using Device %d\n", user_device_id); - - // for (int id = 0; id < g_all_sycl_device_count; ++id) { - // GGML_SYCL_DEBUG("id=%d g_device_caps[%d].device_id=%d g_sycl_device_id2index[%d].index=%d ", id, id, - // g_device_caps[id].device_id, id, g_sycl_device_id2index[id].index); - // } - initialized = true; g_sycl_loaded = true; } @@ -11385,31 +11626,6 @@ catch (sycl::exception const &exc) { std::exit(1); } - -void ggml_sycl_set_tensor_split(const float * tensor_split) { - if (tensor_split == nullptr) { - return; - } - bool all_zero = true; - for (int i = 0; i < g_device_count; ++i) { - if (tensor_split[i] != 0.0f) { - all_zero = false; - break; - } - } - if (all_zero) { - return; - } - float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] /= split_sum; - } -} - void *ggml_sycl_host_malloc(size_t size) try { if (getenv("GGML_SYCL_NO_PINNED") != nullptr) { return nullptr; @@ -11419,28 +11635,14 @@ void *ggml_sycl_host_malloc(size_t size) try { //allow to use dpct::get_in_order_queue() for host malloc dpct::err0 err = CHECK_TRY_ERROR( ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue())); - /* - DPCT1000:82: Error handling if-stmt was detected but could not be rewritten. - */ + if (err != 0) { // clear the error - /* - DPCT1026:83: The call to syclGetLastError was removed because this - functionality is redundant in SYCL. - */ - /* - DPCT1001:81: The statement could not be removed. - */ fprintf( stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", - /* - DPCT1009:84: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ size / 1024.0 / 1024.0, - "syclGetErrorString is not supported" /*syclGetErrorString(err)*/); + "syclGetErrorString is not supported"); return nullptr; } @@ -11480,7 +11682,7 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; SYCL_CHECK(CHECK_TRY_ERROR( - id = get_current_device_index())); + id = get_current_device_id())); // GGML_SYCL_DEBUG("current device index %d\n", id); src_ptr = (char *) extra->data_device[id]; } else { @@ -11714,7 +11916,6 @@ inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); (void) src1; @@ -11737,6 +11938,37 @@ inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } +static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_sycl_op_hardswish(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -11905,7 +12137,7 @@ inline void ggml_sycl_op_mul_mat_q( int device_id; SYCL_CHECK( - CHECK_TRY_ERROR(device_id = dpct::dev_mgr::instance().current_device_id())); + CHECK_TRY_ERROR(device_id = get_current_device_id())); // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into @@ -11957,16 +12189,16 @@ catch (sycl::exception const &exc) { std::exit(1); } -static int64_t get_row_rounding(ggml_type type) { +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; - for (int64_t id = 0; id < g_device_count; ++id) { - if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - if (min_compute_capability > g_device_caps[id].cc) { - min_compute_capability = g_device_caps[id].cc; + for (int i = 0; i < g_device_count; ++i) { + if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) { + if (min_compute_capability > g_device_caps[i].cc) { + min_compute_capability = g_device_caps[i].cc; } - if (max_compute_capability < g_device_caps[id].cc) { - max_compute_capability = g_device_caps[id].cc; + if (max_compute_capability < g_device_caps[i].cc) { + max_compute_capability = g_device_caps[i].cc; } } } @@ -11986,12 +12218,16 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: return max_compute_capability >= VER_GEN9 ? 128 : 64; case GGML_TYPE_Q6_K: return 64; default: GGML_ASSERT(false); } + } inline void ggml_sycl_op_mul_mat_vec_q( @@ -12176,27 +12412,22 @@ inline void ggml_sycl_op_mul_mat_sycl( const int64_t row_diff = row_high - row_low; int id; - int device_id = dpct::dev_mgr::instance().current_device_id(); SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff; + int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check #else bool use_fp16 = false; #endif - // if (compute_capability >= VER_GEN9 && (src0->type == GGML_TYPE_F16 || - // ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == - // src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { - // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n"); sycl_pool_alloc src0_as_f16; if (src0->type != GGML_TYPE_F16) { @@ -12225,7 +12456,6 @@ inline void ggml_sycl_op_mul_mat_sycl( const sycl::half alpha_f16 = 1.0f; const sycl::half beta_f16 = 0.0f; - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( *g_sycl_handles[id], oneapi::mkl::transpose::trans, @@ -12241,14 +12471,21 @@ inline void ggml_sycl_op_mul_mat_sycl( else { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n"); sycl_pool_alloc src0_ddq_as_f32; - + sycl_pool_alloc src1_ddq_as_f32; if (src0->type != GGML_TYPE_F32) { const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type); GGML_ASSERT(to_fp32_sycl != nullptr); src0_ddq_as_f32.alloc(row_diff*ne00); to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); } + if (src1->type != GGML_TYPE_F32) { + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type); + GGML_ASSERT(to_fp32_sycl != nullptr); + src1_ddq_as_f32.alloc(src1_ncols*ne10); + to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); const float alpha = 1.0f; const float beta = 0.0f; @@ -12261,7 +12498,6 @@ inline void ggml_sycl_op_mul_mat_sycl( src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); } - (void) dst; (void) src1_ddq_i; (void) src1_padded_row_size; @@ -12382,6 +12618,48 @@ inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } +static void ggml_sycl_op_pool2d(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE; + sycl::range<3> block_nums(1, 1, num_blocks); + main_stream->parallel_for( + sycl::nd_range<3>(block_nums * + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, + parallel_elements, src0_dd, dst_dd, op, + item_ct1); + }); + + (void) src1; + (void) src1_dd; +} + inline void ggml_sycl_op_im2col(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -12606,12 +12884,12 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, sycl_pool_alloc dst_f; ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; - // GGML_SYCL_DEBUG("g_main_device_index=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", - // g_main_device_index, main_stream, src0_on_device, src1_on_device, dst_on_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device); if (src0_on_device) { - src0_ddf = (float *) src0_extra->data_device[g_main_device_index]; + src0_ddf = (float *) src0_extra->data_device[g_main_device]; } else { src0_ddf = src0_f.alloc(ggml_nelements(src0)); // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0); @@ -12620,15 +12898,14 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, if (use_src1) { if (src1_on_device) { - src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + src1_ddf = (float *) src1_extra->data_device[g_main_device]; } else { src1_ddf = src1_f.alloc(ggml_nelements(src1)); SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); } } if (dst_on_device) { - dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; - // printf("zjy dst_ddf=%p main_stream=%p g_main_device_index=%d\n", dst_ddf, main_stream, g_main_device_index); + dst_ddf = (float *) dst_extra->data_device[g_main_device]; } else { dst_ddf = dst_f.alloc(ggml_nelements(dst)); } @@ -12672,21 +12949,19 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { } #ifdef NDEBUG - for (int id = 0; id < g_device_count; ++id) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); // SYCL_CHECK(syclDeviceSynchronize()); } - for (int id = 0; id < g_device_count; ++id) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - int device_id = g_device_caps[id].device_id; + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); for (int id_other = 0; id_other < g_device_count; ++id_other) { - int device_id_other = g_device_caps[id_other].device_id; - if (device_id == id_other) { + if (i == id_other) { continue; } - if (device_id != g_main_device && device_id_other != g_main_device) { + if (i != g_main_device && id_other != g_main_device) { continue; } @@ -12706,6 +12981,10 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { peer_access_enabled = enable_peer_access; } +struct ggml_backend_sycl_split_buffer_type_context { + std::array tensor_split; +}; + static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, ggml_sycl_op_mul_mat_t op, @@ -12752,80 +13031,90 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); - // dd = data device - char * src0_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; - float * src1_ddf[GGML_SYCL_MAX_DEVICES] = {nullptr}; // float - char * src1_ddq[GGML_SYCL_MAX_DEVICES] = {nullptr}; // q8_1 - float * dst_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; + std::array tensor_split; + if (split) { + // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check + // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context; + tensor_split = buft_ctx->tensor_split; + } - // as = actual size - size_t src0_as[GGML_SYCL_MAX_DEVICES] = {0}; - size_t src1_asf[GGML_SYCL_MAX_DEVICES] = {0}; - size_t src1_asq[GGML_SYCL_MAX_DEVICES] = {0}; - size_t dst_as[GGML_SYCL_MAX_DEVICES] = {0}; + struct dev_data { + sycl_pool_alloc src0_dd_alloc; + sycl_pool_alloc src1_ddf_alloc; + sycl_pool_alloc src1_ddq_alloc; + sycl_pool_alloc dst_dd_alloc; - int64_t row_low[GGML_SYCL_MAX_DEVICES]; - int64_t row_high[GGML_SYCL_MAX_DEVICES]; + char *src0_dd = nullptr; + float *src1_ddf = nullptr; // float + char *src1_ddq = nullptr; // q8_1 + float *dst_dd = nullptr; + + int64_t row_low; + int64_t row_high; + }; + + dev_data dev[GGML_SYCL_MAX_DEVICES]; int used_devices = 0; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int i = 0; i < g_device_count; ++i) { // by default, use all rows - row_low[id] = 0; - row_high[id] = ne01; + dev[i].row_low = 0; + dev[i].row_high = ne01; // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes if (split) { - const int64_t rounding = get_row_rounding(src0->type); + const int64_t rounding = get_row_rounding(src0->type, tensor_split); - if (id != 0) { - row_low[id] = ne01*g_tensor_split[id]; - if (row_low[id] < ne01) { - row_low[id] -= row_low[id] % rounding; + if (i != 0) { + dev[i].row_low = ne01*tensor_split[i]; + if (dev[i].row_low < ne01) { + dev[i].row_low -= dev[i].row_low % rounding; } } - if (id != g_device_count - 1) { - row_high[id] = ne01*g_tensor_split[id + 1]; - if (row_high[id] < ne01) { - row_high[id] -= row_high[id] % rounding; + if (i != g_device_count - 1) { + dev[i].row_high = ne01*tensor_split[i + 1]; + if (dev[i].row_high < ne01) { + dev[i].row_high -= dev[i].row_high % rounding; } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + for (int i = 0; i < g_device_count; ++i) { + if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { continue; } used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][0]; + ggml_sycl_set_device(i); + dpct::queue_ptr stream = g_syclStreams[i][0]; if (src0_on_device && src0_is_contiguous) { - src0_dd[id] = (char *) src0_extra->data_device[id]; + dev[i].src0_dd = (char *) src0_extra->data_device[i]; } else { - // const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_sycl_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0)); } if (src1_on_device && src1_is_contiguous) { - src1_ddf[id] = (float *) src1_extra->data_device[id]; + dev[i].src1_ddf = (float *) src1_extra->data_device[i]; } else { - src1_ddf[id] = (float *) ggml_sycl_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1)); } if (convert_src1_to_q8_1) { - src1_ddq[id] = (char *) ggml_sycl_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_sycl(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); + quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); /* DPCT1010:90: SYCL uses exceptions to report errors and does not use the error codes. The call was replaced with 0. You need to @@ -12836,25 +13125,25 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (dst_on_device) { - dst_dd[id] = (float *) dst_extra->data_device[id]; + dev[i].dst_dd = (float *) dst_extra->data_device[i]; } else { - const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_sycl_pool_malloc(size_dst_ddf, &dst_as[id]); + const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst); + dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf); } } // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data if (split && used_devices > 1) { - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + ggml_sycl_set_device(g_main_device); /* DPCT1024:91: The original code returned the error code that was further consumed by the program logic. This original code was replaced with 0. You may need to rewrite the program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[g_main_device_index][0] = - g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier())); + *src0_extra->events[g_main_device][0] = + g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier())); } const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; @@ -12862,22 +13151,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + for (int i = 0; i < g_device_count; ++i) { + if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const int64_t row_diff = row_high[id] - row_low[id]; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const int64_t row_diff = dev[i].row_high - dev[i].row_low; - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][is]; + ggml_sycl_set_device(i); + dpct::queue_ptr stream = g_syclStreams[i][is]; // wait for main GPU data if necessary - if (split && (id != g_main_device_index || is != 0)) { + if (split && (i != g_main_device || is != 0)) { + /* + DPCT1009:163: SYCL uses exceptions to report errors and does not + use the error codes. The original code was commented out and a + warning string was inserted. You need to rewrite this code. + */ SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier( - {*src0_extra->events[g_main_device_index][0]}))); + {*src0_extra->events[g_main_device][0]}))); } for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { @@ -12887,30 +13181,32 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = src1_ddq[id] + src1_ddq_i_offset; - float * dst_dd_i = dst_dd[id] + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); + char * src0_dd_i = dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; + float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10; + char * src1_ddq_i = dev[i].src1_ddq + src1_ddq_i_offset; + float * dst_dd_i = dev[i].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index) { - dst_dd_i += row_low[id]; // offset is 0 if no tensor split + if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) { + dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { - if (id != g_main_device_index) { + if (i != g_main_device) { if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; + SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( src1_ddq_i, src1_ddq_i_source, src1_ncols * src1_padded_col_size * q8_1_ts / q8_1_bs))); } else { - float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device_index]; + + float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + + SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream, src1_ddf_i, src1_ddf_i_source, src1_ncols * ne10 * sizeof(float)))); } @@ -12933,14 +13229,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream)); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream)); } if (src1->type == GGML_TYPE_F16) { src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10; } // do the computation op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream); + dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream); /* DPCT1010:93: SYCL uses exceptions to report errors and does not use the error codes. The call was replaced with 0. You need to @@ -12956,7 +13252,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, dst_off_device = dst->data; kind = dpct::device_to_host; } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { - dst_off_device = dst_extra->data_device[g_main_device_index]; + dst_off_device = dst_extra->data_device[g_main_device]; kind = dpct::device_to_device; } else { GGML_ASSERT(false); @@ -12969,11 +13265,29 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + row_low[id]; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - dhf_dst_i, ne0 * sizeof(float), dst_dd_i, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, kind, *stream))); + dhf_dst_i += src1_col_0*ne0 + dev[i].row_low; + + //todo, dirty solution. Need be updated when device2device memcpy() is supported. + if (kind == dpct::device_to_device) { + size_t dst_size = ggml_nbytes_pad(dst); + float *host_buf = (float *)malloc(dst_size); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + host_buf, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::device_to_host, *stream))); + dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw(); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), host_buf, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::host_to_device, *main_stream))); + dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw(); + free(host_buf); + } else { + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, kind, *stream))); + } } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); @@ -12985,7 +13299,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } // add event for the main device to wait on until other device is done - if (split && (id != g_main_device_index || is != 0)) { + if (split && (i != g_main_device || is != 0)) { /* DPCT1024:94: The original code returned the error code that was further consumed by the program logic. This original @@ -12993,48 +13307,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[id][is] = + *src0_extra->events[i][is] = stream->ext_oneapi_submit_barrier())); } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { - continue; - } - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - - // free buffers again when done - if (dst_as[id] > 0) { - ggml_sycl_pool_free(dst_dd[id], dst_as[id]); - } - if (src1_asq[id] > 0) { - ggml_sycl_pool_free(src1_ddq[id], src1_asq[id]); - } - if (src1_asf[id] > 0) { - ggml_sycl_pool_free(src1_ddf[id], src1_asf[id]); - } - if (src0_as[id] > 0) { - ggml_sycl_pool_free(src0_dd[id], src0_as[id]); - } - } - // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - for (int64_t id = 0; id < g_device_count; ++id) { - if (row_low[id] == row_high[id]) { + ggml_sycl_set_device(g_main_device); + for (int i = 0; i < g_device_count; ++i) { + if (dev[i].row_low == dev[i].row_high) { continue; } for (int64_t is = 0; is < is_max; ++is) { SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier( - {*src0_extra->events[id][is]}))); + g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier( + {*src0_extra->events[i][is]}))); } } } @@ -13051,110 +13344,132 @@ catch (sycl::exception const &exc) { std::exit(1); } + static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add); - // log_tensor_with_cnt("log_ggml_sycl_add_src0", (struct ggml_tensor *) src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_add_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_add_dst", dst, 6); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul); - // log_tensor_with_cnt("log_ggml_sycl_mul_src0", (struct ggml_tensor *)src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_mul_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_mul_dst", dst, 6); - + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src0", (struct ggml_tensor *)src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_dst", dst, 6); + GGML_SYCL_DEBUG("call %s done\n", __func__); } bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { @@ -13189,16 +13504,16 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); } @@ -13228,16 +13543,16 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; const int64_t row_stride_x = nb01 / sizeof(sycl::half); const int64_t channel_stride_x = nb02 / sizeof(sycl::half); @@ -13280,38 +13595,37 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_TENSOR_BINARY_OP_LOCALS - const int64_t ne_dst = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(g_sycl_handles[g_main_device_index] = main_stream)); + CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream)); ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 sycl_pool_alloc src1_f16_alloc; if (src1->type != GGML_TYPE_F16) { - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - const int64_t ne_src1 = ggml_nelements(src1); - src1_f16_alloc.alloc(ne_src1); - GGML_ASSERT(to_fp16_sycl != nullptr); - to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); } sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf : src1_f16_alloc.get(); @@ -13358,7 +13672,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, int i02 = i12 / r2; SYCL_CHECK( - syclGemmEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + syclGemmEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , SYCL_R_16F, nb01/sizeof(half), (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F, nb11/sizeof(float), @@ -13371,9 +13685,8 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, #else if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - // use syclGemmStridedBatchedEx SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00, @@ -13382,7 +13695,6 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); } else { - // use syclGemmBatchedEx const int ne23 = ne12*ne13; sycl_pool_alloc ptrs_src(2*ne23); @@ -13415,7 +13727,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, }); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00, @@ -13435,6 +13747,7 @@ catch (sycl::exception const &exc) { std::exit(1); } + static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && @@ -13444,9 +13757,9 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; - for (int64_t id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - min_compute_capability = g_device_caps[id].cc; + for (int i = 0; i < g_device_count; ++i) { + if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) { + min_compute_capability = g_device_caps[i].cc; } } @@ -13587,30 +13900,30 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { const int64_t ne = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - syclStream_t main_stream = g_syclStreams[g_main_device_index][0]; + syclStream_t main_stream = g_syclStreams[g_main_device][0]; - SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device_index], main_stream)); + SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream)); //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - //void * src0_ddq = src0_extra->data_device[g_main_device_index]; + //void * src0_ddq = src0_extra->data_device[g_main_device]; //half * src0_as_f16 = (half *) src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); GGML_ASSERT(to_fp16_sycl != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(ne1 * sizeof(half), &src1_as); + half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as); to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_sycl_pool_malloc(ne * sizeof(half), &dst_as); + half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13631,14 +13944,14 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { size_t ptrs_src_s = 0; size_t ptrs_dst_s = 0; - ptrs_src = (const void **) ggml_sycl_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); - ptrs_dst = ( void **) ggml_sycl_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); + ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s); int64_t src0_ne = ggml_nelements(src00); half * src0_as_f16 = nullptr; size_t src0_as = 0; if (src00->type != GGML_TYPE_F16) { - src0_as_f16 = (half *) ggml_sycl_pool_malloc(src0_ne * sizeof(half), &src0_as); + src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as); } static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); @@ -13653,16 +13966,16 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { r2, r3, src00->type, src0_as_f16, src0_ne, src1_as_f16, dst_f16, - (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index], id, - dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device_index] : nullptr + (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id, + dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr, + dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr, + dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr, + dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr ); SYCL_CHECK(syclGetLastError()); SYCL_CHECK( - syclGemmBatchedEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00, (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10, @@ -13672,20 +13985,20 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { CUBLAS_GEMM_DEFAULT_TENSOR_OP)); if (src0_as != 0) { - ggml_sycl_pool_free(src0_as_f16, src0_as); + ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as); } if (ptrs_src_s != 0) { - ggml_sycl_pool_free(ptrs_src, ptrs_src_s); + ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s); } if (ptrs_dst_s != 0) { - ggml_sycl_pool_free(ptrs_dst, ptrs_dst_s); + ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s); } const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream); - ggml_sycl_pool_free(src1_as_f16, src1_as); - ggml_sycl_pool_free(dst_f16, dst_as); + ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as); + ggml_sycl_pool_free(g_main_device, dst_f16, dst_as); } #endif @@ -13706,10 +14019,10 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, std::vector ids_host(ggml_nbytes(ids)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; if (ids->backend == GGML_BACKEND_TYPE_GPU) { - const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index]; + const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); @@ -13733,9 +14046,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, dst_row.extra = &dst_row_extra; char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? - (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index]; + (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? - (char *) dst->data : (char *) dst_extra->data_device[g_main_device_index]; + (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; if (src1->ne[1] == 1) { GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); @@ -13752,10 +14065,10 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const struct ggml_tensor * src0_row = dst->src[row_id + 2]; - src1_row_extra.data_device[g_main_device_index] = src1_original + i01*src1->nb[1]; + src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1]; src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? - dst_row_extra.data_device[g_main_device_index] = dst_original + i01*dst->nb[1]; + dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1]; dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); @@ -13764,8 +14077,8 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); sycl_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); - src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); - dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); + src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); + dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; @@ -13853,13 +14166,13 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, GGML_TENSOR_BINARY_OP_LOCALS; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; - char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; + char * src1_ddc = (char *) src1_extra->data_device[g_main_device]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); @@ -13871,6 +14184,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { @@ -13914,6 +14229,10 @@ static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi); } +static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d); +} + static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col); } @@ -13940,93 +14259,6 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try { - const int64_t nrows = ggml_nrows(tensor); - - const int64_t ne0 = tensor->ne[0]; - - const size_t nb1 = tensor->nb[1]; - - ggml_backend_type backend = tensor->backend; - ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - - for (int64_t id = 0; id < g_device_count; ++id) { - if (backend == GGML_BACKEND_TYPE_GPU && id != g_main_device_index) { - continue; - } - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][0]; - - int64_t row_low, row_high; - if (backend == GGML_BACKEND_TYPE_GPU) { - row_low = 0; - row_high = nrows; - } else if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - const int64_t rounding = get_row_rounding(tensor->type); - - row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; - row_low -= row_low % rounding; - - if (id == g_device_count - 1) { - row_high = nrows; - } else { - row_high = nrows*g_tensor_split[id + 1]; - row_high -= row_high % rounding; - } - } else { - GGML_ASSERT(false); - } - if (row_low == row_high) { - continue; - } - - int64_t nrows_split = row_high - row_low; - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf; - SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *stream))); - char * buf_host = (char *)data + offset_split; - - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memset(buf + original_size, 0, size - original_size) - .wait())); - } - - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(buf, buf_host, original_size) - .wait())); - - extra->data_device[id] = buf; - - if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] = - new sycl::event())); - } - } - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - void ggml_sycl_free_data(struct ggml_tensor *tensor) try { if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) { return; @@ -14034,18 +14266,18 @@ void ggml_sycl_free_data(struct ggml_tensor *tensor) try { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - for (int64_t id = 0; id < g_device_count; ++id) { - const dpct::queue_ptr stream = g_syclStreams[id][0]; - if (extra->data_device[id] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[id], *stream))); + for (int i = 0; i < g_device_count; ++i) { + const dpct::queue_ptr stream = g_syclStreams[i][0]; + if (extra->data_device[i] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(i)); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream))); } for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + if (extra->events[i][is] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(i)); SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[id][is]))); + dpct::destroy_event(extra->events[i][is]))); } } } @@ -14105,22 +14337,22 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, const size_t size = ggml_nbytes(tensor); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; size_t offset = 0; if (tensor->op == GGML_OP_VIEW) { memcpy(&offset, tensor->op_params, sizeof(size_t)); } extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = src0_ddc + offset; + extra->data_device[g_main_device] = src0_ddc + offset; } else if (tensor->op == GGML_OP_CPY) { ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; - void * src1_ddv = src1_extra->data_device[g_main_device_index]; + void * src1_ddv = src1_extra->data_device[g_main_device]; extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = src1_ddv; + extra->data_device[g_main_device] = src1_ddv; } else if (scratch) { GGML_ASSERT(size <= g_scratch_size); if (g_scratch_offset + size > g_scratch_size) { @@ -14135,7 +14367,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, g_scratch_buffer = data; } extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = data + g_scratch_offset; + extra->data_device[g_main_device] = data + g_scratch_offset; g_scratch_offset += size; @@ -14148,44 +14380,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, (*stream).memset(data, 0, size).wait())); extra = new ggml_tensor_extra_gpu; memset(extra, 0, sizeof(*extra)); - extra->data_device[g_main_device_index] = data; - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor, - size_t offset) try { - if (g_scratch_size == 0) { - return; - } - if (g_scratch_buffer == nullptr) { - ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; - SYCL_CHECK( - CHECK_TRY_ERROR(g_scratch_buffer = (void *)sycl::malloc_device( - g_scratch_size, *stream))); - } - - ggml_tensor_extra_gpu * extra = ggml_sycl_alloc_temp_tensor_extra(); - - const bool inplace = tensor->view_src != nullptr; - - if (inplace && (tensor->view_src->backend == GGML_BACKEND_TYPE_GPU || tensor->view_src->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; - size_t view_offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&view_offset, tensor->op_params, sizeof(size_t)); - } - extra->data_device[g_main_device_index] = src0_ddc + view_offset; - } else { - extra->data_device[g_main_device_index] = (char *) g_scratch_buffer + offset; + extra->data_device[g_main_device] = data; } tensor->extra = extra; @@ -14202,9 +14397,9 @@ void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(extra->data_device[g_main_device_index], + .memcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor)) .wait())); } @@ -14231,21 +14426,17 @@ void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) { } void ggml_sycl_set_main_device(const int main_device) try { + if (g_main_device == main_device) return; + check_allow_gpu_index(main_device); + g_main_device = main_device; + g_main_device_id = g_sycl_gpu_mgr->gpus[main_device]; - if (main_device >= g_all_sycl_device_count) { - fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", - main_device, g_all_sycl_device_count, g_main_device); - return; - } - - if (g_main_device != main_device && g_device_count >= 1) { - g_main_device = main_device; - g_main_device_index = get_device_index_by_id(g_main_device); + if (g_ggml_sycl_debug) { dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(g_main_device)))); + prop, dpct::dev_mgr::instance().get_device(g_main_device_id)))); fprintf(stderr, "Using device %d (%s) as main device\n", - g_main_device, prop.get_name()); + g_main_device_id, prop.get_name()); } } catch (sycl::exception const &exc) { @@ -14268,7 +14459,7 @@ void ggml_sycl_free_scratch() try { return; } ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; SYCL_CHECK(CHECK_TRY_ERROR( sycl::free(g_scratch_buffer, *stream))); @@ -14340,6 +14531,12 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_UNARY_OP_RELU: func = ggml_sycl_relu; break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_sycl_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_sycl_hardswish; + break; default: return false; } @@ -14414,6 +14611,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_IM2COL: func = ggml_sycl_im2col; break; + case GGML_OP_POOL_2D: + func = ggml_sycl_pool2d; + break; case GGML_OP_SUM_ROWS: func = ggml_sycl_sum_rows; break; @@ -14439,27 +14639,15 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ } GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { - int max_compute_units = -1; - for(int i=0;igpus.size();i++){ + if (i>=max_len) break; + id_list[i] = g_sycl_gpu_mgr->gpus[i]; } return; } @@ -14486,8 +14674,9 @@ catch (sycl::exception const &exc) { GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size) try { dpct::device_info prop; + int device_id = g_sycl_gpu_mgr->gpus[device]; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device)))); + prop, dpct::dev_mgr::instance().get_device(device_id)))); snprintf(description, description_size, "%s", prop.get_name()); } catch (sycl::exception const &exc) { @@ -14496,17 +14685,36 @@ catch (sycl::exception const &exc) { std::exit(1); } +GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, + size_t *total) try { + ggml_sycl_set_device(device); + + /* + DPCT1009:218: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string was + inserted. You need to rewrite this code. + */ + /* + DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for + device information which may not be supported by all compilers or runtimes. + You may need to adjust the code. + */ + int device_id = g_sycl_gpu_mgr->gpus[device]; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total))); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + //////////////////////////////////////////////////////////////////////////////// // backend interface #define UNUSED GGML_UNUSED -struct ggml_backend_sycl_context { - int device; - std::string name; -}; - // sycl buffer struct ggml_backend_sycl_buffer_context { @@ -14516,7 +14724,12 @@ struct ggml_backend_sycl_buffer_context { size_t temp_tensor_extra_index = 0; std::string name; - ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} + ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : + device(device), dev_ptr(dev_ptr) { + check_allow_gpu_index(device); + int id = g_sycl_gpu_mgr->gpus[device]; + name = (GGML_SYCL_NAME + std::to_string(id)); + } ~ ggml_backend_sycl_buffer_context() { delete[] temp_tensor_extras; @@ -14547,10 +14760,9 @@ GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) static void ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream))); @@ -14563,13 +14775,14 @@ catch (sycl::exception const &exc) { } static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; return ctx->dev_ptr; } -static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; +GGML_CALL static void +ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { assert(tensor->view_src->buffer->buft == buffer->buft); @@ -14581,27 +14794,20 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra(); extra->data_device[ctx->device] = tensor->data; - tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; if (ggml_is_quantized(tensor->type)) { // initialize padding to 0 to avoid possible NaN values - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t original_size = ggml_nbytes_split(tensor, nrows_split); + size_t original_size = ggml_nbytes(tensor); size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset( (char *)tensor->data + original_size, 0, - padded_size - original_size))); + padded_size - original_size).wait())); } } - - UNUSED(buffer); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -14615,13 +14821,12 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t size) try { GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); SYCL_CHECK( CHECK_TRY_ERROR((*stream) @@ -14640,14 +14845,13 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, size_t size) try { GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); SYCL_CHECK(CHECK_TRY_ERROR( (*stream) @@ -14660,13 +14864,73 @@ catch (sycl::exception const &exc) { std::exit(1); } +GGML_CALL static bool +ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *src, + ggml_tensor *dst) try { + if (ggml_backend_buffer_is_sycl(src->buffer)) { + ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; + ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(src_ctx->device); + /* + DPCT1009:198: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); + ggml_sycl_set_device(dst_ctx->device); + /* + DPCT1009:199: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); + /* + DPCT1009:200: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + + dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0]; + dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0]; + size_t size = ggml_nbytes(src); + + //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. + dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); + +//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove +#if 0 + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( + (char *)dst->data, (const char *)src->data, size).wait())); + + /* + DPCT1009:201: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); +#endif + return true; + } + return false; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) try { ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); @@ -14687,7 +14951,7 @@ static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, - /* .cpy_tensor = */ NULL, + /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, /* .clear = */ ggml_backend_sycl_buffer_clear, /* .reset = */ NULL, }; @@ -14698,29 +14962,28 @@ struct ggml_backend_sycl_buffer_type_context { std::string name; }; +struct ggml_backend_sycl_context { + int device; + std::string name; +}; + GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; return ctx->name.c_str(); } - -static ggml_backend_buffer_t +GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) try { ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - int device = (int) buft_ctx->device; - - ggml_sycl_set_device(device); - int device_index = get_device_index_by_id(device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + ggml_sycl_set_device(buft_ctx->device); + const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0]; size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 void * dev_ptr; SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( size, *stream))); - - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(device, dev_ptr); - + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr); return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); } catch (sycl::exception const &exc) { @@ -14729,9 +14992,8 @@ catch (sycl::exception const &exc) { std::exit(1); } -static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; - UNUSED(buft); } @@ -14741,13 +15003,8 @@ static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_typ UNUSED(buft); } -static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t size = ggml_nbytes_split(tensor, nrows_split); - +GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); int64_t ne0 = tensor->ne[0]; if (ggml_is_quantized(tensor->type)) { @@ -14761,10 +15018,13 @@ static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_t UNUSED(buft); } -static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_sycl(backend); - - UNUSED(buft); +GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + if (!ggml_backend_is_sycl(backend)) { + return false; + } + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + return buft_ctx->device == sycl_ctx->device; } static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { @@ -14783,10 +15043,10 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { static bool ggml_backend_sycl_buffer_type_initialized = false; if (!ggml_backend_sycl_buffer_type_initialized) { - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + for (int i = 0; i < g_device_count; i++) { ggml_backend_sycl_buffer_types[i] = { /* .iface = */ ggml_backend_sycl_buffer_type_interface, - /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i)}, + /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(g_sycl_gpu_mgr->gpus[i])}, }; } ggml_backend_sycl_buffer_type_initialized = true; @@ -14795,6 +15055,391 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { return &ggml_backend_sycl_buffer_types[device]; } +// sycl split buffer type +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + if (id == g_device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + +struct ggml_backend_sycl_split_buffer_context { + ~ggml_backend_sycl_split_buffer_context() try { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + if (extra->events[i][is] != nullptr) { + /* + DPCT1009:206: SYCL uses exceptions to report errors and + does not use the error codes. The original code was + commented out and a warning string was inserted. You + need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::destroy_event(extra->events[i][is]))); + } + } + if (extra->data_device[i] != nullptr) { + /* + DPCT1009:207: SYCL uses exceptions to report errors and does + not use the error codes. The original code was commented out + and a warning string was inserted. You need to rewrite this + code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( + extra->data_device[i], *g_syclStreams[i][0]))); + } + } + delete extra; + } + } + catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + std::vector tensor_extras; +}; + +GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_SYCL_NAME "_Split"; + + UNUSED(buffer); +} + +// unused at the moment +//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { +// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +//} + +GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + delete ctx; +} + +GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + UNUSED(buffer); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_sycl_set_device(i); + char * buf; + /* + DPCT1009:208: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( + size, *g_syclStreams[i][0]))); + + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + /* + DPCT1009:209: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memset(buf + original_size, 0, size - original_size) + .wait())); + } + + extra->data_device[i] = buf; + + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + /* + DPCT1009:210: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK( + CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); + } + } + tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, const void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + /* + DPCT1009:211: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memcpy(extra->data_device[i], buf_host, original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + /* + DPCT1009:212: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memcpy(buf_host, extra->data_device[i], original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + UNUSED(buffer); + UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_sycl_split_buffer_clear, + /* .reset = */ NULL, +}; + +GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Split"; + + UNUSED(buft); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); +} + +GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + UNUSED(buft); +} + +GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_sycl(backend); + + UNUSED(buft); +} + +GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, +}; + +GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = g_default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, + /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + // host buffer type GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -14824,6 +15469,7 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; return buffer; @@ -14848,34 +15494,33 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { // backend -static const char * ggml_backend_sycl_name(ggml_backend_t backend) { - return GGML_SYCL_NAME; +GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) { - UNUSED(backend); + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + + return sycl_ctx->name.c_str(); } -static void ggml_backend_sycl_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; delete sycl_ctx; delete backend; } -static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; return ggml_backend_sycl_buffer_type(sycl_ctx->device); } -static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, +GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_tensor *tensor, const void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( (char *)tensor->data + offset, data, size))); } @@ -14885,15 +15530,13 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, +GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, const ggml_tensor *tensor, void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( data, (const char *)tensor->data + offset, size))); } @@ -14903,9 +15546,31 @@ catch (sycl::exception const &exc) { std::exit(1); } +GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, + const ggml_tensor *src, + ggml_tensor *dst) try { + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) { + /* + DPCT1009:215: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + dst->data, src->data, ggml_nbytes(dst)))); + return true; + } + + return false; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); UNUSED(backend); @@ -14916,32 +15581,8 @@ catch (sycl::exception const &exc) { std::exit(1); } -static ggml_backend_graph_plan_t ggml_backend_sycl_graph_plan_create(ggml_backend_t backend, const ggml_cgraph * cgraph) { - GGML_ASSERT(!"not implemented"); - - return nullptr; - - UNUSED(backend); - UNUSED(cgraph); -} - -static void ggml_backend_sycl_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); -} - -static void ggml_backend_sycl_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); -} - -static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - ggml_sycl_set_main_device(sycl_ctx->device); ggml_compute_params params = {}; @@ -14949,63 +15590,41 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - - if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; - - assert(node->backend == GGML_BACKEND_TYPE_GPU); + } +#ifndef NDEBUG + assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU); + assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->src[j]->extra != nullptr); } } - +#endif bool ok = ggml_sycl_compute_forward(¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); - -#if 0 - if (node->type == GGML_TYPE_F32) { - syclDeviceSynchronize(); - std::vector tmp(ggml_nelements(node), 0.0f); - syclMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), syclMemcpyDeviceToHost); - printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op), - ggml_type_name(node->src[0]->type), - node->src[1] ? ggml_type_name(node->src[1]->type) : "none", - node->src[0]->name, - node->src[1] ? node->src[1]->name : "none"); - double sum = 0.0; - double sq_sum = 0.0; - for (int i = 0; i < ggml_nelements(node); i++) { - printf("%f ", tmp[i]); - sum += tmp[i]; - sq_sum += tmp[i]*tmp[i]; - } - printf("\n"); - printf("sum: %f, ", sum); - printf("sq_sum: %f\n", sq_sum); - } -#endif } - UNUSED(backend); return true; } -static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: return true; @@ -15081,16 +15700,17 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { return true; } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return true; + } return false; } break; + case GGML_OP_DUP: + case GGML_OP_REPEAT: case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - if (src0_type == GGML_TYPE_F32) { - return true; - } else { - return false; - } + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; case GGML_OP_NONE: case GGML_OP_RESHAPE: @@ -15098,8 +15718,6 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NORM: - case GGML_OP_REPEAT: - case GGML_OP_DUP: case GGML_OP_ADD: case GGML_OP_MUL: case GGML_OP_DIV: @@ -15113,6 +15731,7 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_ROPE: case GGML_OP_ALIBI: case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: @@ -15134,11 +15753,11 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ NULL, + /* .cpy_tensor_async = */ ggml_backend_sycl_cpy_tensor_async, /* .synchronize = */ ggml_backend_sycl_synchronize, - /* .graph_plan_create = */ ggml_backend_sycl_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_sycl_graph_plan_free, - /* .graph_plan_compute = */ ggml_backend_sycl_graph_plan_compute, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, }; @@ -15148,20 +15767,17 @@ static ggml_guid_t ggml_backend_sycl_guid() { return &guid; } -ggml_backend_t ggml_backend_sycl_init(int device) { +GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) { ggml_init_sycl(); // TODO: remove from ggml.c - if (device < 0 || device >= ggml_sycl_get_device_count()) { - fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); - return nullptr; - } + check_allow_gpu_index(device); // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_sycl_set_main_device(device); - + int id = g_sycl_gpu_mgr->gpus[device]; ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context { /* .device = */ device, - /* .name = */ GGML_SYCL_NAME + std::to_string(device), + /* .name = */ GGML_SYCL_NAME + std::to_string(id), }; ggml_backend_t sycl_backend = new ggml_backend { @@ -15177,22 +15793,33 @@ bool ggml_backend_is_sycl(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); } -static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { +GGML_CALL int ggml_backend_sycl_get_device_count() { + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + return g_sycl_gpu_mgr->get_gpu_count(); +} + +GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data); return sycl_backend; UNUSED(params); } +GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { + return g_sycl_gpu_mgr->get_index(device_id); +} + extern "C" int ggml_backend_sycl_reg_devices(); int ggml_backend_sycl_reg_devices() { - int device_count = ggml_sycl_get_device_count(); - - for (int i = 0; i < device_count; i++) { + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + assert(g_device_count>0); + for (int i = 0; i < g_device_count; i++) { + int id = g_sycl_gpu_mgr->gpus[i]; char name[128]; - snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i); + snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id); ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i); } - return device_count; + return g_device_count; } diff --git a/ggml-sycl.h b/ggml-sycl.h index 891f2d00a..bf5b11b36 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -24,6 +24,11 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); GGML_API void ggml_backend_sycl_print_sycl_devices(void); GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len); GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size); +GGML_API GGML_CALL int ggml_backend_sycl_get_device_count(); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); +GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); +GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); + #ifdef __cplusplus } #endif diff --git a/llama.cpp b/llama.cpp index b1db5b179..cb6266a43 100644 --- a/llama.cpp +++ b/llama.cpp @@ -104,6 +104,7 @@ #define LLAMA_MAX_NODES 8192 #define LLAMA_MAX_EXPERTS 8 + // // logging // @@ -1429,7 +1430,9 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer buft = ggml_backend_cuda_host_buffer_type(); } #elif defined(GGML_USE_SYCL) - buft = ggml_backend_sycl_host_buffer_type(); + if (host_buffer) { + buft = ggml_backend_sycl_host_buffer_type(); + } #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); #elif defined(GGML_USE_VULKAN) @@ -1483,6 +1486,12 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g } #endif +#ifdef GGML_USE_SYCL + if (ggml_backend_sycl_get_device_count() > 1) { + buft = ggml_backend_sycl_split_buffer_type(tensor_split); + } +#endif + if (buft == nullptr) { buft = llama_default_buffer_type_offload(fallback_gpu); } @@ -1494,6 +1503,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g static size_t llama_get_device_count() { #if defined(GGML_USE_CUBLAS) return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_SYCL) + return ggml_backend_sycl_get_device_count(); #elif defined(GGML_USE_VULKAN) return ggml_backend_vk_get_device_count(); #else @@ -1507,6 +1518,11 @@ static size_t llama_get_device_memory(int device) { size_t free; ggml_backend_cuda_get_device_memory(device, &total, &free); return free; +#elif defined(GGML_USE_SYCL) + size_t total; + size_t free; + ggml_backend_sycl_get_device_memory(device, &total, &free); + return free; #elif defined(GGML_USE_VULKAN) size_t total; size_t free; @@ -12075,13 +12091,31 @@ struct llama_context * llama_new_context_with_model( } #elif defined(GGML_USE_SYCL) if (model->n_gpu_layers > 0) { - ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu); - llama_free(ctx); - return nullptr; + // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used + if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { + int main_gpu_index = ggml_backend_sycl_get_device_index(model->main_gpu); + ggml_backend_t backend = ggml_backend_sycl_init(main_gpu_index); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, model->main_gpu, main_gpu_index); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } else { + // LLAMA_SPLIT_LAYER requires a backend for each GPU + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { + int device_id = id_list[i]; + ggml_backend_t backend = ggml_backend_sycl_init(i); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } } - ctx->backends.push_back(backend); } #elif defined(GGML_USE_KOMPUTE) if (model->n_gpu_layers > 0) { @@ -12161,7 +12195,6 @@ struct llama_context * llama_new_context_with_model( ggml_set_name(ctx->inp_cls, "inp_cls"); ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); - LLAMA_LOG_INFO("%s: %10s input buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(ctx->buf_input), ggml_backend_buffer_get_size(ctx->buf_input) / 1024.0 / 1024.0); From 802da0091ba646ecf02e1a8fae2da0b8e76409bd Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Sat, 2 Mar 2024 08:42:56 -0500 Subject: [PATCH 17/86] llama : fix segfault from unknown model arch name (#5820) * llama : fix segfault from unknown model arch name * llama : make all LLM maps const This also requires using `std::map::at` instead of its `operator[]` which does not exist for const maps. * llama : name LLM_ARCH_UNKNOWN to "(unknown)" This avoids errors from `std::map::at` when getting the general name of the model architecture. Using "(unknown)" instead of an empty string as per suggestion https://github.com/ggerganov/llama.cpp/pull/5820#issuecomment-1973735284 * llama : remove redundant inner const for LLM_TENSOR_NAMES The extra const won't do anything here as const maps return const references to values. Co-authored-by: Jared Van Bortel * llama : remove redundant nullptr check in llm_arch_from_string Since LLM_ARCH_NAMES is a const map, no spurious elements with a NULL name are inserted anymore, so this check is dead code. --------- Co-authored-by: Jared Van Bortel --- llama.cpp | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/llama.cpp b/llama.cpp index cb6266a43..790c2740f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -216,7 +216,7 @@ enum llm_arch { LLM_ARCH_UNKNOWN, }; -static std::map LLM_ARCH_NAMES = { +static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GPT2, "gpt2" }, @@ -241,6 +241,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_UNKNOWN, "(unknown)" }, }; enum llm_kv { @@ -301,7 +302,7 @@ enum llm_kv { LLM_KV_TOKENIZER_RWKV, }; -static std::map LLM_KV_NAMES = { +static const std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, @@ -365,7 +366,7 @@ struct LLM_KV { llm_arch arch; std::string operator()(llm_kv kv) const { - return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]); + return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch)); } }; @@ -400,7 +401,7 @@ enum llm_tensor { LLM_TENSOR_LAYER_OUT_NORM, }; -static std::map> LLM_TENSOR_NAMES = { +static const std::map> LLM_TENSOR_NAMES = { { LLM_ARCH_LLAMA, { @@ -833,38 +834,38 @@ struct LLM_TN { llm_arch arch; std::string operator()(llm_tensor tensor) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return LLM_TENSOR_NAMES[arch].at(tensor); + return LLM_TENSOR_NAMES.at(arch).at(tensor); } std::string operator()(llm_tensor tensor, const std::string & suffix) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix; + return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix; } std::string operator()(llm_tensor tensor, int bid) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid); + return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid); } std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix; + return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix; } std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix; + return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix; } }; @@ -872,7 +873,7 @@ struct LLM_TN { // gguf helpers // -static std::map LLAMA_ROPE_SCALING_TYPES = { +static const std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, From 6c32d8c7ad8ba7b6ad2a162e929a21dd04fcdca0 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 2 Mar 2024 15:19:09 +0100 Subject: [PATCH 18/86] llama : refactor internal quantization functions (#5830) --- llama.cpp | 81 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 43 insertions(+), 38 deletions(-) diff --git a/llama.cpp b/llama.cpp index 790c2740f..697e85e89 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10836,7 +10836,7 @@ struct quantize_state_internal { {} }; -static void llama_convert_tensor_internal( +static void llama_tensor_dequantize_internal( struct ggml_tensor * tensor, std::vector> & output, std::vector & workers, const size_t nelements, const int nthread ) { @@ -11177,6 +11177,46 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty return new_type; } +static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, int64_t * hist_cur, const float * imatrix, std::vector & workers, const int nthread) { + std::mutex mutex; + int counter = 0; + size_t new_size = 0; + if (nthread < 2) { + // single-thread + return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur, imatrix); + } + auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, + nrows, n_per_row, imatrix]() { + std::array local_hist = {}; + const int nrows_per_chunk = chunk_size / n_per_row; + size_t local_size = 0; + while (true) { + std::unique_lock lock(mutex); + int first_row = counter; counter += nrows_per_chunk; + if (first_row >= nrows) { + if (local_size > 0) { + for (int j=0; jftype; @@ -11289,7 +11329,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector workers; workers.reserve(nthread); - std::mutex mutex; int idx = 0; @@ -11403,7 +11442,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) { throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type))); } else { - llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread); + llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread); f32_data = (float *) f32_conv_buf.data(); } @@ -11424,41 +11463,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const int nchunk = (nelements + chunk_size - 1)/chunk_size; const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; - if (nthread_use < 2) { - new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur.data(), imatrix); - } else { - int counter = 0; - new_size = 0; - auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, - nrows, n_per_row, imatrix]() { - std::array local_hist = {}; - const int nrows_per_chunk = chunk_size / n_per_row; - size_t local_size = 0; - while (true) { - std::unique_lock lock(mutex); - int first_row = counter; counter += nrows_per_chunk; - if (first_row >= nrows) { - if (local_size > 0) { - for (int j=0; j %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); int64_t tot_count = 0; From ef2cd694c4155fbf25bae61c5178c47eb3676dba Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 2 Mar 2024 16:54:08 +0200 Subject: [PATCH 19/86] scripts : add pod-llama.sh --- scripts/pod-llama.sh | 213 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 213 insertions(+) create mode 100644 scripts/pod-llama.sh diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh new file mode 100644 index 000000000..6cf1ab4f3 --- /dev/null +++ b/scripts/pod-llama.sh @@ -0,0 +1,213 @@ +#!/bin/bash +# +# Use this script only on fresh pods (runpod.io)! +# Otherwise, it can break your environment! +# + +if [ -z "$1" ]; then + echo "Usage: $0 " + echo " 0: no models" + echo " 1: tinyllama-1b" + echo " 2: codellama-7b" + echo " 3: codellama-13b" + echo " 4: codellama-34b" + echo " 5: codellama-7b-instruct" + echo " 6: codellama-13b-instruct" + echo " 7: codellama-34b-instruct" + + exit 1 +fi + +set -x + +# setup deps +apt-get update +apt-get install -y git-lfs cmake cmake-curses-gui vim ruby +git-lfs install + +if [ ! -d "/workspace" ]; then + ln -sfn $(pwd) /workspace +fi + +# download data +cd /workspace + +# this is useful to git clone repos without doubling the disk size due to .git +git clone https://github.com/iboB/git-lfs-download +ln -sfn /workspace/git-lfs-download/git-lfs-download /usr/local/bin/git-lfs-download + +# llama.cpp +cd /workspace +git clone https://github.com/ggerganov/llama.cpp + +cd llama.cpp + +LLAMA_CUBLAS=1 make -j + +ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3 ./models/tinyllama-1b +ln -sfn /workspace/CodeLlama-7b-hf ./models/codellama-7b +ln -sfn /workspace/CodeLlama-13b-hf ./models/codellama-13b +ln -sfn /workspace/CodeLlama-34b-hf ./models/codellama-34b +ln -sfn /workspace/CodeLlama-7b-Instruct-hf ./models/codellama-7b-instruct +ln -sfn /workspace/CodeLlama-13b-Instruct-hf ./models/codellama-13b-instruct +ln -sfn /workspace/CodeLlama-34b-Instruct-hf ./models/codellama-34b-instruct + +pip install -r requirements.txt + +# cmake +cd /workspace/llama.cpp + +mkdir build-cublas +cd build-cublas + +cmake -DLLAMA_CUBLAS=1 ../ +make -j + +if [ "$1" -eq "0" ]; then + exit 0 +fi + +# more models +if [ "$1" -eq "1" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3 + + cd /workspace/llama.cpp + + python3 convert.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "2" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-hf --without *safetensors* + rm -v ./CodeLlama-7b-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "3" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-hf --without *safetensors* + rm -v ./CodeLlama-13b-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "4" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-hf --without *safetensors* + rm -v ./CodeLlama-34b-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "5" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf --without *safetensors* + rm -v ./CodeLlama-7b-Instruct-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "6" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf --without *safetensors* + rm -v ./CodeLlama-13b-Instruct-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "7" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf --without *safetensors* + rm -v ./CodeLlama-34b-Instruct-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "1" ]; then + # perf + perplexity + cd /workspace/llama.cpp/build-cublas + + make -j && ../scripts/run-all-perf.sh tinyllama-1b "f16" "-ngl 99 -t 1 -p 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,64,128,256,512,1024,2048 -n 128" + + ../scripts/get-wikitext-2.sh + unzip wikitext-2-raw-v1.zip + + make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32 + + # batched + cd /workspace/llama.cpp + + LLAMA_CUBLAS=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999 + + # batched-bench + cd /workspace/llama.cpp + + LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32 + + # parallel + cd /workspace/llama.cpp + + LLAMA_CUBLAS=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb + +fi + +# speculative +#if [ "$1" -eq "7" ]; then +# cd /workspace/llama.cpp +# +# LLAMA_CUBLAS=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0 +#fi + +# more benches +#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 +#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 + From bbde6eb2561153aabbdfac5001c690fe00cad639 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sat, 2 Mar 2024 17:00:51 +0200 Subject: [PATCH 20/86] ggml : IQ3_S improvements (#5829) * iq3_s: somewhat faster AVX2 dot product On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using 16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s. PP-512 increases to 28.5 t/s from 23.8 t/s. * iq3_s: somewhat faster ARM_NEON dot product Still dog slow - 10.7 t/s up from 9.9 t/s. * iq3_s: another small ARM_NEON improvement 10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick that works best on AVX2. * iq3_s: minor improvement on Metal 49.4 t/s -> 50.3 t/s * iq3_s: PPL improvement E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653. * iq3_s: use new grid everywhere * Fix ARM_NEON --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 143 ++++++++++++------------ ggml-metal.metal | 152 ++++++++++++------------- ggml-quants.c | 280 +++++++++++++++++++++++++++-------------------- 3 files changed, 310 insertions(+), 265 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0c6501e98..7ed97430f 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2018,74 +2018,73 @@ static const __device__ uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -static const __device__ uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +static const __device__ uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; - static const __device__ uint64_t iq1s_grid[512] = { 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, @@ -2392,9 +2391,9 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint8_t * qs = x[i].qs + 8*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); const uint8_t signs = x[i].signs[4*ib + il]; for (int j = 0; j < 4; ++j) { y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); @@ -5211,8 +5210,8 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( const int8_t * q8 = bq8_1[ib32].qs; int sumi = 0; for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); - const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); + const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); + const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); @@ -5221,7 +5220,7 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( sumi = __dp4a(grid_h, *((int *)q8+1), sumi); q8 += 8; } - const float d = (float)bq2->d * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f; + const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); return d * sumi; #else assert(false); diff --git a/ggml-metal.metal b/ggml-metal.metal index 74a5e0b03..8b9488437 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -4087,71 +4087,71 @@ constexpr constant static uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -constexpr constant static uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +constexpr constant static uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; #define NGRID_IQ1S 512 @@ -4742,7 +4742,7 @@ void kernel_mul_mv_iq3_s_f32_impl( { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3xs_grid[pos + i]; + for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4769,12 +4769,14 @@ void kernel_mul_mv_iq3_s_f32_impl( for (int row = 0; row < N_DST; row++) { const float db = dh[0]; - const float d = db * (0.5f + ((sc[0] >> 4*(ib%2)) & 0xf)); + const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf)); float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values; + const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values; + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]); for (int j = 0; j < 4; ++j) { sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]); sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]); @@ -4795,7 +4797,7 @@ void kernel_mul_mv_iq3_s_f32_impl( for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; } } } @@ -5685,15 +5687,15 @@ void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & device const uint8_t * qs = xb->qs + 8*ib32; device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (0.5f + ((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * 0.5f; - constant uint8_t * grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+0] | ((qh << 8) & 256))); - constant uint8_t * grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+1] | ((qh << 7) & 256))); + const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); + constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); + constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); for (int i = 0; i < 4; ++i) { reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); } - grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+2] | ((qh << 6) & 256))); - grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+3] | ((qh << 5) & 256))); + grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); + grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); for (int i = 0; i < 4; ++i) { reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); diff --git a/ggml-quants.c b/ggml-quants.c index 371826f14..492a1b9a6 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3818,71 +3818,71 @@ static const uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -static const uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +static const uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; #define NGRID_IQ2XXS 512 @@ -4162,11 +4162,11 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in const uint8_t * signs = x[i].signs; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const float db1 = d * (0.5f + (x[i].scales[ib32/2] & 0xf)) * 0.5f; - const float db2 = d * (0.5f + (x[i].scales[ib32/2] >> 4)) * 0.5f; + const float db1 = d * (1 + 2*(x[i].scales[ib32/2] & 0xf)); + const float db2 = d * (1 + 2*(x[i].scales[ib32/2] >> 4)); for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -4176,8 +4176,8 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in qs += 8; signs += 4; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -10089,18 +10089,34 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v #if defined(__ARM_NEON) + typedef union { + uint16x8_t vec_index; + uint16_t index[8]; + } vec_index_t; + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 }; static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); + static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; + + const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const int16x8_t hshift = vld1q_s16(k_shift); + const uint16x8_t m256 = vdupq_n_u16(256); + const uint8x16_t m1 = vdupq_n_u8(1); uint8x16x2_t vs; ggml_int8x16x4_t q3s; ggml_int8x16x4_t q8b; + vec_index_t idx; + +#if QK_K == 256 + uint32_t scales32[2]; + const uint8_t * scales8 = (const uint8_t *)scales32; +#endif float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -10109,47 +10125,63 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const uint8_t * restrict qh = x[i].qh; const uint16_t * restrict signs = (const uint16_t *)x[i].signs; const int8_t * restrict q8 = y[i].qs; + +#if QK_K == 256 + memcpy(scales32, x[i].scales, 4); + scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; + scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; +#endif + int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - const uint32x4_t aux32x4_0 = {iq3xs_grid[qs[ 0] | ((qh[ib32+0] << 8) & 256)], iq3xs_grid[qs[ 1] | ((qh[ib32+0] << 7) & 256)], - iq3xs_grid[qs[ 2] | ((qh[ib32+0] << 6) & 256)], iq3xs_grid[qs[ 3] | ((qh[ib32+0] << 5) & 256)]}; - const uint32x4_t aux32x4_1 = {iq3xs_grid[qs[ 4] | ((qh[ib32+0] << 4) & 256)], iq3xs_grid[qs[ 5] | ((qh[ib32+0] << 3) & 256)], - iq3xs_grid[qs[ 6] | ((qh[ib32+0] << 2) & 256)], iq3xs_grid[qs[ 7] | ((qh[ib32+0] << 1) & 256)]}; - const uint32x4_t aux32x4_2 = {iq3xs_grid[qs[ 8] | ((qh[ib32+1] << 8) & 256)], iq3xs_grid[qs[ 9] | ((qh[ib32+1] << 7) & 256)], - iq3xs_grid[qs[10] | ((qh[ib32+1] << 6) & 256)], iq3xs_grid[qs[11] | ((qh[ib32+1] << 5) & 256)]}; - const uint32x4_t aux32x4_3 = {iq3xs_grid[qs[12] | ((qh[ib32+1] << 4) & 256)], iq3xs_grid[qs[13] | ((qh[ib32+1] << 3) & 256)], - iq3xs_grid[qs[14] | ((qh[ib32+1] << 2) & 256)], iq3xs_grid[qs[15] | ((qh[ib32+1] << 1) & 256)]}; - qs += 16; + + const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; + idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); + const uint32x4_t aux32x4_0 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; + const uint32x4_t aux32x4_1 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); + const uint32x4_t aux32x4_2 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; + const uint32x4_t aux32x4_3 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - q3s.val[0] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_0))), vreinterpretq_s8_u8(vs.val[0])); - q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1])); + q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); signs += 4; - q3s.val[2] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_2))), vreinterpretq_s8_u8(vs.val[0])); - q3s.val[3] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_3))), vreinterpretq_s8_u8(vs.val[1])); + q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); +#if QK_K == 256 + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; + sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; +#else sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); +#endif } sumf += d*(sumi1 + sumi2); } - *s = 0.25f * sumf; + *s = sumf; #elif defined(__AVX2__) @@ -10164,6 +10196,16 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = _mm256_set1_epi32(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + __m256 accumf = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; @@ -10176,24 +10218,25 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+0] << 1) & 256)], - iq3xs_grid[qs[6] | ((qh[ib32+0] << 2) & 256)], - iq3xs_grid[qs[5] | ((qh[ib32+0] << 3) & 256)], - iq3xs_grid[qs[4] | ((qh[ib32+0] << 4) & 256)], - iq3xs_grid[qs[3] | ((qh[ib32+0] << 5) & 256)], - iq3xs_grid[qs[2] | ((qh[ib32+0] << 6) & 256)], - iq3xs_grid[qs[1] | ((qh[ib32+0] << 7) & 256)], - iq3xs_grid[qs[0] | ((qh[ib32+0] << 8) & 256)]); - qs += 8; - const __m256i q2_2 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+1] << 1) & 256)], - iq3xs_grid[qs[6] | ((qh[ib32+1] << 2) & 256)], - iq3xs_grid[qs[5] | ((qh[ib32+1] << 3) & 256)], - iq3xs_grid[qs[4] | ((qh[ib32+1] << 4) & 256)], - iq3xs_grid[qs[3] | ((qh[ib32+1] << 5) & 256)], - iq3xs_grid[qs[2] | ((qh[ib32+1] << 6) & 256)], - iq3xs_grid[qs[1] | ((qh[ib32+1] << 7) & 256)], - iq3xs_grid[qs[0] | ((qh[ib32+1] << 8) & 256)]); - qs += 8; + const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; + idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); + idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); + idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); + idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = _mm256_set_epi32( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = _mm256_set_epi32( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); @@ -10221,7 +10264,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v } - *s = 0.25f * hsum_float_8(accumf); + *s = hsum_float_8(accumf); #else @@ -10238,8 +10281,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; int32_t sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); @@ -10251,8 +10294,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v bsum += sumi * ls1; sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); @@ -10265,7 +10308,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v } sumf += d * bsum; } - *s = 0.25f * sumf; + *s = sumf; #endif } @@ -11912,7 +11955,8 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } float best = 0; float scale = max/(2*kMaxQ-1); - for (int is = -15; is <= 15; ++is) { + for (int k = 0; k < bs4; ++k) is_on_grid[k] = false; + for (int is = -9; is <= 9; ++is) { float id = (2*kMaxQ-1+is*0.2f)/max; float this_scale = 1/id; for (int k = 0; k < bs4; ++k) { @@ -11948,7 +11992,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo if (n_not_ongrid > 0 && scale > 0) { float id = 1/scale; for (int k = 0; k < bs4; ++k) { - if (is_on_grid[k]) continue; + //if (is_on_grid[k]) continue; uint16_t u = 0; for (int i = 0; i < 4; ++i) { int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); @@ -12004,7 +12048,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } float d = max_scale/31; - y[ibl].d = GGML_FP32_TO_FP16(d); + y[ibl].d = GGML_FP32_TO_FP16(d * 1.033f); float id = 1/d; for (int ib = 0; ib < QK_K/block_size; ib += 2) { int l1 = nearest_int(0.5f*(id*scales[ib+0]-1)); From c7a0ad8ec9ebb5ddb1c1c80c82f2ee041c525d47 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 2 Mar 2024 12:21:47 -0500 Subject: [PATCH 21/86] convert-hf : make model class definitions self-contained (#5825) --- convert-hf-to-gguf.py | 204 ++++++++++++++++++------------------ gguf-py/gguf/gguf_writer.py | 2 +- 2 files changed, 101 insertions(+), 105 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 28b92ac38..fa9d4f22f 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -8,9 +8,10 @@ import json import os import re import sys +from abc import ABC, abstractmethod from enum import IntEnum from pathlib import Path -from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast import numpy as np import torch @@ -35,8 +36,11 @@ class SentencePieceTokenTypes(IntEnum): UNUSED = 5 BYTE = 6 +AnyModel = TypeVar("AnyModel", bound="type[Model]") + +class Model(ABC): + _model_classes: dict[str, type[Model]] = {} -class Model: def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): self.dir_model = dir_model self.ftype = ftype @@ -47,10 +51,14 @@ class Model: self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) - self.model_arch = self._get_model_architecture() self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) + @property + @abstractmethod + def model_arch(self) -> gguf.MODEL_ARCH: + pass + def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any: key = next((k for k in keys if k in self.hparams), None) if key is not None: @@ -176,55 +184,21 @@ class Model: with open(dir_model / "config.json", "r", encoding="utf-8") as f: return json.load(f) - @staticmethod - def from_model_architecture(model_architecture): - if model_architecture == "GPTNeoXForCausalLM": - return GPTNeoXModel - if model_architecture == "BloomForCausalLM": - return BloomModel - if model_architecture == "MPTForCausalLM": - return MPTModel - if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return BaichuanModel - if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): - return FalconModel - if model_architecture == "GPTBigCodeForCausalLM": - return StarCoderModel - if model_architecture == "GPTRefactForCausalLM": - return RefactModel - if model_architecture == "PersimmonForCausalLM": - return PersimmonModel - if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return StableLMModel - if model_architecture == "QWenLMHeadModel": - return QwenModel - if model_architecture == "Qwen2ForCausalLM": - return Model - if model_architecture == "MixtralForCausalLM": - return MixtralModel - if model_architecture == "GPT2LMHeadModel": - return GPT2Model - if model_architecture == "PhiForCausalLM": - return Phi2Model - if model_architecture == "PlamoForCausalLM": - return PlamoModel - if model_architecture == "CodeShellForCausalLM": - return CodeShellModel - if model_architecture == "OrionForCausalLM": - return OrionModel - if model_architecture == "InternLM2ForCausalLM": - return InternLM2Model - if model_architecture == "MiniCPMForCausalLM": - return MiniCPMModel - if model_architecture == "BertModel": - return BertModel - if model_architecture == "NomicBertModel": - return NomicBertModel - if model_architecture == "GemmaForCausalLM": - return GemmaModel - if model_architecture == "Starcoder2ForCausalLM": - return Model - return Model + @classmethod + def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: + assert names + def func(modelcls: type[Model]): + for name in names: + cls._model_classes[name] = modelcls + return modelcls + return func + + @classmethod + def from_model_architecture(cls, arch): + try: + return cls._model_classes[arch] + except KeyError: + raise NotImplementedError(f'Architecture {arch!r} not supported!') from None def _is_model_safetensors(self) -> bool: return Model.count_model_parts(self.dir_model, ".safetensors") > 0 @@ -239,57 +213,6 @@ class Model: return ("pytorch_model.bin",) return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) - def _get_model_architecture(self) -> gguf.MODEL_ARCH: - arch = self.hparams["architectures"][0] - if arch == "GPTNeoXForCausalLM": - return gguf.MODEL_ARCH.GPTNEOX - if arch == "BloomForCausalLM": - return gguf.MODEL_ARCH.BLOOM - if arch == "MPTForCausalLM": - return gguf.MODEL_ARCH.MPT - if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return gguf.MODEL_ARCH.BAICHUAN - if arch in ("FalconForCausalLM", "RWForCausalLM"): - return gguf.MODEL_ARCH.FALCON - if arch == "GPTBigCodeForCausalLM": - return gguf.MODEL_ARCH.STARCODER - if arch == "GPTRefactForCausalLM": - return gguf.MODEL_ARCH.REFACT - if arch == "PersimmonForCausalLM": - return gguf.MODEL_ARCH.PERSIMMON - if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return gguf.MODEL_ARCH.STABLELM - if arch == "QWenLMHeadModel": - return gguf.MODEL_ARCH.QWEN - if arch == "Qwen2ForCausalLM": - return gguf.MODEL_ARCH.QWEN2 - if arch == "MixtralForCausalLM": - return gguf.MODEL_ARCH.LLAMA - if arch == "GPT2LMHeadModel": - return gguf.MODEL_ARCH.GPT2 - if arch == "PhiForCausalLM": - return gguf.MODEL_ARCH.PHI2 - if arch == "PlamoForCausalLM": - return gguf.MODEL_ARCH.PLAMO - if arch == "CodeShellForCausalLM": - return gguf.MODEL_ARCH.CODESHELL - if arch == "OrionForCausalLM": - return gguf.MODEL_ARCH.ORION - if arch == "InternLM2ForCausalLM": - return gguf.MODEL_ARCH.INTERNLM2 - if arch == "MiniCPMForCausalLM": - return gguf.MODEL_ARCH.MINICPM - if arch == "BertModel": - return gguf.MODEL_ARCH.BERT - if arch == "NomicBertModel": - return gguf.MODEL_ARCH.NOMIC_BERT - if arch == "GemmaForCausalLM": - return gguf.MODEL_ARCH.GEMMA - if arch == "Starcoder2ForCausalLM": - return gguf.MODEL_ARCH.STARCODER2 - - raise NotImplementedError(f'Architecture "{arch}" not supported!') - def _set_vocab_gpt2(self): dir_model = self.dir_model hparams = self.hparams @@ -457,7 +380,10 @@ class Model: special_vocab.add_to_gguf(self.gguf_writer) +@Model.register("GPTNeoXForCausalLM") class GPTNeoXModel(Model): + model_arch = gguf.MODEL_ARCH.GPTNEOX + def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] @@ -474,7 +400,10 @@ class GPTNeoXModel(Model): self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) +@Model.register("BloomForCausalLM") class BloomModel(Model): + model_arch = gguf.MODEL_ARCH.BLOOM + def set_gguf_parameters(self): self.gguf_writer.add_name("Bloom") n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) @@ -566,7 +495,10 @@ class BloomModel(Model): print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") +@Model.register("MPTForCausalLM") class MPTModel(Model): + model_arch = gguf.MODEL_ARCH.MPT + def set_gguf_parameters(self): block_count = self.hparams["n_layers"] self.gguf_writer.add_name(self.dir_model.name) @@ -629,7 +561,10 @@ class MPTModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("OrionForCausalLM") class OrionModel(Model): + model_arch = gguf.MODEL_ARCH.ORION + def set_vocab(self): self._set_vocab_sentencepiece() @@ -708,7 +643,10 @@ class OrionModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM") class BaichuanModel(Model): + model_arch = gguf.MODEL_ARCH.BAICHUAN + def set_vocab(self): self._set_vocab_sentencepiece() @@ -823,7 +761,10 @@ class BaichuanModel(Model): return weights[r * n_part:r * n_part + r, ...] +@Model.register("FalconForCausalLM", "RWForCausalLM") class FalconModel(Model): + model_arch = gguf.MODEL_ARCH.FALCON + def set_gguf_parameters(self): block_count = self.hparams.get("num_hidden_layers") if block_count is None: @@ -916,7 +857,10 @@ class FalconModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("GPTBigCodeForCausalLM") class StarCoderModel(Model): + model_arch = gguf.MODEL_ARCH.STARCODER + def set_gguf_parameters(self): block_count = self.hparams["n_layer"] @@ -931,7 +875,10 @@ class StarCoderModel(Model): self.gguf_writer.add_file_type(self.ftype) +@Model.register("GPTRefactForCausalLM") class RefactModel(Model): + model_arch = gguf.MODEL_ARCH.REFACT + def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim @@ -1015,7 +962,10 @@ class RefactModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("PersimmonForCausalLM") class PersimmonModel(Model): + model_arch = gguf.MODEL_ARCH.PERSIMMON + def set_gguf_parameters(self): block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) head_count = self.hparams["num_attention_heads"] @@ -1063,7 +1013,10 @@ class PersimmonModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): + model_arch = gguf.MODEL_ARCH.STABLELM + def set_vocab(self): if (self.dir_model / "tokenizer.json").is_file(): self._set_vocab_gpt2() @@ -1087,12 +1040,18 @@ class StableLMModel(Model): self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) +@Model.register("MixtralForCausalLM") class MixtralModel(Model): + model_arch = gguf.MODEL_ARCH.LLAMA + def set_vocab(self): self._set_vocab_sentencepiece() +@Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): + model_arch = gguf.MODEL_ARCH.MINICPM + def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] self.gguf_writer.add_name("MiniCPM") @@ -1169,7 +1128,10 @@ class MiniCPMModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("QWenLMHeadModel") class QwenModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN + @staticmethod def token_bytes_to_string(b): from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode @@ -1249,7 +1211,15 @@ class QwenModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("Qwen2ForCausalLM") +class Qwen2Model(Model): + model_arch = gguf.MODEL_ARCH.QWEN2 + + +@Model.register("GPT2LMHeadModel") class GPT2Model(Model): + model_arch = gguf.MODEL_ARCH.GPT2 + def set_gguf_parameters(self): self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_block_count(self.hparams["n_layer"]) @@ -1311,7 +1281,10 @@ class GPT2Model(Model): self.gguf_writer.add_tensor("output.weight", data) +@Model.register("PhiForCausalLM") class Phi2Model(Model): + model_arch = gguf.MODEL_ARCH.PHI2 + def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) @@ -1333,7 +1306,10 @@ class Phi2Model(Model): self.gguf_writer.add_add_bos_token(False) +@Model.register("PlamoForCausalLM") class PlamoModel(Model): + model_arch = gguf.MODEL_ARCH.PLAMO + def set_vocab(self): self._set_vocab_sentencepiece() @@ -1412,7 +1388,10 @@ class PlamoModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("CodeShellForCausalLM") class CodeShellModel(Model): + model_arch = gguf.MODEL_ARCH.CODESHELL + def set_gguf_parameters(self): block_count = self.hparams["n_layer"] @@ -1477,7 +1456,10 @@ class CodeShellModel(Model): print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") +@Model.register("InternLM2ForCausalLM") class InternLM2Model(Model): + model_arch = gguf.MODEL_ARCH.INTERNLM2 + def set_vocab(self): # (TODO): Is there a better way? # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character @@ -1649,7 +1631,10 @@ in chat mode so that the conversation can end normally.") self.post_write_tensors(tensor_map, name, data_torch) +@Model.register("BertModel") class BertModel(Model): + model_arch = gguf.MODEL_ARCH.BERT + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.vocab_size = None @@ -1679,7 +1664,7 @@ class BertModel(Model): else: raise NotImplementedError("Only MEAN and CLS pooling types supported") - self.gguf_writer.add_pooling_type(pooling_type.value) + self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): path = self.dir_model @@ -1755,7 +1740,10 @@ class BertModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("NomicBertModel") class NomicBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.NOMIC_BERT + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -1792,7 +1780,10 @@ class NomicBertModel(BertModel): yield name, data +@Model.register("GemmaForCausalLM") class GemmaModel(Model): + model_arch = gguf.MODEL_ARCH.GEMMA + def set_vocab(self): self._set_vocab_sentencepiece() @@ -1848,6 +1839,11 @@ class GemmaModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("Starcoder2ForCausalLM") +class StarCoder2Model(Model): + model_arch = gguf.MODEL_ARCH.STARCODER2 + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index e4681475c..801160832 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -362,7 +362,7 @@ class GGUFWriter: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) def add_pooling_type(self, value: PoolingType) -> None: - self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value) + self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) From 4d4d2366fc9c54d4a275065cfe9299c6cf7c5b78 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 2 Mar 2024 12:27:26 -0500 Subject: [PATCH 22/86] convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (#5821) --- README.md | 4 +- convert-llama-ggml-to-gguf.py | 6 +-- convert.py | 72 +++++++++++++++++------------------ examples/infill/infill.cpp | 4 +- 4 files changed, 41 insertions(+), 45 deletions(-) diff --git a/README.md b/README.md index 67717c1e3..939646753 100644 --- a/README.md +++ b/README.md @@ -786,7 +786,7 @@ And after 4.45 hours, you will have the final perplexity. ### Interactive mode If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter. -In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. +In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. Here is an example of a few-shot interaction, invoked with the command @@ -850,7 +850,7 @@ Sample run: ``` == Running in interactive mode. == - Press Ctrl+C to interject at any time. - - Press Return to return control to LLaMa. + - Press Return to return control to LLaMA. - If you want to submit another line, end your input in '\'. Below is an instruction that describes a task. Write a response that appropriately completes the request. diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index b33108062..cd9644fcb 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -373,7 +373,7 @@ def handle_metadata(cfg, hp): raise ValueError('Unable to load metadata') vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir) vocab_factory = convert.VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir) + vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) return params, vocab, special_vocab @@ -398,8 +398,8 @@ def handle_args(): help ='Load HuggingFace/.pth vocab and metadata from the specified directory') parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm", - help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)") + parser.add_argument("--vocabtype", default="spm,hfft", + help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)") return parser.parse_args() diff --git a/convert.py b/convert.py index 63a0a5d78..6e3a0319b 100755 --- a/convert.py +++ b/convert.py @@ -1282,35 +1282,32 @@ def load_some_model(path: Path) -> ModelPlus: class VocabFactory: + _FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"} + def __init__(self, path: Path): self.path = path - self.files: dict[str, Path | None] = { - "tokenizer.model": None, - "vocab.json": None, - "tokenizer.json": None, - } - self._detect_files() + self.file_paths = self._detect_files() + print(f"Found vocab files: {self.file_paths}") - def _detect_files(self): - for file in self.files.keys(): - file_path = self.path / file - parent_file_path = self.path.parent / file - if file_path.exists(): - self.files[file] = file_path - elif parent_file_path.exists(): - self.files[file] = parent_file_path - print(f"Found vocab files: {self.files}") + def _detect_files(self) -> dict[str, Path | None]: + def locate(file: str) -> Path | None: + if (path := self.path / file).exists(): + return path + if (path := self.path.parent / file).exists(): + return path + return None - def _select_file(self, vocabtype: str | None) -> Path: - if vocabtype in ["spm", "bpe"]: - for file_key in self.files.keys(): - if (file := self.files[file_key]) is not None: - return file - raise FileNotFoundError(f"{vocabtype} vocab not found.") - if vocabtype == "hfft": - # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file - return self.path - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + return {vt: locate(f) for vt, f in self._FILES.items()} + + def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]: + for vtype in vocab_types: + try: + path = self.file_paths[vtype] + except KeyError: + raise ValueError(f"Unsupported vocabulary type {vtype}") from None + if path is not None: + return vtype, path + raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}") def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab: load_merges = vocabtype == "bpe" @@ -1322,30 +1319,30 @@ class VocabFactory: n_vocab=n_vocab, ) - def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: - path = self._select_file(vocabtype) - print(f"Loading vocab file '{path}', type '{vocabtype}'") + def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: + vocab_type, path = self._select_file(vocab_types) + print(f"Loading vocab file {path!r}, type {vocab_type!r}") added_tokens_path = path.parent / "added_tokens.json" vocab: Vocab - if vocabtype == "bpe": + if vocab_type == "bpe": vocab = BpeVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocabtype == "spm": + elif vocab_type == "spm": vocab = SentencePieceVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocabtype == "hfft": + elif vocab_type == "hfft": vocab = HfVocab( - path, added_tokens_path if added_tokens_path.exists() else None + path.parent, added_tokens_path if added_tokens_path.exists() else None ) else: - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + raise ValueError(vocab_type) # FIXME: Respect --vocab-dir? special_vocab = self._create_special_vocab( vocab, - vocabtype, + vocab_type, model_parent_path, ) return vocab, special_vocab @@ -1379,15 +1376,14 @@ def main(args_in: list[str] | None = None) -> None: if np.uint32(1) == np.uint32(1).newbyteorder("<"): # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") - vocab_types = ["spm", "bpe", "hfft"] - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") + parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm") + parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") @@ -1448,7 +1444,7 @@ def main(args_in: list[str] | None = None) -> None: model_parent_path = model_plus.paths[0].parent vocab_path = Path(args.vocab_dir or args.model or model_parent_path) vocab_factory = VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path) + vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path) if args.vocab_only: if not args.outfile: diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index d4b8729dd..91c39c5ae 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -378,10 +378,10 @@ int main(int argc, char ** argv) { if (params.interactive) { const char *control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to LLaMA, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to LLaMA.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } From 494c87032613e31c0be99b2735e732871f2c4e4d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 2 Mar 2024 20:00:49 +0200 Subject: [PATCH 23/86] ggml : fix IQ3_S AVX implementation (#5834) ggml-ci --- ggml-quants.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-quants.c b/ggml-quants.c index 492a1b9a6..2a8881d73 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -10231,7 +10231,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); const __m256i q2_1 = _mm256_set_epi32( iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], - iq3s_grid[idx.index[3]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] ); const __m256i q2_2 = _mm256_set_epi32( iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], From 4a6e2d6142ab815c964924896891e9ab3e050632 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Sat, 2 Mar 2024 20:52:25 +0100 Subject: [PATCH 24/86] llama : add abort_callback to interrupt computation (#5409) * using abort_callback from ggml to stop llama computation * format fix * a brief explaining comment --------- Co-authored-by: Georgi Gerganov --- llama.cpp | 18 ++++++++++++++++-- llama.h | 13 +++++++++++-- 2 files changed, 27 insertions(+), 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index 697e85e89..d4c7a965b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1987,6 +1987,9 @@ struct llama_context { std::vector buf_compute_meta; ggml_backend_sched_t sched = nullptr; + ggml_abort_callback abort_callback = nullptr; + void * abort_callback_data = nullptr; + // input tensors ggml_backend_buffer_t buf_input = nullptr; ggml_context * ctx_input = nullptr; @@ -8071,6 +8074,7 @@ static void llama_graph_compute( if (lctx.backend_cpu != nullptr) { ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); + ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data); } ggml_backend_sched_graph_compute(lctx.sched, gf); @@ -11856,6 +11860,8 @@ struct llama_context_params llama_context_default_params() { /*.embedding =*/ false, /*.offload_kqv =*/ true, /*.do_pooling =*/ true, + /*.abort_callback =*/ nullptr, + /*.abort_callback_data =*/ nullptr, }; return result; @@ -12038,8 +12044,11 @@ struct llama_context * llama_new_context_with_model( LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); - ctx->rng = std::mt19937(params.seed); - ctx->logits_all = params.logits_all; + ctx->abort_callback = params.abort_callback; + ctx->abort_callback_data = params.abort_callback_data; + + ctx->rng = std::mt19937(params.seed); + ctx->logits_all = params.logits_all; const ggml_type type_k = params.type_k; const ggml_type type_v = params.type_v; @@ -12989,6 +12998,11 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_ ctx->cparams.n_threads_batch = n_threads_batch; } +void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) { + ctx->abort_callback = abort_callback; + ctx->abort_callback_data = abort_callback_data; +} + struct llama_batch llama_batch_get_one( llama_token * tokens, int32_t n_tokens, diff --git a/llama.h b/llama.h index ed51f478a..6406b5270 100644 --- a/llama.h +++ b/llama.h @@ -255,10 +255,16 @@ extern "C" { enum ggml_type type_v; // data type for V cache // Keep the booleans together to avoid misalignment during copy-by-value. - bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) + bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) + + // Abort callback + // if it returns true, execution of llama_decode() will be aborted + // currently works only with CPU execution + ggml_abort_callback abort_callback; + void * abort_callback_data; }; // model quantization parameters @@ -632,7 +638,10 @@ extern "C" { // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); - // Token logits obtained from the last call to llama_eval() + // Set abort callback + LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); + + // Token logits obtained from the last call to llama_decode() // The logits for the last token are stored in the last row // Logits for which llama_batch.logits[i] == 0 are undefined // Rows: n_tokens provided with llama_batch From 9731134296af3a6839cd682e51d9c2109a871de5 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 2 Mar 2024 22:00:14 +0100 Subject: [PATCH 25/86] server: tests: passkey challenge / self-extend with context shift demo (#5832) * server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test --- .github/workflows/server.yml | 17 +- examples/server/server.cpp | 46 +++- examples/server/tests/README.md | 50 +++- examples/server/tests/features/environment.py | 5 +- examples/server/tests/features/issues.feature | 1 + .../server/tests/features/parallel.feature | 5 +- .../server/tests/features/passkey.feature | 55 ++++ .../server/tests/features/security.feature | 3 +- examples/server/tests/features/server.feature | 23 +- examples/server/tests/features/steps/steps.py | 259 ++++++++++++++---- .../tests/features/wrong_usages.feature | 5 +- examples/server/tests/requirements.txt | 1 + examples/server/tests/tests.sh | 2 +- examples/server/utils.hpp | 3 +- 14 files changed, 363 insertions(+), 112 deletions(-) create mode 100644 examples/server/tests/features/passkey.feature diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 0b6f6669b..8c6312508 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -10,6 +10,8 @@ on: pull_request: types: [opened, synchronize, reopened] paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] + schedule: + - cron: '00 0 * * *' jobs: server: @@ -70,14 +72,15 @@ jobs: run: | pip install -r examples/server/tests/requirements.txt - - name: Download models - id: download_models - run: | - cd examples/server/tests - ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf - - name: Tests - id: server_integration_test + id: server_integration_tests run: | cd examples/server/tests PORT=8888 ./tests.sh + + - name: Slow tests + id: server_integration_tests_slow + if: github.event.schedule != '' + run: | + cd examples/server/tests + PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2b2f4a0f4..52daf9e7a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -441,8 +441,8 @@ struct llama_server_context const int ga_w = params.grp_attn_w; if (ga_n != 1) { - GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT - GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT + GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT + GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT @@ -1709,8 +1709,8 @@ struct llama_server_context } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - // if input prompt is too big, truncate it - if (slot.n_prompt_tokens >= slot.n_ctx) + // if input prompt is too big, truncate it, if group attention self-extend is disabled + if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; @@ -1785,9 +1785,11 @@ struct llama_server_context } LOG_INFO("slot progression", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "n_past", slot.n_past }, + { "slot_id", slot.id }, + { "task_id", slot.task_id }, + { "n_past", slot.n_past }, + { "n_past_se", slot.n_past_se }, + { "ga_i", slot.ga_i }, { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } }); } @@ -2001,6 +2003,17 @@ struct llama_server_context LOG_VERBOSE("slots updated", {}); return true; } + + json model_meta() { + return json{ + {"vocab_type", llama_vocab_type(model)}, + {"n_vocab", llama_n_vocab(model)}, + {"n_ctx_train", llama_n_ctx_train(model)}, + {"n_embd", llama_n_embd(model)}, + {"n_params", llama_model_n_params(model)}, + {"size", llama_model_size(model)}, + }; + } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, @@ -2911,9 +2924,10 @@ int main(int argc, char **argv) for (const auto& metric_def : metrics_def) { std::string name = metric_def["name"]; std::string help = metric_def["help"]; - prometheus << "# HELP llamacpp:" << name << " " << help << "\n" - << "# TYPE llamacpp:" << name << " " << type << "\n" - << "llamacpp:" << name << " " << metric_def["value"] << "\n"; + auto value = json_value(metric_def, "value", 0); + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << value << "\n"; } } @@ -2994,6 +3008,7 @@ int main(int argc, char **argv) state.store(SERVER_STATE_READY); LOG_INFO("model loaded", {}); } + const auto model_meta = llama.model_meta(); if (sparams.chat_template.empty()) { // custom chat template is not supplied // check if the template comes with the model is supported by us @@ -3143,7 +3158,7 @@ int main(int argc, char **argv) } }); - svr.Get("/v1/models", [¶ms](const httplib::Request& req, httplib::Response& res) + svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request& req, httplib::Response& res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); std::time_t t = std::time(0); @@ -3152,10 +3167,11 @@ int main(int argc, char **argv) {"object", "list"}, {"data", { { - {"id", params.model_alias}, - {"object", "model"}, - {"created", t}, - {"owned_by", "llamacpp"} + {"id", params.model_alias}, + {"object", "model"}, + {"created", t}, + {"owned_by", "llamacpp"}, + {"meta", model_meta} }, }} }; diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 0b9fdc4e7..95a0353b6 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -1,22 +1,30 @@ # Server tests -Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/): - * [issues.feature](./features/issues.feature) Pending issues scenario - * [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests - * [security.feature](./features/security.feature) Security, CORS and API Key - * [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... +Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) +and [behave](https://behave.readthedocs.io/en/latest/): + +* [issues.feature](./features/issues.feature) Pending issues scenario +* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests +* [security.feature](./features/security.feature) Security, CORS and API Key +* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... Tests target GitHub workflows job runners with 4 vCPU. -Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client. +Requests are +using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) +based http client. -Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`. +Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. +To mitigate it, you can increase values in `n_predict`, `kv_size`. ### Install dependencies + `pip install -r requirements.txt` ### Run tests + 1. Build the server + ```shell cd ../../.. mkdir build @@ -24,24 +32,36 @@ cd build cmake ../ cmake --build . --target server ``` -2. download required models: - 1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf` -3. Start the test: `./tests.sh` + +2. Start the test: `./tests.sh` It's possible to override some scenario steps values with environment variables: - - `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080` - - `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server` - - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose` - - `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format + +| variable | description | +|--------------------------|------------------------------------------------------------------------------------------------| +| `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | +| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/server` | +| `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` | +| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format | +| `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | ### Run @bug, @wip or @wrong_usage annotated scenario Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope. + - `@bug` annotation aims to link a scenario with a GitHub issue. - `@wrong_usage` are meant to show user issue that are actually an expected behavior - `@wip` to focus on a scenario working in progress +- `@slow` heavy test, disabled by default To run a scenario annotated with `@bug`, start: -`DEBUG=ON ./tests.sh --no-skipped --tags bug` + +```shell +DEBUG=ON ./tests.sh --no-skipped --tags bug +``` After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. + +```shell +./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile" +``` diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 09e826747..9fd330db6 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -7,7 +7,10 @@ from signal import SIGKILL def before_scenario(context, scenario): - print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") + context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' + if context.debug: + print("DEBUG=ON\n") + print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n") port = 8080 if 'PORT' in os.environ: port = int(os.environ['PORT']) diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature index bf5a175a3..7b13e44ca 100644 --- a/examples/server/tests/features/issues.feature +++ b/examples/server/tests/features/issues.feature @@ -1,4 +1,5 @@ # List of ongoing issues +# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug @bug Feature: Issues # No confirmed issue at the moment diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 5f895cf90..86cdf7282 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -1,11 +1,12 @@ @llama.cpp +@parallel Feature: Parallel Background: Server startup Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a model alias tinyllama-2 + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And 42 as server seed + And 512 as batch size And 64 KV cache size And 2 slots And embeddings extraction diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature new file mode 100644 index 000000000..1bde7aab8 --- /dev/null +++ b/examples/server/tests/features/passkey.feature @@ -0,0 +1,55 @@ +# run with: ./tests.sh --no-skipped --tags passkey +@passkey +@slow +Feature: Passkey / Self-extend with context shift + + Background: Server startup + Given a server listening on localhost:8080 + + # Generates a long text of junk and inserts a secret passkey number inside it. + # Then we query the LLM for the secret passkey. + # see #3856 and #4810 + Scenario Outline: Passkey + Given a model file from HF repo + And as batch size + And as number of junk + And server max tokens to predict + And 42 as seed + And KV cache size + And 1 slots + And group attention factor to extend context size through self-extend + And group attention width to extend context size through self-extend + # Can be override with N_GPU_LAYERS + And GPU offloaded layers + Then the server is starting + Then the server is healthy + Given available models + Then model 0 is trained on tokens context + Given a prefix prompt: + """ + here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there. + """ + And a passkey prompt template: + """ + The pass key is Remember it. is the pass key. + """ + And a junk suffix prompt: + """ + The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. + """ + And a suffix prompt: + """ + What is the pass key? The pass key is + """ + Given a "" passkey challenge prompt with the passkey inserted every junk + And a completion request with no api error + Then tokens are predicted matching + + Examples: + | hf_repo | hf_file | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content | + | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 4 | 512 | 250 | 50 | 42 | 1 | 42 | + | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 2 | 512 | 250 | 50 | 42 | 1 | \b((?!42)\w)+\b | + #| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 | + #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0 + # 987 | + diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index db06d3977..42a6709a5 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -1,9 +1,10 @@ @llama.cpp +@security Feature: Security Background: Server startup with an api key defined Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a server api key llama.cpp Then the server is starting Then the server is healthy diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index b571582a7..7c977bcce 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -1,15 +1,17 @@ @llama.cpp +@server Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a model alias tinyllama-2 And 42 as server seed # KV Cache corresponds to the total amount of tokens # that can be stored across all independent sequences: #4130 # see --ctx-size and #5568 And 32 KV cache size + And 512 as batch size And 1 slots And embeddings extraction And 32 server max tokens to predict @@ -29,9 +31,9 @@ Feature: llama.cpp server And prometheus metrics are exposed Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | (readgoing)+ | 8 | - | Write a joke about AI | 64 | (parkfriendsscaredalways)+ | 32 | + | prompt | n_predict | re_content | n_predicted | + | I believe the meaning of life is | 8 | (read\|going)+ | 8 | + | Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 | Scenario Outline: OAI Compatibility Given a model @@ -43,9 +45,9 @@ Feature: llama.cpp server Then tokens are predicted matching Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | - | llama-2 | Book | What is the best book | 8 | (Momwhat)+ | 8 | disabled | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thankshappybird)+ | 32 | enabled | + | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | + | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | Scenario: Embedding When embeddings are computed for: @@ -75,10 +77,15 @@ Feature: llama.cpp server When an OAI compatible embeddings computation request for multiple inputs Then embeddings are generated - Scenario: Tokenize / Detokenize When tokenizing: """ What is the capital of France ? """ Then tokens can be detokenize + + Scenario: Models available + Given available models + Then 1 models are supported + Then model 0 is identified by tinyllama-2 + Then model 0 is trained on 128 tokens context diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 381da105e..319527802 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -13,6 +13,7 @@ import aiohttp import openai from behave import step from behave.api.async_step import async_run_until_complete +from huggingface_hub import hf_hub_download from prometheus_client import parser @@ -26,17 +27,23 @@ def step_server_config(context, server_fqdn, server_port): context.base_url = f'http://{context.server_fqdn}:{context.server_port}' - context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' context.model_alias = None + context.n_batch = None context.n_ctx = None + context.n_ga = None + context.n_ga_w = None + context.n_gpu_layer = None context.n_predict = None context.n_server_predict = None context.n_slots = None + context.prompt_prefix = None + context.prompt_suffix = None context.server_api_key = None context.server_continuous_batching = False context.server_embeddings = False context.server_metrics = False context.server_process = None + context.seed = None context.server_seed = None context.user_api_key = None @@ -45,9 +52,11 @@ def step_server_config(context, server_fqdn, server_port): context.prompts = [] -@step(u'a model file {model_file}') -def step_model_file(context, model_file): - context.model_file = model_file +@step(u'a model file {hf_file} from HF repo {hf_repo}') +def step_download_hf_model(context, hf_file, hf_repo): + context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file) + if context.debug: + print(f"model file: {context.model_file}\n") @step(u'a model alias {model_alias}') @@ -55,24 +64,34 @@ def step_model_alias(context, model_alias): context.model_alias = model_alias -@step(u'{seed} as server seed') +@step(u'{seed:d} as server seed') def step_seed(context, seed): - context.server_seed = int(seed) + context.server_seed = seed -@step(u'{n_ctx} KV cache size') +@step(u'{ngl:d} GPU offloaded layers') +def step_n_gpu_layer(context, ngl): + if 'N_GPU_LAYERS' in os.environ: + new_ngl = int(os.environ['N_GPU_LAYERS']) + if context.debug: + print(f"-ngl upgraded from {ngl} to {new_ngl}") + ngl = new_ngl + context.n_gpu_layer = ngl + + +@step(u'{n_ctx:d} KV cache size') def step_n_ctx(context, n_ctx): - context.n_ctx = int(n_ctx) + context.n_ctx = n_ctx -@step(u'{n_slots} slots') +@step(u'{n_slots:d} slots') def step_n_slots(context, n_slots): - context.n_slots = int(n_slots) + context.n_slots = n_slots -@step(u'{n_predict} server max tokens to predict') +@step(u'{n_predict:d} server max tokens to predict') def step_server_n_predict(context, n_predict): - context.n_server_predict = int(n_predict) + context.n_server_predict = n_predict @step(u'continuous batching') @@ -116,11 +135,13 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): case 'ready' | 'idle': await wait_for_health_status(context, context.base_url, 200, 'ok', + timeout=10, params={'fail_on_no_slot': 0, 'include_slots': 0}, slots_idle=context.n_slots, slots_processing=0, expected_slots=[{'id': slot_id, 'state': 0} - for slot_id in range(context.n_slots)]) + for slot_id in + range(context.n_slots if context.n_slots else 1)]) case 'busy': await wait_for_health_status(context, context.base_url, 503, 'no slot available', @@ -128,7 +149,8 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): slots_idle=0, slots_processing=context.n_slots, expected_slots=[{'id': slot_id, 'state': 1} - for slot_id in range(context.n_slots)]) + for slot_id in + range(context.n_slots if context.n_slots else 1)]) case _: assert False, "unknown status" @@ -157,24 +179,24 @@ async def step_request_completion(context, api_error): context.base_url, debug=context.debug, n_predict=context.n_predict, - server_seed=context.server_seed, + seed=await completions_seed(context), expect_api_error=expect_api_error, user_api_key=context.user_api_key) context.tasks_result.append(completion) if context.debug: - print(f"Completion response: {completion}") + print(f"Completion response: {completion}\n") if expect_api_error: assert completion == 401, f"completion must be an 401 status code: {completion}" -@step(u'{predicted_n} tokens are predicted matching {re_content}') +@step(u'{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content) + assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content) -@step(u'{predicted_n} tokens are predicted') +@step(u'{predicted_n:d} tokens are predicted') def step_n_tokens_predicted(context, predicted_n): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n)) + assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n) @step(u'a user prompt {user_prompt}') @@ -192,9 +214,9 @@ def step_model(context, model): context.model = model -@step(u'{max_tokens} max tokens to predict') +@step(u'{max_tokens:d} max tokens to predict') def step_max_tokens(context, max_tokens): - context.n_predict = int(max_tokens) + context.n_predict = max_tokens @step(u'streaming is {enable_streaming}') @@ -222,11 +244,70 @@ def step_server_api_key(context, server_api_key): context.server_api_key = server_api_key +@step(u'{n_junk:d} as number of junk') +def step_n_junk(context, n_junk): + context.n_junk = n_junk + + +@step(u'{n_batch:d} as batch size') +def step_n_batch(context, n_batch): + context.n_batch = n_batch + + +@step(u'{seed:d} as seed') +def step_seed(context, seed): + context.seed = seed + + +@step(u'a prefix prompt') +def step_prompt_prefix(context): + context.prompt_prefix = context.text + + +@step(u'a junk suffix prompt') +def step_prompt_junk_suffix(context): + context.prompt_junk_suffix = context.text + + +@step(u'a suffix prompt') +def step_prompt_suffix(context): + context.prompt_suffix = context.text + + +@step(u'{n_ga:d} group attention factor' + u' to extend context size through self-extend') +def step_impl(context, n_ga): + context.n_ga = n_ga + + +@step(u'{n_ga_w:d} group attention width to extend context size through self-extend') +def step_impl(context, n_ga_w): + context.n_ga_w = n_ga_w + + +@step(u'a passkey prompt template') +def step_prompt_passkey(context): + context.prompt_passkey = context.text + + +@step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') +def step_prompt_passkey(context, passkey, i_pos): + prompt = "" + for i in range(context.n_junk): + if i % context.n_junk == i_pos: + prompt += context.prompt_passkey # the passkey is already substituted + prompt += context.prompt_junk_suffix + if context.debug: + passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" + print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n") + context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) + + @step(u'an OAI compatible chat completions request with {api_error} api error') @async_run_until_complete async def step_oai_chat_completions(context, api_error): if context.debug: - print(f"Submitting OAI compatible completions request...") + print(f"Submitting OAI compatible completions request...\n") expect_api_error = api_error == 'raised' completion = await oai_chat_completions(context.prompts.pop(), context.system_prompt, @@ -241,8 +322,7 @@ async def step_oai_chat_completions(context, api_error): enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, @@ -276,8 +356,10 @@ async def step_concurrent_completion_requests(context): # prompt is inserted automatically context.base_url, debug=context.debug, + prompt_prefix=context.prompt_prefix, + prompt_suffix=context.prompt_suffix, n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - server_seed=context.server_seed if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -297,8 +379,7 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -318,7 +399,9 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed + seed=context.seed + if hasattr(context, 'seed') else + context.server_seed if hasattr(context, 'server_seed') else None, user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -330,11 +413,10 @@ async def step_all_prompts_are_predicted(context): await all_prompts_are_predicted(context) -@step(u'all prompts are predicted with {n_predict} tokens') +@step(u'all prompts are predicted with {n_expected_predicted:d} tokens') @async_run_until_complete -async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict): - expected_predicted_n = int(n_predict) - await all_prompts_are_predicted(context, expected_predicted_n) +async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted): + await all_prompts_are_predicted(context, n_expected_predicted) async def all_prompts_are_predicted(context, expected_predicted_n=None): @@ -464,6 +546,8 @@ async def step_prometheus_metrics_exported(context): assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4" metrics_raw = await metrics_response.text() metric_exported = False + if context.debug: + print(f"/metrics answer:\n{metrics_raw}\n") for metric in parser.text_string_to_metric_families(metrics_raw): match metric.name: case "llamacpp:kv_cache_usage_ratio": @@ -472,6 +556,37 @@ async def step_prometheus_metrics_exported(context): assert metric_exported, "No metrics exported" +@step(u'available models') +def step_available_models(context): + # openai client always expects an api_key + openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope' + openai.api_base = f'{context.base_url}/v1' + context.models = openai.Model.list().data + + +@step(u'{n_model:d} models are supported') +def step_supported_models(context, n_model): + if context.debug: + print("server models available:", context.models) + assert len(context.models) == n_model + + +@step(u'model {i_model:d} is {param} {preposition} {param_value}') +def step_supported_models(context, i_model, param, preposition, param_value): + assert i_model < len(context.models) + model = context.models[i_model] + + param_value = param_value.split(' ', 1)[0] + match param: + case 'identified': + value = model.id + case 'trained': + value = str(model.meta.n_ctx_train) + case _: + assert False, "param {param} not supported" + assert param_value == value, f"model param {param} {value} != {param_value}" + + async def concurrent_requests(context, f_completion, *args, **kwargs): n_prompts = len(context.prompts) if context.debug: @@ -486,8 +601,10 @@ async def concurrent_requests(context, f_completion, *args, **kwargs): async def request_completion(prompt, base_url, debug=False, + prompt_prefix=None, + prompt_suffix=None, n_predict=None, - server_seed=None, + seed=None, expect_api_error=None, user_api_key=None): if debug: @@ -504,11 +621,14 @@ async def request_completion(prompt, async with aiohttp.ClientSession() as session: async with session.post(f'{base_url}/completion', json={ + "input_prefix": prompt_prefix, "prompt": prompt, - "n_predict": int(n_predict) if n_predict is not None else -1, - "seed": server_seed if server_seed is not None else 42 + "input_suffix": prompt_suffix, + "n_predict": n_predict if n_predict is not None else -1, + "seed": seed if seed is not None else 42 }, - headers=headers) as response: + headers=headers, + timeout=3600) as response: if expect_api_error is None or not expect_api_error: assert response.status == 200 assert response.headers['Access-Control-Allow-Origin'] == origin @@ -526,14 +646,14 @@ async def oai_chat_completions(user_prompt, model=None, n_predict=None, enable_streaming=None, - server_seed=None, + seed=None, user_api_key=None, expect_api_error=None): if debug: print(f"Sending OAI Chat completions request: {user_prompt}") # openai client always expects an api key user_api_key = user_api_key if user_api_key is not None else 'nope' - seed = server_seed if server_seed is not None else 42 + seed = seed if seed is not None else 42 enable_streaming = enable_streaming if enable_streaming is not None else False payload = { "messages": [ @@ -692,20 +812,32 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re content = completion_response['content'] n_predicted = completion_response['timings']['predicted_n'] assert len(content) > 0, "no token predicted" - if expected_predicted_n is not None: + if re_content is not None: + p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL) + matches = p.finditer(content) + last_match = 0 + highlighted = '' + for match in matches: + start, end = match.span() + highlighted += content[last_match: start] + highlighted += '\x1b[33m' + highlighted += content[start: end] + highlighted += '\x1b[0m' + last_match = end + highlighted += content[last_match:] + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + print(f"Checking completion response: {highlighted}\n") + assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```' + if expected_predicted_n and expected_predicted_n > 0: assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' f' {n_predicted} <> {expected_predicted_n}') - if re_content is not None: - re_content = '^.*' + re_content.replace('', '|') + '.*$' - assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), ( - f'invalid tokens predicted:' - f' ```\n{content}\n``` do not match /{re_content}/') + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: - print(f"Waiting for all {n_tasks} tasks results...") + print(f"Waiting for all {n_tasks} tasks results...\n") for task_no in range(n_tasks): context.tasks_result.append(await context.concurrent_tasks.pop()) n_completions = len(context.tasks_result) @@ -716,15 +848,13 @@ async def wait_for_health_status(context, base_url, expected_http_status_code, expected_health_status, + timeout=3, params=None, slots_idle=None, slots_processing=None, expected_slots=None): if context.debug: - print(f"Starting checking for health for expected_health_status={expected_health_status}") - timeout = 3 # seconds - if expected_health_status == 'ok': - timeout = 10 # CI slow inference + print(f"Starting checking for health for expected_health_status={expected_health_status}\n") interval = 0.5 counter = 0 async with aiohttp.ClientSession() as session: @@ -734,7 +864,7 @@ async def wait_for_health_status(context, health = await health_response.json() if context.debug: print(f"HEALTH - response for expected health status='{expected_health_status}' on " - f"'{base_url}/health'?{params} is {health}") + f"'{base_url}/health'?{params} is {health}\n") if (status_code == expected_http_status_code and health['status'] == expected_health_status and (slots_idle is None or health['slots_idle'] == slots_idle) @@ -757,7 +887,7 @@ async def wait_for_health_status(context, if expected_http_status_code == 503: if len(context.tasks_result) == 0: print("\x1b[5;37;43mWARNING: forcing concurrent tasks," - " busy health check missed, probably too fast inference\x1b[0m") + " busy health check missed, probably too fast inference\x1b[0m\n") n_completions = await gather_tasks_results(context) if n_completions > 0: return @@ -791,6 +921,11 @@ def assert_slots_status(slots, expected_slots): f" = {expected[key]} != {slot[key]}") +async def completions_seed(context): + return context.seed if hasattr(context, 'seed') and context.seed is not None \ + else context.server_seed if hasattr(context, 'server_seed') else None + + def start_server_background(context): context.server_path = '../../../build/bin/server' if 'LLAMA_SERVER_BIN_PATH' in os.environ: @@ -800,27 +935,35 @@ def start_server_background(context): '--port', context.server_port, '--model', context.model_file ] + if context.n_batch: + server_args.extend(['--batch-size', context.n_batch]) + if context.n_gpu_layer: + server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.server_continuous_batching: server_args.append('--cont-batching') if context.server_embeddings: server_args.append('--embedding') if context.server_metrics: server_args.append('--metrics') - if context.model_alias is not None: + if context.model_alias: server_args.extend(['--alias', context.model_alias]) - if context.n_ctx is not None: + if context.n_ctx: server_args.extend(['--ctx-size', context.n_ctx]) - if context.n_slots is not None: + if context.n_slots: server_args.extend(['--parallel', context.n_slots]) - if context.n_server_predict is not None: + if context.n_server_predict: server_args.extend(['--n-predict', context.n_server_predict]) - if context.server_api_key is not None: + if context.server_api_key: server_args.extend(['--api-key', context.server_api_key]) + if context.n_ga: + server_args.extend(['--grp-attn-n', context.n_ga]) + if context.n_ga_w: + server_args.extend(['--grp-attn-w', context.n_ga_w]) if context.debug: server_args.append('--verbose') if 'SERVER_LOG_FORMAT_JSON' not in os.environ: server_args.extend(['--log-format', "text"]) - print(f"starting server with: {context.server_path}", *server_args) + print(f"starting server with: {context.server_path} {server_args}\n") context.server_process = subprocess.Popen( [str(arg) for arg in [context.server_path, *server_args]], close_fds=True) diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature index e228b2371..cf14b3b44 100644 --- a/examples/server/tests/features/wrong_usages.feature +++ b/examples/server/tests/features/wrong_usages.feature @@ -1,4 +1,4 @@ -# run with ./test.sh --tags wrong_usage +# run with: ./tests.sh --no-skipped --tags wrong_usage @wrong_usage Feature: Wrong usage of llama.cpp server @@ -7,7 +7,7 @@ Feature: Wrong usage of llama.cpp server # or pass n_predict/max_tokens in the request. Scenario: Infinite loop Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models # Uncomment below to fix the issue #And 64 server max tokens to predict Then the server is starting @@ -18,4 +18,5 @@ Feature: Wrong usage of llama.cpp server # Uncomment below to fix the issue #And 128 max tokens to predict Given concurrent completion requests + Then the server is idle Then all prompts are predicted diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 334fa4a70..5d4210164 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,4 +1,5 @@ aiohttp~=3.9.3 behave~=1.2.6 +huggingface_hub~=0.20.3 openai~=0.25.0 prometheus-client~=0.20.0 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 17a4e6fc6..1c6c5695f 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -5,7 +5,7 @@ set -eu if [ $# -lt 1 ] then # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp + behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp else behave "$@" fi diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d98541f26..b6e49d8b9 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -126,8 +126,7 @@ static inline void server_log(const char *level, const char *function, int line, for (const auto& el : log.items()) { const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace); - snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str()); - ss << buf; + ss << " " << el.key() << "=" << value; } const std::string str = ss.str(); From fa974646e1a2024fc7dc9e6f27cf1f2f5d4a3763 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Mar 2024 06:11:31 +0200 Subject: [PATCH 26/86] flake.lock: Update (#5842) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) → 'github:hercules-ci/flake-parts/f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2' (2024-03-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8?dir=lib' (2024-02-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/cbc4211f0afffe6dfd2478a62615dd5175a13f9a' (2024-02-23) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8' (2024-02-29) Co-authored-by: github-actions[bot] --- flake.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/flake.lock b/flake.lock index 9f659ba8f..b1b091656 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1706830856, - "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=", + "lastModified": 1709336216, + "narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f", + "rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1708655239, - "narHash": "sha256-ZrP/yACUvDB+zbqYJsln4iwotbH6CTZiTkANJ0AgDv4=", + "lastModified": 1709237383, + "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "cbc4211f0afffe6dfd2478a62615dd5175a13f9a", + "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8", "type": "github" }, "original": { @@ -37,11 +37,11 @@ "nixpkgs-lib": { "locked": { "dir": "lib", - "lastModified": 1706550542, - "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=", + "lastModified": 1709237383, + "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652", + "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8", "type": "github" }, "original": { From 8ef969afcec1645d2d9c3ab1fc82263bba968989 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 3 Mar 2024 08:48:36 +0100 Subject: [PATCH 27/86] server : init http requests thread pool with --parallel if set (#5836) --- examples/server/README.md | 2 +- examples/server/server.cpp | 10 ++++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 397ee8252..21da7a0a0 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -18,7 +18,7 @@ The project is under active development, and we are [looking for feedback and co - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. -- `--threads-http N`: number of threads in the http server pool to process requests (default: `std::thread::hardware_concurrency()`) +- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 52daf9e7a..0ca388f47 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2026,7 +2026,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, 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(" --threads-http N number of threads in the http server pool to process requests (default: hardware concurrency)\n"); + printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); @@ -3468,10 +3468,12 @@ int main(int argc, char **argv) }*/ //); - if (sparams.n_threads_http > 0) { - log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + if (sparams.n_threads_http < 1) { + // +2 threads for monitoring endpoints + sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } + log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); + svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below From e6029348e86c3810d4435faee54ba822cb43e2ef Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 3 Mar 2024 09:35:23 +0100 Subject: [PATCH 28/86] ci : schedule slow server tests only on Release or on demand (#5839) --- .github/workflows/server.yml | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 8c6312508..04e3fc0c1 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -3,6 +3,11 @@ name: Server on: workflow_dispatch: # allows manual triggering + inputs: + slow_tests: + description: 'Run slow tests' + required: true + type: boolean push: branches: - master @@ -11,7 +16,7 @@ on: types: [opened, synchronize, reopened] paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] schedule: - - cron: '00 0 * * *' + - cron: '0 0 * * *' jobs: server: @@ -80,7 +85,7 @@ jobs: - name: Slow tests id: server_integration_tests_slow - if: github.event.schedule != '' + if: ${{ github.event.schedule != '' && matrix.build_type == 'Release' || github.event.inputs.slow_tests == 'true' }} run: | cd examples/server/tests PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow From de9692a7d2db66e29e5cb373c6551acc49145ccd Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Sun, 3 Mar 2024 03:41:55 -0500 Subject: [PATCH 29/86] llama : fix llama_copy_state_data with fragmented KV cache (#5840) The row size of the saved states was based on kv_self.head while it should be based on llama_kv_cache_cell_max. Existing session files should still work. * llama : fix llama_kv_cache_cell_max inability to return 1 I've also changed its return type to uint32_t, because this function is always used to set the value of uint32_t variables, and because the index already has this type. * llama : fix state size calculation Some bytes in the state were unaccounted for in llama_get_state_size. Since the logits reserve so much space, it did not cause problems. --- llama.cpp | 47 ++++++++++++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/llama.cpp b/llama.cpp index d4c7a965b..41d0000da 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2156,10 +2156,12 @@ static bool llama_kv_cache_find_slot( } // find how many cells are currently in use -static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { - for (uint32_t i = cache.size - 1; i > 0; --i) { - if (cache.cells[i].pos >= 0 && !cache.cells[i].is_empty()) { - return i + 1; +static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { + for (uint32_t i = cache.size; i > 0; --i) { + const llama_kv_cell & cell = cache.cells[i - 1]; + + if (cell.pos >= 0 && !cell.is_empty()) { + return i; } } @@ -8178,7 +8180,7 @@ static int llama_decode_internal( // a heuristic, to avoid attending the full cache if it is not yet utilized // after enough generations, the benefit from this heuristic disappears // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); //kv_self.n = llama_kv_cache_cell_max(kv_self); //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); @@ -12615,9 +12617,14 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_logits = ctx->logits.capacity() * sizeof(float); const size_t s_embedding_size = sizeof(size_t); const size_t s_embedding = ctx->embedding.size() * sizeof(float); - const size_t s_kv_size = sizeof(size_t); - const size_t s_kv_ntok = sizeof(int); + const size_t s_kv_buf_size = sizeof(size_t); + const size_t s_kv_head = sizeof(uint32_t); + const size_t s_kv_size = sizeof(uint32_t); + const size_t s_kv_used = sizeof(uint32_t); const size_t s_kv = ctx->kv_self.total_size(); + // TODO: assume the max is more than 1 seq_id per KV cell + const size_t s_kv_cell = sizeof(llama_pos) + sizeof(size_t) + sizeof(llama_seq_id); + const size_t s_kv_cells = ctx->kv_self.size * s_kv_cell; const size_t s_total = ( + s_rng_size @@ -12626,9 +12633,12 @@ size_t llama_get_state_size(const struct llama_context * ctx) { + s_logits + s_embedding_size + s_embedding + + s_kv_buf_size + + s_kv_head + s_kv_size - + s_kv_ntok + + s_kv_used + s_kv + + s_kv_cells ); return s_total; @@ -12728,15 +12738,13 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat { const auto & kv_self = ctx->kv_self; const auto & hparams = ctx->model.hparams; - const auto & cparams = ctx->cparams; const uint32_t n_layer = hparams.n_layer; const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const uint32_t n_ctx = cparams.n_ctx; const size_t kv_buf_size = kv_self.total_size(); - const uint32_t kv_head = kv_self.head; + const uint32_t kv_head = llama_kv_cache_cell_max(kv_self); const uint32_t kv_size = kv_self.size; const uint32_t kv_used = kv_self.used; @@ -12756,7 +12764,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); - const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); tmp_buf.resize(v_row_size); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { @@ -12766,7 +12774,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat } } - for (uint32_t i = 0; i < kv_size; ++i) { + for (uint32_t i = 0; i < kv_head; ++i) { const auto & cell = kv_self.cells[i]; const llama_pos pos = cell.pos; @@ -12842,12 +12850,10 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { { const auto & kv_self = ctx->kv_self; const auto & hparams = ctx->model.hparams; - const auto & cparams = ctx->cparams; const uint32_t n_layer = hparams.n_layer; const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const uint32_t n_ctx = cparams.n_ctx; size_t kv_buf_size; uint32_t kv_head; @@ -12870,7 +12876,7 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); - const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size); @@ -12879,13 +12885,15 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { } } + GGML_ASSERT(kv_self.size == kv_size); + ctx->kv_self.head = kv_head; ctx->kv_self.size = kv_size; ctx->kv_self.used = kv_used; ctx->kv_self.cells.resize(kv_size); - for (uint32_t i = 0; i < kv_size; ++i) { + for (uint32_t i = 0; i < kv_head; ++i) { llama_pos pos; size_t seq_id_size; @@ -12901,6 +12909,11 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { ctx->kv_self.cells[i].seq_id.insert(seq_id); } } + + for (uint32_t i = kv_head; i < kv_size; ++i) { + ctx->kv_self.cells[i].pos = -1; + ctx->kv_self.cells[i].seq_id.clear(); + } } const size_t nread = inp - src; From 87c2e8b2797860a06af3d6c06b8488a8ff1a09ab Mon Sep 17 00:00:00 2001 From: Nindaleth Date: Sun, 3 Mar 2024 09:43:42 +0100 Subject: [PATCH 30/86] gguf-dump : support i-quants (#5841) Co-authored-by: Black_Fox --- gguf-py/gguf/constants.py | 72 ++++++++++++++++++++++++--------------- 1 file changed, 44 insertions(+), 28 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5db760cb1..a62139811 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -604,20 +604,28 @@ class PoolingType(IntEnum): class GGMLQuantizationType(IntEnum): - F32 = 0 - F16 = 1 - Q4_0 = 2 - Q4_1 = 3 - Q5_0 = 6 - Q5_1 = 7 - Q8_0 = 8 - Q8_1 = 9 - Q2_K = 10 - Q3_K = 11 - Q4_K = 12 - Q5_K = 13 - Q6_K = 14 - Q8_K = 15 + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + IQ2_XXS = 16 + IQ2_XS = 17 + IQ3_XXS = 18 + IQ1_S = 19 + IQ4_NL = 20 + IQ3_S = 21 + IQ2_S = 22 + IQ4_XS = 23 class GGUFEndian(IntEnum): @@ -662,20 +670,28 @@ class GGUFValueType(IntEnum): QK_K = 256 # Items here are (block size, type size) GGML_QUANT_SIZES = { - GGMLQuantizationType.F32: (1, 4), - GGMLQuantizationType.F16: (1, 2), - GGMLQuantizationType.Q4_0: (32, 2 + 16), - GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), - GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), - GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), - GGMLQuantizationType.Q8_0: (32, 2 + 32), - GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), - GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), - GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), - GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), - GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4), + GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32), + GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8), + GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16), + GGMLQuantizationType.IQ4_NL: (32, 2 + 16), + GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), + GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), } From 475df1d6cf817060028d3ff763cb8097d4ec40d6 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Sun, 3 Mar 2024 04:40:27 -0600 Subject: [PATCH 31/86] llama : allow for user specified embedding pooling type (#5849) * allow for user specified pooling type * llama : use enum types over int --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 13 +++++++++++++ common/common.h | 7 +++++-- convert-hf-to-gguf.py | 18 +++++++++--------- llama.cpp | 44 +++++++++++++++++++++++++++---------------- llama.h | 7 +++++-- 5 files changed, 60 insertions(+), 29 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 1c0b7c403..dbe7e9229 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -335,6 +335,16 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.yarn_beta_slow = std::stof(argv[i]); + } else if (arg == "--pooling") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; break; } } else if (arg == "--defrag-thold" || arg == "-dt") { if (++i >= argc) { invalid_param = true; @@ -1014,6 +1024,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" --pooling {none,mean,cls}\n"); + printf(" pooling type for embeddings, use model default if unspecified\n"); printf(" -dt N, --defrag-thold N\n"); printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); @@ -1296,6 +1308,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.yarn_orig_ctx = params.yarn_orig_ctx; + cparams.pooling_type = params.pooling_type; cparams.defrag_thold = params.defrag_thold; cparams.offload_kqv = !params.no_kv_offload; diff --git a/common/common.h b/common/common.h index ab62bdb82..d3682b7ad 100644 --- a/common/common.h +++ b/common/common.h @@ -76,8 +76,11 @@ struct gpt_params { float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold - int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; - ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; + llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings // // sampling parameters struct llama_sampling_params sparams; diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index fa9d4f22f..ffdba7444 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1644,16 +1644,17 @@ class BertModel(Model): self.gguf_writer.add_causal_attention(False) # get pooling path - with open(self.dir_model / "modules.json", encoding="utf-8") as f: - modules = json.load(f) pooling_path = None - for mod in modules: - if mod["type"] == "sentence_transformers.models.Pooling": - pooling_path = mod["path"] - break + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break # get pooling type - pooling_type = gguf.PoolingType.NONE if pooling_path is not None: with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: pooling = json.load(f) @@ -1663,8 +1664,7 @@ class BertModel(Model): pooling_type = gguf.PoolingType.CLS else: raise NotImplementedError("Only MEAN and CLS pooling types supported") - - self.gguf_writer.add_pooling_type(pooling_type) + self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): path = self.dir_model diff --git a/llama.cpp b/llama.cpp index 41d0000da..c1f015791 100644 --- a/llama.cpp +++ b/llama.cpp @@ -873,16 +873,16 @@ struct LLM_TN { // gguf helpers // -static const std::map LLAMA_ROPE_SCALING_TYPES = { +static const std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, }; -static int32_t llama_rope_scaling_type_from_string(const std::string & name) { +static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) { for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { if (kv.second == name) { - return kv.first; + return (llama_rope_scaling_type) kv.first; } } @@ -1612,7 +1612,6 @@ struct llama_hparams { float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; - int32_t rope_scaling_type_train; float f_clamp_kqv = 0.0f; float f_max_alibi_bias = 0.0f; @@ -1620,8 +1619,9 @@ struct llama_hparams { bool causal_attn = true; bool need_kq_pos = false; - enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; - enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; + enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; + enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; + enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE; bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; @@ -1670,8 +1670,8 @@ struct llama_cparams { uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - float rope_freq_base; - float rope_freq_scale; + float rope_freq_base; + float rope_freq_scale; uint32_t n_yarn_orig_ctx; // These hyperparameters are not exposed in GGUF, because all @@ -1683,7 +1683,7 @@ struct llama_cparams { float defrag_thold; bool offload_kqv; - bool do_pooling; + enum llama_pooling_type pooling_type; ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; @@ -2933,7 +2933,11 @@ template<> bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { uint32_t tmp; const bool found = get_key(kid, tmp, required); - result = (enum llama_pooling_type) tmp; + if (found) { + result = (enum llama_pooling_type) tmp; + } else { + result = LLAMA_POOLING_TYPE_UNSPECIFIED; + } return found; } @@ -3210,7 +3214,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); switch (hparams.n_layer) { case 3: @@ -5175,7 +5179,7 @@ struct llm_build_context { n_kv (worst_case ? n_ctx : kv_self.n), kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), - pooling_type (cparams.do_pooling ? hparams.pooling_type : LLAMA_POOLING_TYPE_NONE), + pooling_type (cparams.pooling_type), rope_type (hparams.rope_type), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { @@ -8015,7 +8019,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { + if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -8043,7 +8047,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { + if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -11846,6 +11850,7 @@ struct llama_context_params llama_context_default_params() { /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, + /*.pooling_type =*/ LLAMA_POOLING_TYPE_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, /*.yarn_ext_factor =*/ -1.0f, @@ -11861,7 +11866,6 @@ struct llama_context_params llama_context_default_params() { /*.logits_all =*/ false, /*.embedding =*/ false, /*.offload_kqv =*/ true, - /*.do_pooling =*/ true, /*.abort_callback =*/ nullptr, /*.abort_callback_data =*/ nullptr, }; @@ -12012,7 +12016,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; cparams.offload_kqv = params.offload_kqv; - cparams.do_pooling = params.do_pooling; + cparams.pooling_type = params.pooling_type; cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; @@ -12038,6 +12042,14 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { + if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { + cparams.pooling_type = LLAMA_POOLING_TYPE_NONE; + } else { + cparams.pooling_type = hparams.pooling_type; + } + } + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } diff --git a/llama.h b/llama.h index 6406b5270..70da4cb3f 100644 --- a/llama.h +++ b/llama.h @@ -129,6 +129,7 @@ extern "C" { }; enum llama_pooling_type { + LLAMA_POOLING_TYPE_UNSPECIFIED = -1, LLAMA_POOLING_TYPE_NONE = 0, LLAMA_POOLING_TYPE_MEAN = 1, LLAMA_POOLING_TYPE_CLS = 2, @@ -236,7 +237,10 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + + enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id + // (ignored if no pooling layer) // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model @@ -258,7 +262,6 @@ extern "C" { bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU - bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) // Abort callback // if it returns true, execution of llama_decode() will be aborted From 231ae28f078c3148d097b301f2145f1e3e816cc1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Mar 2024 12:44:03 +0200 Subject: [PATCH 32/86] readme : add API changes section --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 939646753..45c5d06f3 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,10 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ +### Recent API changes + +- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 + ### Hot topics - The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) From 67be2ce1015d070b3b2cd488bcb041eefb61de72 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 3 Mar 2024 14:26:18 +0100 Subject: [PATCH 33/86] cuda : fix data race in soft max (#5853) --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7ed97430f..04c6cb1b8 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6904,6 +6904,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f // find the sum of exps in the block tmp = warp_reduce_sum(tmp); if (block_size > WARP_SIZE) { + __syncthreads(); if (warp_id == 0) { buf_iw[lane_id] = 0.0f; } From 5a51cc1bb4592f0d71f9af89cd08b11a066ba447 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Mon, 4 Mar 2024 02:57:20 -0500 Subject: [PATCH 34/86] main : support special tokens as reverse/anti prompt (#5847) * Support special tokens as reverse/anti prompt. * Tokenize antiprompts only once. * main : minor --------- Co-authored-by: Georgi Gerganov --- examples/main/main.cpp | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 34e84d0d4..47059e582 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -511,6 +511,14 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; + // tokenized antiprompts + std::vector> antiprompt_ids; + + antiprompt_ids.reserve(params.antiprompt.size()); + for (const std::string & antiprompt : params.antiprompt) { + antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true)); + } + struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); while ((n_remain != 0 && !is_antiprompt) || params.interactive) { @@ -769,6 +777,18 @@ int main(int argc, char ** argv) { } } + // check for reverse prompt using special tokens + llama_token last_token = llama_sampling_last(ctx_sampling); + for (std::vector ids : antiprompt_ids) { + if (ids.size() == 1 && last_token == ids[0]) { + if (params.interactive) { + is_interacting = true; + } + is_antiprompt = true; + break; + } + } + if (is_antiprompt) { LOG("found antiprompt: %s\n", last_output.c_str()); } From 82f3e668adafba647de703f835991e91a96b5ac4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Mon, 4 Mar 2024 03:08:19 -0500 Subject: [PATCH 35/86] common : use LLAMA_DEFAULT_SEED (#5855) --- common/common.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.h b/common/common.h index d3682b7ad..b2868833b 100644 --- a/common/common.h +++ b/common/common.h @@ -43,7 +43,7 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed int32_t n_threads = get_num_physical_cores(); int32_t n_threads_draft = -1; From 7d43c585dc174bb586775c22c15e5db9242b5b4b Mon Sep 17 00:00:00 2001 From: leejet Date: Sun, 3 Mar 2024 20:23:52 +0800 Subject: [PATCH 36/86] add some new ops, fix some operators and add batch operations to certain operators. (ggml/747) * cuda: fix group_norm * cuda: add batch inference support for ggml_pad/ggml_upscale * add ggml_arrange * add ggml_timestep_embedding * update ggml_arange/ggml_timestep_embedding tests * cuda: fix im2col * add ggml_arange/ggml_timestep_embbeding support for metal backend * fix some bugs * fix some bugs * Update ggml.h Co-authored-by: Georgi Gerganov * Update ggml-cuda.cu Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.metal Co-authored-by: Georgi Gerganov * modify according to the review comments * ggml : fix compile warnings + code style * ggml : normalize compute_forward calls + fix seg fault in debug * minor --------- Co-authored-by: Georgi Gerganov Co-authored-by: slaren --- ggml-cuda.cu | 227 ++++++++++++++++++++++++++++++------- ggml-metal.m | 62 +++++++++- ggml-metal.metal | 43 +++++++ ggml.c | 207 +++++++++++++++++++++++++++++++-- ggml.h | 17 +++ tests/test-backend-ops.cpp | 46 ++++++++ 6 files changed, 550 insertions(+), 52 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 04c6cb1b8..7d027a30a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -616,6 +616,8 @@ static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + Q #define CUDA_UPSCALE_BLOCK_SIZE 256 #define CUDA_CONCAT_BLOCK_SIZE 256 #define CUDA_PAD_BLOCK_SIZE 256 +#define CUDA_ARANGE_BLOCK_SIZE 256 +#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 #define CUDA_POOL2D_BLOCK_SIZE 256 @@ -990,17 +992,21 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst, nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; + dst[offset_dst] = x[offset_src]; } else { int offset_src = nidx + blockIdx.y * ne0 + (blockIdx.z - ne02) * ne0 * gridDim.y; - dst[offset_dst] = y[offset_src]; + dst[offset_dst] = y[offset_src]; } } -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) { +static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { + // blockIdx.z: idx of ne02*ne03 + // blockIdx.y: idx of ne01*scale_factor， aka ne1 + // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE + // ne00xne01: ne00 * ne01 int ne0 = ne00 * scale_factor; int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { @@ -1012,7 +1018,7 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00, int offset_src = i00 + i01 * ne00 + - blockIdx.z * nb02; + blockIdx.z * ne00xne01; int offset_dst = nidx + blockIdx.y * ne0 + @@ -1020,7 +1026,10 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00, dst[offset_dst] = x[offset_src]; } -static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) { +static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) { + // blockIdx.z: idx of ne2*ne3, aka ne02*ne03 + // blockIdx.y: idx of ne1 + // blockIDx.x: idx of ne0 / BLOCK_SIZE int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; @@ -1031,19 +1040,53 @@ static __global__ void pad_f32(const float * x, float * dst, const int ne0, cons nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; - if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) { + if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) { int offset_src = nidx + blockIdx.y * ne00 + blockIdx.z * ne00 * ne01; - dst[offset_dst] = x[offset_src]; + dst[offset_dst] = x[offset_src]; } else { dst[offset_dst] = 0.0f; } } +static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) { + // blockIDx.x: idx of ne0 / BLOCK_SIZE + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + dst[nidx] = start + step * nidx; +} + +static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) { + // blockIDx.y: idx of timesteps->ne[0] + // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE + int i = blockIdx.y; + int j = threadIdx.x + blockIdx.x * blockDim.x; + float * embed_data = (float *)((char *)dst + i*nb1); + + if (dim % 2 != 0 && j == ((dim + 1) / 2)) { + embed_data[dim] = 0.f; + } + + int half = dim / 2; + if (j >= half) { + return; + } + + float timestep = timesteps[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); +} + template static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) { + // blockIdx.x: num_groups idx + // threadIdx.x: block_size idx int start = blockIdx.x * group_size; int end = start + group_size; @@ -6448,7 +6491,7 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { return; @@ -6456,17 +6499,17 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i03 = i/(ne00 * ne01 * ne02); - const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; + const int64_t i03 = i/(ne00 * ne01 * ne02); + const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i13 = i/(ne10 * ne11 * ne12); - const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; + const int64_t i13 = i/(ne10 * ne11 * ne12); + const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -6956,23 +6999,23 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, template static __global__ void im2col_kernel( - const float * x, T * dst, int batch_offset, - int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW, + const float * x, T * dst, int64_t batch_offset, + int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW, int s0, int s1, int p0, int p1, int d0, int d1) { - const int i = threadIdx.x + blockIdx.x * blockDim.x; + const int64_t i = threadIdx.x + blockIdx.x * blockDim.x; if (i >= pelements) { return; } - const int ksize = OW * (KH > 1 ? KW : 1); - const int kx = i / ksize; - const int kd = kx * ksize; - const int ky = (i - kd) / OW; - const int ix = i % OW; + const int64_t ksize = OW * (KH > 1 ? KW : 1); + const int64_t kx = i / ksize; + const int64_t kd = kx * ksize; + const int64_t ky = (i - kd) / OW; + const int64_t ix = i % OW; - const int oh = blockIdx.y; - const int batch = blockIdx.z / IC; - const int ic = blockIdx.z % IC; + const int64_t oh = blockIdx.y; + const int64_t batch = blockIdx.z / IC; + const int64_t ic = blockIdx.z % IC; const int64_t iiw = ix * s0 + kx * d0 - p0; const int64_t iih = oh * s1 + ky * d1 - p1; @@ -7298,19 +7341,33 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, const concat_f32<<>>(x, y, dst, ne0, ne02); } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) { +static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, + const int scale_factor, cudaStream_t stream) { int ne0 = (ne00 * scale_factor); int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02); + dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); } static void pad_f32_cuda(const float * x, float * dst, - const int ne00, const int ne01, const int ne02, - const int ne0, const int ne1, const int ne2, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int ne03, + const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) { int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne1, ne2); - pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02); + dim3 gridDim(num_blocks, ne1, ne2*ne3); + pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02, ne03); +} + +static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE; + arange_f32<<>>(dst, ne0, start, step); +} + +static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1, + const int dim, const int max_period, cudaStream_t stream) { + int half_ceil = (dim + 1) / 2; + int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne00, 1); + timestep_embedding_f32<<>>(x, dst, nb1, dim, max_period); } static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { @@ -8443,8 +8500,8 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float * template static void im2col_cuda(const float* x, T* dst, - int IW, int IH, int OW, int OH, int KW, int KH, int IC, - int batch, int batch_offset, int offset_delta, + int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC, + int64_t batch, int64_t batch_offset, int64_t offset_delta, int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { const int parallel_elements = OW * KW * KH; const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; @@ -9123,7 +9180,7 @@ static void ggml_cuda_op_group_norm( int num_groups = dst->op_params[0]; int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream); + group_norm_f32_cuda(src0_dd, dst_dd, num_groups * src0->ne[3], group_size, ggml_nelements(src0), main_stream); (void) src1; (void) dst; @@ -9156,7 +9213,7 @@ static void ggml_cuda_op_upscale( const int scale_factor = dst->op_params[0]; - upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream); + upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, main_stream); (void) src1; (void) dst; @@ -9172,8 +9229,49 @@ static void ggml_cuda_op_pad( GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors pad_f32_cuda(src0_dd, dst_dd, - src0->ne[0], src0->ne[1], src0->ne[2], - dst->ne[0], dst->ne[1], dst->ne[2], main_stream); + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_cuda_op_arange( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + float start; + float stop; + float step; + memcpy(&start, (float *)dst->op_params + 0, sizeof(float)); + memcpy(&stop, (float *)dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *)dst->op_params + 2, sizeof(float)); + + int64_t steps = (int64_t)ceil((stop - start) / step); + GGML_ASSERT(ggml_nelements(dst) == steps); + + arange_f32_cuda(dst_dd, dst->ne[0], start, step, main_stream); + + (void) src0; + (void) src1; + (void) src0_dd; + (void) src1_dd; +} + +static void ggml_cuda_op_timestep_embedding( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; + + timestep_embedding_f32_cuda(src0_dd, dst_dd, src0->ne[0], dst->nb[1], dim, max_period, main_stream); (void) src1; (void) dst; @@ -10458,6 +10556,45 @@ static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, gg ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad); } +static void ggml_cuda_arange(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; + + // dd = data device + float * src0_ddf = nullptr; + float * src1_ddf = nullptr; + float * dst_ddf = nullptr; + + cuda_pool_alloc dst_f; + + ggml_cuda_set_device(g_main_device); + cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + + if (dst_on_device) { + dst_ddf = (float *) dst_extra->data_device[g_main_device]; + } else { + dst_ddf = dst_f.alloc(ggml_nelements(dst)); + } + + // do the computation + ggml_cuda_op_arange(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + CUDA_CHECK(cudaGetLastError()); + + // copy dst to host if necessary + if (!dst_on_device) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); + } + + if (dst->backend == GGML_BACKEND_TYPE_CPU) { + CUDA_CHECK(cudaDeviceSynchronize()); + } +} + +static void ggml_cuda_timestep_embedding(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_timestep_embedding); +} + static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); } @@ -11358,6 +11495,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_OP_PAD: func = ggml_cuda_pad; break; + case GGML_OP_ARANGE: + func = ggml_cuda_arange; + break; + case GGML_OP_TIMESTEP_EMBEDDING: + func = ggml_cuda_timestep_embedding; + break; case GGML_OP_LEAKY_RELU: func = ggml_cuda_leaky_relu; break; @@ -12253,6 +12396,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_GROUP_NORM: case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: return true; default: diff --git a/ggml-metal.m b/ggml-metal.m index 71fcca560..6b5a8fdf5 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -163,6 +163,8 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, + GGML_METAL_KERNEL_TYPE_ARANGE_F32, + GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, @@ -569,6 +571,8 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); @@ -697,6 +701,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const return false; case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: return true; @@ -1091,7 +1097,8 @@ static bool ggml_metal_graph_compute( { GGML_ASSERT(ggml_is_contiguous(src0)); - const float scale = *(const float *) dst->op_params; + float scale; + memcpy(&scale, dst->op_params, sizeof(scale)); int64_t n = ggml_nelements(dst); @@ -1250,11 +1257,15 @@ static bool ggml_metal_graph_compute( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } - const float scale = ((float *) dst->op_params)[0]; - const float max_bias = ((float *) dst->op_params)[1]; + float scale; + float max_bias; + + memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); const int64_t nrows_x = ggml_nrows(src0); const int64_t nrows_y = src0->ne[1]; + const uint32_t n_head_kv = nrows_x/nrows_y; const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); @@ -2086,6 +2097,7 @@ static bool ggml_metal_graph_compute( //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); @@ -2300,6 +2312,50 @@ static bool ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + float start; + float step; + + memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&step, ((int32_t *) dst->op_params) + 2, sizeof(float)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; + [encoder setBytes:&start length:sizeof(start) atIndex:2]; + [encoder setBytes:&step length:sizeof(step) atIndex:3]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; + + const int half = dim / 2; + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; + [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; + + const int nth = MIN(1024, half); + + [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_ARGSORT: { GGML_ASSERT(src0->type == GGML_TYPE_F32); diff --git a/ggml-metal.metal b/ggml-metal.metal index 8b9488437..a65d12641 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1959,6 +1959,49 @@ kernel void kernel_pad_f32( } } +kernel void kernel_arange_f32( + device char * dst, + constant int64_t & ne0, + constant float & start, + constant float & step, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + device float * dst_ptr = (device float *) dst; + + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + dst_ptr[i0] = start + step * i0; + } +} + +kernel void kernel_timestep_embedding_f32( + device const char * src0, + device char * dst, + constant uint64_t & nb1, + constant int & dim, + constant int & max_period, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + int i = tgpig.x; + device float * embed_data = (device float *)(dst + i*nb1); + + int half_ = dim / 2; + for (int j = tpitg.x; j < half_; j += ntg.x) { + float timestep = ((device float *)src0)[i]; + float freq = (float)exp(-log((float)max_period) * j / half_); + float arg = timestep * freq; + embed_data[j ] = cos(arg); + embed_data[j + half_] = sin(arg); + } + + if (dim % 2 != 0 && tpitg.x == 0) { + embed_data[dim] = 0.f; + } +} + // bitonic sort implementation following the CUDA kernels as reference typedef void (argsort_t)( device const float * x, diff --git a/ggml.c b/ggml.c index f29b9f13f..870e41614 100644 --- a/ggml.c +++ b/ggml.c @@ -1822,6 +1822,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "POOL_2D", "UPSCALE", "PAD", + "ARANGE", + "TIMESTEP_EMBEDDING", "ARGSORT", "LEAKY_RELU", @@ -1850,7 +1852,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1908,6 +1910,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "pool_2d(x)", "upscale(x)", "pad(x)", + "arange(start, stop, step)", + "timestep_embedding(timesteps, dim, max_period)", "argsort(x)", "leaky_relu(x)", @@ -1936,7 +1940,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -2895,11 +2899,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_ return ((const int32_t *)(tensor->op_params))[i]; } +static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + return ((const float *)(tensor->op_params))[i]; +} + static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); ((int32_t *)(tensor->op_params))[i] = value; } +static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + ((float *)(tensor->op_params))[i] = value; +} + struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { memset(tensor->data, 0, ggml_nbytes(tensor)); return tensor; @@ -5898,6 +5912,55 @@ struct ggml_tensor * ggml_upscale( return ggml_upscale_impl(ctx, a, scale_factor); } +struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step) { + + GGML_ASSERT(stop > start); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps); + + result->op = GGML_OP_ARANGE; + ggml_set_op_params_f32(result, 0, start); + ggml_set_op_params_f32(result, 1, stop); + ggml_set_op_params_f32(result, 2, step); + + return result; +} + +struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period) { + bool is_node = false; + + if (timesteps->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + int actual_dim = dim; + if (dim % 2 != 0) { + actual_dim = dim + 1; + } + + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]); + + result->op = GGML_OP_TIMESTEP_EMBEDDING; + ggml_set_op_params_i32(result, 0, dim); + ggml_set_op_params_i32(result, 1, max_period); + + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = timesteps; + + return result; +} + // ggml_argsort struct ggml_tensor * ggml_argsort( @@ -10231,7 +10294,7 @@ static void ggml_compute_forward_group_norm_f32( int n_channels = src0->ne[2]; int n_groups = dst->op_params[0]; int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i+=nth) { + for (int i = ith; i < n_groups; i += nth) { int start = i * n_channels_per_group; int end = start + n_channels_per_group; if (end > n_channels) { @@ -10245,28 +10308,32 @@ static void ggml_compute_forward_group_norm_f32( for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; + sumr += (ggml_float)x[i00]; } + sum += sumr; } } - float mean = sum / (ne00 * ne01 * step); - ggml_float sum2 = 0.0; + const float mean = sum / (ne00 * ne01 * step); + ggml_float sum2 = 0.0; for (int64_t i02 = start; i02 < end; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { float v = x[i00] - mean; y[i00] = v; - sum2 += (ggml_float)(v * v); + sumr += (ggml_float)(v * v); } + sum2 += sumr; } } - float variance = sum2 / (ne00 * ne01 * step); + const float variance = sum2 / (ne00 * ne01 * step); const float scale = 1.0f / sqrtf(variance + eps); for (int64_t i02 = start; i02 < end; i02++) { @@ -13547,6 +13614,106 @@ static void ggml_compute_forward_pad( } } + +// ggml_compute_forward_arange + +static void ggml_compute_forward_arange_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const float start = ggml_get_op_params_f32(dst, 0); + const float stop = ggml_get_op_params_f32(dst, 1); + const float step = ggml_get_op_params_f32(dst, 2); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + GGML_ASSERT(ggml_nelements(dst) == steps); + + for (int64_t i = ith; i < steps; i+= nth) { + float value = start + step * i; + ((float *)dst->data)[i] = value; + } +} + +static void ggml_compute_forward_arange( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_arange_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +static void ggml_compute_forward_timestep_embedding_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int dim = ggml_get_op_params_i32(dst, 0); + const int max_period = ggml_get_op_params_i32(dst, 1); + + int half = dim / 2; + + for (int64_t i = 0; i < ne00; i++) { + float * embed_data = (float *)((char *) dst->data + i*nb1); + for (int64_t j = ith; j < half; j += nth) { + float timestep = ((float *)src0->data)[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); + } + if (dim % 2 != 0 && ith == 0) { + embed_data[dim] = 0.f; + } + } +} + +static void ggml_compute_forward_timestep_embedding( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_timestep_embedding_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_argsort static void ggml_compute_forward_argsort_f32( @@ -15615,6 +15782,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_pad(params, tensor); } break; + case GGML_OP_ARANGE: + { + ggml_compute_forward_arange(params, tensor); + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + ggml_compute_forward_timestep_embedding(params, tensor); + } break; case GGML_OP_ARGSORT: { ggml_compute_forward_argsort(params, tensor); @@ -16617,6 +16792,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_ARGSORT: { GGML_ASSERT(false); // TODO: not implemented @@ -17368,6 +17551,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = n_threads; } break; + case GGML_OP_ARANGE: + { + n_tasks = n_threads; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + n_tasks = n_threads; + } break; case GGML_OP_ARGSORT: { n_tasks = n_threads; diff --git a/ggml.h b/ggml.h index 0a6d3c051..98cfc7bf8 100644 --- a/ggml.h +++ b/ggml.h @@ -454,6 +454,8 @@ extern "C" { GGML_OP_POOL_2D, GGML_OP_UPSCALE, // nearest interpolate GGML_OP_PAD, + GGML_OP_ARANGE, + GGML_OP_TIMESTEP_EMBEDDING, GGML_OP_ARGSORT, GGML_OP_LEAKY_RELU, @@ -1661,6 +1663,15 @@ extern "C" { int p2, int p3); + // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151 + // timesteps: [N,] + // return: [N, dim] + GGML_API struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period); + // sort rows enum ggml_sort_order { GGML_SORT_ORDER_ASC, @@ -1672,6 +1683,12 @@ extern "C" { struct ggml_tensor * a, enum ggml_sort_order order); + GGML_API struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step); + // top k elements per row GGML_API struct ggml_tensor * ggml_top_k( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d4cea805f..8a6999f21 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1412,6 +1412,50 @@ struct test_pad : public test_case { } }; +// GGML_OP_ARANGE +struct test_arange : public test_case { + const ggml_type type; + const float start; + const float stop; + const float step; + + std::string vars() override { + return VARS_TO_STR4(type, start, stop, step); + } + + test_arange(ggml_type type = GGML_TYPE_F32, + float start = 0.f, float stop = 10.f, float step = 1.f) + : type(type), start(start), stop(stop), step(step) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * out = ggml_arange(ctx, start, stop, step); + return out; + } +}; + +// GGML_OP_TIMESTEP_EMBEDDING +struct test_timestep_embedding : public test_case { + const ggml_type type; + const std::array ne_a; + const int dim; + const int max_period; + + std::string vars() override { + return VARS_TO_STR4(type, ne_a, dim, max_period); + } + + test_timestep_embedding(ggml_type type = GGML_TYPE_F32, + std::array ne_a = {2, 1, 1, 1}, + int dim = 320, int max_period=10000) + : type(type), ne_a(ne_a), dim(dim), max_period(max_period) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period); + return out; + } +}; + // GGML_OP_LEAKY_RELU struct test_leaky_relu : public test_case { const ggml_type type; @@ -2126,6 +2170,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_group_norm()); test_cases.emplace_back(new test_acc()); test_cases.emplace_back(new test_pad()); + test_cases.emplace_back(new test_arange()); + test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); // these tests are disabled to save execution time, but they can be handy for debugging From a0fc62661f0fd2a9edd10ae5617345bbbf972f42 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 10:40:04 +0200 Subject: [PATCH 37/86] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 389c0bdfe..4a1b0bab4 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -b458250b736a7473f7ff3560d47c93f1644f3290 +274680868e12427373bab4bec87554431b954704 From 4ffcdce2ff877ebb683cd217ea38faf20faa5ffe Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 4 Mar 2024 12:22:08 +0100 Subject: [PATCH 38/86] add alias for chat template (#5858) --- examples/server/server.cpp | 2 +- llama.cpp | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0ca388f47..208edd571 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -413,7 +413,7 @@ struct llama_server_context int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); if (res < 0) { LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template + sparams.chat_template = "chatml"; } } diff --git a/llama.cpp b/llama.cpp index c1f015791..de579d9e3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13282,7 +13282,7 @@ static int32_t llama_chat_apply_template_internal( std::string & dest, bool add_ass) { // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527 std::stringstream ss; - if (tmpl.find("<|im_start|>") != std::string::npos) { + if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) { // chatml template for (auto message : chat) { ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n"; @@ -13290,7 +13290,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|im_start|>assistant\n"; } - } else if (tmpl.find("[INST]") != std::string::npos) { + } else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) { // llama2 template and its variants // [variant] support system message bool support_system_message = tmpl.find("<>") != std::string::npos; @@ -13325,7 +13325,7 @@ static int32_t llama_chat_apply_template_internal( } } // llama2 templates seem to not care about "add_generation_prompt" - } else if (tmpl.find("<|user|>") != std::string::npos) { + } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) { // zephyr template for (auto message : chat) { ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n"; @@ -13333,7 +13333,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } - } else if (tmpl.find("bos_token + message['role']") != std::string::npos) { + } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) { // mlabonne/AlphaMonarch-7B template (the is included inside history) for (auto message : chat) { std::string bos = (message == chat.front()) ? "" : ""; // skip BOS for first message @@ -13342,7 +13342,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "assistant\n"; } - } else if (tmpl.find("") != std::string::npos) { + } else if (tmpl == "gemma" || tmpl.find("") != std::string::npos) { // google/gemma-7b-it std::string system_prompt = ""; for (auto message : chat) { @@ -13389,7 +13389,7 @@ LLAMA_API int32_t llama_chat_apply_template( int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); if (res < 0) { // worst case: there is no information about template, we will use chatml by default - curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal + curr_tmpl = "chatml"; // see llama_chat_apply_template_internal } else { curr_tmpl = std::string(model_template.data(), model_template.size()); } From 6d341ab6c53cd51f2921d986d0090cc8b049b39a Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Tue, 5 Mar 2024 03:24:00 +0900 Subject: [PATCH 39/86] speculative : implement stochastic speculative sampling (#5625) * (WIP) Implement stochastic speculative decoding * sample from residual distribution on draft accept failure * fix #5657: force greedy sampling with probs when temp is 0 * remove p_accept parameter * fix style * remove unused variables * add srand() in speculative.cpp * replace use of rand() with mt19937 sampling * fixes based on review (@JohannesGaessler) * fix r random generation * randomly select next sequence to verify + fix bug in memory freeing * fix bug in active_seqs sync * fix uniform int distribution initialization * remove warnings from comparison between int and size_t * check grammar in `llama_sample_probability_distribution_impl` * remove malloc code by utilizing vectors * add PR link to README --- common/common.cpp | 7 - common/common.h | 3 +- common/sampling.cpp | 79 ++++++++++ common/sampling.h | 7 + examples/speculative/README.md | 1 + examples/speculative/speculative.cpp | 224 ++++++++++++++++++++------- 6 files changed, 260 insertions(+), 61 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index dbe7e9229..036a98134 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -513,12 +513,6 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_sequences = std::stoi(argv[i]); - } else if (arg == "--p-accept" || arg == "-pa") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.p_accept = std::stof(argv[i]); } else if (arg == "--p-split" || arg == "-ps") { if (++i >= argc) { invalid_param = true; @@ -1044,7 +1038,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); - printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept); printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); diff --git a/common/common.h b/common/common.h index b2868833b..977ce419f 100644 --- a/common/common.h +++ b/common/common.h @@ -53,11 +53,10 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 8; // number of tokens to draft during speculative decoding + int32_t n_draft = 5; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode - float p_accept = 0.5f; // speculative decoding accept probability float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) diff --git a/common/sampling.cpp b/common/sampling.cpp index e67096bea..823031feb 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -295,6 +295,77 @@ static llama_token llama_sampling_sample_impl( return id; } +static llama_token_data_array llama_sample_probability_distribution_impl( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + const llama_sampling_params & params = ctx_sampling->params; + + const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); + + const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; + const float penalty_repeat = params.penalty_repeat; + const float penalty_freq = params.penalty_freq; + const float penalty_present = params.penalty_present; + const bool penalize_nl = params.penalize_nl; + + auto & prev = ctx_sampling->prev; + auto & cur = ctx_sampling->cur; + + // Get a pointer to the logits + float * logits = llama_get_logits_ith(ctx_main, idx); + + // Declare original_logits at the beginning of the function scope + std::vector original_logits; + + // apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + if (ctx_cfg) { + float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); + llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); + } + + cur.clear(); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { cur.data(), cur.size(), false }; + + // apply penalties + const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; + const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); + if (penalty_tokens_used_size) { + const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))]; + + llama_sample_repetition_penalties(ctx_main, &cur_p, + penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size, + penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + // apply grammar checks + if (ctx_sampling->grammar != NULL) { + llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); + } + + llama_sample_softmax(ctx_main, &cur_p); + return cur_p; +} + llama_token llama_sampling_sample( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, @@ -304,6 +375,14 @@ llama_token llama_sampling_sample( return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); } +llama_token_data_array llama_sampling_probability_distribution( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx); +} + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, diff --git a/common/sampling.h b/common/sampling.h index 95d875394..48b2459d1 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -131,6 +131,13 @@ llama_token llama_sampling_sample( struct llama_context * ctx_cfg, int idx = 0); +// returns the probability that token of given id will be sampled +llama_token_data_array llama_sampling_probability_distribution( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + int idx = 0); + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, diff --git a/examples/speculative/README.md b/examples/speculative/README.md index 814efa592..a6608c5fe 100644 --- a/examples/speculative/README.md +++ b/examples/speculative/README.md @@ -6,3 +6,4 @@ More info: - https://github.com/ggerganov/llama.cpp/pull/2926 - https://github.com/ggerganov/llama.cpp/pull/3624 +- https://github.com/ggerganov/llama.cpp/pull/5625 diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 3848791d4..85bc0a762 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -5,6 +5,7 @@ #include #include #include +#include #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 @@ -18,6 +19,7 @@ struct seq_draft { std::vector i_batch_tgt; std::vector tokens; + std::vector> dists; struct llama_sampling_context * ctx_sampling; }; @@ -37,12 +39,15 @@ int main(int argc, char ** argv) { // max number of parallel drafting sequences (i.e. tree branches) const int n_seq_dft = params.n_parallel; - // probability threshold for accepting a token from the draft model - const float p_accept = params.p_accept; - // probability threshold for splitting a draft branch (only for n_seq_dft > 1) const float p_split = params.p_split; + if (params.seed == LLAMA_DEFAULT_SEED) { + params.seed = time(NULL); + } + std::default_random_engine rng(params.seed); + std::uniform_real_distribution<> u_dist; + #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); LOG_TEE("Log start\n"); @@ -166,7 +171,9 @@ int main(int argc, char ** argv) { std::vector drafts(n_seq_dft); params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar - params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model + if (params.sparams.temp == 0) { + params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model + } for (int s = 0; s < n_seq_dft; ++s) { drafts[s].ctx_sampling = llama_sampling_init(params.sparams); @@ -182,12 +189,15 @@ int main(int argc, char ** argv) { drafts[0].i_batch_tgt[0] = 0; while (true) { + std::set active_seqs = {}; + // print current draft sequences for (int s = 0; s < n_seq_dft; ++s) { if (!drafts[s].active) { continue; } + active_seqs.insert(s); const auto & tokens = drafts[s].tokens; LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str()); @@ -196,48 +206,156 @@ int main(int argc, char ** argv) { int i_dft = 0; int s_keep = 0; + llama_token token_id; + std::string token_str; + + // loop until we fail to accept a drafted token or we run out of drafted tokens while (true) { - LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); - - // sample from the target model - llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); - - llama_sampling_accept(ctx_sampling, ctx_tgt, id, true); - - //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); - - const std::string token_str = llama_token_to_piece(ctx_tgt, id); - - if (!params.use_color) { - printf("%s", token_str.c_str()); - } - - if (id == llama_token_eos(model_tgt)) { - has_eos = true; - } - - ++n_predict; // check if the target token matches any of the drafts + // for stochastic sampling, attempt to match the token with the drafted tokens { - bool matches = false; + bool accept = false; + if (params.sparams.temp > 0) { + // stochastic verification - for (int s = 0; s < n_seq_dft; ++s) { - if (!drafts[s].active) { - continue; + llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + float p_tgt = 0, p_dft = 0; + + // GGML_ASSERT(dist_tgt.size() == dist_dft.size()); + + while (active_seqs.size() > 0) { + // randomly select a sequence to verify from active sequences + std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); + int s = *std::next(active_seqs.begin(), u_int_dist(rng)); + if (i_dft >= (int) drafts[s].tokens.size()) { + drafts[s].active = false; + active_seqs.erase(s); + continue; + } + if (accept) { + // if we already accepted a token, we can skip the rest + if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) { + drafts[s].active = false; + active_seqs.erase(s); + } + continue; + } + LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size()); + float r = u_dist(rng); + llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true }; + // acquire the token probabilities assigned by the draft and target models + for (size_t i = 0; i < dist_tgt.size; i++) { + if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) { + p_tgt = dist_tgt.data[i].p; + } + if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) { + p_dft = dist_dft.data[i].p; + } + if (p_tgt && p_dft) { + break; + } + } + LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt); + if (r <= p_tgt / p_dft) { + s_keep = s; + accept = true; + token_id = drafts[s].tokens[i_dft]; + token_str = llama_token_to_piece(ctx_tgt, token_id); + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + + LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str()); + break; + } else { + LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str()); + drafts[s].active = false; + + // calculate residual probability + GGML_ASSERT(dist_tgt.sorted); + GGML_ASSERT(dist_dft.sorted); + float sum_probs = 0.0f; + + // sort dist by id + std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.id < b.id; + }); + std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.id < b.id; + }); + + for (size_t i = 0; i < dist_tgt.size; i++) { + dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p); + sum_probs += dist_tgt.data[i].p; + } + for (size_t i = 0; i < dist_tgt.size; i++) { + dist_tgt.data[i].p /= sum_probs; + } + + // sort dist_tgt by p desc + std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.p > b.p; + }); + } + + active_seqs.erase(s); + for(int i = 0; i < n_seq_dft; i++) { + if (i == s) { + continue; + } + if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) { + // synchronize active status for sequences with the same drafted token + drafts[i].active = drafts[i].active && accept; + if (!drafts[i].active) { + active_seqs.erase(s); + } + } + } } - if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) { - LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str()); + if (!accept) { + // all drafted tokens were rejected + // sample from the target model + LOG("all drafted tokens were rejected, sampling from residual distribution\n"); + token_id = llama_sample_token(ctx_tgt, &dist_tgt); + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + token_str = llama_token_to_piece(ctx_tgt, token_id); + } - s_keep = s; - matches = true; - } else { - drafts[s].active = false; + } else { + // greedy verification + + // sample from the target model + LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); + token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); + + token_str = llama_token_to_piece(ctx_tgt, token_id); + + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].active) { + continue; + } + + if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) { + LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str()); + + s_keep = s; + accept = true; + } else { + drafts[s].active = false; + } } } - if (matches) { + if (token_id == llama_token_eos(model_tgt)) { + has_eos = true; + } + ++n_predict; + + if (accept) { ++n_accept; ++n_past_tgt; ++n_past_dft; @@ -245,17 +363,21 @@ int main(int argc, char ** argv) { if (params.use_color) { // Color token according to its origin sequence printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str()); - fflush(stdout); + } else { + printf("%s", token_str.c_str()); } + fflush(stdout); continue; + } else { + printf("%s", token_str.c_str()); + fflush(stdout); + break; } } - if (params.use_color) { - printf("%s", token_str.c_str()); - } - fflush(stdout); + } - LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); + { + LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str()); // TODO: simplify { @@ -275,21 +397,21 @@ int main(int argc, char ** argv) { drafts[s].active = false; drafts[s].tokens.clear(); drafts[s].i_batch_tgt.clear(); + drafts[s].dists.clear(); } // note: will be erased after the speculation phase - drafts[0].tokens.push_back(id); + drafts[0].tokens.push_back(token_id); + drafts[0].dists.push_back(std::vector()); drafts[0].i_batch_tgt.push_back(0); llama_batch_clear(batch_dft); - llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true); + llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true); llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str()); - llama_decode (ctx_dft, batch_dft); + llama_decode(ctx_dft, batch_dft); ++n_past_dft; - - break; } if (n_predict > params.n_predict || has_eos) { @@ -334,12 +456,6 @@ int main(int argc, char ** argv) { k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str()); } - if (cur_p[0].p < p_accept) { - LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept); - drafts[s].drafting = false; - continue; - } - std::vector sa(1, s); // attempt to split the branch if the probability is high enough @@ -367,6 +483,7 @@ int main(int argc, char ** argv) { drafts[n_seq_cur].skip = true; drafts[n_seq_cur].tokens = drafts[s].tokens; + drafts[n_seq_cur].dists = drafts[s].dists; drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft; drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt; @@ -389,6 +506,8 @@ int main(int argc, char ** argv) { llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true); drafts[s].tokens.push_back(id); + // save cur_p.data into drafts[s].dists + drafts[s].dists.push_back(cur_p); // add unique drafted tokens to the target batch drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens); @@ -440,6 +559,7 @@ int main(int argc, char ** argv) { } drafts[s].tokens.erase(drafts[s].tokens.begin()); + drafts[s].dists.erase(drafts[s].dists.begin()); } } From fe52be11e35358d2fd249f19d7ef5b6f9c08b16b Mon Sep 17 00:00:00 2001 From: Dane Madsen Date: Tue, 5 Mar 2024 05:26:55 +1100 Subject: [PATCH 40/86] cmake : handle cases where git index is not found in .git (#5844) * Update CMakeLists.txt * Update CMakeLists.txt --- common/CMakeLists.txt | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index f79acfef1..350bbdf7f 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -19,7 +19,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") endif() endif() - set(GIT_INDEX "${GIT_DIR}/index") + if(EXISTS "${GIT_DIR}/index") + set(GIT_INDEX "${GIT_DIR}/index") + else() + message(WARNING "Git index not found in git repository.") + set(GIT_INDEX "") + endif() else() message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") set(GIT_INDEX "") From 9fa262734733573fa629ffc97dfcb971fe3f4832 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Mon, 4 Mar 2024 10:05:42 +0100 Subject: [PATCH 41/86] ggml : introduce ggml_status (ggml/750) * using enum as an exit code instead of macros * update return type from enum to unsigned int * indentation fix * compound update ggml_compute_exit_code -> ggml_status changed ggml_status from a bit-field type to simple codes ggml_status to string cast * ggml_status to string cast * GGML_CALL was removed Co-authored-by: slaren --------- Co-authored-by: slaren Co-authored-by: Georgi Gerganov --- ggml-backend-impl.h | 7 ++++--- ggml-backend.c | 39 ++++++++++++++++++--------------------- ggml-backend.h | 31 ++++++++++++++++--------------- ggml-cuda.cu | 4 ++-- ggml-kompute.cpp | 4 ++-- ggml-metal.m | 8 ++++---- ggml-opencl.cpp | 4 ++-- ggml-sycl.cpp | 4 ++-- ggml-vulkan.cpp | 4 ++-- ggml.c | 29 +++++++++++++++++++++++------ ggml.h | 17 +++++++++++++---- 11 files changed, 88 insertions(+), 63 deletions(-) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 0e5bf0ae1..2e9ba58a9 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -91,13 +91,14 @@ extern "C" { // (optional) complete all pending operations void (*GGML_CALL synchronize)(ggml_backend_t backend); - // compute graph with a plan + // create a plan for ggml_cgraph and free it ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + // compute graph with a plan + enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async) - bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); diff --git a/ggml-backend.c b/ggml-backend.c index c86673b04..d60d98414 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -262,11 +262,11 @@ void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_pla backend->iface.graph_plan_free(backend, plan); } -void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - backend->iface.graph_plan_compute(backend, plan); +enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + return backend->iface.graph_plan_compute(backend, plan); } -bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return backend->iface.graph_compute(backend, cgraph); } @@ -732,15 +732,15 @@ GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, g GGML_UNUSED(backend); } -GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); + return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); GGML_UNUSED(backend); } -GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); @@ -755,8 +755,7 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str cplan.abort_callback = cpu_ctx->abort_callback; cplan.abort_callback_data = cpu_ctx->abort_callback_data; - ggml_graph_compute(cgraph, &cplan); - return true; + return ggml_graph_compute(cgraph, &cplan); } GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -1437,7 +1436,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { return true; } -static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { +static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; @@ -1472,8 +1471,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t compute_start_us = ggml_time_us(); if (!sched->callback_eval) { - if (!ggml_backend_graph_compute(split_backend, &split->graph)) { - return false; + enum ggml_status ec = ggml_backend_graph_compute(split_backend, &split->graph); + if (ec != GGML_STATUS_SUCCESS) { + return ec; } //ggml_backend_synchronize(split_backend); // necessary to measure compute time } else { @@ -1494,8 +1494,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1); - if (!ggml_backend_graph_compute(split_backend, &gv)) { - return false; + enum ggml_status ec = ggml_backend_graph_compute(split_backend, &gv); + if (ec != GGML_STATUS_SUCCESS) { + return ec; } if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) { @@ -1519,7 +1520,7 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { } #endif - return true; + return GGML_STATUS_SUCCESS; } ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { @@ -1581,7 +1582,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * return true; } -bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); if (!sched->is_reset) { @@ -1590,14 +1591,10 @@ bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cg ggml_backend_sched_split_graph(sched, graph); if (!ggml_backend_sched_alloc_splits(sched)) { - return false; + return GGML_STATUS_ALLOC_FAILED; } - if (!ggml_backend_sched_compute_splits(sched)) { - return false; - } - - return true; + return ggml_backend_sched_compute_splits(sched); } void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) { diff --git a/ggml-backend.h b/ggml-backend.h index 8fb54bd92..8bed22578 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -66,12 +66,13 @@ extern "C" { GGML_API void ggml_backend_synchronize(ggml_backend_t backend); - GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); - GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op); + GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); + + GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst); @@ -157,26 +158,26 @@ extern "C" { typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); // Initialize a backend scheduler - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); - GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph - GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // Get the number of splits of the last graph - GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); + GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); - GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); + GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); - GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); // Allocate and compute graph on the backend scheduler - GGML_API bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // Reset all assignments and allocators - must be called before changing the node backends - GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); + GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // Set a callback to be called for each resulting node during graph compute - GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data); + GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data); // // Utils diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7d027a30a..72bcec8cd 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -12241,7 +12241,7 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { UNUSED(backend); } -GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -12277,7 +12277,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg GGML_ASSERT(ok); } - return true; + return GGML_STATUS_SUCCESS; } GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index e740a76d1..83a7822fd 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1927,10 +1927,10 @@ static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(g return ggml_backend_kompute_buffer_type(ctx->device); } -static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { auto * ctx = static_cast(backend->context); ggml_vk_graph_compute(ctx, cgraph); - return true; + return GGML_STATUS_SUCCESS; } static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { diff --git a/ggml-metal.m b/ggml-metal.m index 6b5a8fdf5..00df22838 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -748,7 +748,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const } } -static bool ggml_metal_graph_compute( +static enum ggml_status ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @@ -2484,7 +2484,7 @@ static bool ggml_metal_graph_compute( MTLCommandBufferStatus status = [command_buffer status]; if (status != MTLCommandBufferStatusCompleted) { GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); - return false; + return GGML_STATUS_FAILED; } } @@ -2493,7 +2493,7 @@ static bool ggml_metal_graph_compute( } } - return true; + return GGML_STATUS_SUCCESS; } //////////////////////////////////////////////////////////////////////////////// @@ -2795,7 +2795,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe UNUSED(backend); } -GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_graph_compute(metal_ctx, cgraph); diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index df619a884..aa73d67df 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -2231,7 +2231,7 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg GGML_UNUSED(backend); } -static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { +static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { for (int i = 0; i < graph->n_nodes; ++i) { ggml_tensor * node = graph->nodes[i]; switch (node->op) { @@ -2246,7 +2246,7 @@ static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgrap } } - return true; + return GGML_STATUS_SUCCESS; GGML_UNUSED(backend); } diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index cad08d610..47a605b01 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15581,7 +15581,7 @@ catch (sycl::exception const &exc) { std::exit(1); } -GGML_CALL static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; ggml_sycl_set_main_device(sycl_ctx->device); @@ -15613,7 +15613,7 @@ GGML_CALL static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, gg GGML_ASSERT(ok); } - return true; + return GGML_STATUS_SUCCESS; } GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index ae9cb3c1c..bc316c3f3 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5092,7 +5092,7 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { ctx->transfer_ctx = nullptr; } -GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { @@ -5135,7 +5135,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml ggml_vk_graph_cleanup(ctx); - return true; + return GGML_STATUS_SUCCESS; UNUSED(backend); } diff --git a/ggml.c b/ggml.c index 870e41614..3c2e94c1b 100644 --- a/ggml.c +++ b/ggml.c @@ -320,6 +320,16 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; +const char * ggml_status_to_string(enum ggml_status status) { + switch (status) { + case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; + case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; + case GGML_STATUS_SUCCESS: return "GGML status: success"; + case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; + default: GGML_ASSERT(false); + } +} + // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { @@ -17400,6 +17410,7 @@ struct ggml_compute_state { ggml_thread_t thrd; int ith; struct ggml_compute_state_shared * shared; + enum ggml_status ec; }; static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { @@ -17693,7 +17704,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { while (true) { if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { state->shared->node_n += 1; - return (thread_ret_t) GGML_EXIT_ABORTED; + state->ec = GGML_STATUS_ABORTED; + return 0; } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { @@ -17815,7 +17827,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } } - return GGML_EXIT_SUCCESS; + return 0; } struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) { @@ -18011,7 +18023,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa return cplan; } -int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { +enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); GGML_ASSERT(cplan->n_threads > 0); @@ -18055,6 +18067,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { .thrd = 0, .ith = j, .shared = &state_shared, + .ec = GGML_STATUS_SUCCESS, }; const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); @@ -18065,12 +18078,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { workers[0].ith = 0; workers[0].shared = &state_shared; + workers[0].ec = GGML_STATUS_SUCCESS; const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); // this is a work thread too - int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]); + ggml_graph_compute_thread(&workers[0]); + enum ggml_status compute_status = workers[0].ec; // don't leave affinity set on the main thread clear_numa_thread_affinity(); @@ -18080,6 +18095,8 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { for (int j = 1; j < n_threads; j++) { const int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); + if (workers[j].ec != GGML_STATUS_SUCCESS) + compute_status = workers[j].ec; } } @@ -18107,14 +18124,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { return compute_status; } -void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { +enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - ggml_graph_compute(cgraph, &cplan); + return ggml_graph_compute(cgraph, &cplan); } struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { diff --git a/ggml.h b/ggml.h index 98cfc7bf8..0ea4f8847 100644 --- a/ggml.h +++ b/ggml.h @@ -315,6 +315,16 @@ extern "C" { #endif + enum ggml_status { + GGML_STATUS_ALLOC_FAILED = -2, + GGML_STATUS_FAILED = -1, + GGML_STATUS_SUCCESS = 0, + GGML_STATUS_ABORTED = 1, + }; + + // get ggml_status name string + GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status); + typedef uint16_t ggml_fp16_t; // convert FP16 <-> FP32 @@ -1940,12 +1950,11 @@ extern "C" { // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - + GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); + GGML_API enum ggml_status ggml_graph_compute ( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data - GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); + GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); From efd8533ef8d0752cef7119eb5dbee412c4dba270 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 11:06:39 +0200 Subject: [PATCH 42/86] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 4a1b0bab4..afe68077d 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -274680868e12427373bab4bec87554431b954704 +8695910a39102609073d0e099aa7c97d6bcb3bf9 From a1c6d96ed8f906aa1cda439f7386b1171a22bf9f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 20:53:27 +0200 Subject: [PATCH 43/86] ggml : fix unknown status (#0) --- ggml.c | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/ggml.c b/ggml.c index 3c2e94c1b..6a10bbcb4 100644 --- a/ggml.c +++ b/ggml.c @@ -323,11 +323,12 @@ float ggml_table_f32_f16[1 << 16]; const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; - case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; - case GGML_STATUS_SUCCESS: return "GGML status: success"; - case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; - default: GGML_ASSERT(false); + case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; + case GGML_STATUS_SUCCESS: return "GGML status: success"; + case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; } + + return "GGML status: unknown"; } // note: do not use these inside ggml.c From e0843afe1b37890b631bc7d3d2da2ed36c862b91 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 21:50:50 +0200 Subject: [PATCH 44/86] flake : fix --- convert-hf-to-gguf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ffdba7444..f6369af38 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -36,8 +36,10 @@ class SentencePieceTokenTypes(IntEnum): UNUSED = 5 BYTE = 6 + AnyModel = TypeVar("AnyModel", bound="type[Model]") + class Model(ABC): _model_classes: dict[str, type[Model]] = {} @@ -187,6 +189,7 @@ class Model(ABC): @classmethod def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: assert names + def func(modelcls: type[Model]): for name in names: cls._model_classes[name] = modelcls From 29ae62d2ae163e2b68aa0ad3bf2ab4636de0c957 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 22:31:20 +0200 Subject: [PATCH 45/86] llama : fix embeddings (#5796) * llama : fix embeddings ggml-ci * llama : do not use KV cache for non-causal models ggml-ci * embeddings : fix llama_batch_init arg * llama : add pooling switch * llama : distinguish token vs sequence embeddings ggml-ci * llama : assert pooling tensor * llama : simplify causal mask condition ggml-ci * llama : assert input batch with pooling enabled * readme : update API changes list --- README.md | 1 + common/common.cpp | 2 +- examples/embedding/embedding.cpp | 28 ++- examples/server-embd.py | 34 +++ examples/server/server.cpp | 53 ++++- llama.cpp | 357 +++++++++++++++++++++---------- llama.h | 18 +- 7 files changed, 359 insertions(+), 134 deletions(-) create mode 100644 examples/server-embd.py diff --git a/README.md b/README.md index 45c5d06f3..f754022de 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 ### Hot topics diff --git a/common/common.cpp b/common/common.cpp index 036a98134..c244db644 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1292,7 +1292,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; cparams.logits_all = params.logits_all; - cparams.embedding = params.embedding; + cparams.embeddings = params.embedding; cparams.rope_scaling_type = params.rope_scaling_type; cparams.rope_freq_base = params.rope_freq_base; cparams.rope_freq_scale = params.rope_freq_scale; diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index acff715e9..ff5883da6 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -19,11 +19,11 @@ static std::vector split_lines(const std::string & s) { static void batch_add_seq(llama_batch & batch, const std::vector & tokens, int seq_id) { for (size_t i = 0; i < tokens.size(); i++) { - llama_batch_add(batch, tokens[i], i, { seq_id }, false); + llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1); } } -static void normalize(float * vec, float * out, int n) { +static void normalize(const float * vec, float * out, int n) { float norm = 0; for (int i = 0; i < n; i++) { norm += vec[i] * vec[i]; @@ -45,10 +45,23 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } // normalize on copy - for (int k = 0; k < n_seq; k++) { - float * emb = llama_get_embeddings_ith(ctx, k); - float * out = output + k * n_embd; - normalize(emb, out, n_embd); + for (int i = 0; i < batch.n_tokens; i++) { + if (!batch.logits[i]) { + continue; + } + + // try to get sequence embeddings - supported only when pooling_type is not NONE + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i); + continue; + } + } + + float * out = output + batch.seq_id[i][0] * n_embd; + normalize(embd, out, n_embd); } } @@ -132,7 +145,7 @@ int main(int argc, char ** argv) { // initialize batch const int n_prompts = prompts.size(); - struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts); + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); // allocate output const int n_embd = llama_n_embd(model); @@ -145,6 +158,7 @@ int main(int argc, char ** argv) { for (int k = 0; k < n_prompts; k++) { // clamp to n_batch tokens auto & inp = inputs[k]; + const uint64_t n_toks = inp.size(); // encode if at capacity diff --git a/examples/server-embd.py b/examples/server-embd.py new file mode 100644 index 000000000..c5c4ea87b --- /dev/null +++ b/examples/server-embd.py @@ -0,0 +1,34 @@ +import asyncio +import requests +import numpy as np + +n = 8 + +result = [] + +async def requests_post_async(*args, **kwargs): + return await asyncio.to_thread(requests.post, *args, **kwargs) + +async def main(): + model_url = "http://127.0.0.1:6900" + responses: list[requests.Response] = await asyncio.gather(*[requests_post_async( + url= f"{model_url}/embedding", + json= {"content": str(i)*1024} + ) for i in range(n)]) + + for response in responses: + embedding = response.json()["embedding"] + print(embedding[-8:]) + result.append(embedding) + +asyncio.run(main()) + +# compute cosine similarity + +for i in range(n-1): + for j in range(i+1, n): + embedding1 = np.array(result[i]) + embedding2 = np.array(result[j]) + similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) + print(f"Similarity between {i} and {j}: {similarity:.2f}") + diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 208edd571..8fe5e0b19 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1210,7 +1210,7 @@ struct llama_server_context queue_results.send(res); } - void send_embedding(server_slot &slot) + void send_embedding(server_slot & slot, const llama_batch & batch) { task_result res; res.id = slot.task_id; @@ -1219,6 +1219,7 @@ struct llama_server_context res.stop = true; const int n_embd = llama_n_embd(model); + if (!params.embedding) { LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); @@ -1229,12 +1230,29 @@ struct llama_server_context } else { - const float *data = llama_get_embeddings(ctx); - std::vector embedding(data, data + n_embd); - res.result_json = json - { - {"embedding", embedding}, - }; + for (int i = 0; i < batch.n_tokens; ++i) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + continue; + } + + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + LOG_ERROR("failed to get embeddings for token", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}}); + res.result_json = json + { + {"embedding", std::vector(n_embd, 0.0f)}, + }; + continue; + } + } + + res.result_json = json + { + {"embedding", std::vector(embd, embd + n_embd)}, + }; + } } queue_results.send(res); } @@ -1845,7 +1863,7 @@ struct llama_server_context ga_i += ga_w/ga_n; } } - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); slot_npast++; } @@ -1881,7 +1899,7 @@ 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)); + const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); for (auto & slot : slots) { @@ -1954,7 +1972,7 @@ struct llama_server_context // prompt evaluated for embedding if (slot.embedding) { - send_embedding(slot); + send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; continue; @@ -2036,6 +2054,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" --pooling {none,mean,cls}\n"); + printf(" pooling type for embeddings, use model default if unspecified\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"); @@ -2276,6 +2296,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.yarn_beta_slow = std::stof(argv[i]); } + else if (arg == "--pooling") + { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; break; } + } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) @@ -2330,7 +2362,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.n_batch = std::stoi(argv[i]); - params.n_batch = std::min(512, params.n_batch); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { diff --git a/llama.cpp b/llama.cpp index de579d9e3..76afcbc13 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1665,7 +1665,7 @@ struct llama_hparams { }; struct llama_cparams { - uint32_t n_ctx; // context size used during inference + uint32_t n_ctx; // context size used during inference uint32_t n_batch; uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -1682,7 +1682,9 @@ struct llama_cparams { float yarn_beta_slow; float defrag_thold; + bool embeddings; bool offload_kqv; + enum llama_pooling_type pooling_type; ggml_backend_sched_eval_callback cb_eval; @@ -1972,7 +1974,7 @@ struct llama_context { int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1) int32_t n_eval = 0; // number of eval calls - // decode output (2-dimensional array: [n_tokens][n_vocab]) + // logits output (2-dimensional array: [n_tokens][n_vocab]) std::vector logits; #ifndef NDEBUG // guard against access to unset logits @@ -1980,8 +1982,13 @@ struct llama_context { #endif bool logits_all = false; - // input embedding (1-dimensional array: [n_embd]) - std::vector embedding; + // embeddings output (2-dimensional array: [n_tokens][n_embd]) + // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE + std::vector embd; + + // sequence embeddings output (map of [n_embd] vectors) + // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE + std::map> embd_seq; // memory buffers used to evaluate the model std::vector buf_compute_meta; @@ -5092,6 +5099,7 @@ static struct ggml_tensor * llm_build_kv( llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il); struct ggml_tensor * cur; + cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b, q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il); cb(cur, "kqv_out", il); @@ -6085,6 +6093,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); struct ggml_tensor * cur; @@ -6092,9 +6101,10 @@ struct llm_build_context { // get input vectors with right size const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type); - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0); - struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0); + struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0); // construct input embeddings (token, type, position) inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); @@ -6112,39 +6122,38 @@ struct llm_build_context { cb(inpL, "inp_norm", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); // [n_kv, n_tokens] + struct ggml_tensor * KQ_mask = ggml_cont(ctx0, ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_tokens, n_tokens, n_tokens*ggml_type_size(lctx.inp_KQ_mask->type), 0)); + cb(KQ_mask, "KQ_mask", -1); // [n_tokens, n_tokens] // iterate layers for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * cur = inpL; + struct ggml_tensor * Qcur; + struct ggml_tensor * Kcur; + struct ggml_tensor * Vcur; + // self-attention if (model.arch == LLM_ARCH_BERT) { - struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); + Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); cb(Qcur, "Qcur", il); - struct ggml_tensor * Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); cb(Kcur, "Kcur", il); - struct ggml_tensor * Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); + Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); cb(Vcur, "Vcur", il); - // seems like we just need to do this for Q? - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); } else { // compute Q and K and RoPE them cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); cb(cur, "wqkv", il); - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); @@ -6163,13 +6172,41 @@ struct llm_build_context { ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); - - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); + + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); + + kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); + + struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); + cb(v, "v", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); + + ggml_build_forward_expand(gf, cur); + + cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur); + if (model.layers[il].bo) { + cb(cur, "kqv_wo", il); + } + + if (model.layers[il].bo) { + cur = ggml_add(ctx0, cur, model.layers[il].bo); + } + cb(cur, "kqv_out", il); + // re-add the layer input cur = ggml_add(ctx0, cur, inpL); @@ -6209,16 +6246,29 @@ struct llm_build_context { // final output cur = inpL; + cb(cur, "result_embd", -1); // pooling layer - if (pooling_type == LLAMA_POOLING_TYPE_MEAN) { - cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean); - } else if (pooling_type == LLAMA_POOLING_TYPE_CLS) { - cur = ggml_get_rows(ctx0, cur, inp_cls); - } else { - GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type"); + switch (pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + // nop + } break; + case LLAMA_POOLING_TYPE_MEAN: + { + cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean); + cb(cur, "result_embd_pooled", -1); + } break; + case LLAMA_POOLING_TYPE_CLS: + { + cur = ggml_get_rows(ctx0, cur, inp_cls); + cb(cur, "result_embd_pooled", -1); + } break; + case LLAMA_POOLING_TYPE_UNSPECIFIED: + { + GGML_ASSERT(false && "Invalid pooling type"); + } break; } - cb(cur, "result_embd", -1); ggml_build_forward_expand(gf, cur); @@ -7980,7 +8030,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); } - { + if (hparams.causal_attn) { const int64_t n_kv = kv_self.n; const int64_t n_tokens = batch.n_tokens; @@ -7995,16 +8045,40 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { for (int i = 0; i < n_kv; ++i) { float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || - (hparams.causal_attn && lctx.kv_self.cells[i].pos > pos)) { + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { f = -INFINITY; } else { - f = 0; + f = 0.0f; } data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } } + } else { + // non-causal attention attends only the tokens within the batch (i.e. the KV cache is not used) + const int64_t n_tokens = batch.n_tokens; + + assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); + + float * data = (float *) lctx.inp_KQ_mask->data; + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_seq_id seq_id = batch.seq_id[j][0]; + + for (int i = 0; i < n_tokens; ++i) { + float f = -INFINITY; + for (int s = 0; s < batch.n_seq_id[i]; ++s) { + if (batch.seq_id[i][s] == seq_id) { + f = 0.0f; + break; + } + } + + data[h*(n_tokens*n_tokens) + j*n_tokens + i] = f; + } + } + } } if (hparams.need_kq_pos) { @@ -8023,13 +8097,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); - float * data = (float *) lctx.inp_mean->data; + float * data = (float *) lctx.inp_mean->data; memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean)); std::vector sum(n_tokens, 0); for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; + + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); + sum[seq_id] += 1; } @@ -8051,11 +8128,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); + uint32_t * data = (uint32_t *) lctx.inp_cls->data; + memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; - const llama_pos pos = batch.pos[i]; + const llama_pos pos = batch.pos[i]; + + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS"); + if (pos == 0) { data[seq_id] = i; } @@ -8169,24 +8251,27 @@ static int llama_decode_internal( batch.seq_id = seq_id_arr.data(); } - llama_kv_cache_update(&lctx); + // non-causal masks do not use the KV cache + if (hparams.causal_attn) { + llama_kv_cache_update(&lctx); - // if we have enough unused cells before the current head -> - // better to start searching from the beginning of the cache, hoping to fill it - if (kv_self.head > kv_self.used + 2*n_tokens) { - kv_self.head = 0; + // if we have enough unused cells before the current head -> + // better to start searching from the beginning of the cache, hoping to fill it + if (kv_self.head > kv_self.used + 2*n_tokens) { + kv_self.head = 0; + } + + if (!llama_kv_cache_find_slot(kv_self, batch)) { + return 1; + } + + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + //kv_self.n = llama_kv_cache_cell_max(kv_self); } - if (!llama_kv_cache_find_slot(kv_self, batch)) { - return 1; - } - - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); - //kv_self.n = llama_kv_cache_cell_max(kv_self); - //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); ggml_backend_sched_reset(lctx.sched); @@ -8195,20 +8280,26 @@ static int llama_decode_internal( ggml_cgraph * gf = llama_build_graph(lctx, batch, false); // the output is always the last tensor in the graph - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2]; - if (strcmp(res->name, "result_output") == 0) { - // the embeddings could be the second to last tensor, or the third to last tensor - if (strcmp(embeddings->name, "result_norm") != 0) { - embeddings = gf->nodes[gf->n_nodes - 3]; - GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); - } - } else if (strcmp(res->name, "result_embd") == 0) { - embeddings = res; - res = nullptr; + if (!hparams.causal_attn) { + res = nullptr; // do not extract logits for embedding models such as BERT + + // token or sequence embeddings + embd = gf->nodes[gf->n_nodes - 1]; + + GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0); } else { - GGML_ASSERT(false); + if (strcmp(res->name, "result_output") == 0) { + // the token embeddings could be the second to last tensor, or the third to last tensor + if (strcmp(embd->name, "result_norm") != 0) { + embd = gf->nodes[gf->n_nodes - 3]; + GGML_ASSERT(strcmp(embd->name, "result_norm") == 0); + } + } else { + GGML_ASSERT(false && "missing result_output tensor"); + } } // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); @@ -8275,46 +8366,82 @@ static int llama_decode_internal( logits_out.clear(); #endif - ggml_backend_t res_backend = ggml_backend_sched_get_node_backend(lctx.sched, res); - GGML_ASSERT(res_backend != nullptr); + ggml_backend_t backend_res = ggml_backend_sched_get_node_backend(lctx.sched, res); + GGML_ASSERT(backend_res != nullptr); + if (batch.logits) { logits_out.resize(n_vocab * n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { if (batch.logits[i] == 0) { continue; } - ggml_backend_tensor_get_async(res_backend, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(backend_res, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[i] = true; #endif } } else if (lctx.logits_all) { logits_out.resize(n_vocab * n_tokens); - ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); + ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); #endif } else { logits_out.resize(n_vocab); - ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[0] = true; #endif } - ggml_backend_synchronize(res_backend); + ggml_backend_synchronize(backend_res); } // extract embeddings - if (!lctx.embedding.empty()) { - auto & embedding_out = lctx.embedding; + if (cparams.embeddings && embd) { + ggml_backend_t backend_embd = ggml_backend_sched_get_node_backend(lctx.sched, embd); + GGML_ASSERT(backend_embd != nullptr); - const int64_t embd_pos = res ? n_embd * (n_tokens-1) : 0; - const int64_t embd_size = res ? n_embd : n_embd * n_tokens; + switch (cparams.pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + // extract token embeddings + auto & embd_out = lctx.embd; - embedding_out.resize(embd_size); - ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings); - ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embd_pos*sizeof(float), embd_size*sizeof(float)); - ggml_backend_synchronize(embeddings_backend); + if (batch.logits) { + embd_out.resize(n_embd * n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + if (batch.logits[i] == 0) { + continue; + } + + ggml_backend_tensor_get_async(backend_embd, embd, embd_out.data() + (n_embd*i), (n_embd*i)*sizeof(float), n_embd*sizeof(float)); + } + } + } break; + case LLAMA_POOLING_TYPE_CLS: + case LLAMA_POOLING_TYPE_MEAN: + { + GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0); + + // extract sequence embeddings + auto & embd_seq_out = lctx.embd_seq; + embd_seq_out.clear(); + + for (uint32_t i = 0; i < n_tokens; i++) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { + continue; + } + embd_seq_out[seq_id].resize(n_embd); + ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); + } + } break; + case LLAMA_POOLING_TYPE_UNSPECIFIED: + { + GGML_ASSERT(false && "unknown pooling type"); + } break; + } + ggml_backend_synchronize(backend_embd); } // measure the performance only for the single-token evals @@ -8608,19 +8735,19 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { GGML_ASSERT(llama_is_byte_token(vocab, id)); const auto& token_data = vocab.id_to_token.at(id); switch (llama_vocab_get_type(vocab)) { - case LLAMA_VOCAB_TYPE_SPM: { - auto buf = token_data.text.substr(3, 2); - return strtol(buf.c_str(), NULL, 16); - } - case LLAMA_VOCAB_TYPE_BPE: { - GGML_ASSERT(false); - return unicode_to_bytes_bpe(token_data.text); - } - case LLAMA_VOCAB_TYPE_WPM: { - GGML_ASSERT(false); - } - default: - GGML_ASSERT(false); + case LLAMA_VOCAB_TYPE_SPM: { + auto buf = token_data.text.substr(3, 2); + return strtol(buf.c_str(), NULL, 16); + } + case LLAMA_VOCAB_TYPE_BPE: { + GGML_ASSERT(false); + return unicode_to_bytes_bpe(token_data.text); + } + case LLAMA_VOCAB_TYPE_WPM: { + GGML_ASSERT(false); + } + default: + GGML_ASSERT(false); } } @@ -11864,7 +11991,7 @@ struct llama_context_params llama_context_default_params() { /*.type_k =*/ GGML_TYPE_F16, /*.type_v =*/ GGML_TYPE_F16, /*.logits_all =*/ false, - /*.embedding =*/ false, + /*.embeddings =*/ false, /*.offload_kqv =*/ true, /*.abort_callback =*/ nullptr, /*.abort_callback_data =*/ nullptr, @@ -12015,6 +12142,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; + cparams.embeddings = params.embeddings; cparams.offload_kqv = params.offload_kqv; cparams.pooling_type = params.pooling_type; @@ -12192,8 +12320,8 @@ struct llama_context * llama_new_context_with_model( // resized during inference, reserve maximum ctx->logits.reserve(hparams.n_vocab*cparams.n_batch); - if (params.embedding) { - ctx->embedding.resize(hparams.n_embd); + if (params.embeddings) { + ctx->embd.reserve(hparams.n_embd*cparams.n_batch); } // graph inputs @@ -12628,7 +12756,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) { // assume worst case for logits although only currently set ones are serialized const size_t s_logits = ctx->logits.capacity() * sizeof(float); const size_t s_embedding_size = sizeof(size_t); - const size_t s_embedding = ctx->embedding.size() * sizeof(float); + const size_t s_embedding = ctx->embd.capacity() * sizeof(float); const size_t s_kv_buf_size = sizeof(size_t); const size_t s_kv_head = sizeof(uint32_t); const size_t s_kv_size = sizeof(uint32_t); @@ -12737,12 +12865,12 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat // copy embeddings { - const size_t embedding_size = ctx->embedding.size(); + const size_t embeddings_size = ctx->embd.size(); - data_ctx->write(&embedding_size, sizeof(embedding_size)); + data_ctx->write(&embeddings_size, sizeof(embeddings_size)); - if (embedding_size) { - data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float)); + if (embeddings_size) { + data_ctx->write(ctx->embd.data(), embeddings_size * sizeof(float)); } } @@ -12846,15 +12974,17 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { // set embeddings { - size_t embedding_size; + size_t embeddings_size; - memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size); + memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size); - GGML_ASSERT(ctx->embedding.capacity() == embedding_size); + GGML_ASSERT(ctx->embd.capacity() == embeddings_size); - if (embedding_size) { - memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float)); - inp += embedding_size * sizeof(float); + if (embeddings_size) { + ctx->embd.resize(embeddings_size); + + memcpy(ctx->embd.data(), inp, embeddings_size * sizeof(float)); + inp += embeddings_size * sizeof(float); } } @@ -13104,11 +13234,20 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { } float * llama_get_embeddings(struct llama_context * ctx) { - return ctx->embedding.data(); + return ctx->embd.data(); } float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) { - return ctx->embedding.data() + i*ctx->model.hparams.n_embd; + return ctx->embd.data() + i*ctx->model.hparams.n_embd; +} + +float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) { + auto it = ctx->embd_seq.find(seq_id); + if (it == ctx->embd_seq.end()) { + return nullptr; + } + + return it->second.data(); } const char * llama_token_get_text(const struct llama_model * model, llama_token token) { diff --git a/llama.h b/llama.h index 70da4cb3f..3dc162b07 100644 --- a/llama.h +++ b/llama.h @@ -163,7 +163,7 @@ extern "C" { // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - pos : the positions of the respective token in the sequence // - seq_id : the sequence to which the respective token belongs - // - logits : if zero, the logits for the respective token will not be output + // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output // typedef struct llama_batch { int32_t n_tokens; @@ -173,7 +173,7 @@ extern "C" { llama_pos * pos; int32_t * n_seq_id; llama_seq_id ** seq_id; - int8_t * logits; + int8_t * logits; // TODO: rename this to "output" // NOTE: helpers for smooth API transition - can be deprecated in the future // for future-proof code, use the above fields instead and ignore everything below @@ -260,7 +260,7 @@ extern "C" { // Keep the booleans together to avoid misalignment during copy-by-value. bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) - bool embedding; // embedding mode only + bool embeddings; // if true, extract embeddings (together with logits) bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU // Abort callback @@ -655,14 +655,20 @@ extern "C" { // llama_get_logits(ctx) + i*n_vocab LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); - // Get the embeddings for the input - // shape: [n_embd] (1-dimensional) + // Get all output token embeddings + // shape: [n_tokens*n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); - // Get the embeddings for the ith sequence + // Get the embeddings for the ith token // llama_get_embeddings(ctx) + i*n_embd + // shape: [n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); + // Get the embeddings for a sequence id + // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE + // shape: [n_embd] (1-dimensional) + LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id); + // // Vocab // From 1d41d6f7c2a666eb9c18a686a4684c4b03289bf3 Mon Sep 17 00:00:00 2001 From: hutli <6594598+hutli@users.noreply.github.com> Date: Tue, 5 Mar 2024 02:33:08 +0100 Subject: [PATCH 46/86] nix: static build (#5814) --- .devops/nix/package.nix | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 815db6a2d..01c99185b 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -1,5 +1,6 @@ { lib, + glibc, config, stdenv, mkShell, @@ -30,6 +31,11 @@ useRocm ? config.rocmSupport, useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake + + # It's necessary to consistently use backendStdenv when building with CUDA support, + # otherwise we get libstdc++ errors downstream. + effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv, + enableStatic ? effectiveStdenv.hostPlatform.isStatic }@inputs: let @@ -40,11 +46,8 @@ let strings versionOlder ; - - # It's necessary to consistently use backendStdenv when building with CUDA support, - # otherwise we get libstdc++ errors downstream. + stdenv = throw "Use effectiveStdenv instead"; - effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv; suffices = lib.optionals useBlas [ "BLAS" ] @@ -167,6 +170,9 @@ effectiveStdenv.mkDerivation ( # TODO: Replace with autoAddDriverRunpath # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged cudaPackages.autoAddOpenGLRunpathHook + ] + ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ + glibc.static ]; buildInputs = @@ -181,7 +187,7 @@ effectiveStdenv.mkDerivation ( [ (cmakeBool "LLAMA_NATIVE" false) (cmakeBool "LLAMA_BUILD_SERVER" true) - (cmakeBool "BUILD_SHARED_LIBS" true) + (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic)) (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) (cmakeBool "LLAMA_BLAS" useBlas) (cmakeBool "LLAMA_CLBLAST" useOpenCL) @@ -190,6 +196,7 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_METAL" useMetalKit) (cmakeBool "LLAMA_MPI" useMpi) (cmakeBool "LLAMA_VULKAN" useVulkan) + (cmakeBool "LLAMA_STATIC" enableStatic) ] ++ optionals useCuda [ ( From 29eee404746e4696143a4f3a642660a4793a15d8 Mon Sep 17 00:00:00 2001 From: Jeffrey Quesnelle Date: Mon, 4 Mar 2024 19:23:06 -0800 Subject: [PATCH 47/86] fix speculative decoding build on windows (#5874) --- examples/speculative/speculative.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 85bc0a762..e991b8846 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -226,7 +226,7 @@ int main(int argc, char ** argv) { while (active_seqs.size() > 0) { // randomly select a sequence to verify from active sequences - std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); + std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); int s = *std::next(active_seqs.begin(), u_int_dist(rng)); if (i_dft >= (int) drafts[s].tokens.size()) { drafts[s].active = false; From 6a87ac3a52668e117d97bcea07b529c93188b303 Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Tue, 5 Mar 2024 15:12:23 +0900 Subject: [PATCH 48/86] fix editorconfig check break (#5879) --- .devops/nix/package.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 01c99185b..c7fa2203a 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -46,7 +46,7 @@ let strings versionOlder ; - + stdenv = throw "Use effectiveStdenv instead"; suffices = From 21b08674331e1ea1b599f17c5ca91f0ed173be31 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Tue, 5 Mar 2024 16:08:35 +0800 Subject: [PATCH 49/86] [SYCL] fix mul_mat fault in CI/unit-test (#5862) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix mul_mat fault in cpy_f32_f16 * rm unused function * add wait() for memcpy * restore ci/run.sh, rename struct defination, fix bug in ggml_sycl_op_mul_mat_sycl * fix format issue * llama : fix segfault from unknown model arch name (#5820) * llama : fix segfault from unknown model arch name * llama : make all LLM maps const This also requires using `std::map::at` instead of its `operator[]` which does not exist for const maps. * llama : name LLM_ARCH_UNKNOWN to "(unknown)" This avoids errors from `std::map::at` when getting the general name of the model architecture. Using "(unknown)" instead of an empty string as per suggestion https://github.com/ggerganov/llama.cpp/pull/5820#issuecomment-1973735284 * llama : remove redundant inner const for LLM_TENSOR_NAMES The extra const won't do anything here as const maps return const references to values. Co-authored-by: Jared Van Bortel * llama : remove redundant nullptr check in llm_arch_from_string Since LLM_ARCH_NAMES is a const map, no spurious elements with a NULL name are inserted anymore, so this check is dead code. --------- Co-authored-by: Jared Van Bortel * llama : refactor internal quantization functions (#5830) * scripts : add pod-llama.sh * ggml : IQ3_S improvements (#5829) * iq3_s: somewhat faster AVX2 dot product On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using 16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s. PP-512 increases to 28.5 t/s from 23.8 t/s. * iq3_s: somewhat faster ARM_NEON dot product Still dog slow - 10.7 t/s up from 9.9 t/s. * iq3_s: another small ARM_NEON improvement 10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick that works best on AVX2. * iq3_s: minor improvement on Metal 49.4 t/s -> 50.3 t/s * iq3_s: PPL improvement E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653. * iq3_s: use new grid everywhere * Fix ARM_NEON --------- Co-authored-by: Iwan Kawrakow * convert-hf : make model class definitions self-contained (#5825) * convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (#5821) * ggml : fix IQ3_S AVX implementation (#5834) ggml-ci * llama : add abort_callback to interrupt computation (#5409) * using abort_callback from ggml to stop llama computation * format fix * a brief explaining comment --------- Co-authored-by: Georgi Gerganov * server: tests: passkey challenge / self-extend with context shift demo (#5832) * server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test * flake.lock: Update (#5842) Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) → 'github:hercules-ci/flake-parts/f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2' (2024-03-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8?dir=lib' (2024-02-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/cbc4211f0afffe6dfd2478a62615dd5175a13f9a' (2024-02-23) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8' (2024-02-29) Co-authored-by: github-actions[bot] * server : init http requests thread pool with --parallel if set (#5836) * ci : schedule slow server tests only on Release or on demand (#5839) * llama : fix llama_copy_state_data with fragmented KV cache (#5840) The row size of the saved states was based on kv_self.head while it should be based on llama_kv_cache_cell_max. Existing session files should still work. * llama : fix llama_kv_cache_cell_max inability to return 1 I've also changed its return type to uint32_t, because this function is always used to set the value of uint32_t variables, and because the index already has this type. * llama : fix state size calculation Some bytes in the state were unaccounted for in llama_get_state_size. Since the logits reserve so much space, it did not cause problems. * gguf-dump : support i-quants (#5841) Co-authored-by: Black_Fox * llama : allow for user specified embedding pooling type (#5849) * allow for user specified pooling type * llama : use enum types over int --------- Co-authored-by: Georgi Gerganov * readme : add API changes section * cuda : fix data race in soft max (#5853) * main : support special tokens as reverse/anti prompt (#5847) * Support special tokens as reverse/anti prompt. * Tokenize antiprompts only once. * main : minor --------- Co-authored-by: Georgi Gerganov * common : use LLAMA_DEFAULT_SEED (#5855) * add some new ops, fix some operators and add batch operations to certain operators. (ggml/747) * cuda: fix group_norm * cuda: add batch inference support for ggml_pad/ggml_upscale * add ggml_arrange * add ggml_timestep_embedding * update ggml_arange/ggml_timestep_embedding tests * cuda: fix im2col * add ggml_arange/ggml_timestep_embbeding support for metal backend * fix some bugs * fix some bugs * Update ggml.h Co-authored-by: Georgi Gerganov * Update ggml-cuda.cu Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.metal Co-authored-by: Georgi Gerganov * modify according to the review comments * ggml : fix compile warnings + code style * ggml : normalize compute_forward calls + fix seg fault in debug * minor --------- Co-authored-by: Georgi Gerganov Co-authored-by: slaren * sync : ggml * add alias for chat template (#5858) * speculative : implement stochastic speculative sampling (#5625) * (WIP) Implement stochastic speculative decoding * sample from residual distribution on draft accept failure * fix #5657: force greedy sampling with probs when temp is 0 * remove p_accept parameter * fix style * remove unused variables * add srand() in speculative.cpp * replace use of rand() with mt19937 sampling * fixes based on review (@JohannesGaessler) * fix r random generation * randomly select next sequence to verify + fix bug in memory freeing * fix bug in active_seqs sync * fix uniform int distribution initialization * remove warnings from comparison between int and size_t * check grammar in `llama_sample_probability_distribution_impl` * remove malloc code by utilizing vectors * add PR link to README * cmake : handle cases where git index is not found in .git (#5844) * Update CMakeLists.txt * Update CMakeLists.txt * ggml : introduce ggml_status (ggml/750) * using enum as an exit code instead of macros * update return type from enum to unsigned int * indentation fix * compound update ggml_compute_exit_code -> ggml_status changed ggml_status from a bit-field type to simple codes ggml_status to string cast * ggml_status to string cast * GGML_CALL was removed Co-authored-by: slaren --------- Co-authored-by: slaren Co-authored-by: Georgi Gerganov * sync : ggml ggml-ci * ggml : fix unknown status (#0) * flake : fix * llama : fix embeddings (#5796) * llama : fix embeddings ggml-ci * llama : do not use KV cache for non-causal models ggml-ci * embeddings : fix llama_batch_init arg * llama : add pooling switch * llama : distinguish token vs sequence embeddings ggml-ci * llama : assert pooling tensor * llama : simplify causal mask condition ggml-ci * llama : assert input batch with pooling enabled * readme : update API changes list * nix: static build (#5814) * fix speculative decoding build on windows (#5874) * rebase and rm tailing space --------- Co-authored-by: LiangtaoJin Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> Co-authored-by: Jared Van Bortel Co-authored-by: Xuan Son Nguyen Co-authored-by: Georgi Gerganov Co-authored-by: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Co-authored-by: Iwan Kawrakow Co-authored-by: Jared Van Bortel Co-authored-by: Michael Podvitskiy Co-authored-by: Pierrick Hymbert Co-authored-by: github-actions[bot] Co-authored-by: Nindaleth Co-authored-by: Black_Fox Co-authored-by: Douglas Hanley Co-authored-by: slaren Co-authored-by: DAN™ Co-authored-by: leejet Co-authored-by: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Co-authored-by: Dane Madsen Co-authored-by: hutli <6594598+hutli@users.noreply.github.com> Co-authored-by: Jeffrey Quesnelle --- ggml-sycl.cpp | 1966 +++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 1810 insertions(+), 156 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 47a605b01..477f5cb02 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2894,6 +2894,254 @@ namespace dpct using err0 = detail::generic_error_type; using err1 = detail::generic_error_type; + static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) { + detail::dpct_free(ptr, q); + } + + /// dpct accessor used as device function parameter. + template class accessor; + template class accessor { + public: + using memory_t = detail::memory_traits; + using element_t = typename memory_t::element_t; + using pointer_t = typename memory_t::pointer_t; + using accessor_t = typename memory_t::template accessor_t<3>; + accessor(pointer_t data, const sycl::range<3> &in_range) + : _data(data), _range(in_range) {} + template + accessor(typename std::enable_if::type &acc) + : accessor(acc, acc.get_range()) {} + accessor(const accessor_t &acc, const sycl::range<3> &in_range) + : accessor(acc.get_pointer(), in_range) {} + accessor operator[](size_t index) const { + sycl::range<2> sub(_range.get(1), _range.get(2)); + return accessor(_data + index * sub.size(), sub); + } + + pointer_t get_ptr() const { return _data; } + + private: + pointer_t _data; + sycl::range<3> _range; + }; + template class accessor { + public: + using memory_t = detail::memory_traits; + using element_t = typename memory_t::element_t; + using pointer_t = typename memory_t::pointer_t; + using accessor_t = typename memory_t::template accessor_t<2>; + accessor(pointer_t data, const sycl::range<2> &in_range) + : _data(data), _range(in_range) {} + template + accessor(typename std::enable_if::type &acc) + : accessor(acc, acc.get_range()) {} + accessor(const accessor_t &acc, const sycl::range<2> &in_range) + : accessor(acc.get_pointer(), in_range) {} + + pointer_t operator[](size_t index) const { + return _data + _range.get(1) * index; + } + + pointer_t get_ptr() const { return _data; } + + private: + pointer_t _data; + sycl::range<2> _range; + }; + + namespace detail { + /// Device variable with address space of shared, global or constant. + template class device_memory { + public: + using accessor_t = + typename detail::memory_traits::template accessor_t; + using value_t = typename detail::memory_traits::value_t; + using dpct_accessor_t = dpct::accessor; + + device_memory() : device_memory(sycl::range(1)) {} + + /// Constructor of 1-D array with initializer list + device_memory(const sycl::range &in_range, + std::initializer_list &&init_list) + : device_memory(in_range) { + assert(init_list.size() <= in_range.size()); + _host_ptr = (value_t *)std::malloc(_size); + std::memset(_host_ptr, 0, _size); + std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T)); + } + + /// Constructor of 2-D array with initializer list + template + device_memory( + const typename std::enable_if>::type &in_range, + std::initializer_list> &&init_list) + : device_memory(in_range) { + assert(init_list.size() <= in_range[0]); + _host_ptr = (value_t *)std::malloc(_size); + std::memset(_host_ptr, 0, _size); + auto tmp_data = _host_ptr; + for (auto sub_list : init_list) { + assert(sub_list.size() <= in_range[1]); + std::memcpy(tmp_data, sub_list.begin(), + sub_list.size() * sizeof(T)); + tmp_data += in_range[1]; + } + } + + /// Constructor with range + device_memory(const sycl::range &range_in) + : _size(range_in.size() * sizeof(T)), _range(range_in), + _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) { + static_assert( + (Memory == global) || (Memory == constant) || (Memory == shared), + "device memory region should be global, constant or shared"); + // Make sure that singleton class mem_mgr and dev_mgr will destruct + // later than this. + detail::mem_mgr::instance(); + dev_mgr::instance(); + } + + /// Constructor with range + template + device_memory(Args... Arguments) + : device_memory(sycl::range(Arguments...)) {} + + ~device_memory() { + if (_device_ptr && !_reference) + dpct::dpct_free(_device_ptr); + if (_host_ptr) + std::free(_host_ptr); + } + + /// Allocate memory with default queue, and init memory if has initial + /// value. + void init() { init(dpct::get_default_queue()); } + /// Allocate memory with specified queue, and init memory if has initial + /// value. + void init(sycl::queue &q) { + if (_device_ptr) + return; + if (!_size) + return; + allocate_device(q); + if (_host_ptr) + detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size, + host_to_device); + } + + /// The variable is assigned to a device pointer. + void assign(value_t *src, size_t size) { + this->~device_memory(); + new (this) device_memory(src, size); + } + + /// Get memory pointer of the memory object, which is virtual pointer when + /// usm is not used, and device pointer when usm is used. + value_t *get_ptr() { return get_ptr(get_default_queue()); } + /// Get memory pointer of the memory object, which is virtual pointer when + /// usm is not used, and device pointer when usm is used. + value_t *get_ptr(sycl::queue &q) { + init(q); + return _device_ptr; + } + + /// Get the device memory object size in bytes. + size_t get_size() { return _size; } + + template + typename std::enable_if::type &operator[](size_t index) { + init(); + #ifdef DPCT_USM_LEVEL_NONE + return dpct::get_buffer::type>( + _device_ptr) + .template get_access()[index]; + #else + return _device_ptr[index]; + #endif // DPCT_USM_LEVEL_NONE + } + + #ifdef DPCT_USM_LEVEL_NONE + /// Get sycl::accessor for the device memory object when usm is not used. + accessor_t get_access(sycl::handler &cgh) { + return get_buffer(_device_ptr) + .template reinterpret(_range) + .template get_access::mode, + detail::memory_traits::target>(cgh); + } + #else + /// Get dpct::accessor with dimension info for the device memory object + /// when usm is used and dimension is greater than 1. + template + typename std::enable_if::type + get_access(sycl::handler &cgh) { + return dpct_accessor_t((T *)_device_ptr, _range); + } + #endif // DPCT_USM_LEVEL_NONE + + private: + device_memory(value_t *memory_ptr, size_t size) + : _size(size), _range(size / sizeof(T)), _reference(true), + _device_ptr(memory_ptr) {} + + void allocate_device(sycl::queue &q) { + #ifndef DPCT_USM_LEVEL_NONE + if (Memory == shared) { + _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(), + q.get_context()); + return; + } + #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY + if (Memory == constant) { + _device_ptr = (value_t *)sycl::malloc_device( + _size, q.get_device(), q.get_context(), + sycl::ext::oneapi::property::usm::device_read_only()); + return; + } + #endif + #endif + _device_ptr = (value_t *)detail::dpct_malloc(_size, q); + } + + size_t _size; + sycl::range _range; + bool _reference; + value_t *_host_ptr; + value_t *_device_ptr; + }; + template + class device_memory : public device_memory { + public: + using base = device_memory; + using value_t = typename base::value_t; + using accessor_t = + typename detail::memory_traits::template accessor_t<0>; + + /// Constructor with initial value. + device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {} + + /// Default constructor + device_memory() : base(1) {} + + #ifdef DPCT_USM_LEVEL_NONE + /// Get sycl::accessor for the device memory object when usm is not used. + accessor_t get_access(sycl::handler &cgh) { + auto buf = get_buffer(base::get_ptr()) + .template reinterpret(sycl::range<1>(1)); + return accessor_t(buf, cgh); + } + #endif // DPCT_USM_LEVEL_NONE + }; + } // namespace detail + + template + using global_memory = detail::device_memory; + template + using constant_memory = detail::device_memory; + template + using shared_memory = detail::device_memory; + + } // COPY from DPCT head files @@ -2938,6 +3186,15 @@ static int g_work_group_size = 0; #pragma warning(disable: 4244 4267) // possible loss of data #endif +// dmmv = dequantize_mul_mat_vec +#ifndef GGML_SYCL_DMMV_X +#define GGML_SYCL_DMMV_X 32 +#endif +#ifndef GGML_SYCL_MMV_Y +#define GGML_SYCL_MMV_Y 1 +#endif + + static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); static void crash(){ @@ -3060,7 +3317,7 @@ typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0, #define QK4_0 32 #define QR4_0 2 #define QI4_0 (QK4_0 / (4 * QR4_0)) -typedef struct dpct_type_471834 { +typedef struct dpct_type_block_q4_0 { sycl::half d; // delta uint8_t qs[QK4_0 / 2]; // nibbles / quants } block_q4_0; @@ -3069,7 +3326,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 #define QK4_1 32 #define QR4_1 2 #define QI4_1 (QK4_1 / (4 * QR4_1)) -typedef struct dpct_type_143705 { +typedef struct dpct_type_block_q4_1 { sycl::half2 dm; // dm.x = delta, dm.y = min uint8_t qs[QK4_1 / 2]; // nibbles / quants } block_q4_1; @@ -3078,7 +3335,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong #define QK5_0 32 #define QR5_0 2 #define QI5_0 (QK5_0 / (4 * QR5_0)) -typedef struct dpct_type_673649 { +typedef struct dpct_type_block_q5_0 { sycl::half d; // delta uint8_t qh[4]; // 5-th bit of quants uint8_t qs[QK5_0 / 2]; // nibbles / quants @@ -3088,7 +3345,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5 #define QK5_1 32 #define QR5_1 2 #define QI5_1 (QK5_1 / (4 * QR5_1)) -typedef struct dpct_type_135589 { +typedef struct dpct_type_block_q5_1 { sycl::half2 dm; // dm.x = delta, dm.y = min uint8_t qh[4]; // 5-th bit of quants uint8_t qs[QK5_1 / 2]; // nibbles / quants @@ -3098,7 +3355,7 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + #define QK8_0 32 #define QR8_0 1 #define QI8_0 (QK8_0 / (4 * QR8_0)) -typedef struct dpct_type_122878 { +typedef struct dpct_type_block_q8_0 { sycl::half d; // delta int8_t qs[QK8_0]; // quants } block_q8_0; @@ -3107,7 +3364,7 @@ static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 blo #define QK8_1 32 #define QR8_1 1 #define QI8_1 (QK8_1 / (4 * QR8_1)) -typedef struct dpct_type_143721 { +typedef struct dpct_type_block_q8_1 { sycl::half2 ds; // ds.x = delta, ds.y = sum int8_t qs[QK8_0]; // quants } block_q8_1; @@ -3141,7 +3398,7 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( #define QR2_K 4 #define QI2_K (QK_K / (4*QR2_K)) -typedef struct dpct_type_619598 { +typedef struct dpct_type_block_q2_K { uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits uint8_t qs[QK_K/4]; // quants sycl::half2 dm; // super-block scale for quantized scales/mins @@ -3150,7 +3407,7 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "w #define QR3_K 4 #define QI3_K (QK_K / (4*QR3_K)) -typedef struct dpct_type_138576 { +typedef struct dpct_type_block_q3_K { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits #ifdef GGML_QKK_64 @@ -3166,13 +3423,13 @@ typedef struct dpct_type_138576 { #define QI4_K (QK_K / (4*QR4_K)) #ifdef GGML_QKK_64 typedef struct { - half dm[2]; // super-block scales/mins + sycl::half dm[2]; // super-block scales/mins uint8_t scales[2]; // 4-bit block scales/mins uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; -static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); +static_assert(sizeof(block_q4_K) == sizeof(sycl::half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); #else -typedef struct dpct_type_154943 { +typedef struct dpct_type_block_q4_K { sycl::half2 dm; // super-block scale for quantized scales/mins uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits uint8_t qs[QK_K/2]; // 4--bit quants @@ -3184,14 +3441,14 @@ static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, #define QI5_K (QK_K / (4*QR5_K)) #ifdef GGML_QKK_64 typedef struct { - half d; // super-block scale + sycl::half d; // super-block scale int8_t scales[QK_K/16]; // block scales uint8_t qh[QK_K/8]; // quants, high bit uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); #else -typedef struct dpct_type_866817 { +typedef struct dpct_type_block_q5_K { sycl::half2 dm; // super-block scale for quantized scales/mins uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits uint8_t qh[QK_K/8]; // quants, high bit @@ -3202,7 +3459,7 @@ static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/ #define QR6_K 2 #define QI6_K (QK_K / (4*QR6_K)) -typedef struct dpct_type_107281 { +typedef struct dpct_type_block_q6_K { uint8_t ql[QK_K/2]; // quants, lower 4 bits uint8_t qh[QK_K/4]; // quants, upper 2 bits int8_t scales[QK_K/16]; // scales @@ -3210,6 +3467,31 @@ typedef struct dpct_type_107281 { } block_q6_K; static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding"); +#define QR2_XXS 8 +#define QI2_XXS (QK_K / (4*QR2_XXS)) +typedef struct dpct_type_block_iq2_xxs { + sycl::half d; + uint16_t qs[QK_K/8]; +} block_iq2_xxs; +static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); + +#define QR2_XS 8 +#define QI2_XS (QK_K / (4*QR2_XS)) +typedef struct dpct_type_block_iq2_xs { + sycl::half d; + uint16_t qs[QK_K/8]; + uint8_t scales[QK_K/32]; +} block_iq2_xs; +static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); + +#define QR3_XXS 8 +#define QI3_XXS (QK_K / (4*QR3_XXS)) +typedef struct dpct_type_block_iq3_xxs { + sycl::half d; + uint8_t qs[3*(QK_K/8)]; +} block_iq3_xxs; +static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -3478,7 +3760,7 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo local_buf = (float *) ggml_sycl_host_malloc(total_size); ggml_sycl_set_device(g_main_device); dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - main_stream->memcpy(local_buf, src, total_size); + main_stream->memcpy(local_buf, src, total_size).wait(); } else { local_buf = (float *)src; @@ -4129,6 +4411,66 @@ static __dpct_inline__ void dequantize_q8_0(const void *vx, const int ib, #endif // GGML_SYCL_F16 } +template +static void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_group(2); + + // assume 32 threads + const int tid = item_ct1.get_local_id(2); + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_0 * x = (const block_q4_0 *)vx + ib; + const float d = sycl::vec(x->d) + .convert()[0]; + const float dm = -8*d; + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d * (q[l] & 0xF) + dm; + y[l+16] = d * (q[l] >> 4) + dm; + } +} + +template +static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_group(2); + + // assume 32 threads + const int tid = item_ct1.get_local_id(2); + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_1 * x = (const block_q4_1 *)vx + ib; + const sycl::float2 d = + x->dm.convert(); + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l + 0] = d.x() * (q[l] & 0xF) + d.y(); + y[l + 16] = d.x() * (q[l] >> 4) + d.y(); + } +} + + //================================== k-quants template @@ -4158,8 +4500,9 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri const int il = tid%16; // 0...15 const uint8_t q = x[i].qs[il] >> (2*is); dst_t * y = yy + i*QK_K + 16*is + il; - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); + + float dall = x[i].dm[0]; + float dmin = x[i].dm[1]; y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); #endif @@ -4198,7 +4541,7 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); #else - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const int is = tid/16; // 0 or 1 const int il = tid%16; // 0...15 const int im = il/8; // 0...1 @@ -4264,7 +4607,7 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri y[l +32] = d2 * (q[l] >> 4) - m2; } #else - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const uint8_t * q = x[i].qs; dst_t * y = yy + i*QK_K; const float d = (float)x[i].dm[0]; @@ -4309,7 +4652,7 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; #else - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const uint8_t q = x[i].qs[tid]; const int im = tid/8; // 0...3 const int in = tid%8; // 0...7 @@ -4351,7 +4694,7 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri #else // assume 32 threads - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const int ip = tid/16; // 0 or 1 const int il = tid - 16*ip; // 0...15 @@ -4368,6 +4711,474 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri #endif } +static dpct::global_memory + iq2xxs_grid(sycl::range<1>(256), + { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, + 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, + 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, + 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, + 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b082b2b, 0x080808082b2b082b, + 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, + 0x080808192b192b08, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b082b082b, 0x0808082b2b08082b, 0x0808190808080819, + 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, + 0x0808190819082b08, 0x08081908192b0808, 0x080819082b080819, + 0x080819082b081908, 0x080819082b190808, 0x080819082b2b1908, + 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, + 0x080819192b080808, 0x080819192b190819, 0x0808192b08082b19, + 0x0808192b08190808, 0x0808192b19080808, 0x0808192b2b081908, + 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, + 0x08082b0819080819, 0x08082b0819081908, 0x08082b0819190808, + 0x08082b081919082b, 0x08082b082b082b08, 0x08082b1908081908, + 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, + 0x08190808082b0819, 0x0819080819080808, 0x08190808192b0808, + 0x081908082b081908, 0x081908082b190808, 0x081908082b191919, + 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, + 0x0819082b082b1908, 0x0819082b19081919, 0x0819190808080808, + 0x0819190808082b08, 0x08191908082b0808, 0x08191908082b1919, + 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, + 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, + 0x08192b0819080808, 0x08192b082b080819, 0x08192b1908080808, + 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, + 0x082b080819081908, 0x082b0808192b0819, 0x082b08082b080808, + 0x082b08082b08082b, 0x082b0819082b2b19, 0x082b081919082b08, + 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, + 0x082b19081919192b, 0x082b191908080808, 0x082b191919080819, + 0x082b1919192b1908, 0x082b192b2b190808, 0x082b2b0808082b08, + 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, + 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x1908080819082b08, 0x190808081919192b, + 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, + 0x19080819192b0819, 0x190808192b080808, 0x190808192b081919, + 0x1908082b08080819, 0x1908082b08190808, 0x1908082b19082b08, + 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, + 0x190819082b192b19, 0x190819190819082b, 0x19081919082b1908, + 0x1908192b08080808, 0x19082b0808080819, 0x19082b0808081908, + 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, + 0x19082b192b08082b, 0x19082b2b19081919, 0x19082b2b2b190808, + 0x1919080808080808, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, + 0x191908192b2b1908, 0x1919082b2b190819, 0x191919082b190808, + 0x191919082b19082b, 0x1919191908082b2b, 0x1919192b08080819, + 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, + 0x19192b2b08082b08, 0x192b080808081908, 0x192b080808190808, + 0x192b080819080808, 0x192b0808192b2b08, 0x192b081908080808, + 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, + 0x192b19190819082b, 0x192b19192b081908, 0x192b2b081908082b, + 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808082b2b, + 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, + 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, + 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, + 0x2b082b080808082b, 0x2b082b1908081908, 0x2b082b2b08190819, + 0x2b19080808081908, 0x2b19080808190808, 0x2b190808082b1908, + 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, + 0x2b191908082b082b, 0x2b19190819081908, 0x2b19191919190819, + 0x2b192b082b080819, 0x2b192b19082b0808, 0x2b2b08080808082b, + 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, + 0x2b2b2b1908081908, + }); + +static dpct::global_memory + iq2xs_grid(sycl::range<1>(512), + { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, + 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, + 0x0808080808191908, 0x080808080819192b, 0x0808080808192b19, + 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, + 0x080808081908192b, 0x0808080819082b19, 0x0808080819190808, + 0x080808081919082b, 0x0808080819191919, 0x0808080819192b08, + 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, + 0x080808082b190819, 0x080808082b191908, 0x080808082b192b19, + 0x080808082b2b0808, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, + 0x0808081908192b2b, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, + 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x0808082b08080808, + 0x0808082b0808082b, 0x0808082b08081919, 0x0808082b08082b08, + 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, + 0x0808082b19191919, 0x0808082b2b080808, 0x0808082b2b082b2b, + 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, + 0x0808190819081919, 0x0808190819082b08, 0x0808190819190819, + 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x0808191908080808, 0x080819190808082b, 0x0808191908081919, + 0x0808191908082b08, 0x0808191908190819, 0x0808191908191908, + 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, + 0x0808192b08080819, 0x0808192b08081908, 0x0808192b08190808, + 0x0808192b082b192b, 0x0808192b19080808, 0x0808192b1908082b, + 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, + 0x08082b0808190819, 0x08082b0808191908, 0x08082b08082b0808, + 0x08082b08082b1919, 0x08082b0819080819, 0x08082b0819081908, + 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b1919080808, + 0x08082b192b080819, 0x08082b192b082b19, 0x08082b2b08080808, + 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, + 0x081908080819082b, 0x0819080808191919, 0x0819080808192b08, + 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, + 0x0819080819190819, 0x0819080819191908, 0x08190808192b0808, + 0x08190808192b2b2b, 0x081908082b080819, 0x081908082b081908, + 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, + 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, + 0x0819081908191908, 0x08190819082b0808, 0x0819081919080819, + 0x0819081919081908, 0x0819081919190808, 0x081908192b080808, + 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, + 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, + 0x0819082b19080808, 0x0819082b192b0808, 0x0819190808080808, + 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, + 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, + 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, + 0x0819190819190808, 0x08191908192b1908, 0x081919082b080808, + 0x0819191908080819, 0x0819191908081908, 0x0819191908190808, + 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, + 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, + 0x08192b0808190808, 0x08192b080819082b, 0x08192b0819080808, + 0x08192b0819191908, 0x08192b082b08192b, 0x08192b1908080808, + 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, + 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, + 0x082b080808081919, 0x082b080808082b08, 0x082b080808082b2b, + 0x082b080808190819, 0x082b080808191908, 0x082b0808082b0808, + 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, + 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, + 0x082b081908081908, 0x082b081908190808, 0x082b081919080808, + 0x082b081919082b08, 0x082b0819192b1919, 0x082b082b08080808, + 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, + 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, + 0x082b1908082b2b19, 0x082b190819080808, 0x082b191908080808, + 0x082b191919080819, 0x082b19191919082b, 0x082b19192b192b19, + 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, + 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, + 0x082b2b08082b0808, 0x082b2b0819191919, 0x082b2b082b082b08, + 0x082b2b082b2b082b, 0x082b2b19192b2b08, 0x082b2b192b190808, + 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, + 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, + 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, + 0x1908080808190808, 0x190808080819082b, 0x1908080808191919, + 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, + 0x1908080819082b08, 0x1908080819082b2b, 0x1908080819190819, + 0x1908080819191908, 0x19080808192b0808, 0x19080808192b1919, + 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, + 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, + 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, + 0x19080819082b0808, 0x1908081919080819, 0x1908081919081908, + 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, + 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, + 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b082b2b19, + 0x1908082b19080808, 0x1908190808080808, 0x190819080808082b, + 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, + 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, + 0x1908190819080819, 0x1908190819081908, 0x1908190819190808, + 0x190819082b080808, 0x190819082b191908, 0x1908191908080819, + 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, + 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, + 0x1908192b08082b2b, 0x1908192b19081908, 0x1908192b19190808, + 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808190808, + 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, + 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, + 0x19082b1919081908, 0x19082b1919190808, 0x19082b19192b2b19, + 0x19082b2b08081908, 0x1919080808080808, 0x191908080808082b, + 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, + 0x1919080819080819, 0x1919080819081908, 0x1919080819190808, + 0x191908082b080808, 0x1919081908080819, 0x1919081908081908, + 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, + 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, + 0x1919082b2b2b2b2b, 0x1919190808080819, 0x1919190808081908, + 0x1919190808190808, 0x19191908082b0819, 0x1919190819080808, + 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, + 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, + 0x191919192b082b08, 0x1919192b082b0819, 0x1919192b192b2b08, + 0x1919192b2b2b0819, 0x19192b0808080808, 0x19192b0808191908, + 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, + 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, + 0x19192b2b2b081919, 0x192b080808080819, 0x192b080808081908, + 0x192b080808190808, 0x192b080819080808, 0x192b080819191908, + 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, + 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, + 0x192b082b2b19082b, 0x192b190808080808, 0x192b19080819192b, + 0x192b191908190808, 0x192b191919080808, 0x192b191919081919, + 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, + 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, + 0x192b2b2b192b082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, + 0x2b0808082b080808, 0x2b0808082b08082b, 0x2b0808082b2b2b08, + 0x2b0808082b2b2b2b, 0x2b08081908080819, 0x2b08081908081908, + 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, + 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, + 0x2b08082b082b0808, 0x2b08082b2b080808, 0x2b08082b2b08082b, + 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, + 0x2b0819082b082b19, 0x2b08191908080808, 0x2b08191919081908, + 0x2b0819192b2b1919, 0x2b08192b08192b08, 0x2b08192b192b2b2b, + 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, + 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, + 0x2b082b082b2b2b08, 0x2b082b190808192b, 0x2b082b2b082b082b, + 0x2b082b2b2b080808, 0x2b082b2b2b082b08, 0x2b082b2b2b19192b, + 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, + 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, + 0x2b1908082b081908, 0x2b19081908080808, 0x2b190819082b082b, + 0x2b190819192b1908, 0x2b19082b1919192b, 0x2b19082b2b082b19, + 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, + 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, + 0x2b1919192b190808, 0x2b1919192b19082b, 0x2b19192b19080819, + 0x2b192b0819190819, 0x2b192b082b2b192b, 0x2b192b1919082b19, + 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, + 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, + 0x2b2b0808082b0808, 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, + 0x2b2b081919190819, 0x2b2b081919192b19, 0x2b2b08192b2b192b, + 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, + 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, + 0x2b2b190819080808, 0x2b2b19082b191919, 0x2b2b192b192b1919, + 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, 0x2b2b2b08082b0808, + 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, + 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, + 0x2b2b2b192b08192b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, + 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, + }); + +static dpct::global_memory iq3xxs_grid( + sycl::range<1>(256), + { + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, + 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, + 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, 0x040c140c, 0x040c142c, + 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, + 0x04141c1c, 0x04141c3e, 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, + 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, 0x041c3e04, 0x04240c1c, + 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, + 0x043e0c24, 0x043e0c34, 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, + 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, 0x0c041c04, 0x0c041c14, + 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, + 0x0c14140c, 0x0c141c04, 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, + 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, 0x0c24042c, 0x0c242c04, + 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, + 0x14041414, 0x14041434, 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, + 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, 0x140c1c04, 0x140c341c, + 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, + 0x141c0c04, 0x141c0c24, 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, + 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, 0x143e040c, 0x143e041c, + 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, + 0x1c0c1404, 0x1c0c1c0c, 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, + 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, 0x1c1c0c0c, 0x1c1c1c1c, + 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, + 0x24040424, 0x24040c3e, 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, + 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, 0x24143404, 0x24143434, + 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, + 0x2c040c14, 0x2c04240c, 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, + 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, 0x2c1c0414, 0x2c1c2c1c, + 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, + 0x34043424, 0x340c140c, 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, + 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, 0x34341c1c, 0x343e041c, + 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, + 0x3e1c0404, 0x3e1c0c2c, 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, + 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, + }); + +static dpct::global_memory ksigns_iq2xs( + sycl::range<1>(128), + { + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, + 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, + 154, 27, 156, 29, 30, 159, 160, 33, 34, 163, 36, 165, 166, + 39, 40, 169, 170, 43, 172, 45, 46, 175, 48, 177, 178, 51, + 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, 192, + 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, + 78, 207, 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, + 219, 92, 221, 222, 95, 96, 225, 226, 99, 228, 101, 102, 231, + 232, 105, 106, 235, 108, 237, 238, 111, 240, 113, 114, 243, 116, + 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, + }); + +static dpct::global_memory + ksigns64(sycl::range<1>(128), + { + 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, + 0x000000000000ffff, 0xff00000000ff0000, 0x0000000000ff00ff, + 0x0000000000ffff00, 0xff00000000ffffff, 0xff000000ff000000, + 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, + 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, + 0x00000000ffffffff, 0xff0000ff00000000, 0x000000ff000000ff, + 0x000000ff0000ff00, 0xff0000ff0000ffff, 0x000000ff00ff0000, + 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, + 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, + 0x000000ffff00ffff, 0xff0000ffffff0000, 0x000000ffffff00ff, + 0x000000ffffffff00, 0xff0000ffffffffff, 0xff00ff0000000000, + 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, + 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, + 0x0000ff0000ffffff, 0x0000ff00ff000000, 0xff00ff00ff0000ff, + 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, 0xff00ff00ffff0000, + 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, + 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, + 0x0000ffff0000ffff, 0xff00ffff00ff0000, 0x0000ffff00ff00ff, + 0x0000ffff00ffff00, 0xff00ffff00ffffff, 0xff00ffffff000000, + 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, + 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, + 0x0000ffffffffffff, 0xffff000000000000, 0x00ff0000000000ff, + 0x00ff00000000ff00, 0xffff00000000ffff, 0x00ff000000ff0000, + 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, + 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, + 0x00ff0000ff00ffff, 0xffff0000ffff0000, 0x00ff0000ffff00ff, + 0x00ff0000ffffff00, 0xffff0000ffffffff, 0x00ff00ff00000000, + 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, + 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, + 0xffff00ff00ffffff, 0xffff00ffff000000, 0x00ff00ffff0000ff, + 0x00ff00ffff00ff00, 0xffff00ffff00ffff, 0x00ff00ffffff0000, + 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, + 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, + 0x00ffff000000ffff, 0xffffff0000ff0000, 0x00ffff0000ff00ff, + 0x00ffff0000ffff00, 0xffffff0000ffffff, 0xffffff00ff000000, + 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, + 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, + 0x00ffff00ffffffff, 0xffffffff00000000, 0x00ffffff000000ff, + 0x00ffffff0000ff00, 0xffffffff0000ffff, 0x00ffffff00ff0000, + 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, + 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, + 0x00ffffffff00ffff, 0xffffffffffff0000, 0x00ffffffffff00ff, + 0x00ffffffffffff00, 0xffffffffffffffff, + }); +//#endif + +static dpct::global_memory + kmask_iq2xs(sycl::range<1>(8), {1, 2, 4, 8, 16, 32, 64, 128}); + +template +static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xxs_grid_ptr, + const uint8_t *ksigns_iq2xs_ptr, + const uint8_t *kmask_iq2xs_ptr) { + + const int i = item_ct1.get_group(2); + const block_iq2_xxs * x = (const block_iq2_xxs *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid_ptr + aux8[il]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; + const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + +template +static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xs_grid, + const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { + + const int i = item_ct1.get_group(2); + const block_iq2_xs * x = (const block_iq2_xs *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + +template +static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3xxs_grid, + const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { + + const int i = item_ct1.get_group(2); + const block_iq3_xxs * x = (const block_iq3_xxs *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * q3 = x[i].qs + 8*ib; + const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } +#else + assert(false); +#endif + +} + /* DPCT1110:4: The total declared local variable size in device function dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register @@ -4446,13 +5257,16 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int tid = item_ct1.get_local_id(2) / + (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = item_ct1.get_local_id(2) % + (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3 const int offset = tid * K_QUANTS_PER_ITERATION; uint32_t uaux[2]; const uint8_t * d = (const uint8_t *)uaux; + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { const float * y = yy + i * QK_K + offset; @@ -4462,7 +5276,8 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, uaux[0] = s[0] & 0x0f0f0f0f; uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - const float2 dall = __half22float2(x[i].dm); + const sycl::float2 dall = + x[i].dm.convert(); float sum1 = 0, sum2 = 0; for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { @@ -4473,8 +5288,9 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, + y[l+48] * d[3] * ((ql >> 6) & 3); sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; } - tmp += dall.x * sum1 - dall.y * sum2; + tmp += dall.x() * sum1 - dall.y() * sum2; } + #endif // sum up partial sums and write back result @@ -4569,8 +5385,8 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 const int in = offset/8; // 0 or 1 const int im = offset%8; // 0...7 @@ -4719,8 +5535,8 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); const int step = tid * K_QUANTS_PER_ITERATION; @@ -4860,8 +5676,8 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); const int step = tid * K_QUANTS_PER_ITERATION; const int im = step/8; const int in = step%8; @@ -4969,8 +5785,8 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3 const int step = tid * K_QUANTS_PER_ITERATION; @@ -5170,6 +5986,21 @@ static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y[iybs + iqs + y_offset] = v.y(); } +template +static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + const src_t * x = (src_t *) vx; + + y[i] = x[i]; +} + // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q @@ -6588,8 +7419,8 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, const float dall = bq4_K->dm[0]; const float dmin = bq4_K->dm[1]; - const float d8_1 = __low2float(bq8_1[0].ds); - const float d8_2 = __low2float(bq8_1[1].ds); + const float d8_1 = bq8_1[0].ds[0]; + const float d8_2 = bq8_1[1].ds[1]; const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); @@ -6600,10 +7431,10 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, const int v1 = q4[0]; const int v2 = q4[4]; - const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); - const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); + const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0)); + const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); + const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0)); + const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0)); sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); @@ -6772,8 +7603,8 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, const float d = bq5_K->d; - const float d8_1 = __low2half(bq8_1[0].ds); - const float d8_2 = __low2half(bq8_1[1].ds); + const float d8_1 = bq8_1[0].ds[0]; + const float d8_2 = bq8_1[1].ds[1]; const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); @@ -6794,8 +7625,8 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); + const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1]) + + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]); return d * sumf_d; @@ -7051,6 +7882,150 @@ static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat( return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); } + +static __dpct_inline__ float +vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { +#if QK_K == 256 + const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; + +#if QR2_XXS == 8 + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = q2[2] | (q2[3] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[aux32 & 127]; + for (int j = 0; j < 8; ++j) { + sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f; + return d * sumi; +#else + // iqs is 0...15 + const int ib32 = iqs/2; + const int il = iqs%2; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f; + const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; + const int8_t * q8 = bq8_1[ib32].qs + 16*il; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); + sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); + } + return d * (sumi1 + sumi2); +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __dpct_inline__ float +vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint64_t *iq2xs_grid, const uint64_t *ksigns64) { +#if DPCT_COMPATIBILITY_TEMP >= \ + MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; + + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = dpct::vectorized_binary( + grid[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid[1] ^ signs[1], signs[1], std::minus<>()); + sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1); + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = dpct::vectorized_binary( + grid[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid[1] ^ signs[1], signs[1], std::minus<>()); + sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2); + q8 += 8; + } + const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __dpct_inline__ float +vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) { +#if DPCT_COMPATIBILITY_TEMP >= \ + MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; + + const int ib32 = iqs; + const uint8_t * q3 = bq2->qs + 8*ib32; + const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = gas[0] | (gas[1] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0]; + const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1]; + const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127)); + const int grid_l = dpct::vectorized_binary( + grid1[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid2[0] ^ signs[1], signs[1], std::minus<>()); + sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.5f; + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + + template @@ -7632,7 +8607,8 @@ template static void template static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, - const sycl::nd_item<3> &item_ct1) { + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3xxs_grid_ptr, const uint64_t *ksigns64_ptr) { const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); @@ -7649,12 +8625,11 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = 0; i < blocks_per_row; i += blocks_per_warp) { - const int ibx = row * blocks_per_row + i + - item_ct1.get_local_id(2) / (qi / vdr); // x block index + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index - const int iby = (i + item_ct1.get_local_id(2) / (qi / vdr)) * - (qk / QK8_1); // y block index that aligns with ibx + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx const int iqs = vdr * @@ -7676,6 +8651,145 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ } } +template +static void mul_mat_vec_q_iq2_xxs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xxs_grid_ptr, const uint8_t *ksigns_iq2xs_ptr, + const uint8_t *kmask_iq2xs_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid_ptr, ksigns_iq2xs_ptr, kmask_iq2xs_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +template +static void mul_mat_vec_q_iq2_xs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xs_grid_ptr, const uint64_t *ksigns64_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid_ptr, ksigns64_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +template +static void mul_mat_vec_q_iq3_xxs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3xxs_grid_ptr, const uint64_t *ksigns64_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid_ptr, ksigns64_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + template static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows, const sycl::nd_item<3> &item_ct1) { @@ -9109,7 +10223,18 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q2_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q2_K(vx, y, item_ct1); + }); + } + #endif } @@ -9130,10 +10255,57 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q3_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q3_K(vx, y, item_ct1); + }); + } #endif } +template +static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q4_0(vx, y, nb32, item_ct1); + }); + } +} + +template +static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q4_1(vx, y, nb32, item_ct1); + }); + } +} + + template static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { @@ -9168,7 +10340,18 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q5_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q5_K(vx, y, item_ct1); + }); + } + #endif } @@ -9189,11 +10372,132 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q6_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q6_K(vx, y, item_ct1); + }); + } + #endif } -static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) { + +template +static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq2xxs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq2xxs_grid_ptr_ct1 = iq2xxs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq2_xxs( + vx, y, item_ct1, iq2xxs_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq2xs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq2xs_grid_ptr_ct1 = iq2xs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq2_xs( + vx, y, item_ct1, iq2xs_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq3xxs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq3_xxs( + vx, y, item_ct1, iq3xxs_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void convert_unary_sycl(const void *__restrict__ vx, + dst_t *__restrict__ y, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>( + sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + convert_unary(vx, y, k, item_ct1); + }); + } +} + + +static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) try { + int id; switch (type) { case GGML_TYPE_Q4_0: return dequantize_block_sycl; @@ -9215,19 +10519,30 @@ static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) { return dequantize_row_q5_K_sycl; case GGML_TYPE_Q6_K: return dequantize_row_q6_K_sycl; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_sycl; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_sycl; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_sycl; case GGML_TYPE_F32: - return dequantize_block_sycl<1, 1, convert_f32>; + return convert_unary_sycl; default: return nullptr; } } +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: - return dequantize_block_sycl; + return dequantize_row_q4_0_sycl; case GGML_TYPE_Q4_1: - return dequantize_block_sycl; + return dequantize_row_q4_1_sycl; case GGML_TYPE_Q5_0: return dequantize_block_sycl; case GGML_TYPE_Q5_1: @@ -9244,8 +10559,14 @@ static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { return dequantize_row_q5_K_sycl; case GGML_TYPE_Q6_K: return dequantize_row_q6_K_sycl; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_sycl; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_sycl; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_sycl; case GGML_TYPE_F16: - return dequantize_block_sycl<1, 1, convert_f16>; + return convert_unary_sycl; default: return nullptr; } @@ -9455,24 +10776,385 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, } } -template -static void mul_mat_vec_q_sycl_submitter(const void *vx, const void *vy, + +static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_1 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK5_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK5_1 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK8_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq2xxs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + + stream->submit([&](sycl::handler &cgh) { + auto iq2xxs_grid_ptr_ct1 = iq2xxs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq2_xxs_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq2xxs_grid_ptr_ct1, ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK4_0 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), [= - ](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq2xs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq2xs_grid_ptr_ct1 = iq2xs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq2_xs_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq2xs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } } +static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq3_xxs_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + + static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, @@ -11451,7 +13133,7 @@ struct sycl_pool_alloc { device_id = get_current_device_id(); device_index = g_sycl_gpu_mgr->get_index(device_id); ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size); - // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); + // GGML_SYCL_DEBUG("sycl_pool_alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); return ptr; } @@ -12242,63 +13924,46 @@ inline void ggml_sycl_op_mul_mat_vec_q( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; - // TODO: support these quantization types - GGML_ASSERT(!(src0->type == GGML_TYPE_IQ2_XXS || - src0->type == GGML_TYPE_IQ2_XS || - src0->type == GGML_TYPE_IQ3_XXS || - src0->type == GGML_TYPE_IQ1_S)); - switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q4_1: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_0: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_1: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q8_0: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q2_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q3_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q4_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q6_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ2_XXS: + mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ2_XS: + mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ3_XXS: + mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; @@ -12311,6 +13976,7 @@ inline void ggml_sycl_op_mul_mat_vec_q( (void) src1_padded_row_size; } + inline void ggml_sycl_op_dequantize_mul_mat_vec( const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, @@ -12318,10 +13984,11 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( const int64_t src1_ncols, const int64_t src1_padded_row_size, const dpct::queue_ptr &stream) { - GGML_TENSOR_BINARY_OP_LOCALS; - + const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + GGML_ASSERT(src1->type == GGML_TYPE_F32); + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_SYCL_F16 sycl_pool_alloc src1_dfloat_a; @@ -12333,15 +14000,10 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; if (src1_convert_f16) { - if (src1->type == GGML_TYPE_F16) { - src1_dfloat = (sycl::half *)src1->data + src1_padded_row_size; - } else { - src1_dfloat = src1_dfloat_a.alloc(ne00); - ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat, - ne00, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, - nb13, stream); - } + src1_dfloat = src1_dfloat_a.alloc(ne00); + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream); } #else const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion @@ -12495,7 +14157,7 @@ inline void ggml_sycl_op_mul_mat_sycl( *g_sycl_handles[id], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00, - src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), + src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); } (void) dst; @@ -12923,7 +14585,7 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, // copy dst to host if necessary if (!dst_on_device) { SYCL_CHECK(CHECK_TRY_ERROR( - main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)))); + main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)).wait())); } if (dst->backend == GGML_BACKEND_TYPE_CPU) { @@ -13200,7 +14862,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( src1_ddq_i, src1_ddq_i_source, src1_ncols * src1_padded_col_size * q8_1_ts / - q8_1_bs))); + q8_1_bs).wait())); } else { float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; @@ -13294,7 +14956,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, dhf_dst_i += src1_col_0*ne0; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(dhf_dst_i, dst_dd_i, - src1_ncols * ne0 * sizeof(float)))); + src1_ncols * ne0 * sizeof(float)).wait())); } } @@ -13852,13 +15514,13 @@ static __global__ void k_compute_batched_ptrs_id( src0_f16 = (half *) srcs_ar[i]; } else { src0_f16 = src0_as_f16; - if (threadIdx.x == 0 && threadIdx.y == 0) { + if (item_ct1.get_local_id(2) == 0 && threadIdx.y == 0) { const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type); to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget); } } - int i13 = blockIdx.x * blockDim.x + threadIdx.x; + int i13 = blockIdx.x * blockDim.x + item_ct1.get_local_id(2); int i12 = blockIdx.y * blockDim.y + threadIdx.y; if (i13 >= ne13 || i12 >= ne12) { @@ -14024,8 +15686,8 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, if (ids->backend == GGML_BACKEND_TYPE_GPU) { const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); - SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); + stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)).wait())); + // SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); } else { memcpy(ids_host.data(), ids->data, ggml_nbytes(ids)); } @@ -14095,7 +15757,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11, - src1_original + i01 * nb11, nb11))); + src1_original + i01 * nb11, nb11).wait())); num_src1_rows++; } @@ -14128,7 +15790,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( dst_original + i01 * nb1, - dst_contiguous.get() + num_src1_rows * nb1, nb1))); + dst_contiguous.get() + num_src1_rows * nb1, nb1).wait())); num_src1_rows++; } } @@ -15522,7 +17184,7 @@ GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( - (char *)tensor->data + offset, data, size))); + (char *)tensor->data + offset, data, size).wait())); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -15538,7 +17200,7 @@ GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( - data, (const char *)tensor->data + offset, size))); + data, (const char *)tensor->data + offset, size).wait())); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -15557,7 +17219,7 @@ GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, was inserted. You need to rewrite this code. */ SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( - dst->data, src->data, ggml_nbytes(dst)))); + dst->data, src->data, ggml_nbytes(dst)).wait())); return true; } @@ -15647,20 +17309,12 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons if (a->ne[3] != b->ne[3]) { return false; } - - if (a->type == GGML_TYPE_IQ1_S) { + ggml_type a_type = a->type; + if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || + a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || + a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { return false; } - if (a->type == GGML_TYPE_IQ3_XXS) { - return false; - } - if (a->type == GGML_TYPE_IQ2_XXS) { - return false; - } - if (a->type == GGML_TYPE_IQ2_XS) { - return false; - } - return true; } break; case GGML_OP_GET_ROWS: @@ -15705,15 +17359,15 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons } return false; } break; - case GGML_OP_DUP: - case GGML_OP_REPEAT: case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; + case GGML_OP_DUP: case GGML_OP_NONE: case GGML_OP_RESHAPE: + case GGML_OP_REPEAT: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: From 61d1c88e155515dd03940913a5707ea84a8b119b Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Tue, 5 Mar 2024 13:33:42 +0100 Subject: [PATCH 50/86] Vulkan Improvements (#5835) * Improve dequant shaders, add fast q4_0 dequant * Optimize dmmv non-kquants for GCN Remove unnecessary SPIR-V shader duplication * Fix q4_0 dequant dispatch sizes Fix backend free bug * Optimize dequant shaders for q4_1, q5_0, q5_1 and q8_0 * Add unary and binary op shader templates * Fix Vulkan check results * Enable non-contiguous support for simple ops * Add argsort Basic q4_0 mmq shader and unit test * Speed up q4_0 dequant code, enable mmq for q4_0 * Rework matmul pipeline selection * Add soft_max alibi support * Add q4_1, q5_0, q5_1 and q8_0 dequant mat mat mul shaders * Add environment variable GGML_VK_FORCE_MAX_ALLOCATION_SIZE to limit max buffer size Rename GGML_VULKAN_DISABLE_F16 to GGML_VK_DISABLE_F16 for consistency --- ggml-vulkan-shaders.hpp | 86931 ++++++++++++++++------------------ ggml-vulkan.cpp | 2128 +- ggml-vulkan.h | 1 + ggml_vk_generate_shaders.py | 1207 +- llama.cpp | 4 +- 5 files changed, 43476 insertions(+), 46795 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index e5e7a8414..1eca4d6e0 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -2,50 +2,1847 @@ unsigned char add_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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argsort_f32_len = 3752; + +unsigned char clamp_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00, +0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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cpy_f32_f16_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x9a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00, +0xc7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00, 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0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -173,3186 +2877,389 @@ unsigned char clamp_f32_data[] = { 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00, -0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 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clamp_f32_len = 1448; - -unsigned char cpy_f16_f16_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x9f,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t dequant_q4_1_len = 5248; + +unsigned char dequant_q4_K_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xae,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 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dequant_q5_0_len = 13952; +const uint64_t dequant_q5_0_len = 6644; unsigned char dequant_q5_1_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x95,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xb4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x55,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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-0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, -0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x13,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x47,0x00,0x00,0x00, -0x1a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x4a,0x00,0x00,0x00, -0x4b,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x2b,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x3e,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x73,0x00,0x04,0x00, -0x32,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, -0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x3e,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xf9,0x00,0x02,0x00,0x31,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x31,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t f32_to_f16_len = 1596; - unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -9897,9 +8558,9 @@ unsigned char get_rows_q5_1_data[] = { 0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00, 0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x20,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x70,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x80,0x00,0x00,0x00, @@ -9971,68 +8632,66 @@ unsigned char get_rows_q5_1_data[] = { 0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x73,0x00,0x04,0x00, 0x1c,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x41,0x00,0x07,0x00,0x69,0x00,0x00,0x00,0x6a,0x00,0x00,0x00, +0x41,0x00,0x07,0x00,0x67,0x00,0x00,0x00,0x68,0x00,0x00,0x00, 0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00, 0x12,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x6b,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0xc2,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6b,0x00,0x00,0x00, +0x69,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0xc2,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00, 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get_rows_q8_0_f32_data[] = { }; const uint64_t get_rows_q8_0_f32_len = 2280; -unsigned char matmul_f16_aligned_l_data[] = { +unsigned char matmul_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -10671,9 +9329,9 @@ unsigned char matmul_f16_aligned_l_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00, -0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,0x10,0x00,0x06,0x00, 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matmul_f16_f32_aligned_l_len = 12096; +const uint64_t matmul_f16_len = 10316; -unsigned char matmul_f16_f32_aligned_l_fp32_data[] = { +unsigned char matmul_f16_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00, -0x59,0x01,0x00,0x00,0x51,0x02,0x00,0x00,0x9a,0x02,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -18201,9 +10193,9 @@ unsigned char matmul_f16_f32_aligned_m_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8f,0x01,0x00,0x00, -0xc6,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,0x10,0x00,0x06,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00, +0x69,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ 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-unsigned char matmul_f16_f32_aligned_s_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00, -0x59,0x01,0x00,0x00,0x51,0x02,0x00,0x00,0x9a,0x02,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00, 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-const uint64_t matmul_f16_l_fp32_len = 10116; - -unsigned char matmul_f16_m_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xcc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xf1,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x47,0x01,0x00,0x00, -0x2c,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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-32306,169 +17388,151 @@ unsigned char matmul_f32_aligned_l_data[] = { 0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x77,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x81,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x7c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x8d,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x13,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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matmul_f32_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xd3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00, +0x44,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,0x34,0x02,0x00,0x00, +0x7d,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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matmul_q4_0_f32_aligned_fp32_len = 10408; + +unsigned char matmul_q4_0_f32_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x15,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00, +0x27,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x63,0x01,0x00,0x00, +0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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+const uint64_t matmul_q4_1_f32_len = 10632; + +unsigned char matmul_q4_1_f32_aligned_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x19,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2d,0x01,0x00,0x00, +0x62,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x78,0x02,0x00,0x00, +0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+const uint64_t matmul_q5_0_f32_len = 11076; + +unsigned char matmul_q5_0_f32_aligned_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x62,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x51,0x01,0x00,0x00, +0x86,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x9c,0x02,0x00,0x00, +0xe5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x7a,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t matmul_q5_0_f32_fp32_len = 10956; + +unsigned char matmul_q5_1_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x17,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, +0x7e,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x74,0x02,0x00,0x00, +0xbd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 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+unsigned char matmul_q5_1_f32_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x10,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00, +0x4b,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x87,0x01,0x00,0x00, +0x6d,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, 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0x07,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00, @@ -33512,1633 +36671,612 @@ unsigned char matmul_f32_aligned_l_fp32_data[] = { 0x64,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00, 0x5d,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x74,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x73,0x00,0x00,0x00, -0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x78,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x78,0x00,0x00,0x00, -0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, -0x47,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x05,0x00, -0x15,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x7c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 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const uint64_t mul_mat_vec_p021_f16_f32_len = 2768; unsigned char mul_mat_vec_q2_K_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xdc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -43715,34 +39160,676 @@ unsigned char mul_mat_vec_q2_K_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x4b,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x8e,0x00,0x00,0x00, -0xb4,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 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mul_mat_vec_q4_1_f32_len = 3252; unsigned char mul_mat_vec_q4_K_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xa2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x9e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -45676,3575 +41119,3566 @@ unsigned char mul_mat_vec_q4_K_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xef,0x01,0x00,0x00, -0x94,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 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10960; + +unsigned char mul_mat_vec_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xc4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x52,0x00,0x00,0x00, +0x74,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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unsigned char split_k_reduce_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -51610,105 +47313,277 @@ const uint64_t split_k_reduce_len = 1416; unsigned char sqr_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x37,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00, +0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 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+0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00, +0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, +0x9b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, +0xef,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe0,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xf0,0x00,0x00,0x00, +0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0xf4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t sqr_f32_len = 1188; +const uint64_t sqr_f32_len = 3252; diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index bc316c3f3..5a1b3f477 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -69,6 +69,33 @@ struct vk_queue { vk::PipelineStageFlags stage_flags; }; +struct vk_pipeline_struct { + std::string name; + vk::ShaderModule shader_module; + vk::DescriptorSetLayout dsl; + std::vector descriptor_pools; + std::vector descriptor_sets; + uint32_t descriptor_set_idx; + vk::PipelineLayout layout; + vk::Pipeline pipeline; + uint32_t push_constant_size; + uint32_t parameter_count; + std::array wg_denoms; + uint32_t align; +}; + +typedef std::shared_ptr vk_pipeline; +typedef std::weak_ptr vk_pipeline_ref; + +static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline); + +struct vk_matmul_pipeline_struct { + vk_pipeline l, m, s; + vk_pipeline a_l, a_m, a_s; +}; + +typedef std::shared_ptr vk_matmul_pipeline; + struct vk_device { vk::PhysicalDevice physical_device; vk::PhysicalDeviceProperties properties; @@ -84,10 +111,61 @@ struct vk_device { uint32_t subgroup_size; bool uma; + bool initialized; + size_t idx; + + vk_matmul_pipeline pipeline_matmul_f32; + vk_matmul_pipeline pipeline_matmul_f16; + vk_matmul_pipeline pipeline_matmul_f16_f32; + vk_pipeline pipeline_matmul_split_k_reduce; + + vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES]; + + vk_pipeline pipeline_dequant[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; + + vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; + vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; + vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; + vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_add_f32; + vk_pipeline pipeline_scale_f32; + vk_pipeline pipeline_sqr_f32; + vk_pipeline pipeline_clamp_f32; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_norm_f32; + vk_pipeline pipeline_rms_norm_f32; + vk_pipeline pipeline_gelu_f32; + vk_pipeline pipeline_silu_f32; + vk_pipeline pipeline_relu_f32; + vk_pipeline pipeline_diag_mask_inf_f32; + vk_pipeline pipeline_soft_max_f32; + vk_pipeline pipeline_rope_f32, pipeline_rope_f16; + vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; + vk_pipeline pipeline_argsort_f32; + + std::vector pipelines; + ~vk_device() { #ifdef GGML_VULKAN_DEBUG std::cerr << "destroy device " << name << std::endl; #endif + device.destroyCommandPool(compute_queue.pool); + if (!single_queue) { + device.destroyCommandPool(transfer_queue.pool); + } + + for (auto& pipeline : pipelines) { + if (pipeline.expired()) { + continue; + } + + vk_pipeline pl = pipeline.lock(); + ggml_vk_destroy_pipeline(device, pl); + } + pipelines.clear(); + device.destroy(); } }; @@ -125,21 +203,6 @@ struct vk_subbuffer { uint64_t size; }; -struct vk_pipeline { - std::string name; - vk::ShaderModule shader_module; - vk::DescriptorSetLayout dsl; - std::vector descriptor_pools; - std::vector descriptor_sets; - uint32_t descriptor_set_idx; - vk::PipelineLayout layout; - vk::Pipeline pipeline; - uint32_t push_constant_size; - uint32_t parameter_count; - std::array wg_denoms; - uint32_t align; -}; - struct vk_semaphore { vk::Semaphore s; uint64_t value; @@ -160,11 +223,21 @@ struct vk_op_push_constants { float param2; }; -struct vk_op_cpy_push_constants { +struct vk_op_unary_push_constants { uint32_t ne; - uint32_t ne00; uint32_t ne01; uint32_t nb00; uint32_t nb01; uint32_t nb02; - uint32_t ne10; uint32_t ne11; uint32_t nb10; uint32_t nb11; uint32_t nb12; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; uint32_t d_offset; + float param1; float param2; +}; + +struct vk_op_binary_push_constants { + uint32_t ne; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; + uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23; + uint32_t d_offset; + float param1; float param2; }; struct vk_op_diag_mask_push_constants { @@ -196,6 +269,22 @@ struct vk_op_rope_neox_push_constants { float inv_ndims; }; +struct vk_op_soft_max_push_constants { + uint32_t KX; + uint32_t KY; + uint32_t KZ; + float scale; + float max_bias; + float m0; + float m1; + uint32_t n_head_log2; +}; + +struct vk_op_argsort_push_constants { + uint32_t ncols; + bool ascending; +}; + // Allow pre-recording command buffers struct vk_staging_memcpy { vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} @@ -236,7 +325,6 @@ struct ggml_tensor_extra_gpu { }; struct ggml_vk_garbage_collector { - std::vector pipelines; std::vector tl_semaphores; std::vector semaphores; std::vector events; @@ -247,35 +335,7 @@ struct ggml_vk_garbage_collector { struct ggml_backend_vk_context { std::string name; - std::weak_ptr device; - vk_pipeline pipeline_matmul_f32_l, pipeline_matmul_f32_m, pipeline_matmul_f32_s; - vk_pipeline pipeline_matmul_f32_aligned_l, pipeline_matmul_f32_aligned_m, pipeline_matmul_f32_aligned_s; - vk_pipeline pipeline_matmul_f16_l, pipeline_matmul_f16_m, pipeline_matmul_f16_s; - vk_pipeline pipeline_matmul_f16_aligned_l, pipeline_matmul_f16_aligned_m, pipeline_matmul_f16_aligned_s; - vk_pipeline pipeline_matmul_f16_f32_l, pipeline_matmul_f16_f32_m, pipeline_matmul_f16_f32_s; - vk_pipeline pipeline_matmul_f16_f32_aligned_l, pipeline_matmul_f16_f32_aligned_m, pipeline_matmul_f16_f32_aligned_s; - vk_pipeline pipeline_matmul_split_k_reduce; - vk_pipeline pipeline_dequant[VK_NUM_TYPES]; - vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; - vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; - vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; - vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; - vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; - vk_pipeline pipeline_mul_f32; - vk_pipeline pipeline_add_f32; - vk_pipeline pipeline_scale_f32; - vk_pipeline pipeline_sqr_f32; - vk_pipeline pipeline_clamp_f32; - vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; - vk_pipeline pipeline_norm_f32; - vk_pipeline pipeline_rms_norm_f32; - vk_pipeline pipeline_gelu_f32; - vk_pipeline pipeline_silu_f32; - vk_pipeline pipeline_relu_f32; - vk_pipeline pipeline_diag_mask_inf_f32; - vk_pipeline pipeline_soft_max_f32; - vk_pipeline pipeline_rope_f32, pipeline_rope_f16; - vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; + std::shared_ptr device; size_t semaphore_idx, event_idx; ggml_vk_garbage_collector gc; @@ -304,13 +364,31 @@ struct vk_instance { std::vector device_indices; - std::shared_ptr devices[GGML_VK_MAX_DEVICES]; ggml_backend_t backends[GGML_VK_MAX_DEVICES]; ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES]; ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES]; bool initialized[GGML_VK_MAX_DEVICES]; }; +static std::shared_ptr ggml_vk_get_device(size_t idx) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl; +#endif + static std::weak_ptr devices[GGML_VK_MAX_DEVICES]; + + if (devices[idx].expired()) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "Initializing new vk_device" << std::endl; +#endif + std::shared_ptr device = std::make_shared(); + device->initialized = false; + devices[idx] = device; + return device; + } + + return devices[idx].lock(); +} + #ifdef GGML_VULKAN_CHECK_RESULTS static size_t vk_skip_checks; static size_t vk_output_tensor; @@ -334,14 +412,15 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - pipeline.name = name; - pipeline.parameter_count = parameter_count; - pipeline.push_constant_size = push_constant_size; - pipeline.wg_denoms = wg_denoms; - pipeline.align = align; + pipeline = std::make_shared(); + pipeline->name = name; + pipeline->parameter_count = parameter_count; + pipeline->push_constant_size = push_constant_size; + pipeline->wg_denoms = wg_denoms; + pipeline->align = align; vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); - pipeline.shader_module = ctx->device.lock()->device.createShaderModule(shader_module_create_info); + pipeline->shader_module = ctx->device->device.createShaderModule(shader_module_create_info); std::vector dsl_binding; std::vector dsl_binding_flags; @@ -355,49 +434,49 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& vk::PushConstantRange pcr( vk::ShaderStageFlagBits::eCompute, 0, - pipeline.push_constant_size + pipeline->push_constant_size ); vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info( {}, dsl_binding); descriptor_set_layout_create_info.setPNext(&dslbfci); - pipeline.dsl = ctx->device.lock()->device.createDescriptorSetLayout(descriptor_set_layout_create_info); + pipeline->dsl = ctx->device->device.createDescriptorSetLayout(descriptor_set_layout_create_info); // Check if device supports multiple descriptors per pool - if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { + if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { const uint32_t alloc_count = 2; // Try allocating multiple sets from one pool // This fails on AMD for some reason, so add a fall back to allocating one pool per set - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); - vk::DescriptorPool pool = ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info); + vk::DescriptorPool pool = ctx->device->device.createDescriptorPool(descriptor_pool_create_info); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline.dsl; + layouts[i] = pipeline->dsl; } try { vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); - std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info); } catch(vk::OutOfPoolMemoryError const&) { - ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; + ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; } - ctx->device.lock()->device.destroyDescriptorPool(pool); + ctx->device->device.destroyDescriptorPool(pool); } - if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); - pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); + pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info)); } - pipeline.descriptor_set_idx = 0; + pipeline->descriptor_set_idx = 0; - vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr); - pipeline.layout = ctx->device.lock()->device.createPipelineLayout(pipeline_layout_create_info); + vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr); + pipeline->layout = ctx->device->device.createPipelineLayout(pipeline_layout_create_info); std::vector specialization_entries(specialization_constants.size()); @@ -417,72 +496,75 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( vk::PipelineShaderStageCreateFlags(), vk::ShaderStageFlagBits::eCompute, - pipeline.shader_module, + pipeline->shader_module, entrypoint.c_str(), &specialization_info); vk::ComputePipelineCreateInfo compute_pipeline_create_info( vk::PipelineCreateFlags(), pipeline_shader_create_info, - pipeline.layout); - pipeline.pipeline = ctx->device.lock()->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + pipeline->layout); + pipeline->pipeline = ctx->device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - ctx->gc.pipelines.push_back(&pipeline); + ctx->device->pipelines.push_back(pipeline); } -static void ggml_vk_destroy_pipeline(ggml_backend_vk_context * ctx, vk_pipeline * pipeline) { +static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl; +#endif for (auto& pool : pipeline->descriptor_pools) { - ctx->device.lock()->device.destroyDescriptorPool(pool); + device.destroyDescriptorPool(pool); } pipeline->descriptor_pools.clear(); pipeline->descriptor_sets.clear(); pipeline->descriptor_set_idx = 0; - ctx->device.lock()->device.destroyDescriptorSetLayout(pipeline->dsl); + device.destroyDescriptorSetLayout(pipeline->dsl); - ctx->device.lock()->device.destroyPipelineLayout(pipeline->layout); + device.destroyPipelineLayout(pipeline->layout); - ctx->device.lock()->device.destroyShaderModule(pipeline->shader_module); + device.destroyShaderModule(pipeline->shader_module); - ctx->device.lock()->device.destroyPipeline(pipeline->pipeline); + device.destroyPipeline(pipeline->pipeline); } static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; + std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl; #endif - if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) { + if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) { // Enough descriptors are available return; } - if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { - const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size(); + if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size(); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline.dsl; + layouts[i] = pipeline->dsl; } - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data()); - std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end()); + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data()); + std::vector sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info); + pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end()); } else { - for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); - pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); + pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info)); - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl); - std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline.descriptor_sets.push_back(sets[0]); + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl); + std::vector sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info); + pipeline->descriptor_sets.push_back(sets[0]); } } } static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_cleanup(" << pipeline.name << ")" << std::endl; + std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl; #endif - pipeline.descriptor_set_idx = 0; + pipeline->descriptor_set_idx = 0; } static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) { @@ -498,7 +580,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx q.pool, vk::CommandBufferLevel::ePrimary, 1); - const std::vector cmd_buffers = ctx->device.lock()->device.allocateCommandBuffers(command_buffer_alloc_info); + const std::vector cmd_buffers = ctx->device->device.allocateCommandBuffers(command_buffer_alloc_info); auto buf = cmd_buffers.front(); q.cmd_buffers.push_back(buf); @@ -643,11 +725,11 @@ static void ggml_vk_create_queue(ggml_backend_vk_context * ctx, vk_queue& q, uin q.queue_family_index = queue_family_index; vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index); - q.pool = ctx->device.lock()->device.createCommandPool(command_pool_create_info_compute); + q.pool = ctx->device->device.createCommandPool(command_pool_create_info_compute); q.cmd_buffer_idx = 0; - q.queue = ctx->device.lock()->device.getQueue(queue_family_index, queue_index); + q.queue = ctx->device->device.getQueue(queue_family_index, queue_index); q.stage_flags = stage_flags; } @@ -671,7 +753,7 @@ static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); ctx->gc.semaphores.push_back({ semaphore, 0 }); return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; } @@ -684,7 +766,7 @@ static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); } return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; @@ -692,7 +774,7 @@ static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { if (ctx->event_idx >= ctx->gc.events.size()) { - ctx->gc.events.push_back(ctx->device.lock()->device.createEvent({})); + ctx->gc.events.push_back(ctx->device->device.createEvent({})); } return ctx->gc.events[ctx->event_idx++]; } @@ -703,7 +785,7 @@ static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { #endif // Requires command buffers to be done - ctx->device.lock()->device.resetCommandPool(q.pool); + ctx->device->device.resetCommandPool(q.pool); q.cmd_buffer_idx = 0; } @@ -740,11 +822,11 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz nullptr, }; - buf->buffer = ctx->device.lock()->device.createBuffer(buffer_create_info); + buf->buffer = ctx->device->device.createBuffer(buffer_create_info); - vk::MemoryRequirements mem_req = ctx->device.lock()->device.getBufferMemoryRequirements(buf->buffer); + vk::MemoryRequirements mem_req = ctx->device->device.getBufferMemoryRequirements(buf->buffer); - vk::PhysicalDeviceMemoryProperties mem_props = ctx->device.lock()->physical_device.getMemoryProperties(); + vk::PhysicalDeviceMemoryProperties mem_props = ctx->device->physical_device.getMemoryProperties(); uint32_t memory_type_index = UINT32_MAX; @@ -757,30 +839,30 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz } if (memory_type_index == UINT32_MAX) { - ctx->device.lock()->device.destroyBuffer(buf->buffer); + ctx->device->device.destroyBuffer(buf->buffer); buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); } try { - buf->device_memory = ctx->device.lock()->device.allocateMemory({ mem_req.size, memory_type_index }); + buf->device_memory = ctx->device->device.allocateMemory({ mem_req.size, memory_type_index }); } catch (const vk::SystemError& e) { // Out of Host/Device memory, clean up buffer - ctx->device.lock()->device.destroyBuffer(buf->buffer); + ctx->device->device.destroyBuffer(buf->buffer); buf->size = 0; throw e; } buf->ptr = nullptr; if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); + buf->ptr = ctx->device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); } - ctx->device.lock()->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); + ctx->device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); buf->ctx = ctx; - buf->device = ctx->device.lock(); + buf->device = ctx->device; #ifdef GGML_VULKAN_DEBUG std::cerr << "Created buffer " << buf->buffer << std::endl; @@ -802,7 +884,7 @@ static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) { vk_buffer buf; try { - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { // Fall back to host memory type buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { @@ -883,10 +965,16 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl; #endif + const std::shared_ptr device = ctx->device; + // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, ctx->device.lock()->subgroup_size * 2, 64, 2, 4, 4, ctx->device.lock()->subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, ctx->device.lock()->subgroup_size, 32, 2, 4, 2, ctx->device.lock()->subgroup_size }; - std::initializer_list warptile_s = { ctx->device.lock()->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + std::initializer_list warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + std::initializer_list warptile_s = { device->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + + std::initializer_list warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + std::initializer_list warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + std::initializer_list warptile_mmq_s = { device->subgroup_size, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -896,126 +984,206 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { uint32_t m_align = 64; uint32_t s_align = 32; - if (ctx->device.lock()->fp16) { - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ctx->device->pipeline_matmul_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f16_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f16 = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared(); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + if (device->fp16) { + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); } else { - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); } - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); // dequant shaders - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), { 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F16 ], "dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); // get_rows - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); } static void ggml_vk_print_gpu_info(size_t idx) { @@ -1057,8 +1225,8 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } - const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); + bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; @@ -1188,140 +1356,152 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { throw std::runtime_error("Device not found"); } - vk_instance.devices[idx] = std::make_shared(); - ctx->device = vk_instance.devices[idx]; - ctx->device.lock()->physical_device = devices[dev_num]; - const std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); + ctx->device = ggml_vk_get_device(idx); + if (!ctx->device->initialized) { + ctx->device->physical_device = devices[dev_num]; + const std::vector ext_props = ctx->device->physical_device.enumerateDeviceExtensionProperties(); - bool maintenance4_support = false; + bool maintenance4_support = false; - // Check if maintenance4 is supported - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { - maintenance4_support = true; + // Check if maintenance4 is supported + for (const auto& properties : ext_props) { + if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { + maintenance4_support = true; + } } - } - vk::PhysicalDeviceProperties2 props2; - vk::PhysicalDeviceMaintenance3Properties props3; - vk::PhysicalDeviceMaintenance4Properties props4; - vk::PhysicalDeviceSubgroupProperties subgroup_props; - props2.pNext = &props3; - props3.pNext = &subgroup_props; - if (maintenance4_support) { - subgroup_props.pNext = &props4; - } - ctx->device.lock()->physical_device.getProperties2(&props2); - ctx->device.lock()->properties = props2.properties; - - if (maintenance4_support) { - ctx->device.lock()->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); - } else { - ctx->device.lock()->max_memory_allocation_size = props3.maxMemoryAllocationSize; - } - - ctx->device.lock()->vendor_id = ctx->device.lock()->properties.vendorID; - ctx->device.lock()->subgroup_size = subgroup_props.subgroupSize; - ctx->device.lock()->uma = ctx->device.lock()->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; - - bool fp16_storage = false; - bool fp16_compute = false; - - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { - fp16_storage = true; - } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { - fp16_compute = true; + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceMaintenance4Properties props4; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + if (maintenance4_support) { + subgroup_props.pNext = &props4; } - } + ctx->device->physical_device.getProperties2(&props2); + ctx->device->properties = props2.properties; - const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE"); - ctx->device.lock()->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) { + ctx->device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE); + } else if (maintenance4_support) { + ctx->device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + } else { + ctx->device->max_memory_allocation_size = props3.maxMemoryAllocationSize; + } - std::vector queue_family_props = ctx->device.lock()->physical_device.getQueueFamilyProperties(); + ctx->device->vendor_id = ctx->device->properties.vendorID; + ctx->device->subgroup_size = subgroup_props.subgroupSize; + ctx->device->uma = ctx->device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; - // Try to find a non-graphics compute queue and transfer-focused queues - const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); - const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); + bool fp16_storage = false; + bool fp16_compute = false; - const float priorities[] = { 1.0f, 1.0f }; - ctx->device.lock()->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + for (const auto& properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } + } - std::vector device_queue_create_infos; - if (compute_queue_family_index != transfer_queue_family_index) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!ctx->device.lock()->single_queue) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); - } else { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - } - vk::DeviceCreateInfo device_create_info; - std::vector device_extensions; - vk::PhysicalDeviceFeatures device_features = ctx->device.lock()->physical_device.getFeatures(); + const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); + const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; - VkPhysicalDeviceFeatures2 device_features2; - device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; - device_features2.pNext = nullptr; - device_features2.features = (VkPhysicalDeviceFeatures)device_features; + ctx->device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - VkPhysicalDeviceVulkan11Features vk11_features; - vk11_features.pNext = nullptr; - vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; - device_features2.pNext = &vk11_features; + std::vector queue_family_props = ctx->device->physical_device.getQueueFamilyProperties(); - VkPhysicalDeviceVulkan12Features vk12_features; - vk12_features.pNext = nullptr; - vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; - vk11_features.pNext = &vk12_features; + // Try to find a non-graphics compute queue and transfer-focused queues + const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); + const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); - vkGetPhysicalDeviceFeatures2(ctx->device.lock()->physical_device, &device_features2); + const float priorities[] = { 1.0f, 1.0f }; + ctx->device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; - ctx->device.lock()->fp16 = ctx->device.lock()->fp16 && vk12_features.shaderFloat16; + std::vector device_queue_create_infos; + if (compute_queue_family_index != transfer_queue_family_index) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); + } else if(!ctx->device->single_queue) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); + } else { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + } + vk::DeviceCreateInfo device_create_info; + std::vector device_extensions; + vk::PhysicalDeviceFeatures device_features = ctx->device->physical_device.getFeatures(); - if (!vk11_features.storageBuffer16BitAccess) { - std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; - throw std::runtime_error("Unsupported device"); - } + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; - device_extensions.push_back("VK_KHR_16bit_storage"); + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + vkGetPhysicalDeviceFeatures2(ctx->device->physical_device, &device_features2); + + ctx->device->fp16 = ctx->device->fp16 && vk12_features.shaderFloat16; + + if (!vk11_features.storageBuffer16BitAccess) { + std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; + throw std::runtime_error("Unsupported device"); + } + + device_extensions.push_back("VK_KHR_16bit_storage"); #ifdef GGML_VULKAN_VALIDATE - device_extensions.push_back("VK_KHR_shader_non_semantic_info"); + device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif - if (ctx->device.lock()->fp16) { - device_extensions.push_back("VK_KHR_shader_float16_int8"); - } - ctx->device.lock()->name = ctx->device.lock()->properties.deviceName.data(); + if (ctx->device->fp16) { + device_extensions.push_back("VK_KHR_shader_float16_int8"); + } + ctx->device->name = ctx->device->properties.deviceName.data(); - device_create_info = { - vk::DeviceCreateFlags(), - device_queue_create_infos, - {}, - device_extensions - }; - device_create_info.setPNext(&device_features2); - ctx->device.lock()->device = ctx->device.lock()->physical_device.createDevice(device_create_info); + device_create_info = { + vk::DeviceCreateFlags(), + device_queue_create_infos, + {}, + device_extensions + }; + device_create_info.setPNext(&device_features2); + ctx->device->device = ctx->device->physical_device.createDevice(device_create_info); - ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; + ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; - // Shaders - ggml_vk_load_shaders(ctx); + // Queues + ggml_vk_create_queue(ctx, ctx->device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - // Queues - ggml_vk_create_queue(ctx, ctx->device.lock()->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - if (!ctx->device.lock()->single_queue) { - const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - ggml_vk_create_queue(ctx, ctx->device.lock()->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); - } else { - // TODO: Use pointer or reference to avoid copy - ctx->device.lock()->transfer_queue = ctx->device.lock()->compute_queue; + // Shaders + ggml_vk_load_shaders(ctx); + + if (!ctx->device->single_queue) { + const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; + ggml_vk_create_queue(ctx, ctx->device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + } else { + // TODO: Use pointer or reference to avoid copy + ctx->device->transfer_queue = ctx->device->compute_queue; + } + + ctx->device->idx = dev_num; + ctx->device->initialized = true; + } else if (ctx->device->idx != dev_num) { + std::cerr << "ggml_vulkan: Device " << ctx->device->name << " already initialized with index " << ctx->device->idx << ", but trying to reinitialize with index " << dev_num << std::endl; + throw std::runtime_error("Device already initialized"); } - ctx->fence = ctx->device.lock()->device.createFence({}); + ctx->fence = ctx->device->device.createFence({}); ctx->compute_ctx = nullptr; ctx->transfer_ctx = nullptr; @@ -1339,7 +1519,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { #endif } -static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { +static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif @@ -1360,10 +1540,36 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type return nullptr; } - return &ctx->pipeline_dequant[type]; + return ctx->device->pipeline_dequant[type]; } -static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { +static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl; +#endif + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f32; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f16_f32; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f16; + } + + GGML_ASSERT(src1_type == GGML_TYPE_F32); + + switch (src0_type) { + case GGML_TYPE_Q4_0: + break; + default: + return nullptr; + } + + return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; +} + +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif @@ -1384,7 +1590,7 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * return nullptr; } - return &ctx->pipeline_dequant_mul_mat_vec_f32[type]; + return ctx->device->pipeline_dequant_mul_mat_vec_f32[type]; } static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { @@ -1463,8 +1669,8 @@ static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", size/1024.0/1024.0); - ctx->device.lock()->device.freeMemory(buf->device_memory); - ctx->device.lock()->device.destroyBuffer(buf->buffer); + ctx->device->device.freeMemory(buf->device_memory); + ctx->device->device.destroyBuffer(buf->buffer); return nullptr; } @@ -1528,30 +1734,30 @@ static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_ } static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { - const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); - const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); - const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); + const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]); + const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); + const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; + std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; #endif std::vector descriptor_buffer_infos; std::vector write_descriptor_sets; - GGML_ASSERT(pipeline.descriptor_set_idx < pipeline.descriptor_sets.size()); - GGML_ASSERT(buffers.size() == pipeline.parameter_count); - vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++]; - for (uint32_t i = 0; i < pipeline.parameter_count; i++) { + GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size()); + GGML_ASSERT(buffers.size() == pipeline->parameter_count); + vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++]; + for (uint32_t i = 0; i < pipeline->parameter_count; i++) { descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size}); } - for (uint32_t i = 0; i < pipeline.parameter_count; i++) { + for (uint32_t i = 0; i < pipeline->parameter_count; i++) { write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); } - ctx->device.lock()->device.updateDescriptorSets(write_descriptor_sets, {}); + ctx->device->device.updateDescriptorSets(write_descriptor_sets, {}); - subctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); - subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); + subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); + subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, - pipeline.layout, + pipeline->layout, 0, { descriptor_set }, {}); @@ -1810,7 +2016,7 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); } } else { - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true); ggml_vk_ctx_end(subctx); @@ -1820,8 +2026,9 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds } ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); } } @@ -1906,18 +2113,19 @@ static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, s memcpy(dst, (uint8_t *) src->ptr + offset, size); } else { - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); } } @@ -1941,15 +2149,13 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr // Copy within the device ggml_backend_vk_context * ctx = src->ctx; - VkBufferCopy bc{ src_offset, dst_offset, size }; - - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); } else { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl; @@ -1977,14 +2183,14 @@ static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, // Make sure ctx owns the buffer GGML_ASSERT(dst->ctx == ctx); - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); } static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { @@ -2045,176 +2251,63 @@ static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * su static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"; + std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl; #endif if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " = 4" << std::endl; -#endif return 4; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " = 1" << std::endl; -#endif return 1; } -static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, int m, int n) { +static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { + if (m <= 32 || n <= 32) { + return aligned ? mmp->a_s : mmp->s; + } + return aligned ? mmp->a_m : mmp->m; + + GGML_UNUSED(ctx); +} + +static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) { + return aligned ? mmp->a_m : mmp->m; + + GGML_UNUSED(ctx); +} + +static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) { + return aligned ? mmp->a_s : mmp->s; + + GGML_UNUSED(ctx); +} + +static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl; +#endif + switch (ctx->device->vendor_id) { + case VK_VENDOR_ID_AMD: + return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned); + case VK_VENDOR_ID_APPLE: + return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned); + case VK_VENDOR_ID_INTEL: + return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned); + } + + if (m <= 32 || n <= 32) { + return aligned ? mmp->a_s : mmp->s; + } + if (m <= 64 || n <= 64) { + return aligned ? mmp->a_m : mmp->m; + } + return aligned ? mmp->a_l : mmp->l; +} + +static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif - if (m <= 32 || n <= 32) { - return ctx->pipeline_matmul_f32_aligned_s.align; - } - if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { - return ctx->pipeline_matmul_f32_aligned_m.align; - } - return ctx->pipeline_matmul_f32_aligned_l.align; -} - -static vk_pipeline* ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { - if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; - } - if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } - - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; -} - -static vk_pipeline* ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - if (bit16_x && bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; - } - if (bit16_x && !bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } - return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; -} - -static vk_pipeline* ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - if (bit16_x && bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; - } - if (bit16_x && !bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } - return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; -} - -static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; -#endif - switch (ctx->device.lock()->vendor_id) { - case VK_VENDOR_ID_AMD: - return ggml_vk_guess_matmul_pipeline_amd(ctx, bit16_x, bit16_y, m, n, aligned); - case VK_VENDOR_ID_APPLE: - return ggml_vk_guess_matmul_pipeline_apple(ctx, bit16_x, bit16_y, aligned); - case VK_VENDOR_ID_INTEL: - return ggml_vk_guess_matmul_pipeline_intel(ctx, bit16_x, bit16_y, aligned); - } - - if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; - } - if (m <= 64 || n <= 64) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " L" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l; - } - if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; - } - if (m <= 64 || n <= 64) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " L" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_l : &ctx->pipeline_matmul_f16_f32_l; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } - - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; - } - if (m <= 64 || n <= 64) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " L" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_l : &ctx->pipeline_matmul_f32_l; + return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, false)->align; } static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { @@ -2232,10 +2325,10 @@ static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, v const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); ggml_vk_sync_buffers(subctx); const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -2245,41 +2338,39 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { +static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return &ctx->pipeline_cpy_f32_f32; + return ctx->device->pipeline_cpy_f32_f32; } if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return &ctx->pipeline_cpy_f32_f16; + return ctx->device->pipeline_cpy_f32_f16; } if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return &ctx->pipeline_cpy_f16_f16; + return ctx->device->pipeline_cpy_f16_f16; } std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; GGML_ASSERT(false); } -static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { +static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); - const int dst_type_size = ggml_type_size(buffer_type); - const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2]; + const uint32_t ne = ggml_nelements(tensor); - const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; - - const vk_op_cpy_push_constants pc = { + const vk_op_unary_push_constants pc = { (uint32_t)ne, - (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, - (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], 1 , (uint32_t)tensor->ne[0] , nb2, + (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size, + (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], 1 , (uint32_t)tensor->ne[0] , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]), 0, + 0.0f, 0.0f, }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 }); } static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -2319,7 +2410,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su bool src0_uma = false; bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; @@ -2332,10 +2423,17 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); - const bool f16_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; - const bool qx_needs_dequant = src0->type != GGML_TYPE_F16 || x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; + vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type); + + const bool qx_needs_dequant = mmp == nullptr || x_non_contig; + const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; + + if (mmp == nullptr) { + // Fall back to dequant + f16 mulmat + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16); + } // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT @@ -2344,17 +2442,17 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, ne01, ne11)); + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); const bool aligned = ne10 == kpad; const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(ctx, true, !f16_f32_kernel, ne01, ne11, aligned); + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned); const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = sizeof(ggml_fp16_t) * x_ne; - const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; + const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -2385,7 +2483,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } else { d_X = d_Qx; x_buf_offset = qx_buf_offset; - GGML_ASSERT(qx_sz == x_sz); // NOLINT + GGML_ASSERT(qx_sz == x_sz); } if (qy_needs_dequant) { d_Y = ctx->prealloc_y; @@ -2396,8 +2494,8 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline * to_fp16_vk_0 = nullptr; - vk_pipeline * to_fp16_vk_1 = nullptr; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); @@ -2413,19 +2511,19 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1); if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1); } if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1); } if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, dst->type, false); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); } else if (load_x || qx_needs_dequant) { if (load_x) { // copy data to device @@ -2434,13 +2532,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } if (qx_needs_dequant) { - const std::vector pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 }; + const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } } if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, dst->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } else if (load_y) { ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } @@ -2457,7 +2555,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } // compute - ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + ggml_vk_matmul(ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2505,7 +2603,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context bool src0_uma = false; bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; @@ -2527,9 +2625,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context const uint64_t y_ne = ne11 * ne10; const uint64_t d_ne = ne11 * ne01; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; @@ -2569,8 +2667,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline * to_fp16_vk_0 = nullptr; - vk_pipeline* to_fp16_vk_1 = nullptr; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); } @@ -2579,30 +2677,30 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); // Allocate descriptor sets if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } - ggml_pipeline_allocate_descriptor_sets(ctx, *dmmv, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13); if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, src0->type); + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); } else if (load_x) { // copy data to device ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); } if (y_non_contig) { GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, src1->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } else if (load_y) { ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } @@ -2619,22 +2717,22 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; - const uint64_t y_buffer_offset = (y_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t y_buffer_offset = (y_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t y_shader_offset = y_offset - y_buffer_offset; - const uint64_t d_buffer_offset = (d_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_offset - d_buffer_offset; if (!y_non_contig && qy_needs_dequant) { - const std::vector pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 }; + const std::vector pc = { (uint32_t)ne11, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(y_ne / 32) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)y_ne, 1, 1}); } // compute - const std::array pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))}; + const std::array pc = { (uint32_t)ne00, (uint32_t)(y_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type))}; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2680,7 +2778,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2691,7 +2789,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c const uint64_t y_ne = ne10 * ne11 * ne12; const uint64_t d_ne = ne01 * ne11 * ne12; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; @@ -2710,12 +2808,12 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c } // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { @@ -2725,7 +2823,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2772,7 +2870,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2803,12 +2901,12 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con } // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { @@ -2818,7 +2916,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2856,6 +2954,10 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, } } +// static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +// +// } + static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat const uint64_t ne0 = dst->ne[0]; @@ -2927,40 +3029,40 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx } -static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { +static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_add_f32; + return ctx->device->pipeline_add_f32; } return nullptr; case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); if (dst->type == GGML_TYPE_F16) { - return &ctx->pipeline_get_rows[src0->type]; + return ctx->device->pipeline_get_rows[src0->type]; } if (dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_get_rows_f32[src0->type]; + return ctx->device->pipeline_get_rows_f32[src0->type]; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_mul_f32; + return ctx->device->pipeline_mul_f32; } return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_scale_f32; + return ctx->device->pipeline_scale_f32; } return nullptr; case GGML_OP_SQR: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_sqr_f32; + return ctx->device->pipeline_sqr_f32; } return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_clamp_f32; + return ctx->device->pipeline_clamp_f32; } return nullptr; case GGML_OP_CPY: @@ -2969,29 +3071,29 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_norm_f32; + return ctx->device->pipeline_norm_f32; } return nullptr; case GGML_OP_RMS_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_rms_norm_f32; + return ctx->device->pipeline_rms_norm_f32; } return nullptr; case GGML_OP_UNARY: switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_SILU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_silu_f32; + return ctx->device->pipeline_silu_f32; } break; case GGML_UNARY_OP_GELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_gelu_f32; + return ctx->device->pipeline_gelu_f32; } break; case GGML_UNARY_OP_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_relu_f32; + return ctx->device->pipeline_relu_f32; } break; default: @@ -3000,12 +3102,12 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_DIAG_MASK_INF: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_diag_mask_inf_f32; + return ctx->device->pipeline_diag_mask_inf_f32; } return nullptr; case GGML_OP_SOFT_MAX: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_soft_max_f32; + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_soft_max_f32; } return nullptr; case GGML_OP_ROPE: @@ -3020,21 +3122,26 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_rope_neox_f32; + return ctx->device->pipeline_rope_neox_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &ctx->pipeline_rope_neox_f16; + return ctx->device->pipeline_rope_neox_f16; } } else { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_rope_f32; + return ctx->device->pipeline_rope_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &ctx->pipeline_rope_f16; + return ctx->device->pipeline_rope_f16; } } return nullptr; } + case GGML_OP_ARGSORT: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) { + return ctx->device->pipeline_argsort_f32; + } + return nullptr; default: return nullptr; } @@ -3050,17 +3157,19 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; } + if (src2 != nullptr) { + std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; + } std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; #endif GGML_ASSERT(!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))); // NOLINT GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT - GGML_ASSERT(src1 == nullptr || ggml_vk_dim01_contiguous(src1)); // NOLINT GGML_ASSERT(dst->extra != nullptr); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3077,7 +3186,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t nb2 = dst->nb[2]; const uint64_t nb3 = dst->nb[3]; - vk_pipeline * pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); + const bool use_src2 = src2 != nullptr; + const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -3098,29 +3210,39 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; vk_buffer d_X = nullptr; size_t x_buf_offset = 0; vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; + vk_buffer d_Z = nullptr; + size_t z_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool src2_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); src0_uma = d_X != nullptr; if (use_src1) { ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } + if (use_src2) { + ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset); + src2_uma = d_Z != nullptr; + } } const bool transfer_src0 = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma; const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; + const bool transfer_src2 = use_src2 && src2->backend != GGML_BACKEND_TYPE_GPU && !src2_uma; - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); + uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -3131,7 +3253,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = (extra->offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT if (transfer_src0) { d_X = ctx->prealloc_qx; @@ -3148,6 +3270,13 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c GGML_ASSERT(d_Y != nullptr); } + GGML_ASSERT(!transfer_src2); + if (use_src2 && !src2_uma) { + d_Z = extra_src2->buffer_gpu.lock(); + z_buf_offset = extra_src2->offset; + GGML_ASSERT(d_Z != nullptr); + } + if (op == GGML_OP_CPY) { GGML_ASSERT(!transfer_src0); GGML_ASSERT(!transfer_src1); @@ -3175,7 +3304,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c // Single call if dimension 2 is contiguous if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { - ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1); switch (dst->op) { case GGML_OP_NORM: @@ -3204,16 +3333,30 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } } - if (!use_src1 && op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible on soft_max, but the shader needs a buffer + if (op == GGML_OP_SOFT_MAX) { + // Empty src1 and src2 are possible on soft_max, but the shader needs buffers + vk_subbuffer subbuf_y; + if (use_src1) { + subbuf_y = { d_Y, y_buf_offset, y_sz }; + } else { + subbuf_y = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + } + + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; + } else { + subbuf_z = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + } + ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) { ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); @@ -3223,7 +3366,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); } } else { - ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne02 * ne03); + GGML_ASSERT(op != GGML_OP_SOFT_MAX); + + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: @@ -3248,16 +3393,12 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint32_t y_offset = y_sz * it_idx1; const uint32_t d_offset = d_sz * it_idx0; - if (!use_src1 && op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible on soft_max, but the shader needs a buffer + if (use_src1) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (use_src1) { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } else { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -3269,69 +3410,141 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); } static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); } static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + op_params[0], 0.0f + }); } static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); } static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + op_params[0], op_params[1], + }); } static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - const int src0_type_size = ggml_type_size(src0->type); - const int dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = (extra->offset % ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, d_offset, + 0.0f, 0.0f, }); } static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); } static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); + + float scale = op_params[0]; + float max_bias = op_params[1]; + + const uint32_t ncols = (uint32_t)src0->ne[0]; + const uint32_t nrows_x = (uint32_t)ggml_nrows(src0); + const uint32_t nrows_y = (uint32_t)src0->ne[1]; + + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { + ncols, + nrows_y, + src2 != nullptr ? (uint32_t)1 : (uint32_t)0, + scale, max_bias, + m0, m1, + n_head_log2, + }); } static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3357,12 +3570,17 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); } } +static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + int32_t * op_params = (int32_t *)dst->op_params; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC }); +} + static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { // If backend is CPU, data from src0 has to be copied off the device if (dst->backend == GGML_BACKEND_TYPE_CPU) { @@ -3414,43 +3632,43 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t const size_t y_ne = k * n * batch; const size_t d_ne = m * n * batch; - vk_pipeline * p; + vk_pipeline p; std::string shname; if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_aligned_s; + p = ctx->device->pipeline_matmul_f32->a_s; shname = "F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_aligned_s; + p = ctx->device->pipeline_matmul_f16_f32->a_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_aligned_s; + p = ctx->device->pipeline_matmul_f16->a_s; shname = "F16_ALIGNED_S"; } else { GGML_ASSERT(false); } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_aligned_m; + p = ctx->device->pipeline_matmul_f32->a_m; shname = "F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_aligned_m; + p = ctx->device->pipeline_matmul_f16_f32->a_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_aligned_m; + p = ctx->device->pipeline_matmul_f16->a_m; shname = "F16_ALIGNED_M"; } else { GGML_ASSERT(false); } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_aligned_l; + p = ctx->device->pipeline_matmul_f32->a_l; shname = "F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_aligned_l; + p = ctx->device->pipeline_matmul_f16_f32->a_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_aligned_l; + p = ctx->device->pipeline_matmul_f16->a_l; shname = "F16_ALIGNED_L"; } else { GGML_ASSERT(false); @@ -3464,43 +3682,43 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (k != kpad) { if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_s; + p = ctx->device->pipeline_matmul_f32->s; shname = "F32_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_s; + p = ctx->device->pipeline_matmul_f16_f32->s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_s; + p = ctx->device->pipeline_matmul_f16->s; shname = "F16_S"; } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_m; + p = ctx->device->pipeline_matmul_f32->m; shname = "F32_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_m; + p = ctx->device->pipeline_matmul_f16_f32->m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_m; + p = ctx->device->pipeline_matmul_f16->m; shname = "F16_M"; } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_l; + p = ctx->device->pipeline_matmul_f32->l; shname = "F32_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_l; + p = ctx->device->pipeline_matmul_f16_f32->l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_l; + p = ctx->device->pipeline_matmul_f16->l; shname = "F16_L"; } } } - ggml_pipeline_allocate_descriptor_sets(ctx, *p, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it); if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it); if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer @@ -3530,9 +3748,11 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t } for (size_t i = 0; i < y_ne; i++) { if (std::is_same()) { - y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + y[i] = (i % k == i / k) ? 1.0f : 0.0f; } else if (std::is_same()) { - y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f); } else { GGML_ASSERT(false); } @@ -3541,17 +3761,17 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch); ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); for (size_t i = 0; i < num_it; i++) { ggml_vk_ctx_begin(ctx, subctx); - ggml_vk_matmul(ctx, subctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); ggml_vk_ctx_end(subctx); } auto begin = std::chrono::high_resolution_clock::now(); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double time = std::chrono::duration_cast(end-begin).count() / 1000.0; @@ -3630,6 +3850,8 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; std::cerr << "Actual result: " << std::endl << std::endl; ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b); std::cerr << "Expected result: " << std::endl << std::endl; ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); @@ -3655,15 +3877,15 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t free(d_chk); - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue); ggml_vk_destroy_buffer(d_X); ggml_vk_destroy_buffer(d_Y); ggml_vk_destroy_buffer(d_D); - ggml_pipeline_cleanup(*p); - ggml_pipeline_cleanup(ctx->pipeline_matmul_split_k_reduce); + ggml_pipeline_cleanup(p); + ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce); free(x); free(y); @@ -3736,7 +3958,7 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_ data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; } - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, subctx); vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); @@ -3745,8 +3967,8 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_ ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor)); @@ -3818,7 +4040,7 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool x[i] = rand() / (float)RAND_MAX; } - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, subctx); auto begin = std::chrono::high_resolution_clock::now(); @@ -3832,8 +4054,8 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); @@ -3847,8 +4069,8 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); @@ -3879,6 +4101,48 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool } } +static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) { + std::vector hist_cur(1 << 4, 0); + + switch(quant) { + case GGML_TYPE_F32: + memcpy(to, from, sizeof(float) * ne); + break; + case GGML_TYPE_Q4_0: + ggml_quantize_q4_0(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_1: + ggml_quantize_q4_1(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_0: + ggml_quantize_q5_0(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_1: + ggml_quantize_q5_1(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q8_0: + ggml_quantize_q8_0(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q2_K: + ggml_quantize_q2_K(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q3_K: + ggml_quantize_q3_K(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_K: + ggml_quantize_q4_K(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_K: + ggml_quantize_q5_K(from, to, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q6_K: + ggml_quantize_q6_K(from, to, ne, ne, hist_cur.data()); + break; + default: + GGML_ASSERT(false); + } +} + static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; @@ -3896,72 +4160,59 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_ x[i] = rand() / (float)RAND_MAX; } - std::vector hist_cur(1 << 4, 0); + vk_pipeline p = ctx->device->pipeline_dequant[quant]; - vk_pipeline& p = ctx->pipeline_dequant[quant]; - - switch(quant) { - case GGML_TYPE_Q4_0: - ggml_quantize_q4_0(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q4_1: - ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_0: - ggml_quantize_q5_0(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_1: - ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q8_0: - ggml_quantize_q8_0(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q2_K: - ggml_quantize_q2_K(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q3_K: - ggml_quantize_q3_K(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q4_K: - ggml_quantize_q4_K(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_K: - ggml_quantize_q5_K(x, qx, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q6_K: - ggml_quantize_q6_K(x, qx, ne, ne, hist_cur.data()); - break; - default: - GGML_ASSERT(false); - } + ggml_vk_quantize_data(x, qx, ne, quant); ggml_pipeline_allocate_descriptor_sets(ctx, p, 1); ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, subctx); - const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; + const std::vector pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne }; ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16); + int first_err = -1; + double avg_err = 0.0; for (size_t i = 0; i < ne; i++) { - avg_err += std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + avg_err += error; + + if (first_err < 0 && error > 0.05) { + first_err = i; + } } - std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err / ne << std::endl; + avg_err /= ne; + + std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1) { + std::cerr << "first_error = " << first_err << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { + std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", "; + } + std::cerr << std::endl << "Expected result: " << std::endl << std::endl; + for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { + std::cerr << x[i] << ", "; + } + std::cerr << std::endl; + } ggml_vk_destroy_buffer(x_buf); ggml_vk_destroy_buffer(qx_buf); @@ -3970,6 +4221,190 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_ free(qx); free(x_chk); } + +static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl; +#endif + const size_t x_ne = m * k * batch; + const size_t y_ne = k * n * batch; + const size_t d_ne = m * n * batch; + + vk_pipeline p; + std::string shname; + if (shader_size == 0) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S"; + } else if (shader_size == 1) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M"; + } else if (shader_size == 2) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L"; + } else { + GGML_ASSERT(0); + } + + const size_t kpad = ggml_vk_align_size(k, p->align); + + if (k != kpad) { + if (shader_size == 0) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s; + shname = std::string(ggml_type_name(quant)) + "_S"; + } else if (shader_size == 1) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m; + shname = std::string(ggml_type_name(quant)) + "_M"; + } else if (shader_size == 2) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l; + shname = std::string(ggml_type_name(quant)) + "_L"; + } else { + GGML_ASSERT(0); + } + } + + const size_t x_sz = sizeof(float) * x_ne; + const size_t y_sz = sizeof(float) * y_ne; + const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant); + const size_t d_sz = sizeof(float) * d_ne; + float * x = (float *) malloc(x_sz); + float * y = (float *) malloc(y_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + float * d = (float *) malloc(d_sz); + float * d_chk = (float *) malloc(d_sz); + + for (size_t i = 0; i < x_ne; i++) { + x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + } + + ggml_vk_quantize_data(x, qx, x_ne, quant); + + for (size_t i = 0; i < y_ne; i++) { + // y[i] = rand() / (float)RAND_MAX; + y[i] = (i % k == i / k) ? 1.0f : 0.0f; + } + + ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it); + if (split_k > 1) { + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it); + + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { + // Resize buffer + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + } + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + } + + ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); + ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz); + + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); + for (size_t i = 0; i < num_it; i++) { + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_ctx_end(subctx); + } + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + + auto end = std::chrono::high_resolution_clock::now(); + + double time_ms = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz); + + ggml_init_params iparams = { + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ggml_ctx = ggml_init(iparams); + + ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch); + ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch); + ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); + + src0_ggml->data = qx; + src1_ggml->data = y; + tensor_ggml->data = d_chk; + + ctx->disable = true; + + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); + ggml_build_forward_expand(cgraph, tensor_ggml); + + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); + + ctx->disable = false; + + ggml_free(ggml_ctx); + + double avg_err = 0.0; + int first_err_n = -1; + int first_err_m = -1; + int first_err_b = -1; + + for (size_t i = 0; i < m*n*batch; i++) { + double err = std::fabs(d[i] - d_chk[i]); + avg_err += err; + + if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) { + first_err_b = i / (m * n); + first_err_n = (i % (m * n)) / m; + first_err_m = (i % (m * n)) % m; + } + } + + avg_err /= m * n; + + std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1 || std::isnan(avg_err)) { + std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << std::endl; + std::cerr << "Expected result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + if (split_k > 1) { + float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + + std::cerr << "d_buf0: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf1: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf2: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf3: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + free(split_k_buf); + } + } + + ggml_vk_destroy_buffer(qx_buf); + ggml_vk_destroy_buffer(y_buf); + ggml_vk_destroy_buffer(d_buf); + + free(x); + free(qx); + free(y); + free(d); + free(d_chk); +} #endif static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { @@ -3982,18 +4417,8 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) return extra; } -static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph * graph) { - GGML_ASSERT(node != nullptr); - - for (int i = graph->n_nodes - 1; i >= 0; i--) { - for (int j = 0; j < GGML_MAX_SRC; j++) { - if (graph->nodes[i]->src[j] == node) { - return graph->nodes[i]; - } - } - } - - return nullptr; +static bool ggml_vk_cpu_assist_op(const ggml_tensor * node) { + return node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID; } static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ @@ -4004,7 +4429,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_TYPE_GPU)); - if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(node))) { return; } @@ -4035,7 +4460,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm const bool f16_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32; int split_k; - if (node->op == GGML_OP_MUL_MAT) { + if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); } else { split_k = 1; @@ -4044,11 +4469,11 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm const uint32_t y_ne = ne10 * ne11; const uint32_t d_ne = ne20 * ne21; - const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; + const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; if (extra->buffer_gpu.expired()) { @@ -4076,6 +4501,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: + case GGML_OP_ARGSORT: break; case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { @@ -4088,6 +4514,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm } break; case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: if (ctx->prealloc_size_qx < qx_sz) { ctx->prealloc_size_qx = qx_sz; } @@ -4121,21 +4548,66 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { #endif #if defined(GGML_VULKAN_RUN_TESTS) ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); ggml_vk_test_transfer(ctx, 8192 * 1000, false); ggml_vk_test_transfer(ctx, 8192 * 1000, true); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_0); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_1); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_0); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_1); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q8_0); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q2_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q3_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q6_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K); + + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 0); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 1); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 2); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 0); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 1); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 2); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0); + + std::cerr << std::endl; const std::vector vals { 8, 8, 8, @@ -4225,7 +4697,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_TYPE_GPU); - if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { + if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(node)) || (ggml_vk_cpu_assist_op(node) && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { return; } @@ -4237,6 +4709,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; + const ggml_tensor * src2 = node->src[2]; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; @@ -4271,7 +4744,9 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: case GGML_OP_NONE: + case GGML_OP_ARGSORT: break; default: if (any_on_device) { @@ -4282,7 +4757,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } if (ctx->compute_ctx == nullptr) { - ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, ctx->compute_ctx); } @@ -4353,16 +4828,25 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node); break; case GGML_OP_ROPE: ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); + break; + case GGML_OP_ARGSORT: + ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_MUL_MAT: ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); + break; + case GGML_OP_MUL_MAT_ID: + //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node); + std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl; + GGML_ASSERT(false); + break; default: return; @@ -4389,7 +4873,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); - if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(tensor))) { return false; } @@ -4415,6 +4899,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NONE: + case GGML_OP_ARGSORT: extra = (ggml_tensor_extra_gpu *) tensor->extra; break; @@ -4430,6 +4915,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } break; case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: if (!any_on_device && !ggml_vk_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { return false; } @@ -4475,8 +4961,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } if (tensor == subctx.exit_tensor) { - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); // Do staging buffer copies for (auto& cpy : subctx.out_memcpys) { @@ -4504,20 +4990,25 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { } ctx->gc.temp_buffers.clear(); - for (auto * pipeline : ctx->gc.pipelines) { - ggml_pipeline_cleanup(*pipeline); + for (auto& pipeline : ctx->device->pipelines) { + if (pipeline.expired()) { + continue; + } + + vk_pipeline pl = pipeline.lock(); + ggml_pipeline_cleanup(pl); } - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { - ctx->device.lock()->device.destroySemaphore({ ctx->gc.semaphores[i].s }); + ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s }); } ctx->gc.semaphores.clear(); for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { - ctx->device.lock()->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); + ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); } ctx->gc.tl_semaphores.clear(); ctx->semaphore_idx = 0; @@ -4525,7 +5016,7 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { ctx->event_idx = 0; for (auto& event : ctx->gc.events) { - ctx->device.lock()->device.resetEvent(event); + ctx->device->device.resetEvent(event); } ctx->staging_offset = 0; @@ -4562,21 +5053,11 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { ctx->staging_size = 0; for (auto& event : ctx->gc.events) { - ctx->device.lock()->device.destroyEvent(event); + ctx->device->device.destroyEvent(event); } ctx->gc.events.clear(); - for (auto* pipeline : ctx->gc.pipelines) { - ggml_vk_destroy_pipeline(ctx, pipeline); - } - ctx->gc.pipelines.clear(); - - ctx->device.lock()->device.destroyFence(ctx->fence); - - ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->compute_queue.pool); - if (!ctx->device.lock()->single_queue) { - ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->transfer_queue.pool); - } + ctx->device->device.destroyFence(ctx->fence); } GGML_CALL static int ggml_vk_get_device_count() { @@ -4787,7 +5268,6 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_vk(src->buffer)) { - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; @@ -4799,6 +5279,8 @@ GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t bu return true; } return false; + + UNUSED(buffer); } GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -4845,12 +5327,12 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer( GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - return ctx->ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + return ctx->ctx->device->properties.limits.minStorageBufferOffsetAlignment; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - return ctx->ctx->device.lock()->max_memory_allocation_size; + return ctx->ctx->device->max_memory_allocation_size; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { @@ -4936,7 +5418,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu } GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_instance.contexts[0].device.lock()->properties.limits.minMemoryMapAlignment; + return vk_instance.contexts[0].device->properties.limits.minMemoryMapAlignment; UNUSED(buft); } @@ -4981,8 +5463,7 @@ GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { ggml_vk_cleanup(ctx); - // Release device - vk_instance.devices[ctx->idx].reset(); + ctx->device.reset(); ctx->initialized = false; vk_instance.initialized[idx] = false; @@ -5011,7 +5492,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } @@ -5032,7 +5513,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } @@ -5052,7 +5533,7 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } @@ -5082,8 +5563,8 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { } ggml_vk_submit(ctx->transfer_ctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); for (auto& cpy : ctx->transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); @@ -5153,6 +5634,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } break; case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: { struct ggml_tensor * a; struct ggml_tensor * b; @@ -5226,6 +5708,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: + case GGML_OP_ARGSORT: return true; default: return false; @@ -5505,6 +5988,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; struct ggml_init_params iparams = { /*.mem_size =*/ 1024*1024*1024, @@ -5516,13 +6000,16 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ struct ggml_tensor * src0_clone = nullptr; struct ggml_tensor * src1_clone = nullptr; + struct ggml_tensor * src2_clone = nullptr; struct ggml_tensor * tensor_clone = nullptr; size_t src0_size; size_t src1_size; + size_t src2_size; void * src0_buffer; void * src1_buffer; + void * src2_buffer; if (src0 != nullptr) { src0_clone = ggml_dup_tensor(ggml_ctx, src0); @@ -5536,12 +6023,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) { for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5552,7 +6039,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); if (offset + src0_size >= buffer_gpu->size) { src0_size = buffer_gpu->size - offset; } @@ -5581,12 +6067,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } else if (src1->backend == GGML_BACKEND_TYPE_GPU) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) { for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5597,7 +6083,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); if (offset + src1_size >= buffer_gpu->size) { src1_size = buffer_gpu->size - offset; } @@ -5630,6 +6115,66 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); } + if (src2 != nullptr) { + src2_clone = ggml_dup_tensor(ggml_ctx, src2); + + src2_size = ggml_nbytes(src2); + + src2_buffer = malloc(src2_size); + src2_clone->data = src2_buffer; + if (src2->backend == GGML_BACKEND_TYPE_CPU) { + memcpy(src2_clone->data, src2->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (src2->backend == GGML_BACKEND_TYPE_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; + vk_buffer buf = extra->buffer_gpu.lock(); + uint64_t offset = extra->offset; + if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { + for (int i3 = 0; i3 < src2->ne[3]; i3++) { + for (int i2 = 0; i2 < src2->ne[2]; i2++) { + const int idx = i3*src2->ne[2] + i2; + ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); + } + } + + src2_clone->nb[0] = src2->nb[0]; + src2_clone->nb[1] = src2->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; + } + } else { + if (offset + src2_size >= buf->size) { + src2_size = buf->size - offset; + } + ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ASSERT(false); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(ctx, src2, "src2"); + std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; + if (src2->src[0] != nullptr) { + std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; + } + if (src2->src[1] != nullptr) { + std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(src2_clone, done); + } + + ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone); + } if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); @@ -5648,7 +6193,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { + if (src1 != nullptr) { + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); + } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { @@ -5728,6 +6277,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { free(src1_buffer); } + if (src2 != nullptr) { + free(src1_buffer); + } ggml_free(ggml_ctx); } diff --git a/ggml-vulkan.h b/ggml-vulkan.h index 9645126b4..e4317c3e0 100644 --- a/ggml-vulkan.h +++ b/ggml-vulkan.h @@ -10,6 +10,7 @@ extern "C" { #define GGML_VK_NAME "Vulkan" #define GGML_VK_MAX_DEVICES 16 +GGML_API void ggml_vk_instance_init(void); GGML_API void ggml_vk_init_cpu_assist(void); GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index b2e86e182..4a6f5e323 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -64,6 +64,7 @@ struct block_q5_0 #define A_TYPE block_q5_0 """ shader_q5_1_defines = """ +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require #define QUANT_K 32 #define QUANT_R 2 @@ -187,7 +188,8 @@ v = (v - 16.0f) * d; shader_q5_1_dequant_func = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ -const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ +const uint uint_qh = data_a[ib].qh; \ +const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ const uint vui = uint(data_a[ib].qs[iqs]); \ vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ v = v*d + m; @@ -206,12 +208,15 @@ mulmat_head = """#version 450 #extension GL_EXT_control_flow_attributes : enable #extension GL_EXT_shader_16bit_storage : require -#ifndef LOAD_VEC -#define LOAD_VEC 1 +#ifndef LOAD_VEC_A +#define LOAD_VEC_A 1 +#endif +#ifndef LOAD_VEC_B +#define LOAD_VEC_B 1 #endif """ -mulmat_body = """ +mulmat_body1 = """ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; @@ -240,7 +245,7 @@ layout (push_constant) uniform parameter layout (constant_id = 1) const uint BM = 64; layout (constant_id = 2) const uint BN = 64; -layout (constant_id = 3) const uint BK = 16; +layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant layout (constant_id = 4) const uint WM = 32; layout (constant_id = 5) const uint WN = 32; layout (constant_id = 6) const uint WMITER = 2; @@ -277,16 +282,19 @@ void main() { const uint tiwr = tiw % (WSUBM / TM); const uint tiwc = tiw / (WSUBM / TM); - const uint loadr = gl_LocalInvocationID.x % (BK / LOAD_VEC); - const uint loadc = gl_LocalInvocationID.x / (BK / LOAD_VEC); + const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A); + const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A); + const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B); + const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B); - const uint loadstride = gl_WorkGroupSize.x * LOAD_VEC / BK; + const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK; + const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK; const uint start_k = ik * p.k_split; const uint end_k = min(p.K, (ik + 1) * p.k_split); - uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC; - uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC; + uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; + uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B; float sums[WMITER * TM * WNITER * TN]; FLOAT_TYPE cache_a[WMITER * TM]; @@ -297,61 +305,145 @@ void main() { } [[unroll]] for (uint block = start_k; block < end_k; block += BK) { - [[unroll]] for (uint l = 0; l < BM; l += loadstride) { -#if LOAD_VEC == 8 - const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr; - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx][0].x); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx][0].y); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx][0].z); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx][0].w); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_a[idx][1].x); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_a[idx][1].y); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_a[idx][1].z); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_a[idx][1].w); -#elif LOAD_VEC == 4 - const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr; - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx].x); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx].y); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx].z); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx].w); + [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {""" + +mulmat_load_scalar = """ +#if LOAD_VEC_A == 8 + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; + buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx][0].x); + buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y); + buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z); + buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w); + buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x); + buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y); + buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z); + buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w); +#elif LOAD_VEC_A == 4 + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; + buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx].x); + buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y); + buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z); + buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w); #else - if (ir * BM + loadc + l < p.M && block + loadr < end_k) { - buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_a[pos_a + (loadc + l) * p.stride_a + loadr]); + if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) { + buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]); } else { - buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f); + buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f); } #endif +""" + +mulmat_load_q4_0 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q4_1 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q5_0 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q5_1 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint uint_qh = data_a[ib].qh; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q8_0 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; + + const uint ib = idx / 16; + const uint iqs = (idx & 0xF) * 2; + + const float d = float(data_a[ib].d); + const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);""" + +mulmat_body2 = """ } - [[unroll]] for (uint l = 0; l < BN; l += loadstride) { -#if LOAD_VEC == 8 - const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr; - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx][0].x); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx][0].y); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx][0].z); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx][0].w); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_b[idx][1].x); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_b[idx][1].y); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_b[idx][1].z); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_b[idx][1].w); -#elif LOAD_VEC == 4 - const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr; - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx].x); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx].y); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx].z); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx].w); + [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) { +#if LOAD_VEC_B == 8 + const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; + const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; + buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x); + buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y); + buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z); + buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w); + buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x); + buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y); + buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z); + buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w); +#elif LOAD_VEC_B == 4 + const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; + const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; + buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x); + buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y); + buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z); + buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w); #else - if (ic * BN + loadc + l < p.N && block + loadr < end_k) { - buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_b[pos_b + (loadc + l) * p.stride_b + loadr]); + if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) { + buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); } else { - buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f); + buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); } #endif } barrier(); - pos_a += BK / LOAD_VEC; - pos_b += BK / LOAD_VEC; + pos_a += BK / LOAD_VEC_A; + pos_b += BK / LOAD_VEC_B; for (uint i = 0; i < BK; i++) { // Load from shared into cache @@ -438,45 +530,191 @@ dequant_head = """#version 450 #extension GL_EXT_control_flow_attributes : require #extension GL_EXT_shader_16bit_storage : require -""" - -dequant_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; layout (push_constant) uniform parameter { - int M; - int K; - int stride_a; - int stride_b; + uint M; + uint K; + uint stride_a; + uint stride_b; + uint nel; } p; +""" + +dequant_f32_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; void main() { - const int i = int(gl_GlobalInvocationID.x); + const uint i = gl_GlobalInvocationID.x * 16; - // Transposed - const int row = i % (p.K / QUANT_K); - const int col = i / (p.K / QUANT_K); - - if (row * QUANT_K >= p.K || col >= p.M) { + if (i >= p.nel) { return; } - const int stride_a = p.stride_a / QUANT_K; + [[unroll]] for (uint l = 0; l < 16; l++) { + data_b[i + l] = D_TYPE(data_a[i + l]); + } +} +""" - const int ib = col * stride_a + row; +dequant_q4_0_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - const int step = QUANT_R == 1 ? 2 : 1; +layout (binding = 0) readonly buffer A {block_q4_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - [[unroll]] for (int iqs = 0; iqs < QUANT_K/QUANT_R; iqs += step) { - DEQUANT_FUNC +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - data_b[col * p.stride_b + row*QUANT_K + iqs + 0 ] = D_TYPE(v.x); - data_b[col * p.stride_b + row*QUANT_K + iqs + y_offset] = D_TYPE(v.y); + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float dm = -8.0f * d; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm); + data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + dm); + } +} +""" + +dequant_q4_1_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q4_1 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m); + data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + m); + } +} +""" + +dequant_q5_0_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q5_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = q_idx + l; + const uint vui = uint(data_a[ib].qs[iqs]); + data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f)); + data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f)); + } +} +""" + +dequant_q5_1_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q5_1 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint qh = data_a[ib].qh; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = q_idx + l; + const uint vui = uint(data_a[ib].qs[iqs]); + data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m); + data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10))) + m); + } +} +""" + +dequant_q8_0_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q8_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 16*il; + + const float d = float(data_a[ib].d); + + const uint q_idx = 16*il; + + [[unroll]] for (uint l = 0; l < 16; l += 2) { + data_b[b_idx + l ] = D_TYPE(d * data_a[ib].qs[q_idx + l ]); + data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]); } } """ @@ -488,29 +726,21 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int ip = tid / 32; - const int il = tid - 32 * ip; - const int is = 8 * ip + il / 16; + const uint tid = gl_LocalInvocationID.x; + const uint ip = tid / 32; + const uint il = tid - 32 * ip; + const uint is = 8 * ip + il / 16; - const int y_idx = i * QUANT_K + 128 * ip + il; + const uint y_idx = i * QUANT_K + 128 * ip + il; - const int ql_idx = 32 * ip + il; + const uint ql_idx = 32 * ip + il; const uint8_t qs = data_a[i].qs[32 * ip + il]; FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); @@ -528,31 +758,23 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = uint(gl_WorkGroupID.x * 256 + wgy); if (i >= p.M * p.K / QUANT_K) { return; } - const int r = int(gl_LocalInvocationID.x) / 4; - const int tid = r / 2; - const int is0 = r % 2; - const int l0 = 16 * is0 + 4 * (int(gl_LocalInvocationID.x) % 4); - const int n = tid / 4; - const int j = tid - 4*n; + const uint r = gl_LocalInvocationID.x / 4; + const uint tid = r / 2; + const uint is0 = r % 2; + const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4); + const uint n = tid / 4; + const uint j = tid - 4*n; const uint8_t m = uint8_t(1 << (4*n + j)); - const int is = 8*n + 2*j + is0; - const int shift = 2*j; + const uint is = 8*n + 2*j + is0; + const uint shift = 2*j; const int8_t us = int8_t(is < 4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) : is < 8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) : @@ -561,10 +783,10 @@ void main() { const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d); const FLOAT_TYPE dl = d_all * FLOAT_TYPE(us - 32); - const int y_idx = i * QUANT_K + 128 * n + 32 * j; - const int qs_idx = 32*n; + const uint y_idx = i * QUANT_K + 128 * n + 32 * j; + const uint qs_idx = 32*n; - for (int l = l0; l < l0 + 4; ++l) { + for (uint l = l0; l < l0 + 4; ++l) { data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4))); } } @@ -576,32 +798,24 @@ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int il = tid / 8; - const int ir = tid % 8; - const int is = 2 * il; - const int n = 4; + const uint tid = gl_LocalInvocationID.x; + const uint il = tid / 8; + const uint ir = tid % 8; + const uint is = 2 * il; + const uint n = 4; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - const int y_idx = i * QUANT_K + 64 * il + n * ir; - const int qs_idx = 32*il + n * ir; + const uint y_idx = i * QUANT_K + 64 * il + n * ir; + const uint qs_idx = 32*il + n * ir; uint8_t sc; uint8_t m; @@ -625,7 +839,7 @@ void main() { const FLOAT_TYPE d2 = dall * sc; const FLOAT_TYPE m2 = dmin * m; - [[unroll]] for (int l = 0; l < n; ++l) { + [[unroll]] for (uint l = 0; l < n; ++l) { data_b[y_idx + l ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1); data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >> 4) - m2); } @@ -638,32 +852,24 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int il = tid / 16; - const int ir = tid % 16; - const int is = 2 * il; + const uint tid = gl_LocalInvocationID.x; + const uint il = tid / 16; + const uint ir = tid % 16; + const uint is = 2 * il; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - const int y_idx = i * QUANT_K + 64 * il + 2 * ir; - const int qs_idx = 32*il + 2 * ir; - const int qh_idx = 2 * ir; + const uint y_idx = i * QUANT_K + 64 * il + 2 * ir; + const uint qs_idx = 32*il + 2 * ir; + const uint qh_idx = 2 * ir; uint8_t sc; uint8_t m; @@ -702,28 +908,20 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int ip = tid / 32; - const int il = tid - 32 * ip; - const int is = 8 * ip + il / 16; + const uint tid = gl_LocalInvocationID.x; + const uint ip = tid / 32; + const uint il = tid - 32 * ip; + const uint is = 8 * ip + il / 16; - const int y_idx = i * QUANT_K + 128 * ip + il; + const uint y_idx = i * QUANT_K + 128 * ip + il; - const int ql_idx = 64 * ip + il; + const uint ql_idx = 64 * ip + il; const uint8_t qh = data_a[i].qh[32 * ip + il]; const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d); @@ -742,49 +940,50 @@ mul_mat_vec_head = """#version 450 #extension GL_EXT_control_flow_attributes : enable #extension GL_EXT_shader_16bit_storage : require #extension GL_EXT_shader_8bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ncols; + uint b_offset; + uint d_offset; +} p; """ mul_mat_vec_body = """ -layout(local_size_x = QUANT_K, local_size_y = 1, local_size_z = 1) in; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; +layout (constant_id = 0) const uint BLOCK_SIZE = 32; -shared FLOAT_TYPE tmp[QUANT_K]; +shared FLOAT_TYPE tmp[BLOCK_SIZE]; void main() { - const int block_size = int(gl_WorkGroupSize.x); - const int row = int(gl_WorkGroupID.x); - const int tid = int(gl_LocalInvocationID.x); + const uint row = gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; - const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; tmp[tid] = FLOAT_TYPE(0.0f); - [[unroll]] for (int i = 0; i < p.ncols/block_size; i += 2) { - const int col = i*block_size + 2*tid; - const int ib = (row*p.ncols + col)/QUANT_K; // block index - const int iqs = (col%QUANT_K)/QUANT_R; // quant index - const int iybs = col - col%QUANT_K; // y block start index + [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) { + const uint col = i*BLOCK_SIZE + 2*tid; + const uint ib = (row*p.ncols + col)/QUANT_K; // block index + const uint iqs = (col%QUANT_K)/QUANT_R; // quant index + const uint iybs = col - col%QUANT_K; // y block start index DEQUANT_FUNC // matrix multiplication - tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]); - tmp[tid] += FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]); + tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]) + + FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]); } // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = block_size/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -804,38 +1003,31 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 - const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const int q_offset = 32*v_im + l0; - const int s_offset = 8*v_im; - const int y_offset = 128*v_im + l0; + const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint s_offset = 8*v_im; + const uint y_offset = 128*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y_idx = i * QUANT_K + y_offset; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); @@ -865,7 +1057,7 @@ void main() { // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -883,41 +1075,34 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 const uint8_t m = uint8_t(1 << (4 * v_im)); - const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const int q_offset = 32*v_im + l0; - const int y_offset = 128*v_im + l0; + const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 128*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp const uint s_shift = 4 * v_im; - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y_idx = i * QUANT_K + y_offset; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -937,7 +1122,7 @@ void main() { // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -955,42 +1140,35 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 + const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - const int il = tid/step; // 0...3 - const int ir = tid - step*il; // 0...7 or 0...3 - const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 + const uint il = tid/step; // 0...3 + const uint ir = tid - step*il; // 0...7 or 0...3 + const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - const int v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int v_in = il % 2; + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; - const int l0 = n * (2 * ir + v_in); // 0...15 - const int q_offset = 32*v_im + l0; - const int y_offset = 64*v_im + l0; + const uint l0 = n * (2 * ir + v_in); // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y1_idx = i * QUANT_K + y_offset; - const int y2_idx = y1_idx + 128; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); @@ -1058,7 +1236,7 @@ void main() { // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -1076,42 +1254,35 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/2; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%2; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 - const int il = tid/4; // 0...3 - const int ir = tid - 4*il; // 0...7 or 0...3 + const uint il = tid/4; // 0...3 + const uint ir = tid - 4*il; // 0...7 or 0...3 - const int v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int v_in = il % 2; + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; - const int l0 = 4*ir + 2*v_in; // 0...15 - const int q_offset = 32*v_im + l0; - const int y_offset = 64*v_im + l0; + const uint l0 = 4*ir + 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; const uint8_t hm1 = uint8_t(1 << (2*v_im)); const uint8_t hm2 = uint8_t(hm1 << 4); tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += 2) { - const int y1_idx = i * QUANT_K + y_offset; - const int y2_idx = y1_idx + 128; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); @@ -1175,7 +1346,7 @@ void main() { // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -1193,46 +1364,40 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint block_size = gl_WorkGroupSize.x; + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 #if K_QUANTS_PER_ITERATION == 1 - const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const int is = 0; + const uint l0 = v_in; // 0...15 + const uint is = 0; #else - const int l0 = 4 * v_in; // 0, 4, 8, ..., 28 - const int is = v_in / 4; + const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 + const uint is = v_in / 4; #endif - const int ql_offset = 64*v_im + l0; - const int qh_offset = 32*v_im + l0; - const int s_offset = 8*v_im + is; - const int y_offset = 128*v_im + l0; + const uint ql_offset = 64*v_im + l0; + const uint qh_offset = 32*v_im + l0; + const uint s_offset = 8*v_im + is; + const uint y_offset = 128*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y_idx = i * QUANT_K + y_offset; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -1260,7 +1425,7 @@ void main() { // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -1421,34 +1586,6 @@ void main() { } """ -# F16 to F32 -f32_to_f16_src = """#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {float16_t data_b[];}; - -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - -void main() { - const int row = int(gl_GlobalInvocationID.x % p.K); - const int col = int(gl_GlobalInvocationID.x / p.K); - - if (row < p.K && col < p.M) { - data_b[col * p.stride_b + row] = float16_t(data_a[col * p.stride_a + row]); - } -} -""" - generic_head = """ #version 450 @@ -1463,107 +1600,17 @@ layout (push_constant) uniform parameter } p; """ -# MUL F32 -mul_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(data_b[idx % p.KY])); -} -""" - -# ADD -add_body = """ -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) + FLOAT_TYPE(data_b[idx % p.KY])); -} -""" - -# SCALE -scale_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); -} -""" - -# SQR -sqr_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]); - data_d[idx] = D_TYPE(val * val); -} -""" - -# CLAMP -clamp_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]); - data_d[idx] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); -} -""" - -# CPY -cpy_src = """#version 450 +generic_unary_op_head = """#version 450 #extension GL_EXT_shader_16bit_storage : require layout (push_constant) uniform parameter { uint ne; - uint ne00; uint ne01; uint nb00; uint nb01; uint nb02; - uint ne10; uint ne11; uint nb10; uint nb11; uint nb12; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; uint d_offset; + float param1; float param2; } p; layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; @@ -1571,28 +1618,129 @@ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; +uint src0_idx(uint idx) { + const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint dst_idx(uint idx) { + const uint i13 = idx / (p.ne12*p.ne11*p.ne10); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; +} + void main() { if (gl_GlobalInvocationID.x >= p.ne) { return; } - - const uint i02 = gl_GlobalInvocationID.x / (p.ne00*p.ne01); - const uint i01 = (gl_GlobalInvocationID.x - i02*p.ne01*p.ne00) / p.ne00; - const uint i00 = gl_GlobalInvocationID.x - i02*p.ne01*p.ne00 - i01*p.ne00; - const uint a_idx = i00*p.nb00 + i01*p.nb01 + i02*p.nb02; - - const uint i12 = gl_GlobalInvocationID.x / (p.ne10*p.ne11); - const uint i11 = (gl_GlobalInvocationID.x - i12*p.ne11*p.ne10) / p.ne10; - const uint i10 = gl_GlobalInvocationID.x - i12*p.ne11*p.ne10 - i11*p.ne10; - const uint d_idx = i10*p.nb10 + i11*p.nb11 + i12*p.nb12; """ + +generic_binary_op_head = """#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; + uint d_offset; + uint param1; uint param2; +} p; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {A_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +uint src0_idx(uint idx) { + const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint src1_idx(uint idx) { + const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + + return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10; +} + +uint dst_idx(uint idx) { + const uint i23 = idx / (p.ne22*p.ne21*p.ne20); + const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; + const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20); + const uint i22_offset = i22*p.ne21*p.ne20; + const uint i21 = (idx - i23_offset - i22_offset) / p.ne20; + const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20; + return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20; +} + +void main() { + if (gl_GlobalInvocationID.x >= p.ne) { + return; + } +""" + +# MUL F32 +mul_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); +} +""" + +# ADD +add_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); +} +""" + +# SCALE +scale_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(p.param1)); +} +""" + +# SQR +sqr_body = """ + const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val); +} +""" + +# CLAMP +clamp_body = """ + const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); +} +""" + +# CPY cpy_end = """ - data_d[p.d_offset + d_idx] = D_TYPE(data_a[a_idx]); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); } """ # Causes an optimization error otherwise cpy_f16_f16_end = """ - data_d[p.d_offset + d_idx] = data_a[a_idx]; + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)]; } """ @@ -1815,6 +1963,24 @@ void main() { """ # SOFT_MAX +soft_max_head = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + uint KZ; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; +} p; +""" + soft_max_body = """ #extension GL_EXT_control_flow_attributes : enable #define BLOCK_SIZE 512 @@ -1823,7 +1989,8 @@ layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) buffer D {D_TYPE data_d[];}; +layout (binding = 2) readonly buffer Z {C_TYPE data_c[];}; +layout (binding = 3) buffer D {D_TYPE data_d[];}; shared FLOAT_TYPE vals[BLOCK_SIZE]; @@ -1832,11 +1999,23 @@ void main() { const uint rowx = gl_WorkGroupID.x; const uint rowy = rowx % p.KY; + float slope = 0.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = rowx/p.KY; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + // Find max vals[tid] = uintBitsToFloat(0xFF800000); [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); + vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * data_c[col] : 0.0f)); } barrier(); @@ -1855,7 +2034,7 @@ void main() { [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); vals[tid] += val; data_d[i] = D_TYPE(val); } @@ -2028,6 +2207,65 @@ void main() { } """ +argsort_src = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) buffer D {int data_d[];}; + +layout (push_constant) uniform parameter { + uint ncols; + bool ascending; +} p; + +void swap(uint idx0, uint idx1) { + int tmp = data_d[idx0]; + data_d[idx0] = data_d[idx1]; + data_d[idx1] = tmp; +} + +void main() { + // bitonic sort + const int col = int(gl_LocalInvocationID.x); + const uint row = gl_WorkGroupID.y; + + if (col >= p.ncols) { + return; + } + + const uint a_idx = row * p.ncols; + const uint d_idx = row * p.ncols; + + // initialize indices + if (col < p.ncols) { + data_d[col] = col; + } + barrier(); + + for (uint k = 2; k <= p.ncols; k *= 2) { + for (uint j = k / 2; j > 0; j /= 2) { + const uint ixj = col ^ j; + if (ixj > col) { + if ((col & k) == 0) { + if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) { + swap(d_idx + col, d_idx + ixj); + } + } else { + if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) { + swap(d_idx + col, d_idx + ixj); + } + } + } + barrier(); + } + } +} +""" + GLSLC = "glslc" VK_NUM_TYPES = 16 @@ -2129,6 +2367,8 @@ async def main(): tasks = [] + stream = [] + for fp16 in (False, True): # mulmat if fp16: @@ -2142,28 +2382,41 @@ async def main(): vec_type_f16 = "f16vec4" vec_type = "vec4" - stream = [] - stream.extend((mulmat_head, shader_float_type, mulmat_body)) - tasks.append(string_to_spv("matmul_f32_l", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_m", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_s", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + stream.clear() + stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) + tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) + tasks.append(string_to_spv("matmul_q4_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q4_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2)) + tasks.append(string_to_spv("matmul_q4_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q4_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2)) + tasks.append(string_to_spv("matmul_q5_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q5_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2)) + tasks.append(string_to_spv("matmul_q5_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q5_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2)) + tasks.append(string_to_spv("matmul_q8_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q8_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) # Shaders where precision is needed, so no fp16 version @@ -2205,18 +2458,18 @@ async def main(): stream.extend((dequant_head, shader_int8_ext, shader_f32)) - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func, dequant_body)) + if i == GGML_TYPE_F32: + stream.append(dequant_f32_body) elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body)) + stream.extend((shader_q4_0_defines, dequant_q4_0_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body)) + stream.extend((shader_q4_1_defines, dequant_q4_1_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body)) + stream.extend((shader_q5_0_defines, dequant_q5_0_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body)) + stream.extend((shader_q5_1_defines, dequant_q5_1_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body)) + stream.extend((shader_q8_0_defines, dequant_q8_0_body)) elif i == GGML_TYPE_Q2_K: stream.extend((shader_q2_K_defines, dequant_q2_K_body)) elif i == GGML_TYPE_Q3_K: @@ -2232,8 +2485,6 @@ async def main(): tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"})) - tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {})) - # get_rows for i in range(0, VK_NUM_TYPES): stream.clear() @@ -2264,20 +2515,20 @@ async def main(): tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f32_f32", f"{generic_unary_op_head}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("cpy_f32_f16", f"{generic_unary_op_head}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f16_f16", f"{generic_unary_op_head}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("add_f32", f"{generic_binary_op_head}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) - tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_head}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_head}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_head}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("clamp_f32", f"{generic_unary_op_head}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) @@ -2285,7 +2536,7 @@ async def main(): tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) @@ -2293,6 +2544,8 @@ async def main(): tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"})) + # Helper to decorate tasks with semaphore acquisition. async def withSemaphore(sem, task): async with sem: diff --git a/llama.cpp b/llama.cpp index 76afcbc13..e9192b4fa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5014,8 +5014,8 @@ static struct ggml_tensor * llm_build_kqv( ggml_mul_mat_set_prec(kq, GGML_PREC_F32); } -#if defined(GGML_USE_VULKAN) || defined(GGML_USE_KOMPUTE) -#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Vulkan, and Kompute") +#if defined(GGML_USE_KOMPUTE) +#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute") #pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") if (hparams.f_max_alibi_bias > 0.0f) { From b1a4e994fde929300d4aeb1deb8320c59cb6edec Mon Sep 17 00:00:00 2001 From: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com> Date: Tue, 5 Mar 2024 14:44:29 +0100 Subject: [PATCH 51/86] grammars : don't allow to output unescaped new line in string (#5885) * Don't allow grammar json array to output unescaped new line in string * Don't allow new line in json object string --- grammars/json.gbnf | 2 +- grammars/json_arr.gbnf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index a9537cdf9..34e014d55 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -15,7 +15,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\n] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index ef53e77a0..fd5740eeb 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -24,7 +24,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\n] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws From 82cb31eb93fd19b74115e0f0133225d1dfdbfdbc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 5 Mar 2024 15:56:24 +0200 Subject: [PATCH 52/86] Revert "grammars : don't allow to output unescaped new line in string (#5885)" This reverts commit b1a4e994fde929300d4aeb1deb8320c59cb6edec. --- grammars/json.gbnf | 2 +- grammars/json_arr.gbnf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index 34e014d55..a9537cdf9 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -15,7 +15,7 @@ array ::= string ::= "\"" ( - [^"\\\n] | + [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index fd5740eeb..ef53e77a0 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -24,7 +24,7 @@ array ::= string ::= "\"" ( - [^"\\\n] | + [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws From 3de31677d36aa4f82d4d99898902d7bcf398e666 Mon Sep 17 00:00:00 2001 From: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com> Date: Tue, 5 Mar 2024 17:33:08 +0100 Subject: [PATCH 53/86] grammars : blacklists character control set (#5888) * Prevent control characters from being served in json string * Prevent control characters from being served in json string (array) --- grammars/json.gbnf | 2 +- grammars/json_arr.gbnf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index a9537cdf9..a8a80752e 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -15,7 +15,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\x7F\x00-\x1F] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index ef53e77a0..31a3202f8 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -24,7 +24,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\x7F\x00-\x1F] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws From bd836944f826f07e19b7edcf994a78728da49c1c Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 5 Mar 2024 11:56:37 -0500 Subject: [PATCH 54/86] quants : use MM256_SET_M128I consistently to fix gcc 7 build (#5889) --- ggml-quants.c | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 2a8881d73..e0c125d43 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -51,6 +51,7 @@ #define UNUSED GGML_UNUSED +// some compilers don't provide _mm256_set_m128i, e.g. gcc 7 #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) @@ -9563,7 +9564,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); - const __m256i full_signs = _mm256_set_m128i(full_sign_bits, full_sign_bits); + const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits); const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); @@ -9585,8 +9586,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const __m256i sc1 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); + const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); + const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); @@ -9653,8 +9654,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); - const __m256i full_signs_1 = _mm256_set_m128i(full_signs_l, full_signs_l); - const __m256i full_signs_2 = _mm256_set_m128i(full_signs_h, full_signs_h); + const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l); + const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h); __m256i signs; signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); @@ -10551,10 +10552,10 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs); const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs); const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs); - const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); @@ -10661,10 +10662,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; From 652ca2bded3c818320d92c70d2b67f64bdbff5e5 Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 5 Mar 2024 22:27:29 +0100 Subject: [PATCH 55/86] compare-llama-bench.py : remove mul_mat_q (#5892) --- scripts/compare-llama-bench.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 39c3e52e5..54a7771d8 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -18,7 +18,7 @@ except ImportError as e: KEY_PROPERTIES = [ "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas", "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", - "type_k", "type_v", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" + "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: From 8ced9f7e3225adb8501e9821ed1bbd92e3a5c7ae Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 6 Mar 2024 12:08:32 +0800 Subject: [PATCH 56/86] add wait() to make code stable (#5895) --- ci/run.sh | 3 ++- ggml-sycl.cpp | 59 +++++++++++++++++++++++++++++++++++++++------------ 2 files changed, 48 insertions(+), 14 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index 35eb3c7aa..51f4c74cc 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -45,7 +45,8 @@ fi if [ ! -z ${GG_BUILD_SYCL} ]; then if [ -z ${ONEAPI_ROOT} ]; then - echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh" + echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:" + echo "source /opt/intel/oneapi/setvars.sh" exit 1 fi diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 477f5cb02..ddd951dd6 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3769,8 +3769,42 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo std::ofstream logfile; logfile.open(filename); for(size_t i=0; imemcpy(local_buf, src, total_size).wait(); + } + else { + local_buf = (sycl::half *)src; + } + + std::ofstream logfile; + logfile.open(filename); + for(size_t i=0; iwait(); const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); } @@ -14159,6 +14193,7 @@ inline void ggml_sycl_op_mul_mat_sycl( dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); + g_sycl_handles[id]->wait(); } (void) dst; (void) src1_ddq_i; @@ -15295,8 +15330,8 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, sycl_pool_alloc dst_f16; char * dst_t; - dpct::library_data_t cu_compute_type = dpct::library_data_t::real_half; - dpct::library_data_t cu_data_type = dpct::library_data_t::real_half; + dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float; + dpct::library_data_t cu_data_type = dpct::library_data_t::real_float; // dst strides size_t nbd2 = dst->nb[2]; @@ -15308,15 +15343,13 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const float alpha_f32 = 1.0f; const float beta_f32 = 0.0f; - const void * alpha = &alpha_f16; - const void * beta = &beta_f16; + const void * alpha = &alpha_f32; + const void * beta = &beta_f32; // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway - // once oneMKL open source supports half, half, float, float: datatypes - dst_t = (char *) dst_f16.alloc(ne_dst); + // oneMKL open source supports half, half, float, float: datatypes - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); + dst_t = (char *) dst_ddf; GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -15356,6 +15389,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb11 / nb10, nb12 / nb10, beta, (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); + g_sycl_handles[g_main_device]->wait(); } else { const int ne23 = ne12*ne13; @@ -15386,7 +15420,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb02, nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1); }); - }); + }).wait(); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, @@ -15397,11 +15431,10 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); + g_sycl_handles[g_main_device]->wait(); } #endif - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ From 1e35d619a6fb0b9c5e3dc955345980ff056ddbaf Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 6 Mar 2024 09:12:25 +0200 Subject: [PATCH 57/86] convert : remove AWQ remnants (#5768) --- convert.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/convert.py b/convert.py index 6e3a0319b..c15f8c47e 100755 --- a/convert.py +++ b/convert.py @@ -1377,7 +1377,6 @@ def main(args_in: list[str] | None = None) -> None: # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") - parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") @@ -1393,18 +1392,6 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") args = parser.parse_args(args_in) - if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights # type: ignore[import-not-found] - tmp_model_path = args.model / "weighted_model" - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") - else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") - args.model = tmp_model_path if args.dump_single: model_plus = lazy_load_file(args.model) From e25fb4b18fcedb9bed6be4585cf842e9a669b28b Mon Sep 17 00:00:00 2001 From: bobqianic <129547291+bobqianic@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:35:07 +0000 Subject: [PATCH 58/86] ggml : use `uint8x16_t` return type for `ggml_vqtbl1q_u8` (#5894) * use uint8x16_t * Update ggml-quants.c --- ggml-quants.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index e0c125d43..9dcb76def 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -464,8 +464,8 @@ inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { } // NOTE: not tested -inline static int8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { - int8x16_t res; +inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { + uint8x16_t res; res[ 0] = a[b[ 0]]; res[ 1] = a[b[ 1]]; From e04e04f8fad549bb0b3ec1c91f0413aeb08baf29 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Wed, 6 Mar 2024 15:42:23 -0500 Subject: [PATCH 59/86] ggml : use SYS_get_cpu if SYS_getcpu is not defined (#5906) Fixes #5694 Fixes ggerganov/whisper.cpp#1894 --- ggml.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 6a10bbcb4..92b17ee6e 100644 --- a/ggml.c +++ b/ggml.c @@ -2154,7 +2154,10 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) { getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); #else // old glibc doesn't have a wrapper for this call. Fall back on direct syscall - getcpu_ret = syscall(SYS_getcpu,¤t_cpu,&g_state.numa.current_node); +# if !defined(SYS_getcpu) && defined(SYS_get_cpu) +# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name +# endif + getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); #endif if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { From ceca1aef0738b57951cd12c603c3477e75312dec Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Thu, 7 Mar 2024 16:34:31 +0800 Subject: [PATCH 60/86] [SYCL] fix error when set main gpu to non-zero (#5901) * fix error when set main gpu to non-zero * fix delete condition --- ggml-sycl.cpp | 178 ++++++++++++++++++++++++++++++-------------------- ggml-sycl.h | 1 + llama.cpp | 16 +++-- 3 files changed, 119 insertions(+), 76 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ddd951dd6..221d67b8d 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3559,12 +3559,31 @@ class sycl_gpu_mgr { int work_group_size = 0; std::string gpus_list = ""; + /* + Use all GPU with same top max compute units + */ sycl_gpu_mgr() { detect_sycl_gpu_list_with_max_cu(); get_allow_gpus(); create_context_with_gpus(); } + /* + Use the assigned GPU as only one + */ + sycl_gpu_mgr(int main_gpu_id) { + sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id); + dpct::device_info prop; + dpct::get_device_info(prop, device); + gpus.push_back(main_gpu_id); + devices.push_back(device); + work_group_size = prop.get_max_work_group_size(); + max_compute_units = prop.get_max_compute_units(); + + get_allow_gpus(); + create_context_with_gpus(); + } + void create_context_with_gpus() { sycl::context ctx = sycl::context(devices); assert(gpus.size() > 0); @@ -3580,7 +3599,7 @@ class sycl_gpu_mgr { gpus_list += std::to_string(gpus[i]); gpus_list += ","; } - if (gpus_list.length() > 2) { + if (gpus_list.length() > 1) { gpus_list.pop_back(); } } @@ -3629,8 +3648,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - assert(false); - return -1; + printf("miss to get device index by id=%d\n", id); + GGML_ASSERT(false); } int get_next_index(int id) { @@ -3639,8 +3658,7 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - assert(false); - return -1; + GGML_ASSERT(false); } }; @@ -3649,6 +3667,7 @@ static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; static int g_main_device_id = -1; +static bool g_ggml_backend_sycl_buffer_type_initialized = false; static std::array g_default_tensor_split = {}; @@ -13225,7 +13244,7 @@ void ggml_backend_sycl_print_sycl_devices() { } void print_gpu_device_list() { - fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", + fprintf(stderr, "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n", g_sycl_gpu_mgr->get_gpu_count(), g_sycl_gpu_mgr->gpus_list.c_str(), g_sycl_gpu_mgr->max_compute_units); @@ -13264,23 +13283,6 @@ void ggml_init_sycl() try { #else fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif - if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != 0) { - initialized = true; - g_sycl_loaded = false; - return; - } - GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - ggml_backend_sycl_print_sycl_devices(); - - if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); - - g_device_count = g_sycl_gpu_mgr->get_gpu_count(); - g_work_group_size = g_sycl_gpu_mgr->work_group_size; - - print_gpu_device_list(); - - int64_t total_vram = 0; /* NOT REMOVE, keep it for next optimize for XMX. #if defined(SYCL_USE_XMX) @@ -13289,52 +13291,71 @@ void ggml_init_sycl() try { fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); #endif */ - for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_device_caps[id].vmm = 0; - g_device_caps[id].device_id = -1; - g_device_caps[id].cc = 0; - g_tensor_split[id] = 0; - g_default_tensor_split[id] = 0; + + if (CHECK_TRY_ERROR(g_all_sycl_device_count = + dpct::dev_mgr::instance().device_count()) != 0) { + initialized = true; + g_sycl_loaded = false; + return; } - - for (int i = 0; i < g_device_count; ++i) { - int device_id = g_sycl_gpu_mgr->gpus[i]; - g_device_caps[i].vmm = 0; - - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); - - g_default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); - - g_device_caps[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } - - for (int i = 0; i < g_device_count; ++i) { - g_default_tensor_split[i] /= total_vram; - } - - for (int i = 0; i < g_device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); - - // create sycl streams - for (int is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[i][is] = - dpct::get_current_device().create_queue( - g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); - } - - const dpct::queue_ptr stream = g_syclStreams[i][0]; - // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); - } - + GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); + ggml_backend_sycl_print_sycl_devices(); + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + print_gpu_device_list(); initialized = true; g_sycl_loaded = true; } + + + + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; + + int64_t total_vram = 0; + + + for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { + g_device_caps[id].vmm = 0; + g_device_caps[id].device_id = -1; + g_device_caps[id].cc = 0; + g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; + } + + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(device_id)))); + + g_default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); + + g_device_caps[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + } + + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; + } + + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); + + // create sycl streams + for (int is = 0; is < MAX_STREAMS; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); + } + + const dpct::queue_ptr stream = g_syclStreams[i][0]; + // create sycl handle + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); + } } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -16732,22 +16753,24 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .is_host = */ nullptr, }; -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { + if (device_index>=g_device_count or device_index<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device_index, g_device_count-1); + GGML_ASSERT(device_indexgpus[i])}, }; } - ggml_backend_sycl_buffer_type_initialized = true; + g_ggml_backend_sycl_buffer_type_initialized = true; } - - return &ggml_backend_sycl_buffer_types[device]; + return &ggml_backend_sycl_buffer_types[device_index]; } // sycl split buffer type @@ -17496,6 +17519,17 @@ GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { return g_sycl_gpu_mgr->get_index(device_id); } +GGML_API GGML_CALL void ggml_backend_sycl_set_single_device(int main_gpu_id) { + GGML_ASSERT(main_gpu_idbackends.push_back(backend); } else { // LLAMA_SPLIT_LAYER requires a backend for each GPU - int id_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { - int device_id = id_list[i]; ggml_backend_t backend = ggml_backend_sycl_init(i); if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, id_list[i], i); llama_free(ctx); return nullptr; } From 2002bc96bf2cbf5ab981a17d7e994d817c9801f5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 7 Mar 2024 11:41:53 +0200 Subject: [PATCH 61/86] server : refactor (#5882) * server : refactoring (wip) * server : remove llava/clip objects from build * server : fix empty prompt handling + all slots idle logic * server : normalize id vars * server : code style * server : simplify model chat template validation * server : code style * server : minor * llama : llama_chat_apply_template support null buf * server : do not process embedding requests when disabled * server : reorganize structs and enums + naming fixes * server : merge oai.hpp in utils.hpp * server : refactor system prompt update at start * server : disable cached prompts with self-extend * server : do not process more than n_batch tokens per iter * server: tests: embeddings use a real embeddings model (#5908) * server, tests : bump batch to fit 1 embedding prompt * server: tests: embeddings fix build type Debug is randomly failing (#5911) * server: tests: embeddings, use different KV Cache size * server: tests: embeddings, fixed prompt do not exceed n_batch, increase embedding timeout, reduce number of concurrent embeddings * server: tests: embeddings, no need to wait for server idle as it can timout * server: refactor: clean up http code (#5912) * server : avoid n_available var ggml-ci * server: refactor: better http codes * server : simplify json parsing + add comment about t_last * server : rename server structs * server : allow to override FQDN in tests ggml-ci * server : add comments --------- Co-authored-by: Pierrick Hymbert --- .github/workflows/server.yml | 3 +- Makefile | 5 +- examples/server-embd.py | 2 +- examples/server/CMakeLists.txt | 4 +- examples/server/README.md | 2 +- examples/server/oai.hpp | 225 - examples/server/server.cpp | 3895 ++++++++--------- .../server/tests/features/embeddings.feature | 94 + .../server/tests/features/parallel.feature | 46 - examples/server/tests/features/server.feature | 28 - examples/server/tests/features/steps/steps.py | 86 +- examples/server/tests/requirements.txt | 1 + examples/server/utils.hpp | 703 ++- llama.cpp | 6 +- 14 files changed, 2327 insertions(+), 2773 deletions(-) delete mode 100644 examples/server/oai.hpp create mode 100644 examples/server/tests/features/embeddings.feature diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 04e3fc0c1..f9aeefaa8 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -58,7 +58,8 @@ jobs: cmake \ python3-pip \ wget \ - psmisc + psmisc \ + language-pack-en - name: Build id: cmake_build diff --git a/Makefile b/Makefile index 4f26c0463..efce10bb8 100644 --- a/Makefile +++ b/Makefile @@ -724,10 +724,9 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp 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 examples/llava/llava.h examples/llava/llava.cpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) + $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/examples/server-embd.py b/examples/server-embd.py index c5c4ea87b..118e04271 100644 --- a/examples/server-embd.py +++ b/examples/server-embd.py @@ -13,7 +13,7 @@ async def main(): model_url = "http://127.0.0.1:6900" responses: list[requests.Response] = await asyncio.gather(*[requests_post_async( url= f"{model_url}/embedding", - json= {"content": str(i)*1024} + json= {"content": str(0)*1024} ) for i in range(n)]) for response in responses: diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index cc13b2d63..c21eba634 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,12 +1,12 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h) +add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h) install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/README.md b/examples/server/README.md index 21da7a0a0..591f748f8 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -436,7 +436,7 @@ Notice that each `probs` is an array of length `n_probs`. "next_token": { "has_next_token": true, "n_remain": -1, - "num_tokens_predicted": 0, + "n_decoded": 0, "stopped_eos": false, "stopped_limit": false, "stopped_word": false, diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp deleted file mode 100644 index ff4ad6994..000000000 --- a/examples/server/oai.hpp +++ /dev/null @@ -1,225 +0,0 @@ -#pragma once - -#include -#include -#include -#include -#include -#include - -#include "json.hpp" -#include "utils.hpp" - -#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" - -using json = nlohmann::json; - -inline static json oaicompat_completion_params_parse( - const struct llama_model * model, - const json &body, /* openai api json semantics */ - const std::string &chat_template) -{ - json llama_params; - - llama_params["__oaicompat"] = true; - - // Map OpenAI parameters to llama.cpp parameters - // - // For parameters that are defined by the OpenAI documentation (e.g. - // temperature), we explicitly specify OpenAI's intended default; we - // need to do that because sometimes OpenAI disagrees with llama.cpp - // - // https://platform.openai.com/docs/api-reference/chat/create - llama_sampling_params default_sparams; - llama_params["model"] = json_value(body, "model", std::string("unknown")); - llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); - llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); - llama_params["temperature"] = json_value(body, "temperature", 0.0); - llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); - llama_params["top_p"] = json_value(body, "top_p", 1.0); - llama_params["n_predict"] = json_value(body, "max_tokens", -1); - llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); - llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); - llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); - llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); - llama_params["stream"] = json_value(body, "stream", false); - llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); - llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); - llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); - llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); - llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); - - if (body.count("grammar") != 0) { - llama_params["grammar"] = json_value(body, "grammar", json::object()); - } - - // Handle 'stop' field - if (body.contains("stop") && body["stop"].is_string()) { - llama_params["stop"] = json::array({body["stop"].get()}); - } else { - llama_params["stop"] = json_value(body, "stop", json::array()); - } - - // Ensure there is ChatML-specific end sequence among stop words - llama_params["stop"].push_back("<|im_end|>"); - - return llama_params; -} - -inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false) -{ - json result = response.result_json; - - bool stopped_word = result.count("stopped_word") != 0; - bool stopped_eos = json_value(result, "stopped_eos", false); - int num_tokens_predicted = json_value(result, "tokens_predicted", 0); - int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason = "length"; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - - json choices = - streaming ? json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}) - : json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"message", json{{"content", content}, - {"role", "assistant"}}}}}); - - std::time_t t = std::time(0); - - json res = - json{{"choices", choices}, - {"created", t}, - {"model", - json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, - {"usage", - json{{"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, - {"id", gen_chatcmplid()}}; - - if (server_verbose) { - res["__verbose"] = result; - } - - if (result.contains("completion_probabilities")) { - res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); - } - - return res; -} - -// return value is vector as there is one case where we might need to generate two responses -inline static std::vector format_partial_response_oaicompat(const task_result &response) { - json result = response.result_json; - - if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { - return std::vector({response.result_json}); - } - - bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; - std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); - - bool stopped_word = json_value(result, "stopped_word", false); - bool stopped_eos = json_value(result, "stopped_eos", false); - bool stopped_limit = json_value(result, "stopped_limit", false); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - if (stopped_limit) { - finish_reason = "length"; - } - - std::time_t t = std::time(0); - - json choices; - - if (!finish_reason.empty()) { - choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); - } else { - if (first) { - if (content.empty()) { - choices = json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{{"role", "assistant"}}}}}); - } else { - // We have to send this as two updates to conform to openai behavior - json initial_ret = json{{"choices", json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"role", "assistant"} - }}}})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - json second_ret = json{ - {"choices", json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"content", content}}} - }})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({initial_ret, second_ret}); - } - } else { - // Some idiosyncrasy in task processing logic makes several trailing calls - // with empty content, we ignore these at the calee site. - if (content.empty()) { - return std::vector({json::object()}); - } - - choices = json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", - json{ - {"content", content}, - }}, - }}); - } - } - - json ret = json{{"choices", choices}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({ret}); -} - -inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings) -{ - json res = - json{ - {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", "list"}, - {"usage", - json{{"prompt_tokens", 0}, - {"total_tokens", 0}}}, - {"data", embeddings} - }; - return res; -} - diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8fe5e0b19..3bdbde954 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,13 +1,8 @@ +#include "utils.hpp" + #include "common.h" #include "llama.h" #include "grammar-parser.h" -#include "utils.hpp" -#include "oai.hpp" - -#include "../llava/clip.h" -#include "../llava/llava.h" - -#include "stb_image.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -24,46 +19,76 @@ #include "completion.js.hpp" #include "json-schema-to-grammar.mjs.hpp" -#include -#include +#include #include #include -#include +#include +#include +#include +#include #include using json = nlohmann::json; -struct server_params { - std::string hostname = "127.0.0.1"; - std::vector api_keys; - std::string public_path = "examples/server/public"; - std::string chat_template = ""; - int32_t port = 8080; - int32_t read_timeout = 600; - int32_t write_timeout = 600; - bool slots_endpoint = true; - bool metrics_endpoint = false; - int n_threads_http = -1; -}; - bool server_verbose = false; bool server_log_json = true; enum stop_type { - STOP_FULL, - STOP_PARTIAL, + STOP_TYPE_FULL, + STOP_TYPE_PARTIAL, }; -// TODO: can become bool if we can't find use of more states enum slot_state { - IDLE, - PROCESSING, + SLOT_STATE_IDLE, + SLOT_STATE_PROCESSING, }; enum slot_command { - NONE, - LOAD_PROMPT, - RELEASE, + SLOT_COMMAND_NONE, + SLOT_COMMAND_LOAD_PROMPT, + SLOT_COMMAND_RELEASE, +}; + +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded + SERVER_STATE_ERROR // An error occurred, load_model failed +}; + +enum server_task_type { + SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_CANCEL, + SERVER_TASK_TYPE_NEXT_RESPONSE, + SERVER_TASK_TYPE_METRICS +}; + +struct server_task { + int id = -1; // to be filled by server_queue + int id_multi = -1; + int id_target = -1; + + server_task_type type; + json data; + + bool infill = false; + bool embedding = false; +}; + +struct server_task_result { + int id = -1; + int id_multi = -1; + + json data; + + bool stop; + bool error; +}; + +struct server_task_multi { + int id = -1; + + std::set subtasks_remaining; + std::vector results; }; struct slot_params { @@ -80,26 +105,32 @@ struct slot_params { json input_suffix; }; -struct slot_image { - int32_t id; +struct server_params { + int32_t port = 8080; + int32_t read_timeout = 600; + int32_t write_timeout = 600; + int32_t n_threads_http = -1; - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; + std::string hostname = "127.0.0.1"; + std::string public_path = "examples/server/public"; + std::string chat_template = ""; + std::string system_prompt = ""; - clip_image_u8 * img_data; + std::vector api_keys; - std::string prefix_prompt; // before of this image + bool slots_endpoint = true; + bool metrics_endpoint = false; }; struct server_slot { int id; - int task_id = -1; + int id_task = -1; + int id_multi = -1; struct slot_params params; - slot_state state = IDLE; - slot_command command = NONE; + slot_state state = SLOT_STATE_IDLE; + slot_command command = SLOT_COMMAND_NONE; // used to determine the slot that has been used the longest int64_t t_last_used = -1; @@ -116,27 +147,31 @@ struct server_slot { int32_t n_prompt_tokens_processed = 0; json prompt; + + // when a task is submitted, we first tokenize the prompt and store it here + std::vector prompt_tokens; + std::string generated_text; - llama_token sampled; std::vector cache_tokens; std::vector generated_token_probs; - bool infill = false; - bool embedding = false; + bool infill = false; + bool embedding = false; bool has_next_token = true; - bool truncated = false; - bool stopped_eos = false; - bool stopped_word = false; - bool stopped_limit = false; + bool truncated = false; + bool stopped_eos = false; + bool stopped_word = false; + bool stopped_limit = false; bool oaicompat = false; - std::string oaicompat_model; + std::string oaicompat_model; std::string stopping_word; // sampling + llama_token sampled; struct llama_sampling_params sparams; - llama_sampling_context *ctx_sampling = nullptr; + llama_sampling_context * ctx_sampling = nullptr; int32_t ga_i = 0; // group-attention state int32_t ga_n = 1; // group-attention factor @@ -144,48 +179,32 @@ struct server_slot { int32_t n_past_se = 0; // self-extend - // multimodal - std::vector images; - // stats size_t n_sent_text = 0; // number of sent text character size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; - int64_t t_start_genereration; + int64_t t_start_generation; double t_prompt_processing; // ms double t_token_generation; // ms - // multitasks - int multitask_id = -1; - void reset() { - n_prompt_tokens = 0; - generated_text = ""; - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - n_past = 0; - n_sent_text = 0; - n_sent_token_probs = 0; - infill = false; - ga_i = 0; - n_past_se = 0; + n_prompt_tokens = 0; + generated_text = ""; + truncated = false; + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + n_past = 0; + n_sent_text = 0; + n_sent_token_probs = 0; + infill = false; + ga_i = 0; + n_past_se = 0; generated_token_probs.clear(); - - for (slot_image & img : images) { - free(img.image_embedding); - if (img.img_data) { - clip_image_u8_free(img.img_data); - } - img.prefix_prompt = ""; - } - - images.clear(); } bool has_budget(gpt_params &global_params) { @@ -205,32 +224,29 @@ struct server_slot { } bool available() const { - return state == IDLE && command == NONE; + return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE; } bool is_processing() const { - return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING; } - void add_token_string(const completion_token_output &token) { - if (command == RELEASE) { + void add_token_string(const completion_token_output & token) { + if (command == SLOT_COMMAND_RELEASE) { return; } - cache_tokens.push_back(token.tok); generated_token_probs.push_back(token); } void release() { - if (state == PROCESSING) - { - t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; - command = RELEASE; + if (state == SLOT_STATE_PROCESSING) { + t_token_generation = (ggml_time_us() - t_start_generation) / 1e3; + command = SLOT_COMMAND_RELEASE; } } - json get_formated_timings() { - return json - { + json get_formated_timings() const { + return json { {"prompt_n", n_prompt_tokens_processed}, {"prompt_ms", t_prompt_processing}, {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, @@ -243,16 +259,47 @@ struct server_slot { }; } + size_t find_stopping_strings(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_TYPE_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_TYPE_FULL) { + stopped_word = true; + stopping_word = word; + has_next_token = false; + } + stop_pos = pos; + } + } + + return stop_pos; + } + void print_timings() const { - char buffer[512]; + char buffer[512]; + double t_token = t_prompt_processing / n_prompt_tokens_processed; double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; - sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", + + snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second); + LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, + {"id_slot", id}, + {"id_task", id_task}, {"t_prompt_processing", t_prompt_processing}, {"n_prompt_tokens_processed", n_prompt_tokens_processed}, {"t_token", t_token}, @@ -261,22 +308,25 @@ struct server_slot { t_token = t_token_generation / n_decoded; n_tokens_second = 1e3 / t_token_generation * n_decoded; - sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", + + snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", t_token_generation, n_decoded, t_token, n_tokens_second); + LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, + {"id_slot", id}, + {"id_task", id_task}, {"t_token_generation", t_token_generation}, {"n_decoded", n_decoded}, {"t_token", t_token}, {"n_tokens_second", n_tokens_second}, }); - sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation); + snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation); + LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, + {"id_slot", id}, + {"id_task", id_task}, {"t_prompt_processing", t_prompt_processing}, {"t_token_generation", t_token_generation}, {"t_total", t_prompt_processing + t_token_generation}, @@ -291,9 +341,8 @@ struct server_metrics { uint64_t n_prompt_tokens_processed = 0; uint64_t t_prompt_processing = 0; - uint64_t n_tokens_predicted = 0; - uint64_t t_tokens_generation = 0; - + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; void on_prompt_eval(const server_slot &slot) { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; @@ -315,23 +364,261 @@ struct server_metrics { } }; -struct llama_server_context -{ - llama_model *model = nullptr; - llama_context *ctx = nullptr; +struct server_queue { + int id = 0; + bool running; - clip_ctx *clp_ctx = nullptr; + // queues + std::vector queue_tasks; + std::vector queue_tasks_deferred; + + std::vector queue_multitasks; + + std::mutex mutex_tasks; + std::condition_variable condition_tasks; + + // callback functions + std::function callback_new_task; + std::function callback_finish_multitask; + std::function callback_run_slots; + + // Add a new task to the end of the queue + int post(server_task task) { + std::unique_lock lock(mutex_tasks); + if (task.id == -1) { + task.id = id++; + LOG_VERBOSE("new task id", {{"new_id", task.id}}); + } + queue_tasks.push_back(std::move(task)); + condition_tasks.notify_one(); + return task.id; + } + + // Add a new task, but defer until one slot is available + void defer(server_task task) { + std::unique_lock lock(mutex_tasks); + queue_tasks_deferred.push_back(std::move(task)); + } + + // Get the next id for creating anew task + int get_new_id() { + std::unique_lock lock(mutex_tasks); + int new_id = id++; + LOG_VERBOSE("new task id", {{"new_id", new_id}}); + return new_id; + } + + // Register function to process a new task + void on_new_task(std::function callback) { + callback_new_task = std::move(callback); + } + + // Register function to process a multitask when it is finished + void on_finish_multitask(std::function callback) { + callback_finish_multitask = std::move(callback); + } + + // Register the function to be called when all slots data is ready to be processed + void on_run_slots(std::function callback) { + callback_run_slots = std::move(callback); + } + + // Call when the state of one slot is changed + void notify_slot_changed() { + // move deferred tasks back to main loop + std::unique_lock lock(mutex_tasks); + for (auto & task : queue_tasks_deferred) { + queue_tasks.push_back(std::move(task)); + } + queue_tasks_deferred.clear(); + } + + // end the start_loop routine + void terminate() { + std::unique_lock lock(mutex_tasks); + running = false; + condition_tasks.notify_all(); + } + + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Run all slots + */ + void start_loop() { + running = true; + + while (true) { + LOG_VERBOSE("new task may arrive", {}); + + while (true) { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + lock.unlock(); + break; + } + server_task task = queue_tasks.front(); + queue_tasks.erase(queue_tasks.begin()); + lock.unlock(); + LOG_VERBOSE("callback_new_task", {{"id_task", task.id}}); + callback_new_task(task); + } + + LOG_VERBOSE("update_multitasks", {}); + + // check if we have any finished multitasks + auto queue_iterator = queue_multitasks.begin(); + while (queue_iterator != queue_multitasks.end()) { + if (queue_iterator->subtasks_remaining.empty()) { + // all subtasks done == multitask is done + server_task_multi current_multitask = *queue_iterator; + callback_finish_multitask(current_multitask); + // remove this multitask + queue_iterator = queue_multitasks.erase(queue_iterator); + } else { + ++queue_iterator; + } + } + + // all tasks in the current loop is processed, slots data is now ready + LOG_VERBOSE("callback_run_slots", {}); + + callback_run_slots(); + + LOG_VERBOSE("wait for new task", {}); + { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + if (!running) { + LOG_VERBOSE("ending start_loop", {}); + return; + } + condition_tasks.wait(lock, [&]{ + return (!queue_tasks.empty() || !running); + }); + } + } + } + } + + // + // functions to manage multitasks + // + + // add a multitask by specifying the id of all subtask (subtask is a server_task) + void add_multitask(int id_multi, std::vector & sub_ids) { + std::lock_guard lock(mutex_tasks); + server_task_multi multi; + multi.id = id_multi; + std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); + queue_multitasks.push_back(multi); + } + + // updatethe remaining subtasks, while appending results to multitask + void update_multitask(int id_multi, int id_sub, server_task_result & result) { + std::lock_guard lock(mutex_tasks); + for (auto & multitask : queue_multitasks) { + if (multitask.id == id_multi) { + multitask.subtasks_remaining.erase(id_sub); + multitask.results.push_back(result); + } + } + } +}; + +struct server_response { + typedef std::function callback_multitask_t; + callback_multitask_t callback_update_multitask; + + // for keeping track of all tasks waiting for the result + std::set waiting_task_ids; + + // the main result queue + std::vector queue_results; + + std::mutex mutex_results; + std::condition_variable condition_results; + + // add the id_task to the list of tasks waiting for response + void add_waiting_task_id(int id_task) { + LOG_VERBOSE("waiting for task id", {{"id_task", id_task}}); + + std::unique_lock lock(mutex_results); + waiting_task_ids.insert(id_task); + } + + // when the request is finished, we can remove task associated with it + void remove_waiting_task_id(int id_task) { + LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}}); + + std::unique_lock lock(mutex_results); + waiting_task_ids.erase(id_task); + } + + // This function blocks the thread until there is a response for this id_task + server_task_result recv(int id_task) { + while (true) { + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + return !queue_results.empty(); + }); + + for (int i = 0; i < (int) queue_results.size(); i++) { + if (queue_results[i].id == id_task) { + assert(queue_results[i].id_multi == -1); + server_task_result res = queue_results[i]; + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // should never reach here + } + + // Register the function to update multitask + void on_multitask_update(callback_multitask_t callback) { + callback_update_multitask = std::move(callback); + } + + // Send a new result to a waiting id_task + void send(server_task_result result) { + LOG_VERBOSE("send new result", {{"id_task", result.id}}); + + std::unique_lock lock(mutex_results); + for (const auto & id_task : waiting_task_ids) { + // LOG_TEE("waiting task id %i \n", id_task); + // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result + if (result.id_multi == id_task) { + LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}}); + callback_update_multitask(id_task, result.id, result); + continue; + } + + if (result.id == id_task) { + LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}}); + queue_results.push_back(result); + condition_results.notify_all(); + return; + } + } + } +}; + +struct server_context { + 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; - bool add_bos_token = true; + bool clean_kv_cache = true; + bool add_bos_token = true; - int32_t n_ctx; // total context for all clients / slots + int32_t n_ctx; // total context for all clients / slots // system prompt bool system_need_update = false; @@ -346,60 +633,32 @@ struct llama_server_context std::vector slots; json default_generation_settings_for_props; - llama_server_queue queue_tasks; - llama_server_response queue_results; + server_queue queue_tasks; + server_response queue_results; server_metrics metrics; - ~llama_server_context() - { - if (ctx) - { + ~server_context() { + if (ctx) { llama_free(ctx); ctx = nullptr; } - if (model) - { + + if (model) { llama_free_model(model); model = nullptr; } } - bool load_model(const gpt_params ¶ms_) - { + bool load_model(const gpt_params & params_) { params = params_; - if (!params.mmproj.empty()) { - multimodal = true; - LOG_INFO("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; - } - } std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (model == nullptr) - { + if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } - 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); add_bos_token = llama_should_add_bos_token(model); @@ -407,25 +666,19 @@ struct llama_server_context return true; } - void validate_model_chat_template(server_params & sparams) { + bool validate_model_chat_template() const { llama_chat_message chat[] = {{"user", "test"}}; - std::vector buf(1); - int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); - if (res < 0) { - LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "chatml"; - } + + const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0); + + return res > 0; } void initialize() { - // create slots - all_slots_are_idle = true; - const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); - for (int i = 0; i < params.n_parallel; i++) - { + for (int i = 0; i < params.n_parallel; i++) { server_slot slot; slot.id = i; @@ -433,7 +686,7 @@ struct llama_server_context slot.n_predict = params.n_predict; LOG_INFO("new slot", { - {"slot_id", slot.id}, + {"id_slot", slot.id}, {"n_ctx_slot", slot.n_ctx} }); @@ -447,9 +700,9 @@ struct llama_server_context //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT LOG_INFO("slot self-extend", { - {"slot_id", slot.id}, - {"ga_n", ga_n}, - {"ga_w", ga_w} + {"id_slot", slot.id}, + {"ga_n", ga_n}, + {"ga_w", ga_w} }); } @@ -468,8 +721,7 @@ struct llama_server_context batch = llama_batch_init(n_ctx, 0, params.n_parallel); } - std::vector tokenize(const json & json_prompt, bool add_bos) const - { + std::vector tokenize(const json & json_prompt, bool add_bos) const { // TODO: currently, we tokenize using special tokens by default // this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216) // but it's better compared to completely ignoring ChatML and other chat templates @@ -479,38 +731,30 @@ struct llama_server_context // or the first element of the json_prompt array is a string. std::vector prompt_tokens; - if (json_prompt.is_array()) - { + if (json_prompt.is_array()) { bool first = true; - for (const auto& p : json_prompt) - { - if (p.is_string()) - { + for (const auto & p : json_prompt) { + if (p.is_string()) { auto s = p.template get(); + std::vector p; - if (first) - { + if (first) { p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); first = false; - } - else - { + } else { p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); } + prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); - } - else - { - if (first) - { + } else { + if (first) { first = false; } + prompt_tokens.push_back(p.template get()); } } - } - else - { + } else { auto s = json_prompt.template get(); prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); } @@ -518,19 +762,18 @@ struct llama_server_context return prompt_tokens; } - server_slot* get_slot(int id) { + server_slot * get_slot(int id) { int64_t t_last = ggml_time_us(); - server_slot *last_used = nullptr; - for (server_slot & slot : slots) - { - if (slot.id == id && slot.available()) - { + server_slot * last_used = nullptr; + + for (server_slot & slot : slots) { + if (slot.id == id && slot.available()) { return &slot; } - if (slot.available() && slot.t_last_used < t_last) - { + // among all available slots, find the one that has been least recently used + if (slot.available() && slot.t_last_used < t_last) { last_used = &slot; t_last = slot.t_last_used; } @@ -539,295 +782,204 @@ struct llama_server_context return last_used; } - bool launch_slot_with_data(server_slot* &slot, json data) { + bool launch_slot_with_data(server_slot & slot, json data) const { slot_params default_params; llama_sampling_params default_sparams; if (data.count("__oaicompat") != 0) { - slot->oaicompat = true; - slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); + slot.oaicompat = true; + slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); } else { - slot->oaicompat = false; - slot->oaicompat_model = ""; + slot.oaicompat = false; + slot.oaicompat_model = ""; } - 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.min_p = json_value(data, "min_p", default_sparams.min_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.temp = json_value(data, "temperature", default_sparams.temp); - slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); - slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); - slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - 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->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); - slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); - slot->sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); + 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.min_p = json_value(data, "min_p", default_sparams.min_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.temp = json_value(data, "temperature", default_sparams.temp); + slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + 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.sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); - if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) { + if (slot.params.cache_prompt && slot.ga_n != 1) { + LOG_WARNING("cache_prompt is not supported with group-attention", {}); + slot.params.cache_prompt = false; + } + + if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { // Might be better to reject the request with a 400 ? LOG_WARNING("Max tokens to predict exceeds server configuration", { - {"params.n_predict", slot->params.n_predict}, - {"slot.n_predict", slot->n_predict}, + {"params.n_predict", slot.params.n_predict}, + {"slot.n_predict", slot.n_predict}, }); - slot->params.n_predict = slot->n_predict; + slot.params.n_predict = slot.n_predict; } // infill - if (data.count("input_prefix") != 0) - { - slot->params.input_prefix = data["input_prefix"]; - } - else - { - slot->params.input_prefix = ""; - } + slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); + slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); + slot.prompt = json_value(data, "prompt", std::string("")); - if (data.count("input_suffix") != 0) + // penalize user-provided tokens { - slot->params.input_suffix = data["input_suffix"]; - } - else - { - slot->params.input_suffix = ""; - } + slot.sparams.penalty_prompt_tokens.clear(); + slot.sparams.use_penalty_prompt_tokens = false; - if (data.count("prompt") != 0) - { - slot->prompt = data["prompt"]; - } - else - { - slot->prompt = ""; - } + const auto & penalty_prompt = data.find("penalty_prompt"); - slot->sparams.penalty_prompt_tokens.clear(); - slot->sparams.use_penalty_prompt_tokens = false; - const auto &penalty_prompt = data.find("penalty_prompt"); - if (penalty_prompt != data.end()) - { - if (penalty_prompt->is_string()) - { - const auto penalty_prompt_string = penalty_prompt->get(); - auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false); - slot->sparams.penalty_prompt_tokens.swap(penalty_tokens); - if (slot->params.n_predict > 0) - { - slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict); - } - slot->sparams.use_penalty_prompt_tokens = true; - } - else if (penalty_prompt->is_array()) - { - const auto n_tokens = penalty_prompt->size(); - slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict)); - const int n_vocab = llama_n_vocab(model); - for (const auto &penalty_token : *penalty_prompt) - { - if (penalty_token.is_number_integer()) - { - const auto tok = penalty_token.get(); - if (tok >= 0 && tok < n_vocab) - { - slot->sparams.penalty_prompt_tokens.push_back(tok); - } + if (penalty_prompt != data.end()) { + if (penalty_prompt->is_string()) { + const auto penalty_prompt_string = penalty_prompt->get(); + slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false); + + if (slot.params.n_predict > 0) { + slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict); } - } - slot->sparams.use_penalty_prompt_tokens = true; - } - } + slot.sparams.use_penalty_prompt_tokens = true; - slot->sparams.logit_bias.clear(); - - if (json_value(data, "ignore_eos", false)) - { - slot->sparams.logit_bias[llama_token_eos(model)] = -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) - { - float bias; - if (el[1].is_number()) - { - bias = el[1].get(); - } - else if (el[1].is_boolean() && !el[1].get()) - { - bias = -INFINITY; - } - else - { - continue; - } - - if (el[0].is_number_integer()) - { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) - { - slot->sparams.logit_bias[tok] = bias; - } - } - else if (el[0].is_string()) - { - auto toks = llama_tokenize(model, el[0].get(), false); - for (auto tok : toks) - { - slot->sparams.logit_bias[tok] = bias; - } - } - } - } - } - - 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); - } - } - } - - const auto &samplers_sequence = data.find("samplers"); - if (samplers_sequence != data.end() && samplers_sequence->is_array()) - { - std::vector sampler_names; - for (const auto &sampler_name : *samplers_sequence) - { - if (sampler_name.is_string()) - { - sampler_names.emplace_back(sampler_name); - } - } - slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); - } - else - { - slot->sparams.samplers_sequence = default_sparams.samplers_sequence; - } - - 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) - { - const std::vector image_buffer = base64_decode(img["data"].get()); - - slot_image img_sl; - img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); - img_sl.img_data = clip_image_u8_init(); - if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data)) - { - LOG_ERROR("failed to load image", { - {"slot_id", slot->id}, - {"img_sl_id", img_sl.id} - }); - return false; - } - LOG_VERBOSE("image loaded", { - {"slot_id", slot->id}, - {"img_sl_id", img_sl.id} + LOG_VERBOSE("penalty_prompt_tokens", { + {"id_slot", slot.id}, + {"tokens", slot.sparams.penalty_prompt_tokens}, }); - 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; + else if (penalty_prompt->is_array()) { + const auto n_tokens = penalty_prompt->size(); + slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict)); + + const int n_vocab = llama_n_vocab(model); + for (const auto & penalty_token : *penalty_prompt) { + if (penalty_token.is_number_integer()) { + const auto tok = penalty_token.get(); + if (tok >= 0 && tok < n_vocab) { + slot.sparams.penalty_prompt_tokens.push_back(tok); } } } - slot->prompt = ""; - slot->params.input_suffix = prompt.substr(begin_prefix); - slot->params.cache_prompt = false; // multimodal doesn't support cache prompt + slot.sparams.use_penalty_prompt_tokens = true; + + LOG_VERBOSE("penalty_prompt_tokens", { + {"id_slot", slot.id}, + {"tokens", slot.sparams.penalty_prompt_tokens}, + }); } } } - if (slot->ctx_sampling != nullptr) { - llama_sampling_free(slot->ctx_sampling); - } - slot->ctx_sampling = llama_sampling_init(slot->sparams); - llama_set_rng_seed(ctx, slot->params.seed); - slot->command = LOAD_PROMPT; + slot.sparams.logit_bias.clear(); - all_slots_are_idle = false; + if (json_value(data, "ignore_eos", false)) { + slot.sparams.logit_bias[llama_token_eos(model)] = -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) { + float bias; + if (el[1].is_number()) { + bias = el[1].get(); + } else if (el[1].is_boolean() && !el[1].get()) { + bias = -INFINITY; + } else { + continue; + } + + if (el[0].is_number_integer()) { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) { + slot.sparams.logit_bias[tok] = bias; + } + } else if (el[0].is_string()) { + auto toks = llama_tokenize(model, el[0].get(), false); + for (auto tok : toks) { + slot.sparams.logit_bias[tok] = bias; + } + } + } + } + } + } + + { + 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); + } + } + } + } + + { + const auto & samplers_sequence = data.find("samplers"); + if (samplers_sequence != data.end() && samplers_sequence->is_array()) { + std::vector sampler_names; + for (const auto & sampler_name : *samplers_sequence) { + if (sampler_name.is_string()) { + sampler_names.emplace_back(sampler_name); + } + } + slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); + } else { + slot.sparams.samplers_sequence = default_sparams.samplers_sequence; + } + } + + { + if (slot.ctx_sampling != nullptr) { + llama_sampling_free(slot.ctx_sampling); + } + slot.ctx_sampling = llama_sampling_init(slot.sparams); + llama_set_rng_seed(ctx, slot.params.seed); + } + + slot.command = SLOT_COMMAND_LOAD_PROMPT; + slot.prompt_tokens.clear(); LOG_INFO("slot is processing task", { - {"slot_id", slot->id}, - {"task_id", slot->task_id}, + {"id_slot", slot.id}, + {"id_task", slot.id_task}, }); return true; } void kv_cache_clear() { + LOG_VERBOSE("clearing KV cache", {}); + // clear the entire KV cache llama_kv_cache_clear(ctx); clean_kv_cache = false; } void system_prompt_update() { + LOG_VERBOSE("system prompt update", { + {"system_prompt", system_prompt}, + }); + kv_cache_clear(); system_tokens.clear(); @@ -836,13 +988,11 @@ struct llama_server_context llama_batch_clear(batch); - for (int i = 0; i < (int)system_tokens.size(); ++i) - { + for (int i = 0; i < (int)system_tokens.size(); ++i) { llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) - { + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) { const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { n_tokens, @@ -854,78 +1004,42 @@ struct llama_server_context batch.logits + i, 0, 0, 0, // unused }; - if (llama_decode(ctx, batch_view) != 0) - { + + if (llama_decode(ctx, batch_view) != 0) { 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) - { + for (int32_t i = 1; i < params.n_parallel; ++i) { llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); } } - LOG_TEE("system prompt updated\n"); system_need_update = false; } - void system_prompt_notify() { + void system_prompt_set(const json & sys_props) { + system_prompt = sys_props.value("prompt", ""); + name_user = sys_props.value("anti_prompt", ""); + name_assistant = sys_props.value("assistant_name", ""); + + LOG_VERBOSE("system prompt process", { + {"system_prompt", system_prompt}, + {"name_user", name_user}, + {"name_assistant", name_assistant}, + }); + // release all slots - for (server_slot &slot : slots) - { + for (server_slot & slot : slots) { slot.release(); } system_need_update = true; } - void system_prompt_process(const json &sys_props) { - system_prompt = sys_props.value("prompt", ""); - name_user = sys_props.value("anti_prompt", ""); - name_assistant = sys_props.value("assistant_name", ""); - - - system_prompt_notify(); - } - - static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, - const stop_type type, server_slot &slot) - { - 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; - } - } - - return stop_pos; - } - - bool process_token(completion_token_output &result, server_slot &slot) { + bool process_token(completion_token_output & result, server_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; @@ -934,34 +1048,26 @@ struct llama_server_context slot.generated_text += token_str; slot.has_next_token = true; - if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) - { + if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) { // we can change penalty_prompt_tokens because it is always created from scratch each request slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok); } // check if there is incomplete UTF-8 character at the end bool incomplete = false; - for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) - { + for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) { unsigned char c = slot.generated_text[slot.generated_text.size() - i]; - if ((c & 0xC0) == 0x80) - { + if ((c & 0xC0) == 0x80) { // continuation byte: 10xxxxxx continue; } - if ((c & 0xE0) == 0xC0) - { + if ((c & 0xE0) == 0xC0) { // 2-byte character: 110xxxxx ... incomplete = i < 2; - } - else if ((c & 0xF0) == 0xE0) - { + } else if ((c & 0xF0) == 0xE0) { // 3-byte character: 1110xxxx ... incomplete = i < 3; - } - else if ((c & 0xF8) == 0xF0) - { + } else if ((c & 0xF8) == 0xF0) { // 4-byte character: 11110xxx ... incomplete = i < 4; } @@ -969,206 +1075,181 @@ struct llama_server_context break; } - if (!incomplete) - { + if (!incomplete) { size_t pos = std::min(slot.n_sent_text, 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) - { + + size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL); + 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.n_sent_text, slot.generated_text.size()); - } - else - { + } else { is_stop_full = false; - stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); + stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL); } // 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.n_sent_text += 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); } } - if (incomplete) - { + if (incomplete) { slot.has_next_token = true; } // check the limits - if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) - { - slot.stopped_limit = true; + if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) { + slot.stopped_limit = true; slot.has_next_token = false; + + LOG_VERBOSE("stopped by limit", { + {"id_slot", slot.id}, + {"n_decoded", slot.n_decoded}, + {"n_predict", slot.params.n_predict}, + }); } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) - { - slot.stopped_eos = true; + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) { + 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.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }); + {"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}, + {"n_decoded", slot.n_decoded}, + {"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 } - bool process_images(server_slot &slot) const - { - for (slot_image &img : slot.images) - { - if (!img.request_encode_image) - { - continue; - } - - if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) { - LOG_TEE("Error processing the given image"); - return false; - } - - - img.request_encode_image = false; - } - - return slot.images.size() > 0; - } - - void send_error(task_server& task, const std::string &error) - { - LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); - task_result res; - res.id = task.id; - res.multitask_id = task.multitask_id; - res.stop = false; - res.error = true; - res.result_json = { { "content", error } }; - queue_results.send(res); - } - - json get_formated_generation(server_slot &slot) - { + json get_formated_generation(const server_slot & slot) const { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); - const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); + const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); + std::vector samplers_sequence; - for (const auto &sampler_type : slot.sparams.samplers_sequence) - { + samplers_sequence.reserve(slot.sparams.samplers_sequence.size()); + for (const auto & sampler_type : slot.sparams.samplers_sequence) { samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); } return json { - {"n_ctx", slot.n_ctx}, - {"n_predict", slot.n_predict}, - {"model", params.model_alias}, - {"seed", slot.params.seed}, - {"temperature", slot.sparams.temp}, - {"dynatemp_range", slot.sparams.dynatemp_range}, - {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, - {"top_k", slot.sparams.top_k}, - {"top_p", slot.sparams.top_p}, - {"min_p", slot.sparams.min_p}, - {"tfs_z", slot.sparams.tfs_z}, - {"typical_p", slot.sparams.typical_p}, - {"repeat_last_n", slot.sparams.penalty_last_n}, - {"repeat_penalty", slot.sparams.penalty_repeat}, - {"presence_penalty", slot.sparams.penalty_present}, - {"frequency_penalty", slot.sparams.penalty_freq}, - {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, + {"n_ctx", slot.n_ctx}, + {"n_predict", slot.n_predict}, + {"model", params.model_alias}, + {"seed", slot.params.seed}, + {"temperature", slot.sparams.temp}, + {"dynatemp_range", slot.sparams.dynatemp_range}, + {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, + {"top_k", slot.sparams.top_k}, + {"top_p", slot.sparams.top_p}, + {"min_p", slot.sparams.min_p}, + {"tfs_z", slot.sparams.tfs_z}, + {"typical_p", slot.sparams.typical_p}, + {"repeat_last_n", slot.sparams.penalty_last_n}, + {"repeat_penalty", slot.sparams.penalty_repeat}, + {"presence_penalty", slot.sparams.penalty_present}, + {"frequency_penalty", slot.sparams.penalty_freq}, + {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens}, - {"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}, - {"min_keep", slot.sparams.min_keep}, - {"grammar", slot.sparams.grammar}, - {"samplers", samplers_sequence} + {"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}, + {"min_keep", slot.sparams.min_keep}, + {"grammar", slot.sparams.grammar}, + {"samplers", samplers_sequence} }; } - void send_partial_response(server_slot &slot, completion_token_output tkn) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = false; + void send_error(const server_task & task, const std::string & error) { + LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); - res.result_json = json - { + server_task_result res; + res.id = task.id; + res.id_multi = task.id_multi; + res.stop = false; + res.error = true; + res.data = { { "content", error } }; + + queue_results.send(res); + } + + void send_partial_response(server_slot & slot, completion_token_output tkn) { + server_task_result res; + res.id = slot.id_task; + res.id_multi = slot.id_multi; + res.error = false; + res.stop = false; + res.data = json { {"content", tkn.text_to_send}, {"stop", false}, - {"slot_id", slot.id}, - {"multimodal", multimodal} + {"id_slot", slot.id}, + {"multimodal", false} }; - if (slot.sparams.n_probs > 0) - { - std::vector probs_output = {}; + if (slot.sparams.n_probs > 0) { const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); - size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); - size_t probs_stop_pos = std::min(slot.n_sent_token_probs + 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 size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); + const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); + + std::vector probs_output; + 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.n_sent_token_probs = probs_stop_pos; - res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); + + res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } - if (slot.oaicompat) - { - res.result_json["oaicompat_token_ctr"] = slot.n_decoded; - res.result_json["model"] = slot.oaicompat_model; + if (slot.oaicompat) { + res.data["oaicompat_token_ctr"] = slot.n_decoded; + res.data["model"] = slot.oaicompat_model; } queue_results.send(res); } - void send_final_response(server_slot &slot) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = true; - - res.result_json = json - { + void send_final_response(const server_slot & slot) { + server_task_result res; + res.id = slot.id_task; + res.id_multi = slot.id_multi; + res.error = false; + res.stop = true; + res.data = json { {"content", !slot.params.stream ? slot.generated_text : ""}, - {"slot_id", slot.id}, + {"id_slot", slot.id}, {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, @@ -1184,96 +1265,87 @@ struct llama_server_context {"timings", slot.get_formated_timings()} }; - if (slot.sparams.n_probs > 0) - { - std::vector probs = {}; - if (!slot.params.stream && slot.stopped_word) - { + 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.end()); + 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.end()); } - res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); + + res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs); } - if (slot.oaicompat) - { - res.result_json["oaicompat_token_ctr"] = slot.n_decoded; - res.result_json["model"] = slot.oaicompat_model; + if (slot.oaicompat) { + res.data["oaicompat_token_ctr"] = slot.n_decoded; + res.data["model"] = slot.oaicompat_model; } queue_results.send(res); } - void send_embedding(server_slot & slot, const llama_batch & batch) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = true; + void send_embedding(const server_slot & slot, const llama_batch & batch) { + server_task_result res; + res.id = slot.id_task; + res.id_multi = slot.id_multi; + res.error = false; + res.stop = true; 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)}, + for (int i = 0; i < batch.n_tokens; ++i) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + continue; + } + + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + } + + if (embd == NULL) { + LOG_ERROR("failed to get embeddings", { + {"token", batch.token [i]}, + {"seq_id", batch.seq_id[i][0]} + }); + + res.data = json { + {"embedding", std::vector(n_embd, 0.0f)}, + }; + + continue; + } + + res.data = json { + {"embedding", std::vector(embd, embd + n_embd)}, }; } - else - { - for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { - continue; - } - const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); - if (embd == NULL) { - embd = llama_get_embeddings_ith(ctx, i); - if (embd == NULL) { - LOG_ERROR("failed to get embeddings for token", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}}); - res.result_json = json - { - {"embedding", std::vector(n_embd, 0.0f)}, - }; - continue; - } - } - - res.result_json = json - { - {"embedding", std::vector(embd, embd + n_embd)}, - }; - } - } queue_results.send(res); } - void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id) - { - task_server task; - task.id = task_id; - task.target_id = 0; - task.data = std::move(data); - task.infill_mode = infill; - task.embedding_mode = embedding; - task.type = TASK_TYPE_COMPLETION; - task.multitask_id = multitask_id; + void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) { + server_task task; + task.id = id_task; + task.id_multi = id_multi; + task.id_target = 0; + task.data = std::move(data); + task.infill = infill; + task.embedding = embedding; + task.type = SERVER_TASK_TYPE_COMPLETION; // when a completion task's prompt array is not a singleton, we split it into multiple requests // otherwise, it's a single-prompt task, we actually queue it // if there's numbers in the prompt array it will be treated as an array of tokens if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { bool numbers = false; - for (const auto& e : task.data.at("prompt")) { + for (const auto & e : task.data.at("prompt")) { if (e.is_number()) { numbers = true; break; @@ -1288,106 +1360,23 @@ struct llama_server_context if (numbers) { queue_tasks.post(task); } else { - split_multiprompt_task(task_id, task); + split_multiprompt_task(id_task, task); } } else { - // an empty prompt can make slot become buggy - if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get().empty()) { - task.data["prompt"] = " "; // add a space so that we have one token - } queue_tasks.post(task); } } - // for multiple images processing - bool ingest_images(server_slot &slot, int n_batch) - { - int image_idx = 0; + void request_cancel(int id_task) { + server_task task; + task.type = SERVER_TASK_TYPE_CANCEL; + task.id_target = id_task; - 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) - { - 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; - } - } - - // 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; - } - - 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)) - { - LOG_TEE("%s : failed to eval image\n", __func__); - return false; - } - slot.n_past += n_eval; - } - image_idx++; - - llama_batch_clear(batch); - - // append prefix of next image - 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 < (int) append_tokens.size(); ++i) - { - llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true); - slot.n_past += 1; - } - } - - return true; - } - - void request_cancel(int task_id) - { - task_server task; - task.type = TASK_TYPE_CANCEL; - task.target_id = task_id; queue_tasks.post(task); } - void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) - { - int prompt_count = multiprompt_task.data.at("prompt").size(); + void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) { + const int prompt_count = multiprompt_task.data.at("prompt").size(); if (prompt_count <= 1) { send_error(multiprompt_task, "error while handling multiple prompts"); return; @@ -1395,127 +1384,121 @@ struct llama_server_context // generate all the ID for subtask std::vector subtask_ids(prompt_count); - for (int i = 0; i < prompt_count; i++) - { + for (int i = 0; i < prompt_count; i++) { subtask_ids[i] = queue_tasks.get_new_id(); } // queue up the multitask so we can track its subtask progression - queue_tasks.add_multitask(multitask_id, subtask_ids); + queue_tasks.add_multitask(id_multi, subtask_ids); // add subtasks - for (int i = 0; i < prompt_count; i++) - { + for (int i = 0; i < prompt_count; i++) { json subtask_data = multiprompt_task.data; subtask_data["prompt"] = subtask_data["prompt"][i]; // subtasks inherit everything else (infill mode, embedding mode, etc.) - request_completion(subtask_ids[i], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id); + request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding); } } - void process_single_task(task_server& task) - { - switch (task.type) - { - case TASK_TYPE_COMPLETION: { - server_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); - if (slot == nullptr) + void process_single_task(const server_task & task) { + switch (task.type) { + case SERVER_TASK_TYPE_COMPLETION: { - // if no slot is available, we defer this task for processing later - LOG_VERBOSE("no slot is available", {{"task_id", task.id}}); - queue_tasks.defer(task); - break; - } - - if (task.data.contains("system_prompt")) - { - if (!all_slots_are_idle) { - send_error(task, "system prompt can only be updated when all slots are idle"); + server_slot * slot = get_slot(json_value(task.data, "id_slot", -1)); + if (slot == nullptr) { + // if no slot is available, we defer this task for processing later + LOG_VERBOSE("no slot is available", {{"id_task", task.id}}); + queue_tasks.defer(task); break; } - system_prompt_process(task.data["system_prompt"]); - // reset cache_tokens for all slots - for (server_slot &slot : slots) - { - slot.cache_tokens.clear(); - slot.n_past = 0; - slot.n_past_se = 0; + if (task.data.contains("system_prompt")) { + system_prompt_set(task.data["system_prompt"]); + + for (server_slot & slot : slots) { + slot.n_past = 0; + slot.n_past_se = 0; + } } - } - slot->reset(); + slot->reset(); - slot->infill = task.infill_mode; - slot->embedding = task.embedding_mode; - slot->task_id = task.id; - slot->multitask_id = task.multitask_id; + slot->id_task = task.id; + slot->id_multi = task.id_multi; + slot->infill = task.infill; + slot->embedding = task.embedding; - if (!launch_slot_with_data(slot, task.data)) - { - // send error result - send_error(task, "internal_error"); - break; - } - } break; - case TASK_TYPE_CANCEL: { // 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, "internal_error"); break; } - } - } break; - case TASK_TYPE_NEXT_RESPONSE: { - // do nothing - } break; - case TASK_TYPE_METRICS: { - json slots_data = json::array(); - int n_idle_slots = 0; - int n_processing_slots = 0; - - for (server_slot &slot: slots) { - json slot_data = get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["task_id"] = slot.task_id; - slot_data["state"] = slot.state; - slot_data["prompt"] = slot.prompt; - slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }; - if (slot_data["state"] == IDLE) { - n_idle_slots++; - } else { - n_processing_slots++; + } break; + case SERVER_TASK_TYPE_CANCEL: + { + // release slot linked with the task id + for (auto & slot : slots) { + if (slot.id_task == task.id_target) { + slot.release(); + break; + } } - slots_data.push_back(slot_data); - } - LOG_INFO("slot data", { - {"task_id", task.id}, - {"n_idle_slots", n_idle_slots}, - {"n_processing_slots", n_processing_slots} - }); - LOG_VERBOSE("slot data", { - {"task_id", task.id}, - {"n_idle_slots", n_idle_slots}, - {"n_processing_slots", n_processing_slots}, - {"slots", slots_data} - }); - task_result res; - res.id = task.id; - res.multitask_id = task.multitask_id; - res.stop = true; - res.error = false; - res.result_json = { + } break; + case SERVER_TASK_TYPE_NEXT_RESPONSE: + { + // do nothing + } break; + case SERVER_TASK_TYPE_METRICS: + { + json slots_data = json::array(); + + int n_idle_slots = 0; + int n_processing_slots = 0; + + for (server_slot & slot : slots) { + json slot_data = get_formated_generation(slot); + slot_data["id"] = slot.id; + slot_data["id_task"] = slot.id_task; + slot_data["state"] = slot.state; + slot_data["prompt"] = slot.prompt; + slot_data["next_token"] = { + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"n_decoded", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }; + + if (slot_data["state"] == SLOT_STATE_IDLE) { + n_idle_slots++; + } else { + n_processing_slots++; + } + + slots_data.push_back(slot_data); + } + LOG_INFO("slot data", { + {"id_task", task.id}, + {"n_idle_slots", n_idle_slots}, + {"n_processing_slots", n_processing_slots} + }); + + LOG_VERBOSE("slot data", { + {"id_task", task.id}, + {"n_idle_slots", n_idle_slots}, + {"n_processing_slots", n_processing_slots}, + {"slots", slots_data} + }); + + server_task_result res; + res.id = task.id; + res.id_multi = task.id_multi; + res.stop = true; + res.error = false; + res.data = { { "idle", n_idle_slots }, { "processing", n_processing_slots }, { "deferred", queue_tasks.queue_tasks_deferred.size() }, @@ -1532,71 +1515,104 @@ struct llama_server_context { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, { "slots", slots_data }, - }; - metrics.reset_bucket(); - queue_results.send(res); - } break; + }; + + metrics.reset_bucket(); + queue_results.send(res); + } break; } } - void on_finish_multitask(task_multi& multitask) - { + void on_finish_multitask(const server_task_multi & multitask) { // all subtasks done == multitask is done - task_result result; - result.id = multitask.id; - result.stop = true; + server_task_result result; + result.id = multitask.id; + result.stop = true; result.error = false; // collect json results into one json result std::vector result_jsons; - for (auto& subres : multitask.results) - { - result_jsons.push_back(subres.result_json); + for (const auto & subres : multitask.results) { + result_jsons.push_back(subres.data); result.error = result.error && subres.error; } - result.result_json = json{ { "results", result_jsons } }; + result.data = json { + { "results", result_jsons } + }; + queue_results.send(result); } bool update_slots() { - if (system_need_update) - { - LOG_INFO("updating system prompt", {}); + if (system_need_update) { system_prompt_update(); } - llama_batch_clear(batch); + // release slots + for (auto & slot : slots) { + if (slot.command == SLOT_COMMAND_RELEASE) { + slot.state = SLOT_STATE_IDLE; + slot.command = SLOT_COMMAND_NONE; + slot.t_last_used = ggml_time_us(); - if (all_slots_are_idle) - { - if (system_prompt.empty() && clean_kv_cache) - { - LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {}); - kv_cache_clear(); + LOG_INFO("slot released", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", n_ctx}, + {"n_past", slot.n_past}, + {"n_system_tokens", system_tokens.size()}, + {"n_cache_tokens", slot.cache_tokens.size()}, + {"truncated", slot.truncated} + }); + + queue_tasks.notify_slot_changed(); } - return true; } - LOG_VERBOSE("posting NEXT_RESPONSE", {}); - task_server task; - task.type = TASK_TYPE_NEXT_RESPONSE; - task.target_id = -1; - queue_tasks.post(task); - - for (server_slot &slot : slots) + // check if all slots are idle { - if (slot.ga_n == 1) - { - if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) - { + bool all_idle = true; + + for (auto & slot : slots) { + if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) { + all_idle = false; + break; + } + } + + if (all_idle) { + LOG_INFO("all slots are idle", {}); + if (system_prompt.empty() && clean_kv_cache) { + kv_cache_clear(); + } + + return true; + } + } + + { + LOG_VERBOSE("posting NEXT_RESPONSE", {}); + + server_task task; + task.type = SERVER_TASK_TYPE_NEXT_RESPONSE; + task.id_target = -1; + + queue_tasks.post(task); + } + + // apply context-shift if needed + // TODO: simplify and improve + for (server_slot & slot : slots) { + if (slot.ga_n == 1) { + if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) { // Shift context const int n_keep = slot.params.n_keep + add_bos_token; const int n_left = (int) system_tokens.size() + slot.n_past - n_keep; const int n_discard = n_left / 2; LOG_INFO("slot context shift", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, + {"id_slot", slot.id}, + {"id_task", slot.id_task}, {"n_keep", n_keep}, {"n_left", n_left}, {"n_discard", n_discard}, @@ -1605,15 +1621,17 @@ struct llama_server_context {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()} }); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); - for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) - { - slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; - } + if (slot.params.cache_prompt) { + for (size_t i = n_keep + 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.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + } slot.n_past -= n_discard; @@ -1622,33 +1640,12 @@ struct llama_server_context } } - // decode any currently ongoing sequences - LOG_VERBOSE("decoding ongoing sequences", {}); - for (auto & slot : slots) - { - // release the slot - if (slot.command == RELEASE) - { - slot.state = IDLE; - slot.command = NONE; - slot.t_last_used = ggml_time_us(); + // start populating the batch for this iteration + llama_batch_clear(batch); - LOG_INFO("slot released", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - {"n_ctx", n_ctx}, - {"n_past", slot.n_past}, - {"n_system_tokens", system_tokens.size()}, - {"n_cache_tokens", slot.cache_tokens.size()}, - {"truncated", slot.truncated} - }); - queue_tasks.notify_slot_changed(); - - continue; - } - - if (slot.state == IDLE) - { + // frist, add sampled tokens from any ongoing sequences + for (auto & slot : slots) { + if (slot.state == SLOT_STATE_IDLE) { continue; } @@ -1659,193 +1656,184 @@ struct llama_server_context // TODO: we always have to take into account the "system_tokens" // this is not great and needs to be improved somehow llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + slot.n_past += 1; + + if (slot.params.cache_prompt) { + slot.cache_tokens.push_back(slot.sampled); + } + + LOG_VERBOSE("slot decode token", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", n_ctx}, + {"n_past", slot.n_past}, + {"n_system_tokens", system_tokens.size()}, + {"n_cache_tokens", slot.cache_tokens.size()}, + {"truncated", slot.truncated} + }); } // 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) - { - for (auto & slot : slots) - { - const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()) || !slot.images.empty(); + // next, batch any pending prompts without exceeding n_batch + if (params.cont_batching || batch.n_tokens == 0) { + for (auto & slot : slots) { + const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()); // empty prompt passed -> release the slot and send empty response // note: infill mode allows empty prompt - if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill) - { + if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT && !has_prompt && !slot.infill) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; slot.release(); slot.print_timings(); send_final_response(slot); continue; } - // need process the prompt - if (slot.state == IDLE && 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; + // this slot still has a prompt to be processed + if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) { + auto & prompt_tokens = slot.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; // 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(model)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - prefix_tokens.push_back(llama_token_middle(model)); - prompt_tokens = prefix_tokens; - } - else - { - prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt - } - - slot.n_prompt_tokens = prompt_tokens.size(); - - if (slot.params.n_keep < 0) - { - slot.params.n_keep = slot.n_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it, if group attention self-extend is disabled - if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) - { - const int n_left = slot.n_ctx - slot.params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.n_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); - 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}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + // we haven't tokenized the prompt yet - do it now: + if (prompt_tokens.empty()) { + LOG_VERBOSE("tokenizing prompt", { + {"id_slot", slot.id}, + {"id_task", slot.id_task} }); - slot.truncated = true; - prompt_tokens = new_tokens; - slot.n_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); - } + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_generation = 0; - if (!slot.params.cache_prompt) - { - llama_sampling_reset(slot.ctx_sampling); - - slot.n_past = 0; - slot.n_past_se = 0; - slot.ga_i = 0; - slot.n_prompt_tokens_processed = slot.n_prompt_tokens; - } - else - { - // 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); - - // the last token of the cache is not in the KV cache until the next call to llama_decode - // (it was sampled, pushed into the "cache_tokens", but not yet put in the context) - if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size()) - { - slot.n_past -= 1; - } - - slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past; - - if (slot.ga_n != 1) - { - int ga_i = 0; - int32_t ga_n = slot.ga_n; - int32_t ga_w = slot.ga_w; - int32_t slot_npast = 0; - for (int k = 0; k < slot.n_past; ++k) - { - while (slot_npast >= ga_i + ga_w) { - const int bd = (ga_w/ga_n)*(ga_n - 1); - slot_npast -= bd; - ga_i += ga_w/ga_n; - } - slot_npast++; + 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; } - slot.n_past_se = slot_npast; - slot.ga_i = ga_i; + + auto prefix_tokens = tokenize(slot.params.input_prefix, false); + auto suffix_tokens = tokenize(slot.params.input_suffix, false); + + 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(model)); + prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.push_back(llama_token_middle(model)); + prompt_tokens = prefix_tokens; + } else { + prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt } - LOG_INFO("slot progression", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "n_past", slot.n_past }, - { "n_past_se", slot.n_past_se }, - { "ga_i", slot.ga_i }, - { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } - }); + slot.n_past = 0; + slot.n_prompt_tokens = prompt_tokens.size(); + + if (slot.embedding) { + // this prompt is too large to process - discard it + if (slot.n_prompt_tokens > n_batch) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; + slot.release(); + slot.print_timings(); + send_final_response(slot); + continue; + } + } else { + if (slot.params.n_keep < 0) { + slot.params.n_keep = slot.n_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); + + // if input prompt is too big, truncate it (if group attention self-extend is disabled) + if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) { + const int n_left = slot.n_ctx - slot.params.n_keep; + + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.n_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); + + new_tokens.insert( + new_tokens.end(), + prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, + prompt_tokens.end()); + + prompt_tokens = std::move(new_tokens); + + slot.truncated = true; + slot.n_prompt_tokens = prompt_tokens.size(); + + LOG_VERBOSE("input truncated", { + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, + }); + + GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); + } + + llama_sampling_reset(slot.ctx_sampling); + + if (!slot.params.cache_prompt) { + slot.n_past_se = 0; + slot.ga_i = 0; + } else { + GGML_ASSERT(slot.ga_n == 1); + + // reuse any previously computed tokens that are common with the new prompt + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + + // remove the non-common part from the cache + slot.cache_tokens.resize(slot.n_past); + + // push the prompt into the sampling context (do not apply grammar) + for (int i = 0; i < slot.n_past; ++i) { + llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false); + } + } + } + + if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { + // we have to evaluate at least 1 token to generate logits. + LOG_INFO("we have to evaluate at least 1 token to generate logits", { + { "id_slot", slot.id }, + { "id_task", slot.id_task } + }); + + slot.n_past--; + if (slot.ga_i > 0) { + slot.n_past_se--; + } + } + + slot.n_prompt_tokens_processed = 0; } - slot.cache_tokens = prompt_tokens; - - if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) - { - // we have to evaluate at least 1 token to generate logits. - LOG_INFO("we have to evaluate at least 1 token to generate logits", { - { "slot_id", slot.id }, - { "task_id", slot.task_id } - }); - slot.n_past--; - if (slot.ga_i > 0) - { - slot.n_past_se--; + if (slot.embedding) { + // cannot fit the prompt in the current batch - will try next iter + if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { + continue; } } - int p0 = (int) system_tokens.size() + slot.n_past; - LOG_INFO("kv cache rm [p0, end)", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "p0", p0 } - }); + const int p0 = (int) system_tokens.size() + slot.n_past; llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); - 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())}, - }); - - 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, add_bos_token) : prompt_tokens; + LOG_INFO("kv cache rm [p0, end)", { + { "id_slot", slot.id }, + { "id_task", slot.id_task }, + { "p0", p0 } + }); int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; @@ -1853,61 +1841,82 @@ struct llama_server_context int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; - for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) - { - if (slot.ga_n != 1) - { + // add prompt tokens for processing in the current batch + // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow + for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) { + if (slot.ga_n != 1) { while (slot_npast >= ga_i + ga_w) { const int bd = (ga_w/ga_n)*(ga_n - 1); slot_npast -= bd; ga_i += ga_w/ga_n; } } - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); + + llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); + + if (slot.params.cache_prompt) { + slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); + } + + slot.n_prompt_tokens_processed++; slot_npast++; } - if (has_images && !ingest_images(slot, n_batch)) - { - LOG_ERROR("failed processing images", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - }); - // FIXME @phymbert: to be properly tested - // early returning without changing the slot state will block the slot for ever - // no one at the moment is checking the return value - return false; - } + LOG_VERBOSE("prompt processing progress", { + {"id_slot", slot.id}, + {"n_past", slot.n_past}, + {"n_ctx", n_ctx}, + {"n_tokens", batch.n_tokens}, + {"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens}, + }); - // extract the logits only for the last token - if (batch.n_tokens > 0) - { + // entire prompt has been processed - start decoding new tokens + if (slot.n_past == slot.n_prompt_tokens) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; + + GGML_ASSERT(batch.n_tokens > 0); + + // extract the logits only for the last token batch.logits[batch.n_tokens - 1] = true; - } - slot.n_decoded = 0; - slot.i_batch = batch.n_tokens - 1; + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; + + LOG_VERBOSE("prompt done", { + {"id_slot", slot.id}, + {"n_past", slot.n_past}, + {"n_ctx", n_ctx}, + {"n_tokens", batch.n_tokens}, + }); + } + } + + if (batch.n_tokens >= n_batch) { + break; } } } - if (batch.n_tokens == 0) - { - all_slots_are_idle = true; + if (batch.n_tokens == 0) { + LOG_VERBOSE("no tokens to decode", {}); + return true; } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) - { + LOG_VERBOSE("decoding batch", { + {"n_tokens", batch.n_tokens}, + }); + + // process the created batch of tokens + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); - for (auto & slot : slots) - { - if (slot.ga_n != 1) - { + for (auto & slot : slots) { + if (slot.ga_n != 1) { // context extension via Self-Extend - while (slot.n_past_se >= slot.ga_i + slot.ga_w) - { + // TODO: simplify and/or abstract this + while (slot.n_past_se >= slot.ga_i + slot.ga_w) { const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w; const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1); const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w; @@ -1918,8 +1927,8 @@ struct llama_server_context LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); - llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); + llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); + llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; @@ -1927,12 +1936,12 @@ struct llama_server_context LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i); } + slot.n_past_se += n_tokens; } } - llama_batch batch_view = - { + llama_batch batch_view = { n_tokens, batch.token + i, nullptr, @@ -1945,10 +1954,8 @@ 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; @@ -1959,19 +1966,17 @@ struct llama_server_context // 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)) - { + for (auto & slot : slots) { + if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { continue; } // prompt evaluated for embedding - if (slot.embedding) - { + if (slot.embedding) { send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; @@ -1984,10 +1989,9 @@ struct llama_server_context llama_sampling_accept(slot.ctx_sampling, ctx, id, true); 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; + if (slot.n_decoded == 1) { + slot.t_start_generation = ggml_time_us(); + slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3; metrics.on_prompt_eval(slot); } @@ -1995,19 +1999,19 @@ struct llama_server_context result.tok = id; const int32_t n_probs = slot.sparams.n_probs; - if (slot.sparams.temp <= 0 && n_probs > 0) - { + if (slot.sparams.temp <= 0 && n_probs > 0) { // for llama_sample_token_greedy we need to sort candidates llama_sample_softmax(ctx, &cur_p); } - for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) - { - result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); + for (size_t i = 0; i < std::min(cur_p.size, (size_t) n_probs); ++i) { + result.probs.push_back({ + cur_p.data[i].id, + cur_p.data[i].p + }); } - if (!process_token(result, slot)) - { + if (!process_token(result, slot)) { slot.release(); slot.print_timings(); send_final_response(slot); @@ -2019,24 +2023,23 @@ struct llama_server_context } LOG_VERBOSE("slots updated", {}); + return true; } - json model_meta() { - return json{ - {"vocab_type", llama_vocab_type(model)}, - {"n_vocab", llama_n_vocab(model)}, - {"n_ctx_train", llama_n_ctx_train(model)}, - {"n_embd", llama_n_embd(model)}, - {"n_params", llama_model_n_params(model)}, - {"size", llama_model_size(model)}, + json model_meta() const { + return json { + {"vocab_type", llama_vocab_type (model)}, + {"n_vocab", llama_n_vocab (model)}, + {"n_ctx_train", llama_n_ctx_train (model)}, + {"n_embd", llama_n_embd (model)}, + {"n_params", llama_model_n_params(model)}, + {"size", llama_model_size (model)}, }; } }; -static void server_print_usage(const char *argv0, const gpt_params ¶ms, - const server_params &sparams) -{ +static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) { printf("usage: %s [options]\n", argv0); printf("\n"); printf("options:\n"); @@ -2054,17 +2057,14 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" --pooling {none,mean,cls}\n"); - printf(" pooling type for embeddings, use model default if unspecified\n"); + printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\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_supports_mlock()) - { + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_supports_mmap()) - { + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); @@ -2096,7 +2096,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); 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(" --embeddings 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"); @@ -2105,7 +2105,6 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" KV cache data type for K (default: f16)\n"); printf(" -ctv TYPE, --cache-type-v TYPE\n"); printf(" KV cache data type for V (default: f16)\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-format log output format: json or text (default: json)\n"); printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); @@ -2123,57 +2122,41 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("\n"); } -static void server_params_parse(int argc, char **argv, server_params &sparams, - gpt_params ¶ms, llama_server_context& llama) -{ - gpt_params default_params; +static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) { + gpt_params default_params; server_params default_sparams; + std::string arg; bool invalid_param = false; - for (int i = 1; i < argc; i++) - { + for (int i = 1; i < argc; i++) { arg = argv[i]; - if (arg == "--port") - { - if (++i >= argc) - { + if (arg == "--port") { + if (++i >= argc) { invalid_param = true; break; } sparams.port = std::stoi(argv[i]); - } - else if (arg == "--host") - { - if (++i >= argc) - { + } else if (arg == "--host") { + if (++i >= argc) { invalid_param = true; break; } sparams.hostname = argv[i]; - } - else if (arg == "--path") - { - if (++i >= argc) - { + } else if (arg == "--path") { + if (++i >= argc) { invalid_param = true; break; } sparams.public_path = argv[i]; - } - else if (arg == "--api-key") - { - if (++i >= argc) - { + } else if (arg == "--api-key") { + if (++i >= argc) { invalid_param = true; break; } sparams.api_keys.emplace_back(argv[i]); - } - else if (arg == "--api-key-file") - { - if (++i >= argc) - { + } else if (arg == "--api-key-file") { + if (++i >= argc) { invalid_param = true; break; } @@ -2190,53 +2173,36 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } key_file.close(); - } - else if (arg == "--timeout" || arg == "-to") - { - if (++i >= argc) - { + } else if (arg == "--timeout" || arg == "-to") { + if (++i >= argc) { invalid_param = true; break; } sparams.read_timeout = std::stoi(argv[i]); sparams.write_timeout = std::stoi(argv[i]); - } - else if (arg == "-m" || arg == "--model") - { - if (++i >= argc) - { + } else if (arg == "-m" || arg == "--model") { + if (++i >= argc) { invalid_param = true; break; } params.model = argv[i]; - } - else if (arg == "-a" || arg == "--alias") - { - if (++i >= argc) - { + } else if (arg == "-a" || arg == "--alias") { + if (++i >= argc) { invalid_param = true; break; } params.model_alias = argv[i]; - } - else if (arg == "-h" || arg == "--help") - { + } else if (arg == "-h" || arg == "--help") { server_print_usage(argv[0], default_params, default_sparams); exit(0); - } - else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") - { - if (++i >= argc) - { + } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { + if (++i >= argc) { invalid_param = true; break; } params.n_ctx = std::stoi(argv[i]); - } - else if (arg == "--rope-scaling") - { - if (++i >= argc) - { + } else if (arg == "--rope-scaling") { + if (++i >= argc) { invalid_param = true; break; } @@ -2245,59 +2211,44 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { invalid_param = true; break; } - } - else if (arg == "--rope-freq-base") - { - if (++i >= argc) - { + } else if (arg == "--rope-freq-base") { + if (++i >= argc) { invalid_param = true; break; } params.rope_freq_base = std::stof(argv[i]); - } - else if (arg == "--rope-freq-scale") - { - if (++i >= argc) - { + } else if (arg == "--rope-freq-scale") { + if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = std::stof(argv[i]); - } - else if (arg == "--yarn-ext-factor") - { + } else if (arg == "--yarn-ext-factor") { if (++i >= argc) { invalid_param = true; break; } params.yarn_ext_factor = std::stof(argv[i]); } - else if (arg == "--yarn-attn-factor") - { + else if (arg == "--yarn-attn-factor") { if (++i >= argc) { invalid_param = true; break; } params.yarn_attn_factor = std::stof(argv[i]); - } - else if (arg == "--yarn-beta-fast") - { + } else if (arg == "--yarn-beta-fast") { if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_fast = std::stof(argv[i]); - } - else if (arg == "--yarn-beta-slow") - { + } else if (arg == "--yarn-beta-slow") { if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_slow = std::stof(argv[i]); - } - else if (arg == "--pooling") - { + } else if (arg == "--pooling") { if (++i >= argc) { invalid_param = true; break; @@ -2307,108 +2258,79 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } else { invalid_param = true; break; } - } - else if (arg == "--threads" || arg == "-t") - { + } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) { invalid_param = true; break; } params.n_threads = std::stoi(argv[i]); - } - else if (arg == "--grp-attn-n" || arg == "-gan") - { + } else if (arg == "--grp-attn-n" || arg == "-gan") { if (++i >= argc) { invalid_param = true; break; } params.grp_attn_n = std::stoi(argv[i]); - } - else if (arg == "--grp-attn-w" || arg == "-gaw") - { - if (++i >= argc) - { + } else if (arg == "--grp-attn-w" || arg == "-gaw") { + if (++i >= argc) { invalid_param = true; break; } params.grp_attn_w = std::stoi(argv[i]); - } - else if (arg == "--threads-batch" || arg == "-tb") - { - if (++i >= argc) - { + } 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 == "--threads-http") - { - if (++i >= argc) - { + } else if (arg == "--threads-http") { + if (++i >= argc) { invalid_param = true; break; } sparams.n_threads_http = std::stoi(argv[i]); - } - else if (arg == "-b" || arg == "--batch-size") - { - if (++i >= argc) - { + } else if (arg == "-b" || arg == "--batch-size") { + if (++i >= argc) { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); - } - else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") - { - if (++i >= argc) - { + } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { invalid_param = true; break; } if (llama_supports_gpu_offload()) { params.n_gpu_layers = std::stoi(argv[i]); } else { - LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " - "See main README.md for information on enabling GPU BLAS support", - {{"n_gpu_layers", params.n_gpu_layers}}); + LOG_WARNING( + "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " + "See main README.md for information on enabling GPU BLAS support", + {{"n_gpu_layers", params.n_gpu_layers}}); } - } - else if (arg == "--split-mode" || arg == "-sm") - { + } else if (arg == "--split-mode" || arg == "-sm") { if (++i >= argc) { invalid_param = true; break; } std::string arg_next = argv[i]; - if (arg_next == "none") - { + if (arg_next == "none") { params.split_mode = LLAMA_SPLIT_MODE_NONE; - } - else if (arg_next == "layer") - { + } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } - else if (arg_next == "row") - { + } else if (arg_next == "row") { params.split_mode = LLAMA_SPLIT_MODE_ROW; - } - else { + } else { invalid_param = true; break; } #ifndef GGML_USE_CUBLAS fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); #endif // GGML_USE_CUBLAS - } - else if (arg == "--tensor-split" || arg == "-ts") - { - if (++i >= argc) - { + } else if (arg == "--tensor-split" || arg == "-ts") { + if (++i >= argc) { invalid_param = true; break; } @@ -2421,25 +2343,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, std::vector split_arg{it, {}}; GGML_ASSERT(split_arg.size() <= llama_max_devices()); - for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) - { - if (i_device < split_arg.size()) - { + for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { + if (i_device < split_arg.size()) { params.tensor_split[i_device] = std::stof(split_arg[i_device]); - } - else - { + } else { params.tensor_split[i_device] = 0.0f; } } #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); #endif // GGML_USE_CUBLAS - } - else if (arg == "--main-gpu" || arg == "-mg") - { - if (++i >= argc) - { + } else if (arg == "--main-gpu" || arg == "-mg") { + if (++i >= argc) { invalid_param = true; break; } @@ -2448,98 +2363,70 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); #endif - } - else if (arg == "--lora") - { - if (++i >= argc) - { + } else if (arg == "--lora") { + if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; - } - else if (arg == "--lora-scaled") - { - if (++i >= argc) - { + } else if (arg == "--lora-scaled") { + if (++i >= argc) { invalid_param = true; break; } const char * lora_adapter = argv[i]; - if (++i >= argc) - { + if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; - } - else if (arg == "--lora-base") - { - if (++i >= argc) - { + } else if (arg == "--lora-base") { + if (++i >= argc) { invalid_param = true; break; } params.lora_base = argv[i]; - } - else if (arg == "-v" || arg == "--verbose") - { + } else if (arg == "-v" || arg == "--verbose") { #if SERVER_VERBOSE != 1 LOG_WARNING("server.cpp is not built with verbose logging.", {}); #else server_verbose = true; #endif - } - else if (arg == "--mlock") - { + } else if (arg == "--mlock") { params.use_mlock = true; - } - else if (arg == "--no-mmap") - { + } else if (arg == "--no-mmap") { params.use_mmap = false; - } - else if (arg == "--numa") { + } else if (arg == "--numa") { if (++i >= argc) { invalid_param = true; break; } else { std::string value(argv[i]); /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } - else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } - else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } else { invalid_param = true; break; } } - } - else if (arg == "--embedding") - { + } else if (arg == "--embedding" || arg == "--embeddings") { params.embedding = true; - } - else if (arg == "-cb" || arg == "--cont-batching") - { + } else if (arg == "-cb" || arg == "--cont-batching") { params.cont_batching = true; - } - else if (arg == "-np" || arg == "--parallel") - { - if (++i >= argc) - { + } 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) - { + } else if (arg == "-n" || arg == "--n-predict") { + if (++i >= argc) { invalid_param = true; break; } params.n_predict = std::stoi(argv[i]); - } else if (arg == "-spf" || arg == "--system-prompt-file") - { - if (++i >= argc) - { + } else if (arg == "-spf" || arg == "--system-prompt-file") { + if (++i >= argc) { invalid_param = true; break; } @@ -2549,67 +2436,39 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - std::string systm_content; + std::string system_prompt; std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), - std::back_inserter(systm_content) + std::back_inserter(system_prompt) ); - llama.system_prompt_process(json::parse(systm_content)); - } - else if (arg == "-ctk" || arg == "--cache-type-k") { + sparams.system_prompt = system_prompt; + } else if (arg == "-ctk" || arg == "--cache-type-k") { params.cache_type_k = argv[++i]; - } - else if (arg == "-ctv" || arg == "--cache-type-v") { + } else if (arg == "-ctv" || arg == "--cache-type-v") { params.cache_type_v = argv[++i]; - } - else if(arg == "--mmproj") - { - if (++i >= argc) - { + } else if (arg == "--log-format") { + if (++i >= argc) { invalid_param = true; break; } - params.mmproj = argv[i]; - } - else if (arg == "--log-format") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - if (std::strcmp(argv[i], "json") == 0) - { + if (std::strcmp(argv[i], "json") == 0) { server_log_json = true; - } - else if (std::strcmp(argv[i], "text") == 0) - { + } else if (std::strcmp(argv[i], "text") == 0) { server_log_json = false; - } - else - { + } else { invalid_param = true; break; } - } - else if (arg == "--log-disable") - { + } else if (arg == "--log-disable") { log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); - } - else if (arg == "--slots-endpoint-disable") - { + } else if (arg == "--slots-endpoint-disable") { sparams.slots_endpoint = false; - } - else if (arg == "--metrics") - { + } else if (arg == "--metrics") { sparams.metrics_endpoint = true; - } - else if (arg == "--chat-template") - { - if (++i >= argc) - { + } else if (arg == "--chat-template") { + if (++i >= argc) { invalid_param = true; break; } @@ -2620,9 +2479,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } sparams.chat_template = argv[i]; - } - else if (arg == "--override-kv") - { + } else if (arg == "--override-kv") { if (++i >= argc) { invalid_param = true; break; @@ -2633,6 +2490,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } + struct llama_model_kv_override kvo; std::strncpy(kvo.key, argv[i], sep - argv[i]); kvo.key[sep - argv[i]] = 0; @@ -2663,67 +2521,28 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.kv_overrides.push_back(kvo); - } - else - { + } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } + if (!params.kv_overrides.empty()) { params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } - if (invalid_param) - { + if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } -/* llama.cpp completion api semantics */ -static json format_partial_response( - llama_server_context &llama, server_slot *slot, const std::string &content, const std::vector &probs -) { - json res = json - { - {"content", content }, - {"stop", false}, - {"slot_id", slot->id }, - {"multimodal", llama.multimodal } - }; - - if (slot->sparams.n_probs > 0) - { - res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); - } - - return res; -} - -static json format_tokenizer_response(const std::vector &tokens) -{ - return json { - {"tokens", tokens} - }; -} - -static json format_detokenized_response(std::string content) -{ - return json { - {"content", content} - }; -} - - -static void log_server_request(const httplib::Request &req, const httplib::Response &res) -{ +static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port - if (req.path == "/v1/health" || req.path == "/v1/completions") - { + if (req.path == "/v1/health" || req.path == "/v1/completions") { return; } @@ -2742,24 +2561,9 @@ static void log_server_request(const httplib::Request &req, const httplib::Respo }); } -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, server_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) - { - slot->generated_text.reserve(slot->generated_text.size() + len); - } - for (const completion_token_output & cto : gtps) - { - slot->generated_text += translator(cto); - } -} - std::function shutdown_handler; std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; + inline void signal_handler(int signal) { if (is_terminating.test_and_set()) { // in case it hangs, we can force terminate the server by hitting Ctrl+C twice @@ -2767,40 +2571,45 @@ inline void signal_handler(int signal) { fprintf(stderr, "Received second interrupt, terminating immediately.\n"); exit(1); } + shutdown_handler(signal); } -int main(int argc, char **argv) -{ +int main(int argc, char ** argv) { #if SERVER_VERBOSE != 1 log_disable(); #endif // own arguments required by this example - gpt_params params; + gpt_params params; server_params sparams; // struct that contains llama context and inference - llama_server_context llama; + server_context ctx_server; - server_params_parse(argc, argv, sparams, params, llama); + server_params_parse(argc, argv, sparams, params); - if (params.model_alias == "unknown") - { + if (!sparams.system_prompt.empty()) { + ctx_server.system_prompt_set(json::parse(sparams.system_prompt)); + } + + if (params.model_alias == "unknown") { params.model_alias = params.model; } llama_backend_init(); llama_numa_init(params.numa); - LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER}, - {"commit", LLAMA_COMMIT}}); + LOG_INFO("build info", { + {"build", LLAMA_BUILD_NUMBER}, + {"commit", LLAMA_COMMIT} + }); LOG_INFO("system info", { - {"n_threads", params.n_threads}, - {"n_threads_batch", params.n_threads_batch}, - {"total_threads", std::thread::hardware_concurrency()}, - {"system_info", llama_print_system_info()}, - }); + {"n_threads", params.n_threads}, + {"n_threads_batch", params.n_threads_batch}, + {"total_threads", std::thread::hardware_concurrency()}, + {"system_info", llama_print_system_info()}, + }); httplib::Server svr; @@ -2809,152 +2618,163 @@ int main(int argc, char **argv) svr.set_default_headers({{"Server", "llama.cpp"}}); // CORS preflight - svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + svr.Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res.set_header("Access-Control-Allow-Credentials", "true"); - res.set_header("Access-Control-Allow-Methods", "POST"); - res.set_header("Access-Control-Allow-Headers", "*"); + res.set_header("Access-Control-Allow-Methods", "POST"); + res.set_header("Access-Control-Allow-Headers", "*"); }); - svr.Get("/health", [&](const httplib::Request& req, httplib::Response& res) { + svr.Get("/health", [&](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); - switch(current_state) { - case SERVER_STATE_READY: { - // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + switch (current_state) { + case SERVER_STATE_READY: + { + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.type = SERVER_TASK_TYPE_METRICS; + task.id_target = -1; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - int n_idle_slots = result.result_json["idle"]; - int n_processing_slots = result.result_json["processing"]; + const int n_idle_slots = result.data["idle"]; + const int n_processing_slots = result.data["processing"]; - json health = { + json health = { {"status", "ok"}, {"slots_idle", n_idle_slots}, - {"slots_processing", n_processing_slots}}; - res.status = 200; // HTTP OK - if (sparams.slots_endpoint && req.has_param("include_slots")) { - health["slots"] = result.result_json["slots"]; - } + {"slots_processing", n_processing_slots} + }; - if (n_idle_slots == 0) { - health["status"] = "no slot available"; - if (req.has_param("fail_on_no_slot")) { - res.status = 503; // HTTP Service Unavailable + res.status = 200; // HTTP OK + if (sparams.slots_endpoint && req.has_param("include_slots")) { + health["slots"] = result.data["slots"]; } + + if (n_idle_slots == 0) { + health["status"] = "no slot available"; + if (req.has_param("fail_on_no_slot")) { + res.status = 503; // HTTP Service Unavailable + } + } + + res.set_content(health.dump(), "application/json"); + break; } - res.set_content(health.dump(), "application/json"); - break; - } case SERVER_STATE_LOADING_MODEL: - res.set_content(R"({"status": "loading model"})", "application/json"); - res.status = 503; // HTTP Service Unavailable - break; + { + res.set_content(R"({"status": "loading model"})", "application/json"); + res.status = 503; // HTTP Service Unavailable + } break; case SERVER_STATE_ERROR: - res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); - res.status = 500; // HTTP Internal Server Error - break; + { + res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); + res.status = 500; // HTTP Internal Server Error + } break; } }); if (sparams.slots_endpoint) { - svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) { + svr.Get("/slots", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - res.set_content(result.result_json["slots"].dump(), "application/json"); + res.set_content(result.data["slots"].dump(), "application/json"); res.status = 200; // HTTP OK }); } if (sparams.metrics_endpoint) { - svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) { + svr.Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - json data = result.result_json; + json data = result.data; - uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; - uint64_t t_prompt_processing = data["t_prompt_processing"]; + const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; + const uint64_t t_prompt_processing = data["t_prompt_processing"]; - uint64_t n_tokens_predicted = data["n_tokens_predicted"]; - uint64_t t_tokens_generation = data["t_tokens_generation"]; + const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; + const uint64_t t_tokens_generation = data["t_tokens_generation"]; - int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; + const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names json all_metrics_def = json { - {"counter", {{ - {"name", "prompt_tokens_total"}, - {"help", "Number of prompt tokens processed."}, - {"value", data["n_prompt_tokens_processed_total"]} - }, { - {"name", "tokens_predicted_total"}, - {"help", "Number of generation tokens processed."}, - {"value", data["n_tokens_predicted_total"]} - }}}, - {"gauge", {{ - {"name", "prompt_tokens_seconds"}, - {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} - },{ - {"name", "predicted_tokens_seconds"}, - {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} - },{ - {"name", "kv_cache_usage_ratio"}, - {"help", "KV-cache usage. 1 means 100 percent usage."}, - {"value", 1. * kv_cache_used_cells / params.n_ctx} - },{ - {"name", "kv_cache_tokens"}, - {"help", "KV-cache tokens."}, - {"value", data["kv_cache_tokens_count"]} - },{ - {"name", "requests_processing"}, - {"help", "Number of request processing."}, - {"value", data["processing"]} - },{ - {"name", "requests_deferred"}, - {"help", "Number of request deferred."}, - {"value", data["deferred"]} - }}} + {"counter", {{ + {"name", "prompt_tokens_total"}, + {"help", "Number of prompt tokens processed."}, + {"value", data["n_prompt_tokens_processed_total"]} + }, { + {"name", "tokens_predicted_total"}, + {"help", "Number of generation tokens processed."}, + {"value", data["n_tokens_predicted_total"]} + }}}, + {"gauge", {{ + {"name", "prompt_tokens_seconds"}, + {"help", "Average prompt throughput in tokens/s."}, + {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} + },{ + {"name", "predicted_tokens_seconds"}, + {"help", "Average generation throughput in tokens/s."}, + {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} + },{ + {"name", "kv_cache_usage_ratio"}, + {"help", "KV-cache usage. 1 means 100 percent usage."}, + {"value", 1. * kv_cache_used_cells / params.n_ctx} + },{ + {"name", "kv_cache_tokens"}, + {"help", "KV-cache tokens."}, + {"value", data["kv_cache_tokens_count"]} + },{ + {"name", "requests_processing"}, + {"help", "Number of request processing."}, + {"value", data["processing"]} + },{ + {"name", "requests_deferred"}, + {"help", "Number of request deferred."}, + {"value", data["deferred"]} + }}} }; std::stringstream prometheus; - for (const auto& el : all_metrics_def.items()) { - const auto& type = el.key(); - const auto& metrics_def = el.value(); - for (const auto& metric_def : metrics_def) { - std::string name = metric_def["name"]; - std::string help = metric_def["help"]; + + for (const auto & el : all_metrics_def.items()) { + const auto & type = el.key(); + const auto & metrics_def = el.value(); + + for (const auto & metric_def : metrics_def) { + const std::string name = metric_def["name"]; + const std::string help = metric_def["help"]; + auto value = json_value(metric_def, "value", 0); prometheus << "# HELP llamacpp:" << name << " " << help << "\n" << "# TYPE llamacpp:" << name << " " << type << "\n" @@ -2969,49 +2789,39 @@ int main(int argc, char **argv) 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; charset=utf-8"); - res.status = 500; - }); + svr.set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { + const char fmt[] = "500 Internal Server Error\n%s"; - svr.set_error_handler([](const httplib::Request &, httplib::Response &res) - { - if (res.status == 401) - { - res.set_content("Unauthorized", "text/plain; charset=utf-8"); - } - if (res.status == 400) - { - res.set_content("Invalid request", "text/plain; charset=utf-8"); - } - else if (res.status == 404) - { - res.set_content("File Not Found", "text/plain; charset=utf-8"); - res.status = 404; - } - }); + 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; charset=utf-8"); + res.status = 500; + }); + + svr.set_error_handler([](const httplib::Request &, httplib::Response & res) { + if (res.status == 401) { + res.set_content("Unauthorized", "text/plain; charset=utf-8"); + } + if (res.status == 400) { + res.set_content("Invalid request", "text/plain; charset=utf-8"); + } + if (res.status == 404) { + res.set_content("File Not Found", "text/plain; charset=utf-8"); + } + }); // set timeouts and change hostname and port svr.set_read_timeout (sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); - if (!svr.bind_to_port(sparams.hostname, sparams.port)) - { + if (!svr.bind_to_port(sparams.hostname, sparams.port)) { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } @@ -3020,8 +2830,9 @@ int main(int argc, char **argv) svr.set_base_dir(sparams.public_path); std::unordered_map log_data; + log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); + log_data["port"] = std::to_string(sparams.port); if (sparams.api_keys.size() == 1) { log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4); @@ -3030,20 +2841,23 @@ int main(int argc, char **argv) } // load the model - if (!llama.load_model(params)) - { + if (!ctx_server.load_model(params)) { state.store(SERVER_STATE_ERROR); return 1; } else { - llama.initialize(); + ctx_server.initialize(); state.store(SERVER_STATE_READY); - LOG_INFO("model loaded", {}); } - const auto model_meta = llama.model_meta(); + + LOG_INFO("model loaded", {}); + + const auto model_meta = ctx_server.model_meta(); if (sparams.chat_template.empty()) { // custom chat template is not supplied - // check if the template comes with the model is supported by us - llama.validate_model_chat_template(sparams); + if (!ctx_server.validate_model_chat_template()) { + LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); + sparams.chat_template = "chatml"; + } } // Middleware for API key validation @@ -3055,6 +2869,7 @@ int main(int argc, char **argv) // Check for API key in the header auto auth_header = req.get_header_value("Authorization"); + std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); @@ -3073,179 +2888,173 @@ 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; charset=utf-8"); - return false; - }); + svr.Get("/", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); + 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; charset=utf-8"); - return false; - }); + svr.Get("/index.js", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); + 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; charset=utf-8"); - return false; - }); + svr.Get("/completion.js", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); + 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; charset=utf-8"); - return false; - }); + 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; charset=utf-8"); + return false; + }); - svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - json data = { - { "user_name", llama.name_user.c_str() }, - { "assistant_name", llama.name_assistant.c_str() }, - { "default_generation_settings", llama.default_generation_settings_for_props }, - { "total_slots", llama.params.n_parallel } - }; - res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + svr.Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + json data = { + { "user_name", ctx_server.name_user.c_str() }, + { "assistant_name", ctx_server.name_assistant.c_str() }, + { "default_generation_settings", ctx_server.default_generation_settings_for_props }, + { "total_slots", ctx_server.params.n_parallel } + }; - svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = json::parse(req.body); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); - } - else - { - res.status = 404; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) - { - while (true) - { - task_result result = llama.queue_results.recv(task_id); - if (!result.error) { - const std::string str = - "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())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - if (result.stop) { - break; - } - } else { - const std::string str = - "error: " + - 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())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - break; - } - } + res.set_content(data.dump(), "application/json; charset=utf-8"); + }); - llama.queue_results.remove_waiting_task_id(task_id); - sink.done(); - return true; - }; - - auto on_complete = [task_id, &llama] (bool) - { - // cancel - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - }; - - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); - - svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request& req, httplib::Response& res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - std::time_t t = std::time(0); - - json models = { - {"object", "list"}, - {"data", { - { - {"id", params.model_alias}, - {"object", "model"}, - {"created", t}, - {"owned_by", "llamacpp"}, - {"meta", model_meta} - }, - }} - }; - - res.set_content(models.dump(), "application/json; charset=utf-8"); - }); - - const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) - { + svr.Post("/completion", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; } - json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); + json data = json::parse(req.body); + + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, data, false, false); if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - + server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error && result.stop) { - json oaicompat_result = format_final_response_oaicompat(data, result); - - res.set_content(oaicompat_result.dump(-1, ' ', false, - json::error_handler_t::replace), - "application/json; charset=utf-8"); + res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 500; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); + res.set_content(result.data["content"], "text/plain; charset=utf-8"); } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { - while (true) { - task_result llama_result = llama.queue_results.recv(task_id); - if (!llama_result.error) { - std::vector result_array = format_partial_response_oaicompat( llama_result); - for (auto it = result_array.begin(); it != result_array.end(); ++it) - { + ctx_server.queue_results.remove_waiting_task_id(id_task); + } else { + const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { + while (true) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error) { + const std::string str = + "data: " + + result.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())) { + ctx_server.queue_results.remove_waiting_task_id(id_task); + return false; + } + + if (result.stop) { + break; + } + } else { + const std::string str = + "error: " + + result.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())) { + ctx_server.queue_results.remove_waiting_task_id(id_task); + return false; + } + + break; + } + } + + ctx_server.queue_results.remove_waiting_task_id(id_task); + sink.done(); + + return true; + }; + + auto on_complete = [id_task, &ctx_server] (bool) { + // cancel + ctx_server.request_cancel(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + }; + + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); + + svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + + json models = { + {"object", "list"}, + {"data", { + { + {"id", params.model_alias}, + {"object", "model"}, + {"created", std::time(0)}, + {"owned_by", "llamacpp"}, + {"meta", model_meta} + }, + }} + }; + + res.set_content(models.dump(), "application/json; charset=utf-8"); + }); + + const auto chat_completions = [&ctx_server, &validate_api_key, &sparams](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!validate_api_key(req, res)) { + return; + } + + json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); + + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, data, false, false); + + if (!json_value(data, "stream", false)) { + server_task_result result = ctx_server.queue_results.recv(id_task); + + if (!result.error && result.stop) { + json result_oai = format_final_response_oaicompat(data, result.data); + + res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); + } else { + res.status = 500; + res.set_content(result.data["content"], "text/plain; charset=utf-8"); + } + ctx_server.queue_results.remove_waiting_task_id(id_task); + } else { + const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { + while (true) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error) { + std::vector result_array = format_partial_response_oaicompat(result.data); + + for (auto it = result_array.begin(); it != result_array.end(); ++it) { if (!it->empty()) { const std::string str = "data: " + @@ -3253,251 +3062,235 @@ int main(int argc, char **argv) "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } } } - if (llama_result.stop) { + if (result.stop) { break; } } else { const std::string str = "error: " + - llama_result.result_json.dump(-1, ' ', false, - json::error_handler_t::replace) + + result.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())) { - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } break; } } sink.done(); - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); return true; }; - auto on_complete = [task_id, &llama](bool) { + auto on_complete = [id_task, &ctx_server](bool) { // cancel request - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.request_cancel(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }; - svr.Post("/chat/completions", chat_completions); + svr.Post("/chat/completions", chat_completions); svr.Post("/v1/chat/completions", chat_completions); - svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = json::parse(req.body); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, true, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) - { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); - } - else - { - res.status = 404; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { - while (true) - { - task_result result = llama.queue_results.recv(task_id); - if (!result.error) { - const std::string str = - "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())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - if (result.stop) - { - break; - } - } - else - { - break; - } - } - - llama.queue_results.remove_waiting_task_id(task_id); - sink.done(); - return true; - }; - - 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); - } - }); - - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) - { return res.set_content("", "application/json; charset=utf-8"); }); - - svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - 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; charset=utf-8"); - }); - - svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - 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; charset=utf-8"); - }); - - svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - json prompt; - if (body.count("content") != 0) - { - prompt = body["content"]; - } - else - { - prompt = ""; - } - - json image_data; - if (body.count("image_data") != 0) { - image_data = body["image_data"]; - } - else - { - image_data = ""; - } - - // create and queue the task - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - // send the result - return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); - }); - - svr.Post("/v1/embeddings", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - - json prompt; - if (body.count("input") != 0) - { - prompt = body["input"]; - // batch - if(prompt.is_array()) { - json data = json::array(); - int i = 0; - for (const json &elem : prompt) { - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - json embedding = json{ - {"embedding", json_value(result.result_json, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }; - data.push_back(embedding); - } - json result = format_embeddings_response_oaicompat(body, data); - return res.set_content(result.dump(), "application/json; charset=utf-8"); - } - } - else - { - prompt = ""; - } - - // create and queue the task - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - json data = json::array({json{ - {"embedding", json_value(result.result_json, "embedding", json::array())}, - {"index", 0}, - {"object", "embedding"} - }} - ); - - json root = format_embeddings_response_oaicompat(body, data); - - // send the result - return res.set_content(root.dump(), "application/json; charset=utf-8"); - }); - - // 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([&]() - /*{ - bool running = true; - while (running) - { - running = llama.update_slots(); + svr.Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!validate_api_key(req, res)) { + return; } - }*/ - //); + + json data = json::parse(req.body); + + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, data, true, false); + + if (!json_value(data, "stream", false)) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error && result.stop) { + res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); + } else { + res.status = 404; + res.set_content(result.data["content"], "text/plain; charset=utf-8"); + } + + ctx_server.queue_results.remove_waiting_task_id(id_task); + } else { + const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { + while (true) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error) { + const std::string str = + "data: " + + result.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())) { + ctx_server.queue_results.remove_waiting_task_id(id_task); + return false; + } + + if (result.stop) { + break; + } + } else { + break; + } + } + + ctx_server.queue_results.remove_waiting_task_id(id_task); + sink.done(); + + return true; + }; + + auto on_complete = [id_task, &ctx_server] (bool) { + ctx_server.request_cancel(id_task); + }; + + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); + + svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { + return res.set_content("", "application/json; charset=utf-8"); + }); + + svr.Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + const json body = json::parse(req.body); + + std::vector tokens; + if (body.count("content") != 0) { + tokens = ctx_server.tokenize(body["content"], false); + } + const json data = format_tokenizer_response(tokens); + return res.set_content(data.dump(), "application/json; charset=utf-8"); + }); + + svr.Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + 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(ctx_server.ctx, tokens.cbegin(), tokens.cend()); + } + + const json data = format_detokenized_response(content); + return res.set_content(data.dump(), "application/json; charset=utf-8"); + }); + + svr.Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!params.embedding) { + res.status = 501; + res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); + return; + } + + const json body = json::parse(req.body); + + json prompt; + if (body.count("content") != 0) { + prompt = body["content"]; + } else { + prompt = ""; + } + + // create and queue the task + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0} }, false, true); + + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + // send the result + return res.set_content(result.data.dump(), "application/json; charset=utf-8"); + }); + + svr.Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!params.embedding) { + res.status = 501; + res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); + return; + } + + const json body = json::parse(req.body); + + json prompt; + if (body.count("input") != 0) { + prompt = body["input"]; + if (prompt.is_array()) { + json data = json::array(); + + int i = 0; + for (const json & elem : prompt) { + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", elem}, { "n_predict", 0} }, false, true); + + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + json embedding = json{ + {"embedding", json_value(result.data, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }; + + data.push_back(embedding); + } + + json result = format_embeddings_response_oaicompat(body, data); + + return res.set_content(result.dump(), "application/json; charset=utf-8"); + } + } else { + prompt = ""; + } + + // create and queue the task + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); + + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + json data = json::array({json{ + {"embedding", json_value(result.data, "embedding", json::array())}, + {"index", 0}, + {"object", "embedding"} + }} + ); + + json root = format_embeddings_response_oaicompat(body, data); + + return res.set_content(root.dump(), "application/json; charset=utf-8"); + }); if (sparams.n_threads_http < 1) { // +2 threads for monitoring endpoints @@ -3507,34 +3300,33 @@ int main(int argc, char **argv) svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); + // run the HTTP server in a thread - see comment below - std::thread t([&]() - { - if (!svr.listen_after_bind()) - { - state.store(SERVER_STATE_ERROR); - return 1; - } + std::thread t([&]() { + if (!svr.listen_after_bind()) { + state.store(SERVER_STATE_ERROR); + return 1; + } - return 0; - }); + return 0; + }); - llama.queue_tasks.on_new_task(std::bind( - &llama_server_context::process_single_task, &llama, std::placeholders::_1)); - llama.queue_tasks.on_finish_multitask(std::bind( - &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); - llama.queue_tasks.on_run_slots(std::bind( - &llama_server_context::update_slots, &llama)); - llama.queue_results.on_multitask_update(std::bind( - &llama_server_queue::update_multitask, - &llama.queue_tasks, + ctx_server.queue_tasks.on_new_task(std::bind( + &server_context::process_single_task, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_finish_multitask(std::bind( + &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_run_slots(std::bind( + &server_context::update_slots, &ctx_server)); + ctx_server.queue_results.on_multitask_update(std::bind( + &server_queue::update_multitask, + &ctx_server.queue_tasks, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3 )); shutdown_handler = [&](int) { - llama.queue_tasks.terminate(); + ctx_server.queue_tasks.terminate(); }; #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) @@ -3549,10 +3341,13 @@ int main(int argc, char **argv) }; SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif - llama.queue_tasks.start_loop(); + + ctx_server.queue_tasks.start_loop(); + svr.stop(); t.join(); llama_backend_free(); + return 0; } diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature new file mode 100644 index 000000000..b47661e94 --- /dev/null +++ b/examples/server/tests/features/embeddings.feature @@ -0,0 +1,94 @@ +@llama.cpp +@embeddings +Feature: llama.cpp server + + Background: Server startup + Given a server listening on localhost:8080 + And a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models + And a model alias bert-bge-small + And 42 as server seed + And 2 slots + And 1024 as batch size + And 2048 KV cache size + And embeddings extraction + Then the server is starting + Then the server is healthy + + Scenario: Embedding + When embeddings are computed for: + """ + What is the capital of Bulgaria ? + """ + Then embeddings are generated + + Scenario: OAI Embeddings compatibility + Given a model bert-bge-small + When an OAI compatible embeddings computation request for: + """ + What is the capital of Spain ? + """ + Then embeddings are generated + + Scenario: OAI Embeddings compatibility with multiple inputs + Given a model bert-bge-small + Given a prompt: + """ + In which country Paris is located ? + """ + And a prompt: + """ + Is Madrid the capital of Spain ? + """ + When an OAI compatible embeddings computation request for multiple inputs + Then embeddings are generated + + Scenario: Multi users embeddings + Given a prompt: + """ + Write a very long story about AI. + """ + And a prompt: + """ + Write another very long music lyrics. + """ + And a prompt: + """ + Write a very long poem. + """ + And a prompt: + """ + Write a very long joke. + """ + Given concurrent embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated + + Scenario: Multi users OAI compatibility embeddings + Given a prompt: + """ + In which country Paris is located ? + """ + And a prompt: + """ + Is Madrid the capital of Spain ? + """ + And a prompt: + """ + What is the biggest US city ? + """ + And a prompt: + """ + What is the capital of Bulgaria ? + """ + And a model bert-bge-small + Given concurrent OAI embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated + + Scenario: All embeddings should be the same + Given 10 fixed prompts + And a model bert-bge-small + Given concurrent OAI embedding requests + Then all embeddings are the same diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 86cdf7282..066698c8e 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -9,7 +9,6 @@ Feature: Parallel And 512 as batch size And 64 KV cache size And 2 slots - And embeddings extraction And continuous batching Then the server is starting Then the server is healthy @@ -99,48 +98,3 @@ Feature: Parallel Then the server is busy Then the server is idle Then all prompts are predicted - - Scenario: Multi users embeddings - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - Given concurrent embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated - - Scenario: Multi users OAI compatibility embeddings - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - And a prompt: - """ - What is the biggest US city ? - """ - And a prompt: - """ - What is the capital of Bulgaria ? - """ - And a model tinyllama-2 - Given concurrent OAI embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 7c977bcce..f3b758c79 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -49,34 +49,6 @@ Feature: llama.cpp server | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | - Scenario: Embedding - When embeddings are computed for: - """ - What is the capital of Bulgaria ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility - Given a model tinyllama-2 - When an OAI compatible embeddings computation request for: - """ - What is the capital of Spain ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility with multiple inputs - Given a model tinyllama-2 - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - When an OAI compatible embeddings computation request for multiple inputs - Then embeddings are generated - Scenario: Tokenize / Detokenize When tokenizing: """ diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 319527802..a0b2ffdfe 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -10,6 +10,7 @@ from contextlib import closing from re import RegexFlag import aiohttp +import numpy as np import openai from behave import step from behave.api.async_step import async_run_until_complete @@ -24,6 +25,9 @@ def step_server_config(context, server_fqdn, server_port): if 'PORT' in os.environ: context.server_port = int(os.environ['PORT']) print(f"$PORT set, overriding server port with to {context.server_port}") + if 'FQDN' in os.environ: + context.server_fqdn = os.environ['FQDN'] + print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}") context.base_url = f'http://{context.server_fqdn}:{context.server_port}' @@ -34,6 +38,7 @@ def step_server_config(context, server_fqdn, server_port): context.n_ga_w = None context.n_gpu_layer = None context.n_predict = None + context.n_prompts = 0 context.n_server_predict = None context.n_slots = None context.prompt_prefix = None @@ -202,6 +207,7 @@ def step_n_tokens_predicted(context, predicted_n): @step(u'a user prompt {user_prompt}') def step_user_prompt(context, user_prompt): context.prompts.append(user_prompt) + context.n_prompts = len(context.prompts) @step(u'a system prompt {system_prompt}') @@ -290,6 +296,12 @@ def step_prompt_passkey(context): context.prompt_passkey = context.text +@step(u'{n_prompts:d} fixed prompts') +def step_fixed_prompts(context, n_prompts): + context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)]) + context.n_prompts = n_prompts + + @step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') def step_prompt_passkey(context, passkey, i_pos): prompt = "" @@ -301,6 +313,7 @@ def step_prompt_passkey(context, passkey, i_pos): passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n") context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) + context.n_prompts = len(context.prompts) @step(u'an OAI compatible chat completions request with {api_error} api error') @@ -341,11 +354,13 @@ async def step_oai_chat_completions(context, api_error): @step(u'a prompt') def step_a_prompt(context): context.prompts.append(context.text) + context.n_prompts = len(context.prompts) @step(u'a prompt {prompt}') def step_a_prompt_prompt(context, prompt): context.prompts.append(prompt) + context.n_prompts = len(context.prompts) @step(u'concurrent completion requests') @@ -430,25 +445,47 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None): @step(u'embeddings are computed for') @async_run_until_complete async def step_compute_embedding(context): + context.n_prompts = 1 context.embeddings = await request_embedding(context.text, base_url=context.base_url) +@step(u'all embeddings are the same') +@async_run_until_complete +async def step_all_embeddings_are_the_same(context): + n_embedding_requests = await gather_tasks_results(context) + assert n_embedding_requests > 0 + embeddings = [] + for i in range(n_embedding_requests): + embedding = context.tasks_result.pop().pop() + embeddings.append(embedding) + assert_embeddings(embedding) + n = len(embeddings) + for i in range(n-1): + for j in range(i+1, n): + embedding1 = np.array(embeddings[i]) + embedding2 = np.array(embeddings[j]) + if context.debug: + print(f"embedding1: {embedding1[-8:]}\n") + print(f"embedding2: {embedding2[-8:]}\n") + similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) + msg = f"Similarity between {i} and {j}: {similarity:.10f}" + if context.debug: + print(f"{msg}\n") + assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg + @step(u'embeddings are generated') def step_assert_embeddings(context): - if len(context.prompts) == 0: - assert_embeddings(context.embeddings) - else: - assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n" - f"context.prompts={context.prompts}\n" - f"context.embeddings={context.embeddings}") - for embedding in context.embeddings: - context.prompts.pop() - assert_embeddings(embedding) + assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n" + f"context.n_prompts={context.n_prompts}\n" + f"context.embeddings={context.embeddings}") + for embedding in context.embeddings: + assert_embeddings(embedding) @step(u'an OAI compatible embeddings computation request for') @async_run_until_complete async def step_oai_compute_embeddings(context): + context.n_prompts = 1 context.embeddings = await request_oai_embeddings(context.text, base_url=context.base_url, user_api_key=context.user_api_key, @@ -462,6 +499,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context): base_url=context.base_url, user_api_key=context.user_api_key, model=context.model) + context.prompts.clear() @step(u'concurrent embedding requests') @@ -488,9 +526,9 @@ async def step_concurrent_oai_embedding_requests(context): @async_run_until_complete() async def all_embeddings_are_generated(context): n_embedding_requests = await gather_tasks_results(context) - assert n_embedding_requests > 0 + assert n_embedding_requests == context.n_prompts for i in range(n_embedding_requests): - assert_embeddings(context.tasks_result.pop()) + assert_embeddings(context.tasks_result.pop().pop()) @step(u'tokenizing') @@ -588,11 +626,11 @@ def step_supported_models(context, i_model, param, preposition, param_value): async def concurrent_requests(context, f_completion, *args, **kwargs): - n_prompts = len(context.prompts) + context.n_prompts = len(context.prompts) if context.debug: - print(f"starting {n_prompts} concurrent completion requests...") - assert n_prompts > 0 - for prompt_no in range(n_prompts): + print(f"starting {context.n_prompts} concurrent completion requests...") + assert context.n_prompts > 0 + for prompt_no in range(context.n_prompts): shifted_args = [context.prompts.pop(), *args] context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) await asyncio.sleep(0.1) @@ -765,7 +803,7 @@ async def request_embedding(content, base_url=None): }) as response: assert response.status == 200 response_json = await response.json() - return response_json['embedding'] + return [response_json['embedding']] async def request_oai_embeddings(input, @@ -775,6 +813,7 @@ async def request_oai_embeddings(input, user_api_key = user_api_key if user_api_key is not None else 'nope' if async_client: origin = 'llama.cpp' + headers=[] if user_api_key is not None: headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} async with aiohttp.ClientSession() as session: @@ -783,14 +822,21 @@ async def request_oai_embeddings(input, "input": input, "model": model, }, - headers=headers) as response: + headers=headers, + timeout=3600) as response: assert response.status == 200, f"received status code not expected: {response.status}" assert response.headers['Access-Control-Allow-Origin'] == origin assert response.headers['Content-Type'] == "application/json; charset=utf-8" response_json = await response.json() assert response_json['model'] == model, f"invalid model received: {response_json['model']}" assert response_json['object'] == 'list' - return response_json['data'] + if isinstance(input, collections.abc.Sequence): + embeddings = [] + for an_oai_embeddings in response_json['data']: + embeddings.append(an_oai_embeddings['embedding']) + else: + embeddings = [response_json['data']['embedding']] + return embeddings else: openai.api_key = user_api_key openai.api_base = f'{base_url}/v1' @@ -804,7 +850,7 @@ async def request_oai_embeddings(input, for an_oai_embeddings in oai_embeddings.data: embeddings.append(an_oai_embeddings.embedding) else: - embeddings = oai_embeddings.data.embedding + embeddings = [oai_embeddings.data.embedding] return embeddings @@ -899,6 +945,8 @@ def assert_embeddings(embeddings): assert len(embeddings) > 0 embeddings_computed = False for emb in embeddings: + if not isinstance(emb, float): + assert False, f"Bad embeddings: {embeddings}" if emb != 0: embeddings_computed = True assert embeddings_computed, f"Embeddings: {embeddings}" diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 5d4210164..2e4f42ad2 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,5 +1,6 @@ aiohttp~=3.9.3 behave~=1.2.6 huggingface_hub~=0.20.3 +numpy~=1.24.4 openai~=0.25.0 prometheus-client~=0.20.0 diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index b6e49d8b9..df0a27782 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -1,15 +1,16 @@ #pragma once -#include -#include -#include -#include -#include -#include +#include "llama.h" +#include "common.h" #include "json.hpp" -#include "../llava/clip.h" +#include +#include +#include +#include + +#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" using json = nlohmann::json; @@ -37,83 +38,35 @@ extern bool server_log_json; #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -enum server_state { - SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet - SERVER_STATE_READY, // Server is ready and model is loaded - SERVER_STATE_ERROR // An error occurred, load_model failed -}; - -enum task_type { - TASK_TYPE_COMPLETION, - TASK_TYPE_CANCEL, - TASK_TYPE_NEXT_RESPONSE, - TASK_TYPE_METRICS -}; - -struct task_server { - int id = -1; // to be filled by llama_server_queue - int target_id; - task_type type; - json data; - bool infill_mode = false; - bool embedding_mode = false; - int multitask_id = -1; -}; - -struct task_result { - int id; - int multitask_id = -1; - bool stop; - bool error; - json result_json; -}; - -struct task_multi { - int id; - std::set subtasks_remaining{}; - std::vector results{}; -}; - -// completion token output with probabilities -struct completion_token_output { - struct token_prob - { - llama_token tok; - float prob; - }; - - std::vector probs; - llama_token tok; - std::string text_to_send; -}; - -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); } -}; +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 inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { std::stringstream ss_tid; ss_tid << std::this_thread::get_id(); json log = nlohmann::ordered_json{ - {"tid", ss_tid.str()}, + {"tid", ss_tid.str()}, {"timestamp", time(nullptr)}, }; if (server_log_json) { - log.merge_patch( - { - {"level", level}, - {"function", function}, - {"line", line}, - {"msg", message}, - }); + log.merge_patch( { + {"level", level}, + {"function", function}, + {"line", line}, + {"msg", message}, + }); + if (!extra.empty()) { log.merge_patch(extra); } - std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush; + printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str()); } else { char buf[1024]; snprintf(buf, 1024, "%4s [%24s] %s", level, function, message); @@ -136,22 +89,13 @@ static inline void server_log(const char *level, const char *function, int line, } // -// server utils +// chat template utils // -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; -} - // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid inline bool verify_custom_template(const std::string & tmpl) { llama_chat_message chat[] = {{"user", "test"}}; - std::vector buf(1); - int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size()); + int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0); return res >= 0; } @@ -163,7 +107,7 @@ inline std::string format_chat(const struct llama_model * model, const std::stri std::vector chat(messages.size()); for (size_t i = 0; i < messages.size(); ++i) { - auto &curr_msg = messages[i]; + const auto & curr_msg = messages[i]; str[i*2 + 0] = json_value(curr_msg, "role", std::string("")); str[i*2 + 1] = json_value(curr_msg, "content", std::string("")); alloc_size += str[i*2 + 1].length(); @@ -183,261 +127,13 @@ inline std::string format_chat(const struct llama_model * model, const std::stri res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size()); } - std::string formatted_chat(buf.data(), res); + const std::string formatted_chat(buf.data(), res); + LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}}); return formatted_chat; } -// -// work queue utils -// - -struct llama_server_queue { - int id = 0; - std::mutex mutex_tasks; - bool running; - // queues - std::vector queue_tasks; - std::vector queue_tasks_deferred; - std::vector queue_multitasks; - std::condition_variable condition_tasks; - // callback functions - std::function callback_new_task; - std::function callback_finish_multitask; - std::function callback_run_slots; - - // Add a new task to the end of the queue - int post(task_server task) { - std::unique_lock lock(mutex_tasks); - if (task.id == -1) { - task.id = id++; - LOG_VERBOSE("new task id", {{"new_id", task.id}}); - } - queue_tasks.push_back(std::move(task)); - condition_tasks.notify_one(); - return task.id; - } - - // Add a new task, but defer until one slot is available - void defer(task_server task) { - std::unique_lock lock(mutex_tasks); - queue_tasks_deferred.push_back(std::move(task)); - } - - // Get the next id for creating anew task - int get_new_id() { - std::unique_lock lock(mutex_tasks); - int new_id = id++; - LOG_VERBOSE("new task id", {{"new_id", new_id}}); - return new_id; - } - - // Register function to process a new task - void on_new_task(std::function callback) { - callback_new_task = callback; - } - - // Register function to process a multitask when it is finished - void on_finish_multitask(std::function callback) { - callback_finish_multitask = callback; - } - - // Register the function to be called when all slots data is ready to be processed - void on_run_slots(std::function callback) { - callback_run_slots = callback; - } - - // Call when the state of one slot is changed - void notify_slot_changed() { - // move deferred tasks back to main loop - std::unique_lock lock(mutex_tasks); - for (auto & task : queue_tasks_deferred) { - queue_tasks.push_back(std::move(task)); - } - queue_tasks_deferred.clear(); - } - - // end the start_loop routine - void terminate() { - { - std::unique_lock lock(mutex_tasks); - running = false; - } - condition_tasks.notify_all(); - } - - /** - * Main loop consists of these steps: - * - Wait until a new task arrives - * - Process the task (i.e. maybe copy data into slot) - * - Check if multitask is finished - * - Run all slots - */ - void start_loop() { - running = true; - while (true) { - LOG_VERBOSE("new task may arrive", {}); - { - while (true) - { - std::unique_lock lock(mutex_tasks); - if (queue_tasks.empty()) { - lock.unlock(); - break; - } - task_server task = queue_tasks.front(); - queue_tasks.erase(queue_tasks.begin()); - lock.unlock(); - LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); - callback_new_task(task); - } - LOG_VERBOSE("update_multitasks", {}); - // check if we have any finished multitasks - auto queue_iterator = queue_multitasks.begin(); - while (queue_iterator != queue_multitasks.end()) - { - if (queue_iterator->subtasks_remaining.empty()) - { - // all subtasks done == multitask is done - task_multi current_multitask = *queue_iterator; - callback_finish_multitask(current_multitask); - // remove this multitask - queue_iterator = queue_multitasks.erase(queue_iterator); - } - else - { - ++queue_iterator; - } - } - // all tasks in the current loop is processed, slots data is now ready - LOG_VERBOSE("callback_run_slots", {}); - callback_run_slots(); - } - LOG_VERBOSE("wait for new task", {}); - // wait for new task - { - std::unique_lock lock(mutex_tasks); - if (queue_tasks.empty()) { - if (!running) { - LOG_VERBOSE("ending start_loop", {}); - return; - } - condition_tasks.wait(lock, [&]{ - return (!queue_tasks.empty() || !running); - }); - } - } - } - } - - // - // functions to manage multitasks - // - - // add a multitask by specifying the id of all subtask (subtask is a task_server) - void add_multitask(int multitask_id, std::vector& sub_ids) - { - std::lock_guard lock(mutex_tasks); - task_multi multi; - multi.id = multitask_id; - std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); - queue_multitasks.push_back(multi); - } - - // updatethe remaining subtasks, while appending results to multitask - void update_multitask(int multitask_id, int subtask_id, task_result& result) - { - std::lock_guard lock(mutex_tasks); - for (auto& multitask : queue_multitasks) - { - if (multitask.id == multitask_id) - { - multitask.subtasks_remaining.erase(subtask_id); - multitask.results.push_back(result); - } - } - } -}; - -struct llama_server_response { - typedef std::function callback_multitask_t; - callback_multitask_t callback_update_multitask; - // for keeping track of all tasks waiting for the result - std::set waiting_task_ids; - // the main result queue - std::vector queue_results; - std::mutex mutex_results; - std::condition_variable condition_results; - - // add the task_id to the list of tasks waiting for response - void add_waiting_task_id(int task_id) { - LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); - std::unique_lock lock(mutex_results); - waiting_task_ids.insert(task_id); - } - - // when the request is finished, we can remove task associated with it - void remove_waiting_task_id(int task_id) { - LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); - std::unique_lock lock(mutex_results); - waiting_task_ids.erase(task_id); - } - - // This function blocks the thread until there is a response for this task_id - task_result recv(int task_id) { - while (true) - { - std::unique_lock lock(mutex_results); - condition_results.wait(lock, [&]{ - return !queue_results.empty(); - }); - - for (int i = 0; i < (int) queue_results.size(); i++) - { - if (queue_results[i].id == task_id) - { - assert(queue_results[i].multitask_id == -1); - task_result res = queue_results[i]; - queue_results.erase(queue_results.begin() + i); - return res; - } - } - } - - // should never reach here - } - - // Register the function to update multitask - void on_multitask_update(callback_multitask_t callback) { - callback_update_multitask = callback; - } - - // Send a new result to a waiting task_id - void send(task_result result) { - std::unique_lock lock(mutex_results); - LOG_VERBOSE("send new result", {{"task_id", result.id}}); - for (auto& task_id : waiting_task_ids) { - // LOG_TEE("waiting task id %i \n", task_id); - // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result - if (result.multitask_id == task_id) - { - LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}}); - callback_update_multitask(task_id, result.id, result); - continue; - } - - if (result.id == task_id) - { - LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}}); - queue_results.push_back(result); - condition_results.notify_all(); - return; - } - } - } -}; - // // base64 utils (TODO: move to common in the future) // @@ -447,13 +143,11 @@ 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 == '/')); } -static inline std::vector base64_decode(const std::string & encoded_string) -{ +static inline std::vector base64_decode(const std::string & encoded_string) { int i = 0; int j = 0; int in_ = 0; @@ -465,13 +159,10 @@ static inline std::vector base64_decode(const std::string & encoded_str std::vector ret; - while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) - { + 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++) - { + if (i == 4) { + for (i = 0; i < 4; i++) { char_array_4[i] = base64_chars.find(char_array_4[i]); } @@ -479,23 +170,20 @@ static inline std::vector base64_decode(const std::string & encoded_str 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++) - { + for (i = 0; (i < 3); i++) { ret.push_back(char_array_3[i]); } + i = 0; } } - if (i) - { - for (j = i; j <4; j++) - { + if (i) { + for (j = i; j < 4; j++) { char_array_4[j] = 0; } - for (j = 0; j <4; j++) - { + for (j = 0; j < 4; j++) { char_array_4[j] = base64_chars.find(char_array_4[j]); } @@ -503,8 +191,7 @@ static inline std::vector base64_decode(const std::string & encoded_str 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++) - { + for (j = 0; j < i - 1; j++) { ret.push_back(char_array_3[j]); } } @@ -516,8 +203,7 @@ static inline std::vector base64_decode(const std::string & encoded_str // random string / id // -static std::string random_string() -{ +static std::string random_string() { static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); std::random_device rd; @@ -532,10 +218,10 @@ static std::string random_string() return result; } -static std::string gen_chatcmplid() -{ +static std::string gen_chatcmplid() { std::stringstream chatcmplid; chatcmplid << "chatcmpl-" << random_string(); + return chatcmplid.str(); } @@ -543,91 +229,316 @@ static std::string gen_chatcmplid() // other common utils // -static size_t common_part(const std::vector &a, const std::vector &b) -{ +static size_t common_part(const std::vector & a, const std::vector & b) { size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) - { - } + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} + return i; } -static bool ends_with(const std::string &str, const std::string &suffix) -{ - return str.size() >= suffix.size() && - 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); +static bool ends_with(const std::string & str, const std::string & suffix) { + return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } -static size_t find_partial_stop_string(const std::string &stop, - const std::string &text) -{ - if (!text.empty() && !stop.empty()) - { +static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { + if (!text.empty() && !stop.empty()) { const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) - { - if (stop[char_index] == text_last_char) - { + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { + if (stop[char_index] == text_last_char) { const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) - { + if (ends_with(text, current_partial)) { return text.size() - char_index - 1; } } } } + return std::string::npos; } // TODO: reuse llama_detokenize template -static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) -{ +static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { std::string ret; - for (; begin != end; ++begin) - { + for (; begin != end; ++begin) { ret += llama_token_to_piece(ctx, *begin); } + return ret; } // format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) -{ +static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); + // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) - { + if (out.size() == 1 && (out[0] & 0x80) == 0x80) { std::stringstream ss; ss << std::hex << (out[0] & 0xff); std::string res(ss.str()); out = "byte: \\x" + res; } + return out; } +struct completion_token_output { + llama_token tok; + std::string text_to_send; + + struct token_prob { + llama_token tok; + float prob; + }; + + std::vector probs; +}; + // 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) - { + + for (const auto & prob : probs) { json probs_for_token = json::array(); - for (const auto &p : prob.probs) - { - std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json - { + + for (const auto & p : prob.probs) { + const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json { {"tok_str", tok_str}, {"prob", p.prob}, }); } - std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json{ + + const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json { {"content", tok_str}, {"probs", probs_for_token}, }); } + return out; } + +// +// OAI utils +// + +static json oaicompat_completion_params_parse( + const struct llama_model * model, + const json & body, /* openai api json semantics */ + const std::string & chat_template) { + json llama_params; + + llama_params["__oaicompat"] = true; + + // Map OpenAI parameters to llama.cpp parameters + // + // For parameters that are defined by the OpenAI documentation (e.g. + // temperature), we explicitly specify OpenAI's intended default; we + // need to do that because sometimes OpenAI disagrees with llama.cpp + // + // https://platform.openai.com/docs/api-reference/chat/create + llama_sampling_params default_sparams; + llama_params["model"] = json_value(body, "model", std::string("unknown")); + llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); + llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); + llama_params["temperature"] = json_value(body, "temperature", 0.0); + llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); + llama_params["top_p"] = json_value(body, "top_p", 1.0); + llama_params["n_predict"] = json_value(body, "max_tokens", -1); + llama_params["logit_bias"] = json_value(body, "logit_bias", json::object()); + llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); + llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); + llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); + llama_params["stream"] = json_value(body, "stream", false); + llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); + llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); + llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); + llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); + llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); + llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); + + if (body.count("grammar") != 0) { + llama_params["grammar"] = json_value(body, "grammar", json::object()); + } + + // Handle 'stop' field + if (body.contains("stop") && body["stop"].is_string()) { + llama_params["stop"] = json::array({body["stop"].get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Ensure there is ChatML-specific end sequence among stop words + llama_params["stop"].push_back("<|im_end|>"); + + return llama_params; +} + +static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) { + bool stopped_word = result.count("stopped_word") != 0; + bool stopped_eos = json_value(result, "stopped_eos", false); + int num_tokens_predicted = json_value(result, "tokens_predicted", 0); + int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); + std::string content = json_value(result, "content", std::string("")); + + std::string finish_reason = "length"; + if (stopped_word || stopped_eos) { + finish_reason = "stop"; + } + + json choices = + streaming ? json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}}}) + : json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"message", json{{"content", content}, + {"role", "assistant"}}}}}); + + std::time_t t = std::time(0); + + json res = json { + {"choices", choices}, + {"created", t}, + {"model", + json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, + {"usage", json { + {"completion_tokens", num_tokens_predicted}, + {"prompt_tokens", num_prompt_tokens}, + {"total_tokens", num_tokens_predicted + num_prompt_tokens} + }}, + {"id", gen_chatcmplid()} + }; + + if (server_verbose) { + res["__verbose"] = result; + } + + if (result.contains("completion_probabilities")) { + res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); + } + + return res; +} + +// return value is vector as there is one case where we might need to generate two responses +static std::vector format_partial_response_oaicompat(json result) { + if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { + return std::vector({result}); + } + + bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; + std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); + + bool stopped_word = json_value(result, "stopped_word", false); + bool stopped_eos = json_value(result, "stopped_eos", false); + bool stopped_limit = json_value(result, "stopped_limit", false); + std::string content = json_value(result, "content", std::string("")); + + std::string finish_reason; + if (stopped_word || stopped_eos) { + finish_reason = "stop"; + } + if (stopped_limit) { + finish_reason = "length"; + } + + std::time_t t = std::time(0); + + json choices; + + if (!finish_reason.empty()) { + choices = json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}}}); + } else { + if (first) { + if (content.empty()) { + choices = json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{{"role", "assistant"}}}}}); + } else { + // We have to send this as two updates to conform to openai behavior + json initial_ret = json{{"choices", json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"role", "assistant"} + }}}})}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + json second_ret = json{ + {"choices", json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"content", content}}} + }})}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + return std::vector({initial_ret, second_ret}); + } + } else { + // Some idiosyncrasy in task processing logic makes several trailing calls + // with empty content, we ignore these at the calee site. + if (content.empty()) { + return std::vector({json::object()}); + } + + choices = json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", + json{ + {"content", content}, + }}, + }}); + } + } + + json ret = json { + {"choices", choices}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"} + }; + + return std::vector({ret}); +} + +static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) { + json res = json { + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json { + {"prompt_tokens", 0}, + {"total_tokens", 0} + }}, + {"data", embeddings} + }; + + return res; +} + +static json format_tokenizer_response(const std::vector & tokens) { + return json { + {"tokens", tokens} + }; +} + +static json format_detokenized_response(const std::string & content) { + return json { + {"content", content} + }; +} diff --git a/llama.cpp b/llama.cpp index b27aa2728..478099648 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13541,18 +13541,22 @@ LLAMA_API int32_t llama_chat_apply_template( curr_tmpl = std::string(model_template.data(), model_template.size()); } } + // format the chat to string std::vector chat_vec; chat_vec.resize(n_msg); for (size_t i = 0; i < n_msg; i++) { chat_vec[i] = &chat[i]; } + std::string formatted_chat; int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass); if (res < 0) { return res; } - strncpy(buf, formatted_chat.c_str(), length); + if (buf && length > 0) { + strncpy(buf, formatted_chat.c_str(), length); + } return res; } From 55a2a900ff4a02fc33708ac7858d595d289a3f2a Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Thu, 7 Mar 2024 19:42:39 +0900 Subject: [PATCH 62/86] server : add `/v1/completions` endpoint (#5914) * add-`/v1/completions`-endpoint * add legacy comment to `/completion` endpoint --- examples/server/server.cpp | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3bdbde954..f255ad764 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2923,7 +2923,7 @@ int main(int argc, char ** argv) { res.set_content(data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/completion", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + const auto completions = [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; @@ -3001,7 +3001,11 @@ int main(int argc, char ** argv) { res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } - }); + }; + + svr.Post("/completion", completions); // legacy + svr.Post("/completions", completions); + svr.Post("/v1/completions", completions); svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); From 89fb735fcfd21781a8194b211cf32824beb3f71f Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Thu, 7 Mar 2024 19:14:49 +0800 Subject: [PATCH 63/86] Revert "[SYCL] fix error when set main gpu to non-zero (#5901)" (#5918) This reverts commit ceca1aef0738b57951cd12c603c3477e75312dec. --- ggml-sycl.cpp | 172 ++++++++++++++++++++------------------------------ ggml-sycl.h | 1 - llama.cpp | 16 ++--- 3 files changed, 73 insertions(+), 116 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 221d67b8d..ddd951dd6 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3559,31 +3559,12 @@ class sycl_gpu_mgr { int work_group_size = 0; std::string gpus_list = ""; - /* - Use all GPU with same top max compute units - */ sycl_gpu_mgr() { detect_sycl_gpu_list_with_max_cu(); get_allow_gpus(); create_context_with_gpus(); } - /* - Use the assigned GPU as only one - */ - sycl_gpu_mgr(int main_gpu_id) { - sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id); - dpct::device_info prop; - dpct::get_device_info(prop, device); - gpus.push_back(main_gpu_id); - devices.push_back(device); - work_group_size = prop.get_max_work_group_size(); - max_compute_units = prop.get_max_compute_units(); - - get_allow_gpus(); - create_context_with_gpus(); - } - void create_context_with_gpus() { sycl::context ctx = sycl::context(devices); assert(gpus.size() > 0); @@ -3599,7 +3580,7 @@ class sycl_gpu_mgr { gpus_list += std::to_string(gpus[i]); gpus_list += ","; } - if (gpus_list.length() > 1) { + if (gpus_list.length() > 2) { gpus_list.pop_back(); } } @@ -3648,8 +3629,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - printf("miss to get device index by id=%d\n", id); - GGML_ASSERT(false); + assert(false); + return -1; } int get_next_index(int id) { @@ -3658,7 +3639,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - GGML_ASSERT(false); + assert(false); + return -1; } }; @@ -3667,7 +3649,6 @@ static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; static int g_main_device_id = -1; -static bool g_ggml_backend_sycl_buffer_type_initialized = false; static std::array g_default_tensor_split = {}; @@ -13244,7 +13225,7 @@ void ggml_backend_sycl_print_sycl_devices() { } void print_gpu_device_list() { - fprintf(stderr, "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n", + fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", g_sycl_gpu_mgr->get_gpu_count(), g_sycl_gpu_mgr->gpus_list.c_str(), g_sycl_gpu_mgr->max_compute_units); @@ -13283,15 +13264,6 @@ void ggml_init_sycl() try { #else fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif - -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - if (CHECK_TRY_ERROR(g_all_sycl_device_count = dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; @@ -13300,61 +13272,68 @@ void ggml_init_sycl() try { } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); ggml_backend_sycl_print_sycl_devices(); + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; + print_gpu_device_list(); - initialized = true; - g_sycl_loaded = true; - } + int64_t total_vram = 0; - - g_device_count = g_sycl_gpu_mgr->get_gpu_count(); - g_work_group_size = g_sycl_gpu_mgr->work_group_size; - - int64_t total_vram = 0; - - - for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_device_caps[id].vmm = 0; - g_device_caps[id].device_id = -1; - g_device_caps[id].cc = 0; - g_tensor_split[id] = 0; - g_default_tensor_split[id] = 0; - } - - for (int i = 0; i < g_device_count; ++i) { - int device_id = g_sycl_gpu_mgr->gpus[i]; - g_device_caps[i].vmm = 0; - - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); - - g_default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); - - g_device_caps[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } - - for (int i = 0; i < g_device_count; ++i) { - g_default_tensor_split[i] /= total_vram; - } - - for (int i = 0; i < g_device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); - - // create sycl streams - for (int is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[i][is] = - dpct::get_current_device().create_queue( - g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); +/* NOT REMOVE, keep it for next optimize for XMX. +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif +*/ + for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { + g_device_caps[id].vmm = 0; + g_device_caps[id].device_id = -1; + g_device_caps[id].cc = 0; + g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; } - const dpct::queue_ptr stream = g_syclStreams[i][0]; - // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(device_id)))); + + g_default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); + + g_device_caps[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + } + + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; + } + + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); + + // create sycl streams + for (int is = 0; is < MAX_STREAMS; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); + } + + const dpct::queue_ptr stream = g_syclStreams[i][0]; + // create sycl handle + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); + } + + initialized = true; + g_sycl_loaded = true; } } catch (sycl::exception const &exc) { @@ -16753,24 +16732,22 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .is_host = */ nullptr, }; -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { - if (device_index>=g_device_count or device_index<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device_index, g_device_count-1); - GGML_ASSERT(device_indexgpus[i])}, }; } - g_ggml_backend_sycl_buffer_type_initialized = true; + ggml_backend_sycl_buffer_type_initialized = true; } - return &ggml_backend_sycl_buffer_types[device_index]; + + return &ggml_backend_sycl_buffer_types[device]; } // sycl split buffer type @@ -17519,17 +17496,6 @@ GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { return g_sycl_gpu_mgr->get_index(device_id); } -GGML_API GGML_CALL void ggml_backend_sycl_set_single_device(int main_gpu_id) { - GGML_ASSERT(main_gpu_idbackends.push_back(backend); } else { // LLAMA_SPLIT_LAYER requires a backend for each GPU - + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { + int device_id = id_list[i]; ggml_backend_t backend = ggml_backend_sycl_init(i); if (backend == nullptr) { - int id_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, id_list[i], i); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); llama_free(ctx); return nullptr; } From 6cdabe652695167263c8b447520987b11856f7ca Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 7 Mar 2024 16:32:38 +0200 Subject: [PATCH 64/86] llama-bench : add embeddings option (#5924) * llama-bench : add embeddings option * llama-bench : do not hard code embd default value --------- Co-authored-by: slaren --- examples/llama-bench/llama-bench.cpp | 30 +++++++++++++++++++++++++--- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index aa79d002a..2ff86ef6f 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -173,6 +173,7 @@ struct cmd_params { std::vector no_kv_offload; std::vector> tensor_split; std::vector use_mmap; + std::vector embeddings; int reps; bool verbose; output_formats output_format; @@ -192,6 +193,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ {false}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, + /* embeddings */ {false}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -214,6 +216,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); @@ -382,6 +385,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); + } else if (arg == "-embd" || arg == "--embeddings") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; @@ -453,6 +463,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } + if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } return params; @@ -472,6 +483,7 @@ struct cmd_params_instance { bool no_kv_offload; std::vector tensor_split; bool use_mmap; + bool embeddings; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -502,6 +514,7 @@ struct cmd_params_instance { cparams.type_k = type_k; cparams.type_v = type_v; cparams.offload_kqv = !no_kv_offload; + cparams.embeddings = embeddings; return cparams; } @@ -517,6 +530,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) for (const auto & mmp : params.use_mmap) + for (const auto & embd : params.embeddings) for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) @@ -540,6 +554,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, + /* .embeddings = */ embd, }; instances.push_back(instance); } @@ -562,6 +577,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, + /* .embeddings = */ embd, }; instances.push_back(instance); } @@ -597,6 +613,7 @@ struct test { bool no_kv_offload; std::vector tensor_split; bool use_mmap; + bool embeddings; int n_prompt; int n_gen; std::string test_time; @@ -619,6 +636,7 @@ struct test { no_kv_offload = inst.no_kv_offload; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; + embeddings = inst.embeddings; n_prompt = inst.n_prompt; n_gen = inst.n_gen; // RFC 3339 date-time format @@ -690,7 +708,7 @@ struct test { "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "tensor_split", "use_mmap", + "tensor_split", "use_mmap", "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -710,7 +728,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "use_mmap") { + field == "use_mmap" || field == "embeddings") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -744,7 +762,7 @@ struct test { std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - tensor_split_str, std::to_string(use_mmap), + tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -914,6 +932,9 @@ struct markdown_printer : public printer { if (field == "use_mmap") { return "mmap"; } + if (field == "embeddings") { + return "embd"; + } if (field == "tensor_split") { return "ts"; } @@ -957,6 +978,9 @@ struct markdown_printer : public printer { if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { fields.emplace_back("use_mmap"); } + if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) { + fields.emplace_back("embeddings"); + } fields.emplace_back("test"); fields.emplace_back("t/s"); From 581ed5c4fe3a8909aaa8313633ac443f471ba755 Mon Sep 17 00:00:00 2001 From: "UEXTM.com" <84163508+uextm@users.noreply.github.com> Date: Fri, 8 Mar 2024 04:35:04 -0500 Subject: [PATCH 65/86] log : fix MSVC compile errors (#5643) MSVC gives the following error with the existing macros: `Error C2059 : syntax error: ','` This patch adds `##` as a prefix to `__VA_ARGS__` to address this error. --- common/log.h | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/common/log.h b/common/log.h index e4e1b9f4f..eb111e784 100644 --- a/common/log.h +++ b/common/log.h @@ -297,7 +297,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #ifndef _MSC_VER #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else - #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "") + #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "") #endif // Main TEE macro. @@ -311,7 +311,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #ifndef _MSC_VER #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") #else - #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "") + #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "") #endif // LOG macro variants with auto endline. @@ -319,8 +319,8 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else - #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n") - #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n") + #define LOGLN(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "\n") + #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "\n") #endif // INTERNAL, DO NOT USE From af37fd8b30e37ccbffdd82e6f48559e2fb7ce7dd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 8 Mar 2024 12:40:02 +0200 Subject: [PATCH 66/86] server : fix EOS token detection with disabled cache (#5938) --- 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 f255ad764..1434095fc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1123,7 +1123,7 @@ struct server_context { }); } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) { + if (result.tok == llama_token_eos(model)) { slot.stopped_eos = true; slot.has_next_token = false; From e457fb3540e0aaec47cfde0abf784c213f9216ee Mon Sep 17 00:00:00 2001 From: Don Mahurin Date: Fri, 8 Mar 2024 02:41:50 -0800 Subject: [PATCH 67/86] llama : assume tied weights if lm_head/output weights is missing (#5824) This is to support model configurations with "tie_word_embeddings" set to true. Co-authored-by: Don Mahurin <2797413+dmahurin@users.noreply.github.com> --- llama.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 4225f9555..458382b21 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3888,7 +3888,13 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); if (model.arch != LLM_ARCH_MINICPM){ - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } } } From 76e868821a94072fbc87cb1fcca291694319eae8 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 8 Mar 2024 12:25:04 +0100 Subject: [PATCH 68/86] server: metrics: add llamacpp:prompt_seconds_total and llamacpp:tokens_predicted_seconds_total, reset bucket only on /metrics. Fix values cast to int. Add Process-Start-Time-Unix header. (#5937) Closes #5850 --- examples/server/server.cpp | 47 ++++++++++++++----- examples/server/tests/features/server.feature | 1 + examples/server/tests/features/steps/steps.py | 11 ++++- 3 files changed, 46 insertions(+), 13 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1434095fc..109ff7175 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -335,8 +335,12 @@ struct server_slot { }; struct server_metrics { + const int64_t t_start = ggml_time_us(); + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t t_prompt_processing_total = 0; uint64_t n_tokens_predicted_total = 0; + uint64_t t_tokens_generation_total = 0; uint64_t n_prompt_tokens_processed = 0; uint64_t t_prompt_processing = 0; @@ -348,12 +352,14 @@ struct server_metrics { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; n_prompt_tokens_processed += slot.n_prompt_tokens_processed; t_prompt_processing += slot.t_prompt_processing; + t_prompt_processing_total += slot.t_prompt_processing; } void on_prediction(const server_slot &slot) { - n_tokens_predicted_total += slot.n_decoded; - n_tokens_predicted += slot.n_decoded; - t_tokens_generation += slot.t_token_generation; + n_tokens_predicted_total += slot.n_decoded; + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; + t_tokens_generation_total += slot.t_token_generation; } void reset_bucket() { @@ -1502,9 +1508,12 @@ struct server_context { { "idle", n_idle_slots }, { "processing", n_processing_slots }, { "deferred", queue_tasks.queue_tasks_deferred.size() }, + { "t_start", metrics.t_start}, { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total}, + { "t_tokens_generation_total", metrics.t_tokens_generation_total}, { "n_tokens_predicted_total", metrics.n_tokens_predicted_total}, + { "t_prompt_processing_total", metrics.t_prompt_processing_total}, { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed}, { "t_prompt_processing", metrics.t_prompt_processing}, @@ -1517,7 +1526,9 @@ struct server_context { { "slots", slots_data }, }; - metrics.reset_bucket(); + if (json_value(task.data, "reset_bucket", false)) { + metrics.reset_bucket(); + } queue_results.send(res); } break; } @@ -2709,6 +2720,7 @@ int main(int argc, char ** argv) { task.id_multi = -1; task.id_target = -1; task.type = SERVER_TASK_TYPE_METRICS; + task.data.push_back({{"reset_bucket", true}}); ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task); @@ -2732,20 +2744,28 @@ int main(int argc, char ** argv) { {"counter", {{ {"name", "prompt_tokens_total"}, {"help", "Number of prompt tokens processed."}, - {"value", data["n_prompt_tokens_processed_total"]} + {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} + }, { + {"name", "prompt_seconds_total"}, + {"help", "Prompt process time"}, + {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} }, { {"name", "tokens_predicted_total"}, {"help", "Number of generation tokens processed."}, - {"value", data["n_tokens_predicted_total"]} + {"value", (uint64_t) data["n_tokens_predicted_total"]} + }, { + {"name", "tokens_predicted_seconds_total"}, + {"help", "Predict process time"}, + {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} }}}, {"gauge", {{ {"name", "prompt_tokens_seconds"}, {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} + {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} },{ {"name", "predicted_tokens_seconds"}, {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} + {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} },{ {"name", "kv_cache_usage_ratio"}, {"help", "KV-cache usage. 1 means 100 percent usage."}, @@ -2753,15 +2773,15 @@ int main(int argc, char ** argv) { },{ {"name", "kv_cache_tokens"}, {"help", "KV-cache tokens."}, - {"value", data["kv_cache_tokens_count"]} + {"value", (uint64_t) data["kv_cache_tokens_count"]} },{ {"name", "requests_processing"}, {"help", "Number of request processing."}, - {"value", data["processing"]} + {"value", (uint64_t) data["processing"]} },{ {"name", "requests_deferred"}, {"help", "Number of request deferred."}, - {"value", data["deferred"]} + {"value", (uint64_t) data["deferred"]} }}} }; @@ -2775,13 +2795,16 @@ int main(int argc, char ** argv) { const std::string name = metric_def["name"]; const std::string help = metric_def["help"]; - auto value = json_value(metric_def, "value", 0); + auto value = json_value(metric_def, "value", 0.); prometheus << "# HELP llamacpp:" << name << " " << help << "\n" << "# TYPE llamacpp:" << name << " " << type << "\n" << "llamacpp:" << name << " " << value << "\n"; } } + const int64_t t_start = data["t_start"]; + res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); + res.set_content(prometheus.str(), "text/plain; version=0.0.4"); res.status = 200; // HTTP OK }); diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index f3b758c79..878ac1363 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -29,6 +29,7 @@ Feature: llama.cpp server And a completion request with no api error Then tokens are predicted matching And prometheus metrics are exposed + And metric llamacpp:tokens_predicted is Examples: Prompts | prompt | n_predict | re_content | n_predicted | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index a0b2ffdfe..d7f005836 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -586,14 +586,24 @@ async def step_prometheus_metrics_exported(context): metric_exported = False if context.debug: print(f"/metrics answer:\n{metrics_raw}\n") + context.metrics = {} for metric in parser.text_string_to_metric_families(metrics_raw): match metric.name: case "llamacpp:kv_cache_usage_ratio": assert len(metric.samples) > 0 metric_exported = True + context.metrics[metric.name] = metric + assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time" assert metric_exported, "No metrics exported" +@step(u'metric {metric_name} is {metric_value:d}') +def step_assert_metric_value(context, metric_name, metric_value): + if metric_name not in context.metrics: + assert False, f"no metric {metric_name} in {context.metrics.keys()}" + assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}" + + @step(u'available models') def step_available_models(context): # openai client always expects an api_key @@ -879,7 +889,6 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re f' {n_predicted} <> {expected_predicted_n}') - async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: From 515f7d0d4fce41c752fc253acf30707c3be2531e Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Fri, 8 Mar 2024 10:53:37 -0500 Subject: [PATCH 69/86] llama : fix quantization of shared token_embd (#5944) --- llama.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 458382b21..4a20b7928 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10973,6 +10973,9 @@ struct quantize_state_internal { bool has_imatrix = false; + // used to figure out if a model shares tok_embd with the output weight + bool has_output = false; + quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params) : model(model) , params(params) @@ -11070,8 +11073,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings // with the quantization of the output tensor - if (name == tn(LLM_TENSOR_OUTPUT, "weight") || - (LLM_TENSOR_NAMES.at(arch).find(LLM_TENSOR_OUTPUT) == LLM_TENSOR_NAMES.at(arch).end() && name == "token_embd.weight")) { + if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) { int nx = tensor->ne[0]; if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; @@ -11460,6 +11462,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s else if (name.find("ffn_up") != std::string::npos) { ++qs.n_ffn_up; } + else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { + qs.has_output = true; + } } if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) { LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n", From c2101a2e909ac7c08976d414e64e96c90ee5fa9e Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Fri, 8 Mar 2024 17:31:00 -0500 Subject: [PATCH 70/86] llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`. --- README.md | 2 + common/common.cpp | 1 + convert-hf-to-gguf.py | 118 ++++ examples/batched-bench/batched-bench.cpp | 13 +- examples/batched/batched.cpp | 3 +- examples/parallel/parallel.cpp | 20 +- examples/perplexity/perplexity.cpp | 9 +- examples/server/server.cpp | 51 +- ggml.c | 379 +++++++++++- ggml.h | 19 + gguf-py/gguf/constants.py | 41 ++ gguf-py/gguf/gguf_writer.py | 12 + gguf-py/gguf/tensor_mapping.py | 46 +- llama.cpp | 698 +++++++++++++++++++++-- llama.h | 4 +- 15 files changed, 1342 insertions(+), 74 deletions(-) diff --git a/README.md b/README.md index f754022de..d7dba73e6 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 @@ -110,6 +111,7 @@ Typically finetunes of the base models below are supported as well. - [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [Gemma](https://ai.google.dev/gemma) +- [x] [Mamba](https://github.com/state-spaces/mamba) **Multimodal models:** diff --git a/common/common.cpp b/common/common.cpp index c244db644..d7f650ef4 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1288,6 +1288,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_ctx = params.n_ctx; cparams.n_batch = params.n_batch; + cparams.n_parallel = params.n_parallel; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f6369af38..5eee32016 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1847,6 +1847,124 @@ class StarCoder2Model(Model): model_arch = gguf.MODEL_ARCH.STARCODER2 +@Model.register("MambaForCausalLM", "MambaLMHeadModel") +class MambaModel(Model): + model_arch = gguf.MODEL_ARCH.MAMBA + + def set_vocab(self): + vocab_size = self.hparams["vocab_size"] + # Round vocab size to next multiple of 8 + pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8) + # pad using ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + vocab_size = -(vocab_size // -pad_vocab) * pad_vocab + self.hparams["vocab_size"] = vocab_size + + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + else: + # Use the GPT-NeoX tokenizer when no tokenizer files are present + tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf" + print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") + neox_reader = gguf.GGUFReader(tokenizer_path, "r") + + field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL) + self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1])) + field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST) + self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES) + self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID) + self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID) + self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID) + self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) + + def set_gguf_parameters(self): + d_model = self.find_hparam(["hidden_size", "d_model"]) + d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 + d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model + d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 + # ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 + dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) + rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 + + # Fail early for models which don't have a block expansion factor of 2 + assert d_inner == 2 * d_model + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default + self.gguf_writer.add_embedding_length(d_model) + self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading + self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_ssm_conv_kernel(d_conv) + self.gguf_writer.add_ssm_inner_size(d_inner) + self.gguf_writer.add_ssm_state_size(d_state) + self.gguf_writer.add_ssm_time_step_rank(dt_rank) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams["n_layer"] + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + tok_embd = None + tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight" + output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight" + + for name, data_torch in self.get_tensors(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + if name.endswith(".A_log"): + print("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + # assuming token_embd.weight is seen before output.weight + if tok_embd is not None and new_name == output_name: + if torch.equal(tok_embd, data_torch): + print(f"{output_name} is equivalent to {tok_embd_name}, omitting") + continue + if new_name == tok_embd_name: + tok_embd = data_torch + + data = data_torch.squeeze().numpy() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert big float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + ###### CONVERSION LOGIC ###### diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 19aff18ae..dff6c68ec 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -105,6 +105,9 @@ int main(int argc, char ** argv) { ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + // ensure enough sequences are available + ctx_params.n_parallel = *std::max_element(n_pl.begin(), n_pl.end()); + llama_context * ctx = llama_new_context_with_model(model, ctx_params); if (ctx == NULL) { @@ -174,10 +177,10 @@ int main(int argc, char ** argv) { llama_batch_clear(batch); - const int n_tokens = is_pp_shared ? pp : pl*pp; - - for (int i = 0; i < n_tokens; ++i) { - llama_batch_add(batch, 0, i, { 0 }, false); + for (int i = 0; i < pp; ++i) { + for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) { + llama_batch_add(batch, 0, i, { j }, false); + } } batch.logits[batch.n_tokens - 1] = true; @@ -192,7 +195,7 @@ int main(int argc, char ** argv) { if (is_pp_shared) { for (int32_t i = 1; i < pl; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, pp); + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } } diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 9be7eb56b..dde4d5a06 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -80,6 +80,7 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_len, n_parallel); + ctx_params.n_parallel = n_parallel; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -132,7 +133,7 @@ int main(int argc, char ** argv) { // assign the system KV cache to all parallel sequences // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them for (int32_t i = 1; i < n_parallel; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens); + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } if (n_parallel > 1) { diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 7d11fcd59..a2ef0fb03 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -107,6 +107,9 @@ int main(int argc, char ** argv) { // number of simultaneous "clients" to simulate const int32_t n_clients = params.n_parallel; + // dedicate one sequence to the system prompt + params.n_parallel += 1; + // requests to simulate const int32_t n_seq = params.n_sequences; @@ -196,8 +199,8 @@ int main(int argc, char ** argv) { } // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < n_clients; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system); + for (int32_t i = 1; i <= n_clients; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } LOG_TEE("\n"); @@ -221,15 +224,17 @@ int main(int argc, char ** argv) { client.i_batch = batch.n_tokens; - llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true); + llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true); client.n_decoded += 1; } if (batch.n_tokens == 0) { // all sequences have ended - clear the entire KV cache - for (int i = 0; i < n_clients; ++i) { - llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1); + for (int i = 1; i <= n_clients; ++i) { + llama_kv_cache_seq_rm(ctx, i, -1, -1); + // but keep the system prompt + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } LOG_TEE("%s: clearing the KV cache\n", __func__); @@ -255,7 +260,7 @@ int main(int argc, char ** argv) { tokens_prompt = ::llama_tokenize(ctx, client.prompt, false); for (size_t i = 0; i < tokens_prompt.size(); ++i) { - llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false); + llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false); } // extract the logits only for the last token @@ -366,7 +371,8 @@ int main(int argc, char ** argv) { } // delete only the generated part of the sequence, i.e. keep the system prompt in the cache - llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1); + llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1); + llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1); const auto t_main_end = ggml_time_us(); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 9ec989389..52789ee63 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -809,7 +809,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int max_tasks_per_batch = 32; - const int max_seq = 4*max_tasks_per_batch; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1086,7 +1086,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int max_tasks_per_batch = 128; - const int max_seq = 2*max_tasks_per_batch; + const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1438,7 +1438,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params const int n_batch = params.n_batch; const int max_tasks_per_batch = 32; - const int max_seq = 4*max_tasks_per_batch; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1815,6 +1815,9 @@ int main(int argc, char ** argv) { llama_model * model; llama_context * ctx; + // ensure there's at least enough seq_ids for HellaSwag + params.n_parallel = std::max(4, params.n_parallel); + // load the model and apply lora adapter, if any std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == NULL) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 109ff7175..59a59d56b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -659,7 +659,11 @@ struct server_context { bool load_model(const gpt_params & params_) { params = params_; + // dedicate one sequence to the system prompt + params.n_parallel += 1; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + params.n_parallel -= 1; // but be sneaky about it if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params.model}}); return false; @@ -1018,8 +1022,8 @@ struct 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, system_tokens.size()); + for (int32_t i = 1; i <= params.n_parallel; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } } @@ -1306,7 +1310,7 @@ struct server_context { const int n_embd = llama_n_embd(model); for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { continue; } @@ -1633,8 +1637,8 @@ struct server_context { {"n_cache_tokens", slot.cache_tokens.size()} }); - llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); - llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); if (slot.params.cache_prompt) { for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1666,7 +1670,7 @@ struct server_context { // TODO: we always have to take into account the "system_tokens" // this is not great and needs to be improved somehow - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true); slot.n_past += 1; @@ -1804,9 +1808,6 @@ struct server_context { // reuse any previously computed tokens that are common with the new prompt slot.n_past = common_part(slot.cache_tokens, prompt_tokens); - // remove the non-common part from the cache - slot.cache_tokens.resize(slot.n_past); - // push the prompt into the sampling context (do not apply grammar) for (int i = 0; i < slot.n_past; ++i) { llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false); @@ -1837,8 +1838,28 @@ struct server_context { } } - const int p0 = (int) system_tokens.size() + slot.n_past; - llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); + // keep only the common part + int p0 = (int) system_tokens.size() + slot.n_past; + if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) { + // could not partially delete (likely using a non-Transformer model) + llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1); + + p0 = (int) system_tokens.size(); + if (p0 != 0) { + // copy over the system prompt when there is one + llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1); + } + + // there is no common part left (except for the system prompt) + slot.n_past = 0; + slot.n_past_se = 0; + slot.ga_i = 0; + // TODO: is the system prompt ever in the sampling context? + llama_sampling_reset(slot.ctx_sampling); + } + + // remove the non-common part from the cache + slot.cache_tokens.resize(slot.n_past); LOG_INFO("kv cache rm [p0, end)", { { "id_slot", slot.id }, @@ -1863,7 +1884,7 @@ struct server_context { } } - llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); + llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); @@ -1937,9 +1958,9 @@ struct server_context { LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); - llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); + llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd); + llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); + llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; diff --git a/ggml.c b/ggml.c index 92b17ee6e..6eff98ab6 100644 --- a/ggml.c +++ b/ggml.c @@ -1841,6 +1841,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "FLASH_ATTN", "FLASH_FF", "FLASH_ATTN_BACK", + "SSM_CONV", + "SSM_SCAN", "WIN_PART", "WIN_UNPART", "GET_REL_POS", @@ -1863,7 +1865,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); +static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1929,6 +1931,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "flash_attn(x)", "flash_ff(x)", "flash_attn_back(x)", + "ssm_conv(x)", + "ssm_scan(x)", "win_part(x)", "win_unpart(x)", "get_rel_pos(x)", @@ -1951,7 +1955,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); +static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -6154,6 +6158,108 @@ struct ggml_tensor * ggml_flash_attn_back( return result; } +// ggml_ssm_conv + +struct ggml_tensor * ggml_ssm_conv( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * c, + struct ggml_tensor * sq) { + GGML_ASSERT(ggml_is_3d(s)); + GGML_ASSERT(ggml_is_matrix(x)); + GGML_ASSERT(ggml_is_matrix(c)); + GGML_ASSERT(ggml_is_matrix(sq)); + GGML_ASSERT(sq->type == GGML_TYPE_I32); + + const int64_t d_conv = c->ne[0]; + const int64_t d_inner = c->ne[1]; + const int64_t n_tokens = x->ne[1]; + const int64_t n_kv = s->ne[2]; + + GGML_ASSERT( s->ne[0] == d_conv - 1); + GGML_ASSERT( s->ne[1] == d_inner); + GGML_ASSERT( x->ne[0] == d_inner); + GGML_ASSERT(sq->ne[0] == n_kv); + GGML_ASSERT(sq->ne[1] == n_tokens); + + bool is_node = false; + + if (s->grad || x->grad || c->grad || sq->grad) { + GGML_ASSERT(false); // TODO: implement + is_node = true; + } + + // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv} + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv)); + + result->op = GGML_OP_SSM_CONV; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = s; + result->src[1] = x; + result->src[2] = c; + result->src[3] = sq; + + return result; +} + +// ggml_ssm_scan + +struct ggml_tensor * ggml_ssm_scan( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * dt, + struct ggml_tensor * A, + struct ggml_tensor * B, + struct ggml_tensor * C, + struct ggml_tensor * sq) { + GGML_ASSERT(ggml_is_contiguous(s)); + GGML_ASSERT(ggml_is_contiguous(x)); + GGML_ASSERT(ggml_is_contiguous(dt)); + GGML_ASSERT(ggml_is_contiguous(A)); + GGML_ASSERT(sq->type == GGML_TYPE_I32); + GGML_ASSERT(B->nb[0] == ggml_type_size(B->type)); + GGML_ASSERT(C->nb[0] == ggml_type_size(C->type)); + GGML_ASSERT(ggml_are_same_shape(x, dt)); + + { + const int64_t d_state = s->ne[0]; + const int64_t d_inner = s->ne[1]; + const int64_t n_tokens = x->ne[1]; + + GGML_ASSERT(x->ne[0] == d_inner); + GGML_ASSERT(A->ne[0] == d_state); + GGML_ASSERT(A->ne[1] == d_inner); + GGML_ASSERT(B->ne[0] == d_state); + GGML_ASSERT(B->ne[1] == n_tokens); + GGML_ASSERT(C->ne[0] == d_state); + GGML_ASSERT(C->ne[1] == n_tokens); + } + + bool is_node = false; + + if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) { + GGML_ASSERT(false); // TODO: implement + is_node = true; + } + + // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv} + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s)); + + result->op = GGML_OP_SSM_SCAN; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = s; + result->src[1] = x; + result->src[2] = dt; + result->src[3] = A; + result->src[4] = B; + result->src[5] = C; + result->src[6] = sq; + + return result; +} + // ggml_win_part struct ggml_tensor * ggml_win_part( @@ -14771,6 +14877,257 @@ static void ggml_compute_forward_flash_attn_back( } } +// ggml_compute_forward_ssm_conv + +static void ggml_compute_forward_ssm_conv_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; // conv_state + const struct ggml_tensor * src1 = dst->src[1]; // x + const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight + const struct ggml_tensor * src3 = dst->src[3]; // state_seq + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src2->ne[0]; // d_conv + const int nr = src0->ne[1]; // d_inner + const int n_t = src1->ne[1]; // n_tokens + const int n_kv = src0->ne[2]; // max number of sequences in the batch + + GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(int32_t)); + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // for use with the destination state offset between sequences + GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + if (n_kv > 1) { + // multiple sequences means it's hard to know when it's the first time a state is read, + // so copy them all over to the destination, just to be sure. + for (int i3 = 0; i3 < n_kv; ++i3) { + float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); + float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float)); + // can't use memcpy because of d_conv vs d_conv - 1 + for (int i1 = 0; i1 < ir; ++i1) { + for (int i0 = 0; i0 < nc - 1; ++i0) { + // copy s0 to last (d_conv - 1) columns of s + s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)]; + } + } + } + } + + for (int i2 = 0; i2 < n_t; ++i2) { + int32_t * sq = (int32_t *) ((char *) src3->data + i2*(src3->nb[1])); // {n_kv, n_tokens} + float * x = (float *) ((char *) dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens} + float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv} + float * s0; // {d_conv - 1, d_inner, n_kv} + float * x0 = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} + float * c = (float *) ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner} + int ne0s0; + + GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); + + // avoid needing to copy the state for the first token + if (i2 == 0) { + s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv} + ne0s0 = src0->ne[0]; + } else { + // the source is the last (d_conv - 1) columns of the destination + s0 = s + 1; + ne0s0 = nc; + } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // shift state left + for (int i0 = 0; i0 < nc - 1; ++i0) { + s[i0 + i1*nc] = s0[i0 + i1*ne0s0]; + } + // insert x on the last column + s[(nc - 1) + i1*nc] = x0[i1]; + } + + // handle copies when there are multiple output states + for (int i3 = 1; i3 < n_kv; ++i3) { + int32_t seq = sq[i3]; + if (0 <= seq && seq < n_kv) { + float * s1 = s + (seq - sq[0])*nc*nr; + memcpy(s1, s, nc*ir*sizeof(float)); + } else { + // stop at negative or too big seq_ids + break; + } + } + + // it seems a little faster when this is separate from the state shift + for (int i1 = 0; i1 < ir; ++i1) { + // rowwise dot product + float sumf = 0.0f; + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + sumf += s[i] * c[i]; + } + x[i1] = sumf; + } + } +} + +static void ggml_compute_forward_ssm_conv( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_conv_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_ssm_scan + +static void ggml_compute_forward_ssm_scan_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; // s + const struct ggml_tensor * src1 = dst->src[1]; // x + const struct ggml_tensor * src2 = dst->src[2]; // dt + const struct ggml_tensor * src3 = dst->src[3]; // A + const struct ggml_tensor * src4 = dst->src[4]; // B + const struct ggml_tensor * src5 = dst->src[5]; // C + const struct ggml_tensor * src6 = dst->src[6]; // sq + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nc = src0->ne[0]; // d_state + const int64_t nr = src0->ne[1]; // d_inner + const int64_t n_t = src1->ne[1]; // number of tokens in the batch + const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch + + GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(float)); + GGML_ASSERT(src4->nb[0] == sizeof(float)); + GGML_ASSERT(src5->nb[0] == sizeof(float)); + // required for the dot product between s and C, and when copying the states + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // required for per-sequence offsets for states + GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); + // required to get correct offset for state destination (i.e. src1->nb[2]) + GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + if (n_kv > 1) { + // it's hard to know if the source states have already been copied + // when there are multiple, so copy them already. + for (int i3 = 0; i3 < n_kv; ++i3) { + float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); + float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]); + memcpy(s, s0, nc*ir*sizeof(float)); + } + } + + for (int i2 = 0; i2 < n_t; ++i2) { + int32_t * sq = (int32_t *) ((char *) src6->data + i2*(src6->nb[1])); // {n_kv, n_tokens} + float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} + float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv} + float * s0; + float * x = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} + float * dt = (float *) ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens} + float * A = (float *) ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} + float * B = (float *) ((char *) src4->data + i2*(src4->nb[1])); // {d_state, n_tokens} + float * C = (float *) ((char *) src5->data + i2*(src5->nb[1])); // {d_state, n_tokens} + + GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); + + // avoid needing to copy the state for the first token + if (i2 == 0) { + s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv} + } else { + // otherwise the source is the same as the destination + s0 = s; + } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 + float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; + float x_dt = x[i1] * dt_soft_plus; + float sumf = 0.0f; + // d_state + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + // state = prev_state * dA + dB * x + float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); + // y = rowwise_dotprod(state, C) + sumf += state * C[i0]; + s[i] = state; + } + y[i1] = sumf; + } + + // handle copies when there are multiple output states + for (int i3 = 1; i3 < n_kv; ++i3) { + int32_t seq = sq[i3]; + if (0 <= seq && seq < n_kv) { + float * s1 = s + (seq - sq[0])*nc*nr; + memcpy(s1, s, nc*ir*sizeof(float)); + } else { + // stop at negative or too big seq_ids + break; + } + } + } +} + +static void ggml_compute_forward_ssm_scan( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_scan_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_win_part static void ggml_compute_forward_win_part_f32( @@ -15830,6 +16187,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm bool masked = t != 0; ggml_compute_forward_flash_attn_back(params, masked, tensor); } break; + case GGML_OP_SSM_CONV: + { + ggml_compute_forward_ssm_conv(params, tensor); + } break; + case GGML_OP_SSM_SCAN: + { + ggml_compute_forward_ssm_scan(params, tensor); + } break; case GGML_OP_WIN_PART: { ggml_compute_forward_win_part(params, tensor); @@ -16884,6 +17249,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // not supported } break; + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_UNARY: @@ -17590,6 +17960,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = n_threads; } break; + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: + { + n_tasks = n_threads; + } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_GET_REL_POS: diff --git a/ggml.h b/ggml.h index 0ea4f8847..a13b0cec4 100644 --- a/ggml.h +++ b/ggml.h @@ -472,6 +472,8 @@ extern "C" { GGML_OP_FLASH_ATTN, GGML_OP_FLASH_FF, GGML_OP_FLASH_ATTN_BACK, + GGML_OP_SSM_CONV, + GGML_OP_SSM_SCAN, GGML_OP_WIN_PART, GGML_OP_WIN_UNPART, GGML_OP_GET_REL_POS, @@ -1728,6 +1730,23 @@ extern "C" { struct ggml_tensor * c0, struct ggml_tensor * c1); + GGML_API struct ggml_tensor * ggml_ssm_conv( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * c, + struct ggml_tensor * sq); + + GGML_API struct ggml_tensor * ggml_ssm_scan( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * dt, + struct ggml_tensor * A, + struct ggml_tensor * B, + struct ggml_tensor * C, + struct ggml_tensor * sq); + // partition into non-overlapping windows with padding if needed // example: // a: 768 64 64 1 diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a62139811..b23badb10 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -61,6 +61,12 @@ class Keys: SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + class SSM: + CONV_KERNEL = "{arch}.ssm.conv_kernel" + INNER_SIZE = "{arch}.ssm.inner_size" + STATE_SIZE = "{arch}.ssm.state_size" + TIME_STEP_RANK = "{arch}.ssm.time_step_rank" + class Tokenizer: MODEL = "tokenizer.ggml.model" LIST = "tokenizer.ggml.tokens" @@ -113,6 +119,7 @@ class MODEL_ARCH(IntEnum): MINICPM = auto() GEMMA = auto() STARCODER2 = auto() + MAMBA = auto() class MODEL_TENSOR(IntEnum): @@ -144,6 +151,13 @@ class MODEL_TENSOR(IntEnum): ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() + SSM_IN = auto() + SSM_CONV1D = auto() + SSM_X = auto() + SSM_DT = auto() + SSM_A = auto() + SSM_D = auto() + SSM_OUT = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -171,6 +185,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.GEMMA: "gemma", MODEL_ARCH.STARCODER2: "starcoder2", + MODEL_ARCH.MAMBA: "mamba", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -202,6 +217,13 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}", MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", + MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", + MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", + MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", + MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", + MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", + MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", + MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -543,6 +565,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.MAMBA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_X, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_OUT, + ], # TODO } @@ -734,6 +769,12 @@ KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED +# SSM +KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL +KEY_SSM_INNER_SIZE = Keys.SSM.INNER_SIZE +KEY_SSM_STATE_SIZE = Keys.SSM.STATE_SIZE +KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK + # tokenization KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 801160832..e49c5db68 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -382,6 +382,18 @@ class GGUFWriter: def add_rope_scaling_finetuned(self, value: bool) -> None: self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + def add_ssm_conv_kernel(self, value: int) -> None: + self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) + + def add_ssm_inner_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value) + + def add_ssm_state_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value) + + def add_ssm_time_step_rank(self, value: int) -> None: + self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value) + def add_tokenizer_model(self, model: str) -> None: self.add_string(Keys.Tokenizer.MODEL, model) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index db2ec9704..ed89955d8 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -20,6 +20,9 @@ class TensorNameMap: "wte", # gpt2 "transformer.embd.wte", # phi2 "model.tok_embeddings", # internlm2 + "model.embedding", # mamba-qbert + "backbone.embedding", # mamba + "backbone.embeddings", # mamba-hf ), # Token type embeddings @@ -44,7 +47,7 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 @@ -61,6 +64,8 @@ class TensorNameMap: "language_model.encoder.final_layernorm", # persimmon "model.final_layernorm", # persimmon "lm_head.ln", # phi2 + "model.norm_f", # mamba-qbert + "backbone.norm_f", # mamba ), # Rope frequencies @@ -86,6 +91,8 @@ class TensorNameMap: "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo "model.layers.{bid}.attention_norm", # internlm2 + "model.layers.{bid}.norm", # mamba-qbert + "backbone.layers.{bid}.norm", # mamba ), # Attention norm 2 @@ -282,7 +289,42 @@ class TensorNameMap: MODEL_TENSOR.LAYER_OUT_NORM: ( "encoder.layer.{bid}.output.LayerNorm", # bert "encoder.layers.{bid}.norm2", # nomic-bert - ) + ), + + MODEL_TENSOR.SSM_IN: ( + "model.layers.{bid}.in_proj", + "backbone.layers.{bid}.mixer.in_proj", + ), + + MODEL_TENSOR.SSM_CONV1D: ( + "model.layers.{bid}.conv1d", + "backbone.layers.{bid}.mixer.conv1d", + ), + + MODEL_TENSOR.SSM_X: ( + "model.layers.{bid}.x_proj", + "backbone.layers.{bid}.mixer.x_proj", + ), + + MODEL_TENSOR.SSM_DT: ( + "model.layers.{bid}.dt_proj", + "backbone.layers.{bid}.mixer.dt_proj", + ), + + MODEL_TENSOR.SSM_A: ( + "model.layers.{bid}.A_log", + "backbone.layers.{bid}.mixer.A_log", + ), + + MODEL_TENSOR.SSM_D: ( + "model.layers.{bid}.D", + "backbone.layers.{bid}.mixer.D", + ), + + MODEL_TENSOR.SSM_OUT: ( + "model.layers.{bid}.out_proj", + "backbone.layers.{bid}.mixer.out_proj", + ), } mapping: dict[str, tuple[MODEL_TENSOR, str]] diff --git a/llama.cpp b/llama.cpp index 4a20b7928..8c147a42b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -213,6 +213,7 @@ enum llm_arch { LLM_ARCH_MINICPM, LLM_ARCH_GEMMA, LLM_ARCH_STARCODER2, + LLM_ARCH_MAMBA, LLM_ARCH_UNKNOWN, }; @@ -241,6 +242,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_MAMBA, "mamba" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -284,6 +286,11 @@ enum llm_kv { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, + LLM_KV_SSM_INNER_SIZE, + LLM_KV_SSM_CONV_KERNEL, + LLM_KV_SSM_STATE_SIZE, + LLM_KV_SSM_TIME_STEP_RANK, + LLM_KV_TOKENIZER_MODEL, LLM_KV_TOKENIZER_LIST, LLM_KV_TOKENIZER_TOKEN_TYPE, @@ -342,6 +349,11 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, + { LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" }, + { LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" }, + { LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" }, + { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" }, + { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, { LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" }, @@ -399,6 +411,13 @@ enum llm_tensor { LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, + LLM_TENSOR_SSM_IN, + LLM_TENSOR_SSM_CONV1D, + LLM_TENSOR_SSM_X, + LLM_TENSOR_SSM_DT, + LLM_TENSOR_SSM_A, + LLM_TENSOR_SSM_D, + LLM_TENSOR_SSM_OUT, }; static const std::map> LLM_TENSOR_NAMES = { @@ -801,6 +820,22 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_MAMBA, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" }, + { LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" }, + { LLM_TENSOR_SSM_X, "blk.%d.ssm_x" }, + { LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" }, + { LLM_TENSOR_SSM_A, "blk.%d.ssm_a" }, + { LLM_TENSOR_SSM_D, "blk.%d.ssm_d" }, + { LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1613,6 +1648,12 @@ struct llama_hparams { float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; + // for State Space Models + uint32_t ssm_d_conv = 0; + uint32_t ssm_d_inner = 0; + uint32_t ssm_d_state = 0; + uint32_t ssm_dt_rank = 0; + float f_clamp_kqv = 0.0f; float f_max_alibi_bias = 0.0f; @@ -1641,6 +1682,11 @@ struct llama_hparams { if (this->rope_finetuned != other.rope_finetuned) return true; if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; + if (this->ssm_d_conv != other.ssm_d_conv) return true; + if (this->ssm_d_inner != other.ssm_d_inner) return true; + if (this->ssm_d_state != other.ssm_d_state) return true; + if (this->ssm_dt_rank != other.ssm_dt_rank) return true; + const float EPSILON = 1e-9f; if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; @@ -1652,6 +1698,9 @@ struct llama_hparams { } uint32_t n_gqa() const { + if (n_head_kv == 0) { + return 0; + } return n_head/n_head_kv; } @@ -1662,6 +1711,18 @@ struct llama_hparams { uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads return n_embd_head_v * n_head_kv; } + + uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings + // corresponds to Mamba's conv_states size + // TODO: maybe support other convolution strides than 1 + // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed + return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner; + } + + uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings + // corresponds to Mamba's ssm_states size + return ssm_d_state * ssm_d_inner; + } }; struct llama_cparams { @@ -1739,11 +1800,27 @@ struct llama_layer { struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_up_b; // b3 struct ggml_tensor * ffn_act; + + // mamba proj + struct ggml_tensor * ssm_in; + struct ggml_tensor * ssm_x; + struct ggml_tensor * ssm_dt; + struct ggml_tensor * ssm_out; + + // mamba + struct ggml_tensor * ssm_conv1d; + struct ggml_tensor * ssm_a; + struct ggml_tensor * ssm_d; + + // mamba bias + struct ggml_tensor * ssm_conv1d_b; + struct ggml_tensor * ssm_dt_b; }; struct llama_kv_cell { llama_pos pos = -1; llama_pos delta = 0; + int32_t src = 0; // used by recurrent state models to copy states std::set seq_id; @@ -1764,6 +1841,9 @@ struct llama_kv_cell { struct llama_kv_cache { bool has_shift = false; bool do_defrag = false; + bool do_copy = false; + // with recurrent state models, a cell can hold the state for more than one past token + bool recurrent = false; // Note: The value of head isn't only used to optimize searching // for a free KV slot. llama_decode_internal also uses it, so it @@ -2003,11 +2083,14 @@ struct llama_context { struct ggml_tensor * inp_tokens; // I32 [n_batch] struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] struct ggml_tensor * inp_pos; // I32 [n_batch] - struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] - struct ggml_tensor * inp_KQ_pos; // F32 [n_ctx] - struct ggml_tensor * inp_K_shift; // I32 [n_ctx] + struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch] + struct ggml_tensor * inp_KQ_pos; // F32 [kv_size] + struct ggml_tensor * inp_K_shift; // I32 [kv_size] struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] struct ggml_tensor * inp_cls; // I32 [n_batch] + struct ggml_tensor * inp_s_copy; // I32 [kv_size] + struct ggml_tensor * inp_s_mask; // F32 [kv_size] + struct ggml_tensor * inp_s_seq; // I32 [kv_size, n_batch] #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -2023,25 +2106,42 @@ static bool llama_kv_cache_init( const llama_model & model, ggml_type type_k, ggml_type type_v, - uint32_t n_ctx, + uint32_t kv_size, bool offload) { const struct llama_hparams & hparams = model.hparams; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); const int64_t n_layer = hparams.n_layer; cache.has_shift = false; + // TODO: find a nicer way to add other recurrent model architectures + cache.recurrent = model.arch == LLM_ARCH_MAMBA; + + // TODO: support mixed reccurent Transformer architectues + // NOTE: (!a || b) is a logical implication (a -> b) + GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s()); + GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s()); + GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa()); + GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa()); + cache.head = 0; - cache.size = n_ctx; + cache.size = kv_size; cache.used = 0; cache.type_k = type_k; cache.type_v = type_v; cache.cells.clear(); - cache.cells.resize(n_ctx); + cache.cells.resize(kv_size); + + if (cache.recurrent) { + // init state copy sources + for (uint32_t i = 0; i < cache.size; ++i) { + cache.cells[i].src = i; + } + } #ifdef GGML_USE_CLBLAST offload = false; @@ -2080,8 +2180,8 @@ static bool llama_kv_cache_init( for (int i = 0; i < (int) n_layer; i++) { struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); - ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*n_ctx); - ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*n_ctx); + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); + ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size); ggml_format_name(k, "cache_k_l%d", i); ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); @@ -2115,6 +2215,54 @@ static bool llama_kv_cache_find_slot( const uint32_t n_ctx = cache.size; const uint32_t n_tokens = batch.n_tokens; + if (cache.recurrent) { + // For recurrent state architectures (like Mamba), + // each KV cache cell can store the state for a whole sequence. + + llama_seq_id min = cache.size - 1; + llama_seq_id max = 0; + + for (uint32_t i = 0; i < n_tokens; ++i) { + for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) { + llama_seq_id seq_id = batch.seq_id[i][j]; + // make sure it's a valid seq_id + if ((uint32_t) seq_id < cache.size) { + if (seq_id > max) { + max = seq_id; + } + if (seq_id < min) { + min = seq_id; + } + // Assuming the tokens are in-order + if (batch.pos[i] != cache.cells[seq_id].pos + 1) { + // What should happen when the pos backtracks or skips a value? + // Clearing the state mid-batch would require special-casing which isn't done. + LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n", + __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id); + } + if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) { + cache.used += 1; + } + cache.cells[seq_id].pos = batch.pos[i]; + // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set + } else { + // too big seq_id + // TODO: would it be possible to resize the KV cache size instead? + LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size); + return false; + } + } + } + + // allow getting the range of used cells, from head to head + n + cache.head = min; + cache.n = max - min + 1; + + // sanity check + return max >= min; + } + // otherwise, one cell per token. + if (n_tokens > n_ctx) { LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx); return false; @@ -2184,7 +2332,7 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { cache.used = 0; } -static void llama_kv_cache_seq_rm( +static bool llama_kv_cache_seq_rm( struct llama_kv_cache & cache, llama_seq_id seq_id, llama_pos p0, @@ -2194,6 +2342,25 @@ static void llama_kv_cache_seq_rm( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + // models like Mamba can't have a state partially erased + if (cache.recurrent) { + if (seq_id >= (int64_t) cache.size) { + // could be fatal + return false; + } + if (0 <= seq_id) { + // partial intersection is invalid + if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) { + return false; + } + } else { + // seq_id is negative, then the range should include everything or nothing + if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits::max())) { + return false; + } + } + } + for (uint32_t i = 0; i < cache.size; ++i) { if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { if (seq_id < 0) { @@ -2215,6 +2382,8 @@ static void llama_kv_cache_seq_rm( // If we freed up a slot, set head to it so searching can start there. if (new_head != cache.size && new_head < cache.head) cache.head = new_head; + + return true; } static void llama_kv_cache_seq_cp( @@ -2226,6 +2395,29 @@ static void llama_kv_cache_seq_cp( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + if (cache.recurrent) { + if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) { + seq_id_src = cache.cells[seq_id_src].src; + GGML_ASSERT((uint32_t) seq_id_src < cache.size); + // intent to "copy from" + // supports copy chains thanks to taking the source of the source + cache.cells[seq_id_dst].src = seq_id_src; + + // preserve the "keep or clear" status of the copied sequence + if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) { + cache.cells[seq_id_dst].seq_id.insert(seq_id_dst); + } else { + cache.cells[seq_id_dst].seq_id.erase(seq_id_dst); + } + + cache.do_copy = true; + + cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos; + } + return; + } + // otherwise, this is the KV cache of a Transformer-like model + cache.head = 0; for (uint32_t i = 0; i < cache.size; ++i) { @@ -2265,6 +2457,17 @@ static void llama_kv_cache_seq_add( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + if (cache.recurrent) { + // for Mamba-like models, only the pos needs to be shifted + if (0 <= seq_id && seq_id < (int64_t) cache.size) { + llama_kv_cell & cell = cache.cells[seq_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos += delta; + } + } + return; + } + for (uint32_t i = 0; i < cache.size; ++i) { if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { cache.has_shift = true; @@ -2298,6 +2501,17 @@ static void llama_kv_cache_seq_div( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + if (cache.recurrent) { + // for Mamba-like models, only the pos needs to be changed + if (0 <= seq_id && seq_id < (int64_t) cache.size) { + llama_kv_cell & cell = cache.cells[seq_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos /= d; + } + } + return; + } + for (uint32_t i = 0; i < cache.size; ++i) { if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { cache.has_shift = true; @@ -3117,7 +3331,7 @@ static void llm_load_hparams( // sanity check for n_rot (optional) { - hparams.n_rot = hparams.n_embd / hparams.n_head; + hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); @@ -3130,10 +3344,10 @@ static void llm_load_hparams( // gpt-j n_rot = rotary_dim } - hparams.n_embd_head_k = hparams.n_embd / hparams.n_head; + hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); - hparams.n_embd_head_v = hparams.n_embd / hparams.n_head; + hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); // arch-specific KVs @@ -3383,6 +3597,36 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_MAMBA: + { + ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); + ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner); + ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state); + ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank); + + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 24: + switch (hparams.n_embd) { + case 768: model.type = e_model::MODEL_SMALL; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 48: + switch (hparams.n_embd) { + case 1024: model.type = e_model::MODEL_MEDIUM; break; + case 1536: model.type = e_model::MODEL_LARGE; break; + case 2048: model.type = e_model::MODEL_XL; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 64: + switch (hparams.n_embd) { + case 2560: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3702,6 +3946,10 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); + LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv); + LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner); + LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state); + LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank); LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); if (ml.n_elements >= 1e12) { @@ -4609,6 +4857,57 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); } } break; + case LLM_ARCH_MAMBA: + { + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = hparams.ssm_d_inner; + const int64_t d_state = hparams.ssm_d_state; + const int64_t dt_rank = hparams.ssm_dt_rank; + // only an expansion factor of 2 is supported for now + GGML_ASSERT(2 * n_embd == d_inner); + + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed, duplicated to allow offloading + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + // norm + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}); + + layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}); + layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}); + + layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}); + + layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}); + layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}); + + // no "weight" suffix for these + layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}); + layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner}); + + // out_proj + layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -4834,6 +5133,8 @@ static void llm_build_kv_store( const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(kv.size == n_ctx); + // compute the transposed [n_tokens, n_embd] V matrix struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens)); //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed @@ -5043,6 +5344,8 @@ static struct ggml_tensor * llm_build_kqv( cb(kq, "kq_soft_max_ext", il); } + GGML_ASSERT(kv.size == n_ctx); + // split cached v into n_head heads struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], @@ -5190,8 +5493,8 @@ struct llm_build_context { norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), n_tokens (batch.n_tokens), - n_kv (worst_case ? n_ctx : kv_self.n), - kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), + n_kv (worst_case ? kv_self.size : kv_self.n), + kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), pooling_type (cparams.pooling_type), rope_type (hparams.rope_type), @@ -5220,6 +5523,8 @@ struct llm_build_context { struct ggml_cgraph * build_k_shift() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + GGML_ASSERT(kv_self.size == n_ctx); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions @@ -5238,6 +5543,27 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_s_copy() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + GGML_ASSERT(kv_self.recurrent); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); + struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); + + conv_states = ggml_get_rows(ctx0, conv_states, lctx.inp_s_copy); + ssm_states = ggml_get_rows(ctx0, ssm_states, lctx.inp_s_copy); + + // TODO: name the intermediate tensors with cb() + + ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il])); + ggml_build_forward_expand(gf, ggml_cpy(ctx0, ssm_states, kv_self.v_l[il])); + } + + return gf; + } + struct ggml_cgraph * build_defrag(const std::vector & ids) { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -7835,6 +8161,145 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_mamba() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t d_model = n_embd; + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = hparams.ssm_d_inner; + GGML_ASSERT(2 * d_model == d_inner); + const int64_t d_state = hparams.ssm_d_state; + const int64_t dt_rank = hparams.ssm_dt_rank; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + // {n_embd, n_tokens} + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + struct ggml_tensor * state_mask = ggml_view_2d(ctx0, lctx.inp_s_mask, 1, n_kv, lctx.inp_s_mask->nb[0], 0); + struct ggml_tensor * state_seq = ggml_view_2d(ctx0, lctx.inp_s_seq, n_kv, n_tokens, n_kv*ggml_element_size(lctx.inp_s_seq), 0); + + for (int il = 0; il < n_layer; ++il) { + // (ab)using the KV cache to store the states + struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); + struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); + + // clear states of sequences which are starting at the beginning of this batch + { + conv_states = ggml_mul(ctx0, + ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]), + state_mask); + ssm_states = ggml_mul(ctx0, + ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]), + state_mask); + } + + conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv); + ssm_states = ggml_reshape_3d(ctx0, ssm_states, d_state, d_inner, n_kv); + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens} + struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur); + // split the above in two + // => {d_inner, n_tokens} + struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0); + struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner); + + // conv + { + // Custom operator which is needed only to ease simultaneous sequence processing. + // For a single sequence, the equivalent is to concatenate the columns of conv_states and x, + // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension, + // then element-wise multiply that with the conv1d weigth, + // then sum the elements of each row, + // (the last two steps are a dot product over rows (also doable with mul_mat)) + // then permute away the ne[0] dimension, + // and then you're left with the resulting x tensor. + // The new conv_states is the last (d_conv - 1) columns + // of the last 3rd dimensional "layer" of the self-overlapping view. + // For simultaneous sequences, it's more complicated. + struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq); + + // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, + ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)), + ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_self.head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv)))); + + // extract x from x_conv + x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0); + + // bias + x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b); + + x = ggml_silu(ctx0, x); + } + + // ssm + { + // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens} + struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x); + // split + struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0); + struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank); + struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state)); + + // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens} + dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt); + dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b); + + // Custom operator to optimize the parallel associative scan + // as described in the Annex D of the Mamba paper. + // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined, + // because only a single tensor can be returned. + struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq); + + // store last states (the second part of y_ssm_states) + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, + ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)), + ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_self.head*d_state*d_inner*ggml_element_size(ssm_states)))); + + struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0); + + // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens} + y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d)); + y = ggml_mul(ctx0, y, ggml_silu(ctx0, z)); + + // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens} + cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y); + } + + // residual + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + // final rmsnorm + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -7871,6 +8336,23 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { return result; } +static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) { + llama_batch dummy; + dummy.n_tokens = 0; + + llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; + + struct llm_build_context llm(lctx, dummy, cb, false); + + llm.init(); + + struct ggml_cgraph * result = llm.build_s_copy(); + + llm.free(); + + return result; +} + static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch, @@ -7985,6 +8467,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_starcoder2(); } break; + case LLM_ARCH_MAMBA: + { + result = llm.build_mamba(); + } break; default: GGML_ASSERT(false); } @@ -7995,19 +8481,29 @@ static struct ggml_cgraph * llama_build_graph( } static void llama_set_k_shift(llama_context & lctx) { - const auto & cparams = lctx.cparams; - - const int64_t n_ctx = cparams.n_ctx; + const int64_t kv_size = lctx.kv_self.size; assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); int32_t * data = (int32_t *) lctx.inp_K_shift->data; - for (int i = 0; i < n_ctx; ++i) { + for (int i = 0; i < kv_size; ++i) { data[i] = lctx.kv_self.cells[i].delta; } } +static void llama_set_s_copy(llama_context & lctx) { + const int64_t kv_size = lctx.kv_self.size; + + assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer)); + + int32_t * data = (int32_t *) lctx.inp_s_copy->data; + + for (int i = 0; i < kv_size; ++i) { + data[i] = lctx.kv_self.cells[i].src; + } +} + static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { // // set input data @@ -8044,6 +8540,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { float * data = (float *) lctx.inp_KQ_mask->data; + // For causal attention, use only the previous KV cells + // of the correct sequence for each token of the batch. + // It's assumed that if a token in the batch has multiple sequences, they are equivalent. for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { const llama_pos pos = batch.pos[j]; @@ -8149,6 +8648,53 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } } + + if (kv_self.recurrent) { + const int64_t n_kv = kv_self.n; + + { + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer)); + float * data = (float *) lctx.inp_s_mask->data; + + // states which are not affected by the current batch are left untouched + for (int i = 0; i < n_kv; ++i) { + llama_seq_id seq_id = i + lctx.kv_self.head; + llama_kv_cell & kv_cell = lctx.kv_self.cells[seq_id]; + bool has_self_seq = kv_cell.has_seq_id(seq_id); + + data[i] = (float) has_self_seq; + + // ensure current sequences will be kept + if (!has_self_seq && kv_cell.pos >= 0) { + kv_cell.seq_id.insert(seq_id); + } + } + } + // For Mamba (and other recurrent architectures), + // update the correct state(s)/sequence(s) for each token of the batch. + // Like with the KQ_mask, if a token in the batch has multiple sequences, + // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv). + { + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer)); + int32_t * data = (int32_t *) lctx.inp_s_seq->data; + + for (int j = 0; j < n_tokens; ++j) { + const int32_t n_seq = batch.n_seq_id[j]; + GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence + + for (int i = 0; i < n_kv; ++i) { + if (i < n_seq) { + // for this type of model, the head is the minimum seq_id of the batch + data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head; + } else { + data[j*n_kv + i] = -1; + } + } + } + } + } } static void llama_graph_compute( @@ -8271,11 +8817,13 @@ static int llama_decode_internal( return 1; } - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); - //kv_self.n = llama_kv_cache_cell_max(kv_self); + if (!kv_self.recurrent) { + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + //kv_self.n = llama_kv_cache_cell_max(kv_self); + } } //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); @@ -8701,6 +9249,26 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { } } + if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) { + llama_set_s_copy(lctx); + + { + ggml_cgraph * gf = llama_build_graph_s_copy(lctx); + + llama_graph_compute(lctx, gf, lctx.cparams.n_threads); + } + + { + auto & kv_self = lctx.kv_self; + + kv_self.do_copy = false; + + for (uint32_t i = 0; i < kv_self.size; ++i) { + kv_self.cells[i].src = i; + } + } + } + // defragment the KV cache if needed if (lctx.kv_self.do_defrag) { llama_kv_cache_defrag_internal(lctx); @@ -11535,6 +12103,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight"); quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight"); + // do not quantize Mamba's small yet 2D weights + // NOTE: can't use LLM_TN here because the layer number is not known + quantize &= name.find("ssm_conv1d.weight") == std::string::npos; + quantize &= name.find("ssm_x.weight") == std::string::npos; + quantize &= name.find("ssm_dt.weight") == std::string::npos; + enum ggml_type new_type; void * new_data; size_t new_size; @@ -11985,6 +12559,7 @@ struct llama_context_params llama_context_default_params() { /*.seed =*/ LLAMA_DEFAULT_SEED, /*.n_ctx =*/ 512, /*.n_batch =*/ 512, + /*.n_parallel =*/ 1, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, @@ -12146,6 +12721,7 @@ struct llama_context * llama_new_context_with_model( auto & cparams = ctx->cparams; cparams.n_batch = params.n_batch; + // TODO: maybe add n_parallel here too cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch; cparams.yarn_ext_factor = params.yarn_ext_factor; @@ -12203,8 +12779,18 @@ struct llama_context * llama_new_context_with_model( ctx->rng = std::mt19937(params.seed); ctx->logits_all = params.logits_all; - const ggml_type type_k = params.type_k; - const ggml_type type_v = params.type_v; + uint32_t kv_size = cparams.n_ctx; + ggml_type type_k = params.type_k; + ggml_type type_v = params.type_v; + + // Mamba only needs a constant number of KV cache cells per sequence + if (model->arch == LLM_ARCH_MAMBA) { + // Mamba needs at least as many KV cells as there are sequences kept at any time + kv_size = std::max((uint32_t) 1, params.n_parallel); + // it's probably best to keep as much precision as possible for the states + type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states + type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states + } GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); @@ -12304,7 +12890,7 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(ctx->backend_cpu); - if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, cparams.n_ctx, cparams.offload_kqv)) { + if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -12338,7 +12924,7 @@ struct llama_context * llama_new_context_with_model( // graph inputs { ggml_init_params init_params = { - /* .mem_size */ ggml_tensor_overhead()*8, + /* .mem_size */ ggml_tensor_overhead()*(8 + 3*(ctx->kv_self.recurrent)), /* .mem_buffer */ nullptr, /* .no_alloc */ true, }; @@ -12347,11 +12933,16 @@ struct llama_context * llama_new_context_with_model( ctx->inp_tokens = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ctx->inp_embd = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch); ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); - ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); - ctx->inp_KQ_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx); - ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); + ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, kv_size, cparams.n_batch); + ctx->inp_KQ_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, kv_size); + ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, kv_size); ctx->inp_mean = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); ctx->inp_cls = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); + if (ctx->kv_self.recurrent) { + ctx->inp_s_copy = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, kv_size); + ctx->inp_s_mask = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, kv_size); + ctx->inp_s_seq = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_I32, kv_size, cparams.n_batch); + } ggml_set_name(ctx->inp_tokens, "inp_tokens"); ggml_set_name(ctx->inp_embd, "inp_embd"); @@ -12361,6 +12952,11 @@ struct llama_context * llama_new_context_with_model( ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); ggml_set_name(ctx->inp_mean, "inp_mean"); ggml_set_name(ctx->inp_cls, "inp_cls"); + if (ctx->kv_self.recurrent) { + ggml_set_name(ctx->inp_s_copy, "inp_s_copy"); + ggml_set_name(ctx->inp_s_mask, "inp_s_mask"); + ggml_set_name(ctx->inp_s_seq, "inp_s_seq"); + } ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); LLAMA_LOG_INFO("%s: %10s input buffer size = %8.2f MiB\n", __func__, @@ -12447,6 +13043,10 @@ uint32_t llama_n_batch(const struct llama_context * ctx) { return ctx->cparams.n_batch; } +uint32_t llama_n_max_seq(const struct llama_context * ctx) { + return ctx->kv_self.size; +} + enum llama_vocab_type llama_vocab_type(const struct llama_model * model) { return model->vocab.type; } @@ -12460,6 +13060,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_MPT: case LLM_ARCH_REFACT: case LLM_ARCH_BLOOM: + case LLM_ARCH_MAMBA: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values @@ -12713,8 +13314,8 @@ void llama_kv_cache_clear(struct llama_context * ctx) { llama_kv_cache_clear(ctx->kv_self); } -void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { - llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1); +bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { + return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1); } void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { @@ -12891,8 +13492,8 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); const size_t kv_buf_size = kv_self.total_size(); const uint32_t kv_head = llama_kv_cache_cell_max(kv_self); @@ -12913,6 +13514,17 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); + if (kv_self.recurrent) { + // v is contiguous for recurrent models + // TODO: use other tensors for state models than k and v + const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head); + + tmp_buf.resize(v_size); + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size()); + data_ctx->write(tmp_buf.data(), tmp_buf.size()); + continue; + } + // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); @@ -13005,8 +13617,8 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); size_t kv_buf_size; uint32_t kv_head; @@ -13027,6 +13639,16 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); inp += k_size; + if (kv_self.recurrent) { + // v is contiguous for recurrent models + // TODO: use other tensors for state models than k and v + const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head); + + ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size); + inp += v_size; + continue; + } + // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); diff --git a/llama.h b/llama.h index 3dc162b07..7a107c7f3 100644 --- a/llama.h +++ b/llama.h @@ -235,6 +235,7 @@ extern "C" { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model uint32_t n_batch; // prompt processing maximum batch size + uint32_t n_parallel; // number of parallel sequences (i.e. distinct states for recurrent models) uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -376,6 +377,7 @@ extern "C" { LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_max_seq (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); @@ -502,7 +504,7 @@ extern "C" { // seq_id < 0 : match any sequence // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) - LLAMA_API void llama_kv_cache_seq_rm( + LLAMA_API bool llama_kv_cache_seq_rm( struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, From fd72d2d2a5e79d61ccef6af3d15f16e5e5cbc352 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 9 Mar 2024 10:30:04 +0100 Subject: [PATCH 71/86] server: tests: add truncated prompt tests, better kv cache size (#5933) * server: tests: add truncated prompt tests, better size * server, tests : update regex --------- Co-authored-by: Georgi Gerganov --- examples/server/server.cpp | 23 +++++++++-- .../server/tests/features/parallel.feature | 5 ++- examples/server/tests/features/server.feature | 41 ++++++++++++++----- examples/server/tests/features/steps/steps.py | 35 +++++++++++++--- 4 files changed, 81 insertions(+), 23 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 59a59d56b..6f4449984 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1128,6 +1128,7 @@ struct server_context { LOG_VERBOSE("stopped by limit", { {"id_slot", slot.id}, + {"id_task", slot.id_task}, {"n_decoded", slot.n_decoded}, {"n_predict", slot.params.n_predict}, }); @@ -1141,6 +1142,8 @@ struct server_context { } LOG_VERBOSE("next token", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, {"token", result.tok}, {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, {"has_next_token", slot.has_next_token}, @@ -1750,6 +1753,15 @@ struct server_context { slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); + LOG_VERBOSE("prompt tokenized", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_prompt_tokens", slot.n_prompt_tokens}, + {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, + }); + if (slot.embedding) { // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_batch) { @@ -1788,10 +1800,13 @@ struct server_context { slot.n_prompt_tokens = prompt_tokens.size(); LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"n_prompt_tokens", slot.n_prompt_tokens}, + {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, }); GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 066698c8e..a66fed626 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -6,8 +6,8 @@ Feature: Parallel Given a server listening on localhost:8080 And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And 42 as server seed - And 512 as batch size - And 64 KV cache size + And 128 as batch size + And 256 KV cache size And 2 slots And continuous batching Then the server is starting @@ -76,6 +76,7 @@ Feature: Parallel | disabled | 128 | | enabled | 64 | + Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 Given a prompt: """ diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 878ac1363..aa132fa34 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -10,11 +10,10 @@ Feature: llama.cpp server # KV Cache corresponds to the total amount of tokens # that can be stored across all independent sequences: #4130 # see --ctx-size and #5568 - And 32 KV cache size - And 512 as batch size - And 1 slots - And embeddings extraction - And 32 server max tokens to predict + And 256 KV cache size + And 32 as batch size + And 2 slots + And 64 server max tokens to predict And prometheus compatible metrics exposed Then the server is starting Then the server is healthy @@ -23,18 +22,35 @@ Feature: llama.cpp server Then the server is ready And all slots are idle + Scenario Outline: Completion Given a prompt And max tokens to predict And a completion request with no api error Then tokens are predicted matching + And the completion is truncated + And prompt tokens are processed And prometheus metrics are exposed And metric llamacpp:tokens_predicted is Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | (read\|going)+ | 8 | - | Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 | + | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | + | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | + | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids)+ | 46 | 64 | not | + + Scenario: Completion prompt truncated + Given a prompt: + """ + Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. + Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. + Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. + Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. + """ + And a completion request with no api error + Then 64 tokens are predicted matching fun|Annaks|popcorns + And the completion is truncated + And 109 prompt tokens are processed + Scenario Outline: OAI Compatibility Given a model @@ -44,11 +60,14 @@ Feature: llama.cpp server And streaming is Given an OAI compatible chat completions request with no api error Then tokens are predicted matching + And prompt tokens are processed + And the completion is truncated Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | - | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | + | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | + | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird)+ | -1 | 64 | enabled | | + Scenario: Tokenize / Detokenize When tokenizing: diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index d7f005836..0076f805b 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -196,12 +196,30 @@ async def step_request_completion(context, api_error): @step(u'{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content) + context.completion = context.tasks_result.pop() + assert_n_tokens_predicted(context.completion, predicted_n, re_content) @step(u'{predicted_n:d} tokens are predicted') def step_n_tokens_predicted(context, predicted_n): - assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n) + context.completion = context.tasks_result.pop() + assert_n_tokens_predicted(context.completion, predicted_n) + + +@step(u'the completion is truncated') +def step_assert_completion_truncated(context): + step_assert_completion_truncated(context, '') + + +@step(u'the completion is {truncated} truncated') +def step_assert_completion_truncated(context, truncated): + truncated = truncated != "not" + assert context.completion['truncated'] == truncated, f'{context.completion}' + + +@step(u'{n_prompt:d} prompt tokens are processed') +def step_impl(context, n_prompt): + assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}" @step(u'a user prompt {user_prompt}') @@ -722,7 +740,8 @@ async def oai_chat_completions(user_prompt, completion_response = { 'content': '', 'timings': { - 'predicted_n': 0 + 'predicted_n': 0, + 'prompt_n': 0 } } if async_client: @@ -763,7 +782,8 @@ async def oai_chat_completions(user_prompt, completion_response = { 'content': chat_completion_raw['choices'][0]['message'], 'timings': { - 'predicted_n': chat_completion_raw['usage']['completion_tokens'] + 'predicted_n': chat_completion_raw['usage']['completion_tokens'], + 'prompt_n': chat_completion_raw['usage']['prompt_tokens'] } } else: @@ -792,13 +812,16 @@ async def oai_chat_completions(user_prompt, if 'content' in delta: completion_response['content'] += delta['content'] completion_response['timings']['predicted_n'] += 1 + completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop' else: assert len(chat_completion.choices) == 1 completion_response = { 'content': chat_completion.choices[0].message.content, 'timings': { - 'predicted_n': chat_completion.usage.completion_tokens - } + 'predicted_n': chat_completion.usage.completion_tokens, + 'prompt_n': chat_completion.usage.prompt_tokens + }, + 'truncated': chat_completion.choices[0].finish_reason != 'stop' } if debug: print("OAI response formatted to llama.cpp:", completion_response) From e1fa9569ba8ce276bc7801a3cebdcf8b1aa116ea Mon Sep 17 00:00:00 2001 From: Gabe Goodhart Date: Sat, 9 Mar 2024 02:57:09 -0700 Subject: [PATCH 72/86] server : add SSL support (#5926) * add cmake build toggle to enable ssl support in server Signed-off-by: Gabe Goodhart * add flags for ssl key/cert files and use SSLServer if set All SSL setup is hidden behind CPPHTTPLIB_OPENSSL_SUPPORT in the same way that the base httlib hides the SSL support Signed-off-by: Gabe Goodhart * Update readme for SSL support in server Signed-off-by: Gabe Goodhart * Add LLAMA_SERVER_SSL variable setup to top-level Makefile Signed-off-by: Gabe Goodhart --------- Signed-off-by: Gabe Goodhart --- Makefile | 4 ++ examples/server/CMakeLists.txt | 6 ++ examples/server/README.md | 26 ++++++++ examples/server/server.cpp | 108 ++++++++++++++++++++++----------- 4 files changed, 110 insertions(+), 34 deletions(-) diff --git a/Makefile b/Makefile index efce10bb8..aea969222 100644 --- a/Makefile +++ b/Makefile @@ -201,6 +201,10 @@ ifdef LLAMA_SERVER_VERBOSE MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif +ifdef LLAMA_SERVER_SSL + MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT + MK_LDFLAGS += -lssl -lcrypto +endif ifdef LLAMA_CODE_COVERAGE MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index c21eba634..f94de1e99 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) +option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h) install(TARGETS ${TARGET} RUNTIME) @@ -7,6 +8,11 @@ target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) +if (LLAMA_SERVER_SSL) + find_package(OpenSSL REQUIRED) + target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto) + target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT) +endif() if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/README.md b/examples/server/README.md index 591f748f8..bf8c450b6 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -59,6 +59,10 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--log-disable`: Output logs to stdout only, default: enabled. - `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json) +**If compiled with `LLAMA_SERVER_SSL=ON`** +- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key +- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate + ## Build server is build alongside everything else from the root of the project @@ -75,6 +79,28 @@ server is build alongside everything else from the root of the project cmake --build . --config Release ``` +## Build with SSL + +server can also be built with SSL support using OpenSSL 3 + +- Using `make`: + + ```bash + # NOTE: For non-system openssl, use the following: + # CXXFLAGS="-I /path/to/openssl/include" + # LDFLAGS="-L /path/to/openssl/lib" + make LLAMA_SERVER_SSL=true server + ``` + +- Using `CMake`: + + ```bash + mkdir build + cd build + cmake .. -DLLAMA_SERVER_SSL=ON + make server + ``` + ## Quick Start To get started right away, run the following command, making sure to use the correct path for the model you have: diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6f4449984..c3b87c846 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -27,6 +27,7 @@ #include #include #include +#include using json = nlohmann::json; @@ -118,6 +119,11 @@ struct server_params { std::vector api_keys; +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + std::string ssl_key_file = ""; + std::string ssl_cert_file = ""; +#endif + bool slots_endpoint = true; bool metrics_endpoint = false; }; @@ -2142,6 +2148,10 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + printf(" --ssl-key-file FNAME path to file a PEM-encoded SSL private key\n"); + printf(" --ssl-cert-file FNAME path to file a PEM-encoded SSL certificate\n"); +#endif printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embeddings 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); @@ -2220,7 +2230,24 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, } } key_file.close(); - } else if (arg == "--timeout" || arg == "-to") { + + } +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + else if (arg == "--ssl-key-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.ssl_key_file = argv[i]; + } else if (arg == "--ssl-cert-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.ssl_cert_file = argv[i]; + } +#endif + else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) { invalid_param = true; break; @@ -2658,21 +2685,34 @@ int main(int argc, char ** argv) { {"system_info", llama_print_system_info()}, }); - httplib::Server svr; + std::unique_ptr svr; +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") { + LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}}); + svr.reset( + new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str()) + ); + } else { + LOG_INFO("Running without SSL", {}); + svr.reset(new httplib::Server()); + } +#else + svr.reset(new httplib::Server()); +#endif std::atomic state{SERVER_STATE_LOADING_MODEL}; - svr.set_default_headers({{"Server", "llama.cpp"}}); + svr->set_default_headers({{"Server", "llama.cpp"}}); // CORS preflight - svr.Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { + svr->Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res.set_header("Access-Control-Allow-Credentials", "true"); res.set_header("Access-Control-Allow-Methods", "POST"); res.set_header("Access-Control-Allow-Headers", "*"); }); - svr.Get("/health", [&](const httplib::Request & req, httplib::Response & res) { + svr->Get("/health", [&](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); switch (current_state) { case SERVER_STATE_READY: @@ -2728,7 +2768,7 @@ int main(int argc, char ** argv) { }); if (sparams.slots_endpoint) { - svr.Get("/slots", [&](const httplib::Request &, httplib::Response & res) { + svr->Get("/slots", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); @@ -2749,7 +2789,7 @@ int main(int argc, char ** argv) { } if (sparams.metrics_endpoint) { - svr.Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { + svr->Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); @@ -2846,9 +2886,9 @@ int main(int argc, char ** argv) { }); } - svr.set_logger(log_server_request); + svr->set_logger(log_server_request); - svr.set_exception_handler([](const httplib::Request &, httplib::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]; @@ -2864,7 +2904,7 @@ int main(int argc, char ** argv) { res.status = 500; }); - svr.set_error_handler([](const httplib::Request &, httplib::Response & res) { + svr->set_error_handler([](const httplib::Request &, httplib::Response & res) { if (res.status == 401) { res.set_content("Unauthorized", "text/plain; charset=utf-8"); } @@ -2877,16 +2917,16 @@ int main(int argc, char ** argv) { }); // set timeouts and change hostname and port - svr.set_read_timeout (sparams.read_timeout); - svr.set_write_timeout(sparams.write_timeout); + svr->set_read_timeout (sparams.read_timeout); + svr->set_write_timeout(sparams.write_timeout); - if (!svr.bind_to_port(sparams.hostname, sparams.port)) { + if (!svr->bind_to_port(sparams.hostname, sparams.port)) { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } // Set the base directory for serving static files - svr.set_base_dir(sparams.public_path); + svr->set_base_dir(sparams.public_path); std::unordered_map log_data; @@ -2947,30 +2987,30 @@ 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) { + svr->Get("/", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); 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) { + svr->Get("/index.js", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); 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) { + svr->Get("/completion.js", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); 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) { + 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; charset=utf-8"); return false; }); - svr.Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", ctx_server.name_user.c_str() }, @@ -3062,11 +3102,11 @@ int main(int argc, char ** argv) { } }; - svr.Post("/completion", completions); // legacy - svr.Post("/completions", completions); - svr.Post("/v1/completions", completions); + svr->Post("/completion", completions); // legacy + svr->Post("/completions", completions); + svr->Post("/v1/completions", completions); - svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { + svr->Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json models = { @@ -3161,10 +3201,10 @@ int main(int argc, char ** argv) { } }; - svr.Post("/chat/completions", chat_completions); - svr.Post("/v1/chat/completions", chat_completions); + svr->Post("/chat/completions", chat_completions); + svr->Post("/v1/chat/completions", chat_completions); - svr.Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + svr->Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; @@ -3228,11 +3268,11 @@ int main(int argc, char ** argv) { } }); - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { + svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { return res.set_content("", "application/json; charset=utf-8"); }); - svr.Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3244,7 +3284,7 @@ int main(int argc, char ** argv) { return res.set_content(data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3258,7 +3298,7 @@ int main(int argc, char ** argv) { return res.set_content(data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3289,7 +3329,7 @@ int main(int argc, char ** argv) { return res.set_content(result.data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3360,13 +3400,13 @@ int main(int argc, char ** argv) { sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() { - if (!svr.listen_after_bind()) { + if (!svr->listen_after_bind()) { state.store(SERVER_STATE_ERROR); return 1; } @@ -3407,7 +3447,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.start_loop(); - svr.stop(); + svr->stop(); t.join(); llama_backend_free(); From 950ba1ab84db199f0bbdecdb2bb911f35261b321 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 9 Mar 2024 11:27:53 +0100 Subject: [PATCH 73/86] Server: reorganize some http logic (#5939) * refactor static file handler * use set_pre_routing_handler for validate_api_key * merge embedding handlers * correct http verb for endpoints * fix embedding response * fix test case CORS Options * fix code style --- examples/server/README.md | 4 +- examples/server/server.cpp | 725 +++++++++--------- .../server/tests/features/security.feature | 5 +- examples/server/tests/features/steps/steps.py | 3 +- 4 files changed, 379 insertions(+), 358 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index bf8c450b6..3abb1abe3 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -42,7 +42,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. -- `--path`: path from which to serve static files (default examples/server/public) +- `--path`: path from which to serve static files (default: disabled) - `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. - `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s. - `--embedding`: Enable embedding extraction, Default: disabled. @@ -558,7 +558,7 @@ The HTTP server supports OAI-like API ### Extending or building alternative Web Front End -The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. +You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. Read the documentation in `/completion.js` to see convenient ways to access llama. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c3b87c846..6e0f8328c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -113,7 +113,7 @@ struct server_params { int32_t n_threads_http = -1; std::string hostname = "127.0.0.1"; - std::string public_path = "examples/server/public"; + std::string public_path = ""; std::string chat_template = ""; std::string system_prompt = ""; @@ -2145,7 +2145,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); + printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); #ifdef CPPHTTPLIB_OPENSSL_SUPPORT @@ -2211,7 +2211,7 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, invalid_param = true; break; } - sparams.api_keys.emplace_back(argv[i]); + sparams.api_keys.push_back(argv[i]); } else if (arg == "--api-key-file") { if (++i >= argc) { invalid_param = true; @@ -2712,7 +2712,143 @@ int main(int argc, char ** argv) { res.set_header("Access-Control-Allow-Headers", "*"); }); - svr->Get("/health", [&](const httplib::Request & req, httplib::Response & res) { + 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; charset=utf-8"); + res.status = 500; + }); + + svr->set_error_handler([](const httplib::Request &, httplib::Response & res) { + if (res.status == 401) { + res.set_content("Unauthorized", "text/plain; charset=utf-8"); + } + if (res.status == 400) { + res.set_content("Invalid request", "text/plain; charset=utf-8"); + } + if (res.status == 404) { + res.set_content("File Not Found", "text/plain; charset=utf-8"); + } + }); + + // set timeouts and change hostname and port + svr->set_read_timeout (sparams.read_timeout); + svr->set_write_timeout(sparams.write_timeout); + + if (!svr->bind_to_port(sparams.hostname, sparams.port)) { + fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); + return 1; + } + + std::unordered_map log_data; + + log_data["hostname"] = sparams.hostname; + log_data["port"] = std::to_string(sparams.port); + + if (sparams.api_keys.size() == 1) { + auto key = sparams.api_keys[0]; + log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0)); + } else if (sparams.api_keys.size() > 1) { + log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; + } + + // load the model + if (!ctx_server.load_model(params)) { + state.store(SERVER_STATE_ERROR); + return 1; + } else { + ctx_server.initialize(); + state.store(SERVER_STATE_READY); + } + + LOG_INFO("model loaded", {}); + + const auto model_meta = ctx_server.model_meta(); + + if (sparams.chat_template.empty()) { // custom chat template is not supplied + if (!ctx_server.validate_model_chat_template()) { + LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); + sparams.chat_template = "chatml"; + } + } + + // + // Middlewares + // + + auto middleware_validate_api_key = [&sparams](const httplib::Request & req, httplib::Response & res) { + // TODO: should we apply API key to all endpoints, including "/health" and "/models"? + static const std::set protected_endpoints = { + "/props", + "/completion", + "/completions", + "/v1/completions", + "/chat/completions", + "/v1/chat/completions", + "/infill", + "/tokenize", + "/detokenize", + "/embedding", + "/embeddings", + "/v1/embeddings", + }; + + // If API key is not set, skip validation + if (sparams.api_keys.empty()) { + return true; + } + + // If path is not in protected_endpoints list, skip validation + if (protected_endpoints.find(req.path) == protected_endpoints.end()) { + return true; + } + + // Check for API key in the header + auto auth_header = req.get_header_value("Authorization"); + + std::string prefix = "Bearer "; + if (auth_header.substr(0, prefix.size()) == prefix) { + std::string received_api_key = auth_header.substr(prefix.size()); + if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { + return true; // API key is valid + } + } + + // API key is invalid or not provided + // TODO: make another middleware for CORS related logic + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); + res.status = 401; // Unauthorized + + LOG_WARNING("Unauthorized: Invalid API Key", {}); + + return false; + }; + + // register server middlewares + svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) { + if (!middleware_validate_api_key(req, res)) { + return httplib::Server::HandlerResponse::Handled; + } + return httplib::Server::HandlerResponse::Unhandled; + }); + + // + // Route handlers (or controllers) + // + + const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); switch (current_state) { case SERVER_STATE_READY: @@ -2765,252 +2901,136 @@ int main(int argc, char ** argv) { res.status = 500; // HTTP Internal Server Error } break; } - }); - - if (sparams.slots_endpoint) { - svr->Get("/slots", [&](const httplib::Request &, httplib::Response & res) { - // request slots data using task queue - server_task task; - task.id = ctx_server.queue_tasks.get_new_id(); - task.id_multi = -1; - task.id_target = -1; - task.type = SERVER_TASK_TYPE_METRICS; - - ctx_server.queue_results.add_waiting_task_id(task.id); - ctx_server.queue_tasks.post(task); - - // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); - ctx_server.queue_results.remove_waiting_task_id(task.id); - - res.set_content(result.data["slots"].dump(), "application/json"); - res.status = 200; // HTTP OK - }); - } - - if (sparams.metrics_endpoint) { - svr->Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { - // request slots data using task queue - server_task task; - task.id = ctx_server.queue_tasks.get_new_id(); - task.id_multi = -1; - task.id_target = -1; - task.type = SERVER_TASK_TYPE_METRICS; - task.data.push_back({{"reset_bucket", true}}); - - ctx_server.queue_results.add_waiting_task_id(task.id); - ctx_server.queue_tasks.post(task); - - // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); - ctx_server.queue_results.remove_waiting_task_id(task.id); - - json data = result.data; - - const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; - const uint64_t t_prompt_processing = data["t_prompt_processing"]; - - const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; - const uint64_t t_tokens_generation = data["t_tokens_generation"]; - - const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; - - // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names - json all_metrics_def = json { - {"counter", {{ - {"name", "prompt_tokens_total"}, - {"help", "Number of prompt tokens processed."}, - {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} - }, { - {"name", "prompt_seconds_total"}, - {"help", "Prompt process time"}, - {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} - }, { - {"name", "tokens_predicted_total"}, - {"help", "Number of generation tokens processed."}, - {"value", (uint64_t) data["n_tokens_predicted_total"]} - }, { - {"name", "tokens_predicted_seconds_total"}, - {"help", "Predict process time"}, - {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} - }}}, - {"gauge", {{ - {"name", "prompt_tokens_seconds"}, - {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} - },{ - {"name", "predicted_tokens_seconds"}, - {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} - },{ - {"name", "kv_cache_usage_ratio"}, - {"help", "KV-cache usage. 1 means 100 percent usage."}, - {"value", 1. * kv_cache_used_cells / params.n_ctx} - },{ - {"name", "kv_cache_tokens"}, - {"help", "KV-cache tokens."}, - {"value", (uint64_t) data["kv_cache_tokens_count"]} - },{ - {"name", "requests_processing"}, - {"help", "Number of request processing."}, - {"value", (uint64_t) data["processing"]} - },{ - {"name", "requests_deferred"}, - {"help", "Number of request deferred."}, - {"value", (uint64_t) data["deferred"]} - }}} - }; - - std::stringstream prometheus; - - for (const auto & el : all_metrics_def.items()) { - const auto & type = el.key(); - const auto & metrics_def = el.value(); - - for (const auto & metric_def : metrics_def) { - const std::string name = metric_def["name"]; - const std::string help = metric_def["help"]; - - auto value = json_value(metric_def, "value", 0.); - prometheus << "# HELP llamacpp:" << name << " " << help << "\n" - << "# TYPE llamacpp:" << name << " " << type << "\n" - << "llamacpp:" << name << " " << value << "\n"; - } - } - - const int64_t t_start = data["t_start"]; - res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); - - res.set_content(prometheus.str(), "text/plain; version=0.0.4"); - res.status = 200; // HTTP OK - }); - } - - 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; charset=utf-8"); - res.status = 500; - }); - - svr->set_error_handler([](const httplib::Request &, httplib::Response & res) { - if (res.status == 401) { - res.set_content("Unauthorized", "text/plain; charset=utf-8"); - } - if (res.status == 400) { - res.set_content("Invalid request", "text/plain; charset=utf-8"); - } - if (res.status == 404) { - res.set_content("File Not Found", "text/plain; charset=utf-8"); - } - }); - - // set timeouts and change hostname and port - svr->set_read_timeout (sparams.read_timeout); - svr->set_write_timeout(sparams.write_timeout); - - if (!svr->bind_to_port(sparams.hostname, sparams.port)) { - fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); - return 1; - } - - // Set the base directory for serving static files - svr->set_base_dir(sparams.public_path); - - std::unordered_map log_data; - - log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); - - if (sparams.api_keys.size() == 1) { - log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4); - } else if (sparams.api_keys.size() > 1) { - log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; - } - - // load the model - if (!ctx_server.load_model(params)) { - state.store(SERVER_STATE_ERROR); - return 1; - } else { - ctx_server.initialize(); - state.store(SERVER_STATE_READY); - } - - LOG_INFO("model loaded", {}); - - const auto model_meta = ctx_server.model_meta(); - - if (sparams.chat_template.empty()) { // custom chat template is not supplied - if (!ctx_server.validate_model_chat_template()) { - LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "chatml"; - } - } - - // Middleware for API key validation - auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { - // If API key is not set, skip validation - if (sparams.api_keys.empty()) { - return true; - } - - // Check for API key in the header - auto auth_header = req.get_header_value("Authorization"); - - std::string prefix = "Bearer "; - if (auth_header.substr(0, prefix.size()) == prefix) { - std::string received_api_key = auth_header.substr(prefix.size()); - if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { - return true; // API key is valid - } - } - - // API key is invalid or not provided - res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); - res.status = 401; // Unauthorized - - LOG_WARNING("Unauthorized: Invalid API Key", {}); - - return false; }; - // 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; charset=utf-8"); - return false; - }); + const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) { + if (!sparams.slots_endpoint) { + res.status = 501; + res.set_content("This server does not support slots endpoint.", "text/plain; charset=utf-8"); + return; + } - // 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; charset=utf-8"); - return false; - }); + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; - // 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; charset=utf-8"); - return false; - }); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - // 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; charset=utf-8"); - return false; - }); + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - svr->Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_content(result.data["slots"].dump(), "application/json"); + res.status = 200; // HTTP OK + }; + + const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { + if (!sparams.metrics_endpoint) { + res.status = 501; + res.set_content("This server does not support metrics endpoint.", "text/plain; charset=utf-8"); + return; + } + + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; + task.data.push_back({{"reset_bucket", true}}); + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + json data = result.data; + + const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; + const uint64_t t_prompt_processing = data["t_prompt_processing"]; + + const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; + const uint64_t t_tokens_generation = data["t_tokens_generation"]; + + const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; + + // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names + json all_metrics_def = json { + {"counter", {{ + {"name", "prompt_tokens_total"}, + {"help", "Number of prompt tokens processed."}, + {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} + }, { + {"name", "prompt_seconds_total"}, + {"help", "Prompt process time"}, + {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} + }, { + {"name", "tokens_predicted_total"}, + {"help", "Number of generation tokens processed."}, + {"value", (uint64_t) data["n_tokens_predicted_total"]} + }, { + {"name", "tokens_predicted_seconds_total"}, + {"help", "Predict process time"}, + {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} + }}}, + {"gauge", {{ + {"name", "prompt_tokens_seconds"}, + {"help", "Average prompt throughput in tokens/s."}, + {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} + },{ + {"name", "predicted_tokens_seconds"}, + {"help", "Average generation throughput in tokens/s."}, + {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} + },{ + {"name", "kv_cache_usage_ratio"}, + {"help", "KV-cache usage. 1 means 100 percent usage."}, + {"value", 1. * kv_cache_used_cells / params.n_ctx} + },{ + {"name", "kv_cache_tokens"}, + {"help", "KV-cache tokens."}, + {"value", (uint64_t) data["kv_cache_tokens_count"]} + },{ + {"name", "requests_processing"}, + {"help", "Number of request processing."}, + {"value", (uint64_t) data["processing"]} + },{ + {"name", "requests_deferred"}, + {"help", "Number of request deferred."}, + {"value", (uint64_t) data["deferred"]} + }}} + }; + + std::stringstream prometheus; + + for (const auto & el : all_metrics_def.items()) { + const auto & type = el.key(); + const auto & metrics_def = el.value(); + + for (const auto & metric_def : metrics_def) { + const std::string name = metric_def["name"]; + const std::string help = metric_def["help"]; + + auto value = json_value(metric_def, "value", 0.); + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << value << "\n"; + } + } + + const int64_t t_start = data["t_start"]; + res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); + + res.set_content(prometheus.str(), "text/plain; version=0.0.4"); + res.status = 200; // HTTP OK + }; + + const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", ctx_server.name_user.c_str() }, @@ -3020,13 +3040,10 @@ int main(int argc, char ** argv) { }; res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + }; - const auto completions = [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } json data = json::parse(req.body); @@ -3102,11 +3119,7 @@ int main(int argc, char ** argv) { } }; - svr->Post("/completion", completions); // legacy - svr->Post("/completions", completions); - svr->Post("/v1/completions", completions); - - svr->Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { + const auto handle_models = [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json models = { @@ -3123,14 +3136,10 @@ int main(int argc, char ** argv) { }; res.set_content(models.dump(), "application/json; charset=utf-8"); - }); + }; - const auto chat_completions = [&ctx_server, &validate_api_key, &sparams](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, &sparams](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); const int id_task = ctx_server.queue_tasks.get_new_id(); @@ -3201,14 +3210,8 @@ int main(int argc, char ** argv) { } }; - svr->Post("/chat/completions", chat_completions); - svr->Post("/v1/chat/completions", chat_completions); - - svr->Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + const auto handle_infill = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } json data = json::parse(req.body); @@ -3266,13 +3269,9 @@ int main(int argc, char ** argv) { res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } - }); + }; - svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { - return res.set_content("", "application/json; charset=utf-8"); - }); - - svr->Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3282,9 +3281,9 @@ int main(int argc, char ** argv) { } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + }; - svr->Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3296,9 +3295,9 @@ int main(int argc, char ** argv) { const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + }; - svr->Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_embeddings = [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3307,94 +3306,114 @@ int main(int argc, char ** argv) { } const json body = json::parse(req.body); + bool is_openai = false; - json prompt; - if (body.count("content") != 0) { - prompt = body["content"]; - } else { - prompt = ""; - } - - // create and queue the task - const int id_task = ctx_server.queue_tasks.get_new_id(); - - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0} }, false, true); - - // get the result - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); - - // send the result - return res.set_content(result.data.dump(), "application/json; charset=utf-8"); - }); - - svr->Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!params.embedding) { - res.status = 501; - res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); - return; - } - - const json body = json::parse(req.body); - - json prompt; + // an input prompt can string or a list of tokens (integer) + std::vector prompts; if (body.count("input") != 0) { - prompt = body["input"]; - if (prompt.is_array()) { - json data = json::array(); - - int i = 0; - for (const json & elem : prompt) { - const int id_task = ctx_server.queue_tasks.get_new_id(); - - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", elem}, { "n_predict", 0} }, false, true); - - // get the result - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); - - json embedding = json{ - {"embedding", json_value(result.data, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }; - - data.push_back(embedding); + is_openai = true; + if (body["input"].is_array()) { + // support multiple prompts + for (const json & elem : body["input"]) { + prompts.push_back(elem); } - - json result = format_embeddings_response_oaicompat(body, data); - - return res.set_content(result.dump(), "application/json; charset=utf-8"); + } else { + // single input prompt + prompts.push_back(body["input"]); } + } else if (body.count("content") != 0) { + // only support single prompt here + std::string content = body["content"]; + prompts.push_back(content); } else { - prompt = ""; + // TODO @ngxson : should return an error here + prompts.push_back(""); } - // create and queue the task - const int id_task = ctx_server.queue_tasks.get_new_id(); + // process all prompts + json responses = json::array(); + for (auto & prompt : prompts) { + // TODO @ngxson : maybe support multitask for this endpoint? + // create and queue the task + const int id_task = ctx_server.queue_tasks.get_new_id(); - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); - // get the result - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); - - json data = json::array({json{ - {"embedding", json_value(result.data, "embedding", json::array())}, - {"index", 0}, - {"object", "embedding"} - }} - ); - - json root = format_embeddings_response_oaicompat(body, data); + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + responses.push_back(result.data); + } + // write JSON response + json root; + if (is_openai) { + json res_oai = json::array(); + int i = 0; + for (auto & elem : responses) { + res_oai.push_back(json{ + {"embedding", json_value(elem, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }); + } + root = format_embeddings_response_oaicompat(body, res_oai); + } else { + root = responses[0]; + } return res.set_content(root.dump(), "application/json; charset=utf-8"); - }); + }; + // + // Router + // + + // register static assets routes + if (!sparams.public_path.empty()) { + // Set the base directory for serving static files + svr->set_base_dir(sparams.public_path); + } + + // using embedded static files + auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { + return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(content), len, mime_type); + return false; + }; + }; + + svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { + // TODO @ngxson : I have no idea what it is... maybe this is redundant? + return res.set_content("", "application/json; charset=utf-8"); + }); + svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); + svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); + svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); + svr->Get("/json-schema-to-grammar.mjs", handle_static_file( + json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + + // register API routes + svr->Get ("/health", handle_health); + svr->Get ("/slots", handle_slots); + svr->Get ("/metrics", handle_metrics); + svr->Get ("/props", handle_props); + svr->Get ("/v1/models", handle_models); + svr->Post("/completion", handle_completions); // legacy + svr->Post("/completions", handle_completions); + svr->Post("/v1/completions", handle_completions); + svr->Post("/chat/completions", handle_chat_completions); + svr->Post("/v1/chat/completions", handle_chat_completions); + svr->Post("/infill", handle_infill); + svr->Post("/embedding", handle_embeddings); // legacy + svr->Post("/embeddings", handle_embeddings); + svr->Post("/v1/embeddings", handle_embeddings); + svr->Post("/tokenize", handle_tokenize); + svr->Post("/detokenize", handle_detokenize); + + // + // Start the server + // if (sparams.n_threads_http < 1) { // +2 threads for monitoring endpoints sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index 42a6709a5..1d6aa40ea 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -39,8 +39,9 @@ Feature: Security Scenario Outline: CORS Options - When an OPTIONS request is sent from - Then CORS header is set to + Given a user api key llama.cpp + When an OPTIONS request is sent from + Then CORS header is set to Examples: Headers | origin | cors_header | cors_header_value | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 0076f805b..142048509 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -582,8 +582,9 @@ async def step_detokenize(context): @async_run_until_complete async def step_options_request(context, origin): async with aiohttp.ClientSession() as session: + headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin} async with session.options(f'{context.base_url}/v1/chat/completions', - headers={"Origin": origin}) as response: + headers=headers) as response: assert response.status == 200 context.options_response = response From 9674aaf35cb81478eb38c3f3ebde713ec72fbb79 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 12:34:18 +0200 Subject: [PATCH 74/86] server : simplify logic for empty prompts (#5953) --- examples/server/server.cpp | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6e0f8328c..aedf0afc6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1704,19 +1704,6 @@ struct server_context { // next, batch any pending prompts without exceeding n_batch if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { - const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()); - - // empty prompt passed -> release the slot and send empty response - // note: infill mode allows empty prompt - if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT && !has_prompt && !slot.infill) { - slot.state = SLOT_STATE_PROCESSING; - slot.command = SLOT_COMMAND_NONE; - slot.release(); - slot.print_timings(); - send_final_response(slot); - continue; - } - // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) { auto & prompt_tokens = slot.prompt_tokens; @@ -1768,6 +1755,21 @@ struct server_context { {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, }); + // empty prompt passed -> release the slot and send empty response + if (prompt_tokens.empty()) { + LOG_INFO("empty prompt - releasing slot", { + {"id_slot", slot.id}, + {"id_task", slot.id_task} + }); + + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; + slot.release(); + slot.print_timings(); + send_final_response(slot); + continue; + } + if (slot.embedding) { // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_batch) { From 8a3012a4ad08112bb3dc3f1399afec4e93780c44 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 12:47:57 +0200 Subject: [PATCH 75/86] ggml : add ggml-common.h to deduplicate shared code (#5940) * ggml : add ggml-common.h to shared code ggml-ci * scripts : update sync scripts * sycl : reuse quantum tables ggml-ci * ggml : minor * ggml : minor * sycl : try to fix build --- CMakeLists.txt | 3 +- Makefile | 4 +- ggml-common.h | 779 ++++++++++++++++++++++++++++++++++++++++ ggml-cuda.cu | 743 +------------------------------------- ggml-metal.metal | 707 +----------------------------------- ggml-quants.c | 708 +----------------------------------- ggml-quants.h | 4 +- ggml-sycl.cpp | 384 +------------------- scripts/sync-ggml-am.sh | 2 + scripts/sync-ggml.sh | 1 + 10 files changed, 799 insertions(+), 2536 deletions(-) create mode 100644 ggml-common.h diff --git a/CMakeLists.txt b/CMakeLists.txt index 48880f720..9309ca6bb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -199,7 +199,8 @@ if (LLAMA_METAL) # get full path to the file #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") - # copy ggml-metal.metal to bin directory + # copy ggml-common.h and ggml-metal.metal to bin directory + configure_file(ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) if (LLAMA_METAL_EMBED_LIBRARY) diff --git a/Makefile b/Makefile index aea969222..d809b8b3b 100644 --- a/Makefile +++ b/Makefile @@ -453,7 +453,7 @@ endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) endif -ggml-cuda.o: ggml-cuda.cu ggml-cuda.h +ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml-common.h ifdef JETSON_EOL_MODULE_DETECT $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ else @@ -630,7 +630,7 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h $(CC) $(CFLAGS) -c $< -o $@ -ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h +ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h $(CC) $(CFLAGS) -c $< -o $@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o diff --git a/ggml-common.h b/ggml-common.h new file mode 100644 index 000000000..4b6d248b6 --- /dev/null +++ b/ggml-common.h @@ -0,0 +1,779 @@ +#pragma once + +#if defined(GGML_COMMON_IMPL_C) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = { +#define GGML_TABLE_END() }; + +#define GGML_COMMON_IMPL +#elif defined(GGML_COMMON_IMPL_METAL) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static const constant type name[size] = { +#define GGML_TABLE_END() }; + +#define GGML_COMMON_IMPL +#elif defined(GGML_COMMON_IMPL_CUDA) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static const __device__ __constant__ type name[size] = { +#define GGML_TABLE_END() }; + +#define GGML_COMMON_IMPL +#elif defined(GGML_COMMON_IMPL_SYCL) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static dpct::global_memory name(sycl::range<1>(size), { +#define GGML_TABLE_END() }); + +#define GGML_COMMON_IMPL +#endif + +#if defined(GGML_COMMON_IMPL) + +GGML_TABLE_BEGIN(uint8_t, kmask_iq2xs, 8) + 1, 2, 4, 8, 16, 32, 64, 128 +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint8_t, ksigns_iq2xs, 128) + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, + 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, + 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, + 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, + 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, + 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, + 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, + 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, +GGML_TABLE_END() + +//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +GGML_TABLE_BEGIN(uint64_t, ksigns64, 128) + 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff, + 0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff, + 0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, + 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff, + 0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff, + 0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, + 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff, + 0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff, + 0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, + 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff, + 0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, + 0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, + 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff, + 0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff, + 0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, + 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff, + 0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff, + 0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, + 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff, + 0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff, + 0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, + 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff, + 0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff, + 0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, + 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff, + 0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff, + 0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, + 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff, + 0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff, + 0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, + 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff, + 0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff, +GGML_TABLE_END() +//#endif + + +GGML_TABLE_BEGIN(uint64_t, iq2xxs_grid, 256) + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, + 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, + 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, + 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, + 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, + 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, + 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, + 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, + 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, + 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, + 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, + 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, + 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, + 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, + 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, + 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, + 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, + 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, + 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, + 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, + 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, + 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, + 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, + 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, + 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, + 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, + 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, + 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, + 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, + 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, + 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, + 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, + 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, + 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, + 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, + 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint64_t, iq2xs_grid, 512) + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, + 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, + 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, + 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, + 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, + 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, + 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, + 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, + 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, + 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, + 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, + 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, + 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, + 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, + 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, + 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, + 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, + 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, + 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, + 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, + 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, + 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, + 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, + 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, + 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, + 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, + 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, + 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, + 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, + 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, + 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, + 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, + 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, + 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, + 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, + 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, + 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, + 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, + 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, + 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, + 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, + 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, + 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, + 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, + 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, + 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, + 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, + 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, + 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, + 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, + 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, + 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, + 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, + 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, + 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, + 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, + 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, + 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, + 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, + 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, + 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, + 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, + 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, + 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, + 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, + 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, + 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, + 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, + 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, + 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, + 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, + 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, + 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, + 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, + 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, + 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, + 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, + 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, + 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, + 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, + 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, + 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, + 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, + 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, + 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, + 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, + 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, + 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, + 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, + 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, + 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, + 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, + 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, + 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, + 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, + 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, + 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, + 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, + 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, + 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, + 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, + 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, + 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, + 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint64_t, iq2s_grid, 1024) + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 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0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, + 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, + 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, + 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, + 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, + 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, + 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, + 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, + 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, + 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, + 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, + 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, + 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, + 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, + 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, + 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, + 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, + 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, + 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, + 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, + 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, + 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, + 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, + 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, + 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, + 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, + 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, + 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, + 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, + 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, + 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, + 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, + 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, + 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, + 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, + 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, + 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, + 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, + 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, + 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, + 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, + 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, + 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, + 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, + 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, + 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, + 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, + 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, + 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, + 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, + 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint32_t, iq3xxs_grid, 256) + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, + 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, + 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, + 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, + 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, + 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512) + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, +GGML_TABLE_END() + +#define NGRID_IQ2XXS 512 +GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ2XXS) + 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, + 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, + 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, + 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, + 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, + 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, + 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, + 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, + 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, + 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, + 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, + 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, + 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, + 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, + 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, + 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, + 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, + 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, + 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, + 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, + 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, + 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, + 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, + 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, + 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, + 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, + 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, + 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, + 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, + 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, + 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, + 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, + 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, + 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, + 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, + 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, + 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, + 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, + 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, + 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, + 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, + 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, + 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, + 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, + 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, + 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, + 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, + 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, + 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, + 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, + 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, + 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, + 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, + 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, + 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, + 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, + 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, + 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, + 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, + 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, + 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, + 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, + 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, + 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, + 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, + 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, + 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, + 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, + 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, + 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, + 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, + 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, + 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, + 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, + 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, + 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, + 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, + 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, + 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, + 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, + 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, + 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, + 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, + 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, + 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, + 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, + 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, + 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, + 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, + 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, + 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, + 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, + 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, + 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, + 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, + 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, + 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, + 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, + 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, + 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, + 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, + 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, + 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, + 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, + 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, + 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, + 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, + 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, + 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, + 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, + 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, + 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, + 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, + 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, + 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, + 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, + 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, + 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, + 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, + 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, + 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, + 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, + 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, + 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, + 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, + 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, + 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, + 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, +GGML_TABLE_END() + +#endif // GGML_COMMON_IMPL diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 72bcec8cd..c207ff87a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2,6 +2,9 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#define GGML_COMMON_IMPL_CUDA +#include "ggml-common.h" + #include #include #include @@ -1569,746 +1572,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t #endif } -static const __device__ uint64_t iq2xxs_grid[256] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, - 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, - 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, - 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, - 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, - 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, - 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, - 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, - 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, - 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, - 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, - 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, - 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, - 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, - 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, - 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, - 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, - 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, - 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, - 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, - 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, - 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, - 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, - 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, - 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, - 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, - 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, - 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, - 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, - 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, - 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, - 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, - 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, - 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, - 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, - 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, -}; - -static const __device__ uint64_t iq2xs_grid[512] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, - 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, - 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, - 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, - 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, - 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, - 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, - 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, - 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, - 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, - 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, - 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, - 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, - 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, - 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, - 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, - 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, - 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, - 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, - 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, - 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, - 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, - 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, - 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, - 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, - 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, - 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, - 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, - 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, - 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, - 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, - 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, - 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, - 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, - 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, - 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, - 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, - 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, - 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, - 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, - 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, - 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, - 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, - 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, - 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, - 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, - 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, - 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, - 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, - 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, - 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, - 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, - 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, - 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, - 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, - 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, - 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, - 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, - 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, - 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, - 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, - 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, - 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, - 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, - 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, - 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, - 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, - 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, - 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, - 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, - 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, - 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, - 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, - 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, -}; - -static const __device__ uint64_t iq2s_grid[1024] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, - 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, - 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, - 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, - 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, - 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, - 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, - 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, - 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, - 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, - 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, - 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, - 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, - 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, - 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, - 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, - 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, - 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, - 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, - 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, - 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, - 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, - 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, - 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, - 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, - 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, - 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, - 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, - 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, - 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, - 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, - 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, - 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, - 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, - 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, - 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, - 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, - 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, - 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, - 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, - 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, - 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, - 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, - 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, - 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, - 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, - 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, - 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, - 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, - 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, - 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, - 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, - 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, - 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, - 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, - 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, - 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, - 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, - 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, - 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, - 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, - 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, - 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, - 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, - 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, - 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, - 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, - 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, - 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, - 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, - 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, - 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, - 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, - 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, - 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, - 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, - 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, - 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, - 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, - 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, - 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, - 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, - 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, - 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, - 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, - 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, - 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, - 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, - 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, - 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, - 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, - 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, - 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, - 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, - 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, - 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, - 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, - 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, - 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, - 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, - 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, - 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, - 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, - 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, - 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, - 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, - 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, - 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, - 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, - 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, - 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, - 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, - 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, - 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, - 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, - 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, - 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, - 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, - 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, - 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, - 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, - 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, - 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, - 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, - 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, - 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, - 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, - 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, - 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, - 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, - 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, - 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, - 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, - 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, - 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, - 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, - 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, - 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, - 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, - 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, - 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, - 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, - 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, - 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, - 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, - 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, - 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, - 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, - 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, - 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, - 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, - 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, - 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, - 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, - 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, - 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, - 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, - 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, - 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, - 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, - 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, - 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, - 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, - 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, - 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, - 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, - 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, - 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, - 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, - 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, - 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, - 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, - 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, - 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, - 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, - 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, - 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, - 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, - 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, - 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, - 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, - 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, - 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, - 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, - 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, - 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, - 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, - 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, - 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, - 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, - 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, - 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, - 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, - 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, - 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, - 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, - 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, - 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, - 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, - 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, - 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, - 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, - 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, - 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, - 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, - 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, - 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, - 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, - 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, - 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, - 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, - 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, - 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, - 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, - 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, - 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, - 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, - 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, - 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, - 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, - 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, - 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, - 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, - 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, - 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, - 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, - 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, - 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, - 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, - 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, - 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, - 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, - 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, - 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, - 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, - 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, - 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, -}; - -static const __device__ uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, - 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, - 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, - 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, - 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, - 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, - 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, - 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, - 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, -}; - -static const __device__ uint32_t iq3s_grid[512] = { - 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, - 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, - 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, - 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, - 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, - 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, - 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, - 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, - 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, - 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, - 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, - 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, - 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, - 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, - 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, - 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, - 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, - 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, - 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, - 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, - 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, - 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, - 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, - 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, - 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, - 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, - 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, - 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, - 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, - 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, - 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, - 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, - 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, - 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, - 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, - 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, - 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, - 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, - 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, - 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, - 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, - 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, - 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, - 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, - 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, - 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, - 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, - 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, - 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, - 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, - 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, - 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, - 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, - 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, - 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, - 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, - 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, - 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, - 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, - 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, - 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, - 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, - 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, - 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, -}; - -static const __device__ uint64_t iq1s_grid[512] = { - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, - 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, - 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, - 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, - 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, - 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, - 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, - 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, - 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, - 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, - 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, - 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, - 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, - 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, - 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, - 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, - 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, - 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, - 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, - 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, - 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, - 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, - 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, - 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, - 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, - 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, - 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, - 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, - 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, - 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, - 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, - 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, - 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, - 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, - 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, - 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, - 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, - 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, - 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, - 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, - 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, - 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, - 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, - 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, - 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, - 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, - 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, - 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, - 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, - 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, - 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, - 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, - 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, - 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, - 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, - 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, - 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, - 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, - 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, - 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, - 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, - 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, - 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, - 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, - 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, - 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, -}; - -static const __device__ uint8_t ksigns_iq2xs[128] = { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, - 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, - 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, - 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, - 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, - 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, - 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, - 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, -}; - -//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -static const __device__ uint64_t ksigns64[128] = { - 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff, - 0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff, - 0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, - 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff, - 0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff, - 0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, - 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff, - 0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff, - 0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, - 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff, - 0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, - 0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, - 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff, - 0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff, - 0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, - 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff, - 0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff, - 0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, - 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff, - 0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff, - 0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, - 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff, - 0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff, - 0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, - 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff, - 0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff, - 0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, - 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff, - 0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff, - 0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, - 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff, - 0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff, -}; -//#endif - -static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; - inline bool ggml_cuda_supports_mmq(enum ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: diff --git a/ggml-metal.metal b/ggml-metal.metal index a65d12641..6ebbbd195 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1,5 +1,8 @@ #include +#define GGML_COMMON_IMPL_METAL +#include "ggml-common.h" + using namespace metal; #define MAX(x, y) ((x) > (y) ? (x) : (y)) @@ -3638,710 +3641,6 @@ kernel void kernel_mul_mv_q6_K_f32( // ======================= "True" 2-bit -constexpr constant static uint64_t iq2xxs_grid[256] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, - 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, - 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, - 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, - 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, - 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, - 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, - 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, - 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, - 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, - 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, - 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, - 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, - 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, - 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, - 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, - 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, - 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, - 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, - 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, - 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, - 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, - 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, - 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, - 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, - 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, - 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, - 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, - 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, - 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, - 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, - 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, - 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, - 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, - 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, - 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, -}; - -constexpr constant static uint64_t iq2xs_grid[512] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, - 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, - 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, - 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, - 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, - 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, - 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, - 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, - 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, - 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, - 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, - 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, - 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, - 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, - 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, - 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, - 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, - 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, - 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, - 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, - 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, - 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, - 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, - 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, - 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, - 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, - 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, - 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, - 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, - 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, - 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, - 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, - 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, - 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, - 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, - 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, - 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, - 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, - 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, - 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, - 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, - 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, - 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, - 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, - 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, - 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, - 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, - 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, - 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, - 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, - 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, - 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, - 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, - 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, - 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, - 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, - 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, - 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, - 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, - 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, - 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, - 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, - 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, - 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, - 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, - 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, - 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, - 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, - 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, - 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, - 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, - 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, - 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, - 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, -}; - -constexpr constant static uint64_t iq2s_grid[1024] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, - 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, - 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, - 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, - 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, - 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0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, - 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, - 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, - 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, - 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, - 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, - 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, - 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, - 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, - 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, - 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, - 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, - 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, - 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, - 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, - 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, - 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, - 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, - 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, - 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, - 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, - 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, - 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, - 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, - 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, - 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, - 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, - 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, - 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, - 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, - 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, - 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, - 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, - 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, - 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, - 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, - 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, - 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, - 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, - 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, - 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, - 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, - 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, - 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, - 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, - 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, - 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, - 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, - 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, - 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, - 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, - 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, - 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, - 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, - 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, - 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, - 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, - 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, - 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, -}; - -constexpr constant static uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, - 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, - 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, - 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, - 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, - 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, - 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, - 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, - 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, -}; - -constexpr constant static uint32_t iq3s_grid[512] = { - 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, - 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, - 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, - 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, - 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, - 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, - 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, - 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, - 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, - 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, - 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, - 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, - 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, - 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, - 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, - 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, - 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, - 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, - 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, - 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, - 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, - 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, - 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, - 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, - 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, - 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, - 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, - 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, - 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, - 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, - 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, - 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, - 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, - 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, - 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, - 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, - 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, - 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, - 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, - 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, - 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, - 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, - 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, - 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, - 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, - 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, - 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, - 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, - 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, - 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, - 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, - 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, - 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, - 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, - 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, - 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, - 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, - 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, - 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, - 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, - 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, - 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, - 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, - 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, -}; - -#define NGRID_IQ1S 512 -constexpr constant static uint64_t iq1s_grid[NGRID_IQ1S] = { - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, - 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, - 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, - 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, - 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, - 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, - 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, - 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, - 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, - 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, - 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, - 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, - 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, - 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, - 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, - 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, - 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, - 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, - 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, - 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, - 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, - 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, - 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, - 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, - 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, - 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, - 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, - 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, - 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, - 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, - 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, - 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, - 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, - 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, - 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, - 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, - 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, - 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, - 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, - 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, - 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, - 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, - 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, - 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, - 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, - 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, - 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, - 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, - 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, - 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, - 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, - 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, - 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, - 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, - 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, - 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, - 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, - 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, - 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, - 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, - 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, - 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, - 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, - 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, - 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, - 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, -}; - -constexpr constant static uint8_t ksigns_iq2xs[128] = { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, - 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, - 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, - 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, - 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, - 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, - 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, - 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, -}; - -constexpr constant static uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; - void kernel_mul_mv_iq2_xxs_f32_impl( device const void * src0, device const float * src1, diff --git a/ggml-quants.c b/ggml-quants.c index 9dcb76def..5bb46def3 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1,6 +1,9 @@ #include "ggml-quants.h" #include "ggml-impl.h" +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + #include #include #include @@ -3327,711 +3330,6 @@ size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int // ====================== "True" 2-bit (de)-quantization -static const uint64_t iq2xxs_grid[256] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, - 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, - 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, - 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, - 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, - 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, - 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, - 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, - 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, - 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, - 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, - 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, - 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, - 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, - 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, - 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, - 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, - 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, - 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, - 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, - 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, - 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, - 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, - 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, - 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, - 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, - 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, - 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, - 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, - 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, - 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, - 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, - 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, - 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, - 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, - 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, -}; - -static const uint64_t iq2xs_grid[512] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, - 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, - 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, - 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, - 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, - 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, - 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, - 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, - 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, - 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, - 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, - 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, - 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, - 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, - 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, - 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, - 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, - 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, - 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, - 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, - 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, - 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, - 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, - 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, - 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, - 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, - 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, - 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, - 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, - 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, - 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, - 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, - 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, - 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, - 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, - 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, - 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, - 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, - 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, - 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, - 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, - 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, - 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, - 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, - 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, - 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, - 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, - 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, - 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, - 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, - 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, - 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, - 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, - 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, - 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, - 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, - 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, - 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, - 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, - 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, - 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, - 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, - 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, - 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, - 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, - 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, - 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, - 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, - 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, - 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, - 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, - 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, - 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, - 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, -}; - -static const uint64_t iq2s_grid[1024] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, - 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, - 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, - 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, - 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, - 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, - 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, - 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, - 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, - 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, - 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, - 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, - 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, - 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, - 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, - 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, - 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, - 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, - 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, - 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, - 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, - 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, - 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, - 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, - 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, - 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, - 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, - 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, - 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, - 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, - 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, - 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, - 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, - 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, - 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, - 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, - 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, - 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, - 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, - 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, - 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, - 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, - 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, - 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, - 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, - 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, - 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, - 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, - 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, - 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, - 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, - 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, - 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, - 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, - 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, - 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, - 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, - 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, - 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, - 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, - 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, - 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, - 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, - 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, - 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, - 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, - 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, - 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, - 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, - 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, - 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, - 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, - 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, - 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, - 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, - 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, - 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, - 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, - 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, - 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, - 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, - 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, - 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, - 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, - 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, - 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, - 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, - 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, - 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, - 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, - 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, - 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, - 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, - 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, - 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, - 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, - 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, - 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, - 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, - 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, - 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, - 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, - 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, - 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, - 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, - 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, - 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, - 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, - 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, - 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, - 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, - 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, - 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, - 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, - 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, - 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, - 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, - 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, - 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, - 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, - 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, - 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, - 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, - 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, - 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, - 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, - 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, - 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, - 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, - 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, - 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, - 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, - 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, - 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, - 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, - 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, - 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, - 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, - 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, - 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, - 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, - 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, - 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, - 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, - 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, - 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, - 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, - 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, - 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, - 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, - 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, - 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, - 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, - 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, - 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, - 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, - 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, - 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, - 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, - 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, - 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, - 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, - 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, - 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, - 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, - 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, - 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, - 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, - 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, - 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, - 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, - 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, - 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, - 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, - 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, - 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, - 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, - 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, - 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, - 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, - 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, - 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, - 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, - 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, - 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, - 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, - 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, - 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, - 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, - 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, - 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, - 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, - 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, - 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, - 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, - 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, - 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, - 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, - 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, - 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, - 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, - 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, - 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, - 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, - 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, - 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, - 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, - 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, - 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, - 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, - 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, - 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, - 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, - 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, - 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, - 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, - 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, - 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, - 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, - 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, - 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, - 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, - 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, - 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, - 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, - 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, - 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, - 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, - 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, - 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, - 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, - 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, - 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, - 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, - 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, - 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, - 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, -}; - -static const uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, - 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, - 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, - 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, - 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, - 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, - 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, - 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, - 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, -}; - -static const uint32_t iq3s_grid[512] = { - 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, - 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, - 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, - 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, - 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, - 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, - 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, - 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, - 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, - 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, - 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, - 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, - 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, - 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, - 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, - 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, - 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, - 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, - 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, - 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, - 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, - 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, - 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, - 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, - 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, - 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, - 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, - 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, - 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, - 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, - 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, - 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, - 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, - 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, - 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, - 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, - 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, - 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, - 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, - 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, - 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, - 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, - 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, - 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, - 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, - 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, - 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, - 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, - 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, - 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, - 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, - 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, - 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, - 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, - 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, - 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, - 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, - 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, - 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, - 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, - 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, - 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, - 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, - 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, -}; - -#define NGRID_IQ2XXS 512 -static const uint64_t iq1s_grid[NGRID_IQ2XXS] = { - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, - 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, - 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, - 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, - 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, - 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, - 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, - 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, - 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, - 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, - 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, - 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, - 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, - 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, - 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, - 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, - 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, - 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, - 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, - 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, - 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, - 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, - 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, - 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, - 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, - 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, - 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, - 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, - 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, - 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, - 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, - 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, - 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, - 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, - 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, - 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, - 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, - 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, - 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, - 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, - 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, - 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, - 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, - 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, - 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, - 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, - 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, - 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, - 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, - 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, - 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, - 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, - 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, - 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, - 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, - 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, - 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, - 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, - 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, - 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, - 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, - 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, - 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, - 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, - 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, - 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, - -}; - -static const uint8_t ksigns_iq2xs[128] = { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, - 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, - 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, - 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, - 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, - 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, - 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, - 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, -}; - -static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; - void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; diff --git a/ggml-quants.h b/ggml-quants.h index 316e35687..d2d25eb44 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -1,9 +1,9 @@ #pragma once -#include "ggml-impl.h" - // GGML internal header +#include "ggml-impl.h" + #include #include diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ddd951dd6..6fafa5282 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3144,6 +3144,8 @@ namespace dpct } // COPY from DPCT head files +#define GGML_COMMON_IMPL_SYCL +#include "ggml-common.h" static int g_ggml_sycl_debug=0; #define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) printf(__VA_ARGS__);}while(0) @@ -4745,388 +4747,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri #endif } -static dpct::global_memory - iq2xxs_grid(sycl::range<1>(256), - { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, - 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, - 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, - 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, - 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b082b2b, 0x080808082b2b082b, - 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, - 0x080808192b192b08, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b082b082b, 0x0808082b2b08082b, 0x0808190808080819, - 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, - 0x0808190819082b08, 0x08081908192b0808, 0x080819082b080819, - 0x080819082b081908, 0x080819082b190808, 0x080819082b2b1908, - 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, - 0x080819192b080808, 0x080819192b190819, 0x0808192b08082b19, - 0x0808192b08190808, 0x0808192b19080808, 0x0808192b2b081908, - 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, - 0x08082b0819080819, 0x08082b0819081908, 0x08082b0819190808, - 0x08082b081919082b, 0x08082b082b082b08, 0x08082b1908081908, - 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, - 0x08190808082b0819, 0x0819080819080808, 0x08190808192b0808, - 0x081908082b081908, 0x081908082b190808, 0x081908082b191919, - 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, - 0x0819082b082b1908, 0x0819082b19081919, 0x0819190808080808, - 0x0819190808082b08, 0x08191908082b0808, 0x08191908082b1919, - 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, - 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b0819080808, 0x08192b082b080819, 0x08192b1908080808, - 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, - 0x082b080819081908, 0x082b0808192b0819, 0x082b08082b080808, - 0x082b08082b08082b, 0x082b0819082b2b19, 0x082b081919082b08, - 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, - 0x082b19081919192b, 0x082b191908080808, 0x082b191919080819, - 0x082b1919192b1908, 0x082b192b2b190808, 0x082b2b0808082b08, - 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, - 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x1908080819082b08, 0x190808081919192b, - 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, - 0x19080819192b0819, 0x190808192b080808, 0x190808192b081919, - 0x1908082b08080819, 0x1908082b08190808, 0x1908082b19082b08, - 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, - 0x190819082b192b19, 0x190819190819082b, 0x19081919082b1908, - 0x1908192b08080808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, - 0x19082b192b08082b, 0x19082b2b19081919, 0x19082b2b2b190808, - 0x1919080808080808, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, - 0x191908192b2b1908, 0x1919082b2b190819, 0x191919082b190808, - 0x191919082b19082b, 0x1919191908082b2b, 0x1919192b08080819, - 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, - 0x19192b2b08082b08, 0x192b080808081908, 0x192b080808190808, - 0x192b080819080808, 0x192b0808192b2b08, 0x192b081908080808, - 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, - 0x192b19190819082b, 0x192b19192b081908, 0x192b2b081908082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808082b2b, - 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, - 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, - 0x2b082b080808082b, 0x2b082b1908081908, 0x2b082b2b08190819, - 0x2b19080808081908, 0x2b19080808190808, 0x2b190808082b1908, - 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, - 0x2b191908082b082b, 0x2b19190819081908, 0x2b19191919190819, - 0x2b192b082b080819, 0x2b192b19082b0808, 0x2b2b08080808082b, - 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, - 0x2b2b2b1908081908, - }); - -static dpct::global_memory - iq2xs_grid(sycl::range<1>(512), - { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, - 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, - 0x0808080808191908, 0x080808080819192b, 0x0808080808192b19, - 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, - 0x080808081908192b, 0x0808080819082b19, 0x0808080819190808, - 0x080808081919082b, 0x0808080819191919, 0x0808080819192b08, - 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, - 0x080808082b190819, 0x080808082b191908, 0x080808082b192b19, - 0x080808082b2b0808, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, - 0x0808081908192b2b, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, - 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x0808082b08080808, - 0x0808082b0808082b, 0x0808082b08081919, 0x0808082b08082b08, - 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, - 0x0808082b19191919, 0x0808082b2b080808, 0x0808082b2b082b2b, - 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, - 0x0808190819081919, 0x0808190819082b08, 0x0808190819190819, - 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x0808191908080808, 0x080819190808082b, 0x0808191908081919, - 0x0808191908082b08, 0x0808191908190819, 0x0808191908191908, - 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, - 0x0808192b08080819, 0x0808192b08081908, 0x0808192b08190808, - 0x0808192b082b192b, 0x0808192b19080808, 0x0808192b1908082b, - 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, - 0x08082b0808190819, 0x08082b0808191908, 0x08082b08082b0808, - 0x08082b08082b1919, 0x08082b0819080819, 0x08082b0819081908, - 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b1919080808, - 0x08082b192b080819, 0x08082b192b082b19, 0x08082b2b08080808, - 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, - 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, - 0x081908080819082b, 0x0819080808191919, 0x0819080808192b08, - 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, - 0x0819080819190819, 0x0819080819191908, 0x08190808192b0808, - 0x08190808192b2b2b, 0x081908082b080819, 0x081908082b081908, - 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, - 0x0819081908191908, 0x08190819082b0808, 0x0819081919080819, - 0x0819081919081908, 0x0819081919190808, 0x081908192b080808, - 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, - 0x0819082b19080808, 0x0819082b192b0808, 0x0819190808080808, - 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, - 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, - 0x0819190819190808, 0x08191908192b1908, 0x081919082b080808, - 0x0819191908080819, 0x0819191908081908, 0x0819191908190808, - 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, - 0x08192b0808190808, 0x08192b080819082b, 0x08192b0819080808, - 0x08192b0819191908, 0x08192b082b08192b, 0x08192b1908080808, - 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, - 0x082b080808081919, 0x082b080808082b08, 0x082b080808082b2b, - 0x082b080808190819, 0x082b080808191908, 0x082b0808082b0808, - 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, - 0x082b081908081908, 0x082b081908190808, 0x082b081919080808, - 0x082b081919082b08, 0x082b0819192b1919, 0x082b082b08080808, - 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, - 0x082b1908082b2b19, 0x082b190819080808, 0x082b191908080808, - 0x082b191919080819, 0x082b19191919082b, 0x082b19192b192b19, - 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, - 0x082b2b08082b0808, 0x082b2b0819191919, 0x082b2b082b082b08, - 0x082b2b082b2b082b, 0x082b2b19192b2b08, 0x082b2b192b190808, - 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, - 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, - 0x1908080808190808, 0x190808080819082b, 0x1908080808191919, - 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, - 0x1908080819082b08, 0x1908080819082b2b, 0x1908080819190819, - 0x1908080819191908, 0x19080808192b0808, 0x19080808192b1919, - 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, - 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, - 0x19080819082b0808, 0x1908081919080819, 0x1908081919081908, - 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, - 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b082b2b19, - 0x1908082b19080808, 0x1908190808080808, 0x190819080808082b, - 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, - 0x1908190819080819, 0x1908190819081908, 0x1908190819190808, - 0x190819082b080808, 0x190819082b191908, 0x1908191908080819, - 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, - 0x1908192b08082b2b, 0x1908192b19081908, 0x1908192b19190808, - 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808190808, - 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, - 0x19082b1919081908, 0x19082b1919190808, 0x19082b19192b2b19, - 0x19082b2b08081908, 0x1919080808080808, 0x191908080808082b, - 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, - 0x1919080819080819, 0x1919080819081908, 0x1919080819190808, - 0x191908082b080808, 0x1919081908080819, 0x1919081908081908, - 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, - 0x1919082b2b2b2b2b, 0x1919190808080819, 0x1919190808081908, - 0x1919190808190808, 0x19191908082b0819, 0x1919190819080808, - 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, - 0x191919192b082b08, 0x1919192b082b0819, 0x1919192b192b2b08, - 0x1919192b2b2b0819, 0x19192b0808080808, 0x19192b0808191908, - 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, - 0x19192b2b2b081919, 0x192b080808080819, 0x192b080808081908, - 0x192b080808190808, 0x192b080819080808, 0x192b080819191908, - 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, - 0x192b082b2b19082b, 0x192b190808080808, 0x192b19080819192b, - 0x192b191908190808, 0x192b191919080808, 0x192b191919081919, - 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, - 0x192b2b2b192b082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, - 0x2b0808082b080808, 0x2b0808082b08082b, 0x2b0808082b2b2b08, - 0x2b0808082b2b2b2b, 0x2b08081908080819, 0x2b08081908081908, - 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, - 0x2b08082b082b0808, 0x2b08082b2b080808, 0x2b08082b2b08082b, - 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, - 0x2b0819082b082b19, 0x2b08191908080808, 0x2b08191919081908, - 0x2b0819192b2b1919, 0x2b08192b08192b08, 0x2b08192b192b2b2b, - 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, - 0x2b082b082b2b2b08, 0x2b082b190808192b, 0x2b082b2b082b082b, - 0x2b082b2b2b080808, 0x2b082b2b2b082b08, 0x2b082b2b2b19192b, - 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, - 0x2b1908082b081908, 0x2b19081908080808, 0x2b190819082b082b, - 0x2b190819192b1908, 0x2b19082b1919192b, 0x2b19082b2b082b19, - 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, - 0x2b1919192b190808, 0x2b1919192b19082b, 0x2b19192b19080819, - 0x2b192b0819190819, 0x2b192b082b2b192b, 0x2b192b1919082b19, - 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, - 0x2b2b0808082b0808, 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, - 0x2b2b081919190819, 0x2b2b081919192b19, 0x2b2b08192b2b192b, - 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, - 0x2b2b190819080808, 0x2b2b19082b191919, 0x2b2b192b192b1919, - 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, 0x2b2b2b08082b0808, - 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, - 0x2b2b2b192b08192b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, - 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, - }); - -static dpct::global_memory iq3xxs_grid( - sycl::range<1>(256), - { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, - 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, - 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, 0x040c140c, 0x040c142c, - 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, - 0x04141c1c, 0x04141c3e, 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, - 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, 0x041c3e04, 0x04240c1c, - 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, - 0x043e0c24, 0x043e0c34, 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, - 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, 0x0c041c04, 0x0c041c14, - 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, - 0x0c14140c, 0x0c141c04, 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, - 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, 0x0c24042c, 0x0c242c04, - 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, - 0x14041414, 0x14041434, 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, - 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, 0x140c1c04, 0x140c341c, - 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, - 0x141c0c04, 0x141c0c24, 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, - 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, 0x143e040c, 0x143e041c, - 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, - 0x1c0c1404, 0x1c0c1c0c, 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, - 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, 0x1c1c0c0c, 0x1c1c1c1c, - 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, - 0x24040424, 0x24040c3e, 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, - 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, 0x24143404, 0x24143434, - 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, - 0x2c040c14, 0x2c04240c, 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, - 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, 0x2c1c0414, 0x2c1c2c1c, - 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, - 0x34043424, 0x340c140c, 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, - 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, 0x34341c1c, 0x343e041c, - 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, - 0x3e1c0404, 0x3e1c0c2c, 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, - 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, - }); - -static dpct::global_memory ksigns_iq2xs( - sycl::range<1>(128), - { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, - 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, - 154, 27, 156, 29, 30, 159, 160, 33, 34, 163, 36, 165, 166, - 39, 40, 169, 170, 43, 172, 45, 46, 175, 48, 177, 178, 51, - 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, 192, - 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, - 78, 207, 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, - 219, 92, 221, 222, 95, 96, 225, 226, 99, 228, 101, 102, 231, - 232, 105, 106, 235, 108, 237, 238, 111, 240, 113, 114, 243, 116, - 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, - }); - -static dpct::global_memory - ksigns64(sycl::range<1>(128), - { - 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, - 0x000000000000ffff, 0xff00000000ff0000, 0x0000000000ff00ff, - 0x0000000000ffff00, 0xff00000000ffffff, 0xff000000ff000000, - 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, - 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, - 0x00000000ffffffff, 0xff0000ff00000000, 0x000000ff000000ff, - 0x000000ff0000ff00, 0xff0000ff0000ffff, 0x000000ff00ff0000, - 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, - 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, - 0x000000ffff00ffff, 0xff0000ffffff0000, 0x000000ffffff00ff, - 0x000000ffffffff00, 0xff0000ffffffffff, 0xff00ff0000000000, - 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, - 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, - 0x0000ff0000ffffff, 0x0000ff00ff000000, 0xff00ff00ff0000ff, - 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, 0xff00ff00ffff0000, - 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, - 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, - 0x0000ffff0000ffff, 0xff00ffff00ff0000, 0x0000ffff00ff00ff, - 0x0000ffff00ffff00, 0xff00ffff00ffffff, 0xff00ffffff000000, - 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, - 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, - 0x0000ffffffffffff, 0xffff000000000000, 0x00ff0000000000ff, - 0x00ff00000000ff00, 0xffff00000000ffff, 0x00ff000000ff0000, - 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, - 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, - 0x00ff0000ff00ffff, 0xffff0000ffff0000, 0x00ff0000ffff00ff, - 0x00ff0000ffffff00, 0xffff0000ffffffff, 0x00ff00ff00000000, - 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, - 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, - 0xffff00ff00ffffff, 0xffff00ffff000000, 0x00ff00ffff0000ff, - 0x00ff00ffff00ff00, 0xffff00ffff00ffff, 0x00ff00ffffff0000, - 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, - 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, - 0x00ffff000000ffff, 0xffffff0000ff0000, 0x00ffff0000ff00ff, - 0x00ffff0000ffff00, 0xffffff0000ffffff, 0xffffff00ff000000, - 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, - 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, - 0x00ffff00ffffffff, 0xffffffff00000000, 0x00ffffff000000ff, - 0x00ffffff0000ff00, 0xffffffff0000ffff, 0x00ffffff00ff0000, - 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, - 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, - 0x00ffffffff00ffff, 0xffffffffffff0000, 0x00ffffffffff00ff, - 0x00ffffffffffff00, 0xffffffffffffffff, - }); -//#endif - -static dpct::global_memory - kmask_iq2xs(sycl::range<1>(8), {1, 2, 4, 8, 16, 32, 64, 128}); - template static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy, const sycl::nd_item<3> &item_ct1, diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 2c391e641..83e6359b1 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -94,6 +94,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-alloc.c -> ggml-alloc.c # src/ggml-backend-impl.h -> ggml-backend-impl.h # src/ggml-backend.c -> ggml-backend.c + # src/ggml-common.h -> ggml-common.h # src/ggml-cuda.cu -> ggml-cuda.cu # src/ggml-cuda.h -> ggml-cuda.h # src/ggml-impl.h -> ggml-impl.h @@ -126,6 +127,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \ -e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \ -e 's/src\/ggml-backend\.c/ggml-backend.c/g' \ + -e 's/src\/ggml-common\.h/ggml-common.h/g' \ -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \ -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \ -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \ diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index feb34bbc8..f1e6f0e57 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -4,6 +4,7 @@ cp -rpv ../ggml/src/ggml.c ./ggml.c cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml-backend-impl.h cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c +cp -rpv ../ggml/src/ggml-common.h ./ggml-common.h cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h cp -rpv ../ggml/src/ggml-impl.h ./ggml-impl.h From 0db32beaf09d90b8959d3d0cc493ed1e45685353 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 9 Mar 2024 11:16:53 +0000 Subject: [PATCH 76/86] server : fix passing prompt as tokens (#5955) * server: fix passing prompt as tokens * Update examples/server/server.cpp --------- Co-authored-by: Georgi Gerganov --- examples/server/server.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index aedf0afc6..8cff514f2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -852,7 +852,16 @@ struct server_context { // infill slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); - slot.prompt = json_value(data, "prompt", std::string("")); + + // get prompt + { + const auto & prompt = data.find("prompt"); + if (prompt == data.end()) { + slot.prompt = ""; + } else { + slot.prompt = *prompt; + } + } // penalize user-provided tokens { From 2c4f566c88322ebf2f9bd11b01b5ebdaa0130b89 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 14:17:11 +0200 Subject: [PATCH 77/86] tests : gitignore ggml-common.h --- tests/.gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/.gitignore b/tests/.gitignore index 9427cf13d..620a48ee4 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -1,3 +1,4 @@ * !*.* *.o +ggml-common.h From fb215c3832236fec7380c4fb618bd7154cb196ef Mon Sep 17 00:00:00 2001 From: SeungWon Jeong <65549245+redlion0929@users.noreply.github.com> Date: Sat, 9 Mar 2024 21:27:58 +0900 Subject: [PATCH 78/86] server : normalize embeddings (#5956) * output normalize embedding in '/v1/embeddings' * common : reuse llama_embd_normalize * common : better normalize impl --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 15 +++++++++++++++ common/common.h | 7 +++++++ examples/embedding/embedding.cpp | 14 +------------- examples/server/server.cpp | 8 +++++++- 4 files changed, 30 insertions(+), 14 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index d7f650ef4..16ef4d7f7 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1852,3 +1852,18 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } + +void llama_embd_normalize(const float * inp, float * out, int n) { + double sum = 0.0; + for (int i = 0; i < n; i++) { + sum += inp[i] * inp[i]; + } + sum = sqrt(sum); + + const float norm = sum > 0.0 ? 1.0f / sum : 0.0f; + + for (int i = 0; i < n; i++) { + out[i] = inp[i] * norm; + } +} + diff --git a/common/common.h b/common/common.h index 977ce419f..f8d82b871 100644 --- a/common/common.h +++ b/common/common.h @@ -260,3 +260,10 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); + +// +// Embedding utils +// + +void llama_embd_normalize(const float * inp, float * out, int n); + diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index ff5883da6..a553ae1c3 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -23,17 +23,6 @@ static void batch_add_seq(llama_batch & batch, const std::vector & toke } } -static void normalize(const float * vec, float * out, int n) { - float norm = 0; - for (int i = 0; i < n; i++) { - norm += vec[i] * vec[i]; - } - norm = sqrt(norm); - for (int i = 0; i < n; i++) { - out[i] = vec[i] / norm; - } -} - static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) { // clear previous kv_cache values (irrelevant for embeddings) llama_kv_cache_clear(ctx); @@ -44,7 +33,6 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu fprintf(stderr, "%s : failed to decode\n", __func__); } - // normalize on copy for (int i = 0; i < batch.n_tokens; i++) { if (!batch.logits[i]) { continue; @@ -61,7 +49,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } float * out = output + batch.seq_id[i][0] * n_embd; - normalize(embd, out, n_embd); + llama_embd_normalize(embd, out, n_embd); } } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8cff514f2..796f3499c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1327,6 +1327,8 @@ struct server_context { const int n_embd = llama_n_embd(model); + std::vector embd_res(n_embd, 0.0f); + for (int i = 0; i < batch.n_tokens; ++i) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { continue; @@ -1350,8 +1352,10 @@ struct server_context { continue; } + llama_embd_normalize(embd, embd_res.data(), n_embd); + res.data = json { - {"embedding", std::vector(embd, embd + n_embd)}, + {"embedding", embd_res}, }; } @@ -3354,6 +3358,8 @@ int main(int argc, char ** argv) { // get the result server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); + + // append to the responses responses.push_back(result.data); } From 97c09585d65a95864773b4d25d66d0f708baf38d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 15:47:47 +0200 Subject: [PATCH 79/86] server : clarify some items in the readme (#5957) * server : clarify some items in the readme * server : fix typo --- examples/server/README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 3abb1abe3..23606b32a 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -195,7 +195,11 @@ 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. + `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: + + - The prompt is a string or an array with the first element given as a string + - The model's `tokenizer.ggml.add_bos_token` metadata is `true` + - The system prompt is empty `temperature`: Adjust the randomness of the generated text (default: 0.8). @@ -308,7 +312,7 @@ Notice that each `probs` is an array of length `n_probs`. `content`: Set the text to tokenize. - Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`. + Note that a special `BOS` token is never inserted. - **POST** `/detokenize`: Convert tokens to text. From 5b09797321430f08caf0473143a962916ab2ea89 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 15:53:59 +0200 Subject: [PATCH 80/86] ggml : remove old quantization functions (#5942) * ggml : remove old quantization functions ggml-ci * ggml : simplify ggml_quantize_chunk ggml-ci * ggml : restrict correctness ggml-ci * ggml : remove hist data from the quantization API ggml-ci * tests : remove hist usage in test-backend-ops ggml-ci * vulkan : remove hist and fix typo --- examples/benchmark/benchmark-matmult.cpp | 6 +- examples/llava/clip.cpp | 55 +--- ggml-quants.c | 143 +++------- ggml-quants.h | 42 +-- ggml-vulkan.cpp | 40 +-- ggml.c | 339 +++-------------------- ggml.h | 23 +- llama.cpp | 48 +--- tests/test-backend-ops.cpp | 3 +- 9 files changed, 131 insertions(+), 568 deletions(-) diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index e89f3de2f..47cb16c69 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -189,12 +189,10 @@ int main(int argc, char ** argv) { int32_t nelements = sizex*sizey; - std::vector hist_cur(1 << 4, 0); - // Set up a the benchmark matrices // printf("Creating new tensor q11 & Running quantize\n"); struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr); + ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr); // Set up a the compute graph // printf("Creating new tensor q31\n"); @@ -207,7 +205,7 @@ int main(int argc, char ** argv) { // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr); + ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr); // printf("Creating new tensor q32\n"); struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index ef9e4ba7a..6653b815d 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1862,7 +1862,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i std::vector work(512); std::vector conv_buf(512); - std::vector hist_all(1 << 4, 0); size_t total_size_org = 0; size_t total_size_new = 0; @@ -1917,48 +1916,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i } new_data = work.data(); - std::vector hist_cur(1 << 4, 0); - - switch (new_type) { - case GGML_TYPE_Q4_0: { - new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q4_1: { - new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_0: { - new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_1: { - new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q8_0: { - new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q2_K: { - new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q3_K: { - new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q4_K: { - new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_K: { - new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q6_K: { - new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - default: { - fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type); - return false; - } - } - - for (size_t j = 0; j < hist_cur.size(); ++j) { - hist_all[j] += hist_cur[j]; - } + new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr); } else { new_type = cur->type; new_data = cur->data; @@ -1993,17 +1951,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i { printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); - - int64_t sum_all = 0; - for (size_t i = 0; i < hist_all.size(); ++i) { - sum_all += hist_all[i]; - } - - printf("%s: hist: ", __func__); - for (size_t i = 0; i < hist_all.size(); ++i) { - printf("%5.3f ", hist_all[i] / (float)sum_all); - } - printf("\n"); } return true; diff --git a/ggml-quants.c b/ggml-quants.c index 5bb46def3..4ee4e0604 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1704,16 +1704,6 @@ void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q2_K_reference(x, vy, k); } -size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K; - quantize_row_q2_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q2_K)); -} - static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights, uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, float rmin, float rdelta, int nstep, bool use_mad) { @@ -1966,8 +1956,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } } -size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q2_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row); if (!quant_weights) { quantize_row_q2_K_reference(src, dst, nrow*n_per_row); @@ -2186,16 +2175,6 @@ void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q3_K_reference(x, vy, k); } -size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K; - quantize_row_q3_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q3_K)); -} - static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -2285,8 +2264,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri #endif } -size_t quantize_q3_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q3_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row); if (!quant_weights) { quantize_row_q3_K_reference(src, dst, nrow*n_per_row); @@ -2456,17 +2434,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q4_K_reference(x, y, k); } -size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K; - quantize_row_q4_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q4_K)); -} - static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -2545,8 +2512,7 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri #endif } -size_t quantize_q4_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q4_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row); if (!quant_weights) { quantize_row_q4_K_reference(src, dst, nrow*n_per_row); @@ -2757,17 +2723,6 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q5_K_reference(x, y, k); } -size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K; - quantize_row_q5_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q5_K)); -} - static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -2866,8 +2821,7 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri #endif } -size_t quantize_q5_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q5_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row); if (!quant_weights) { quantize_row_q5_K_reference(src, dst, nrow*n_per_row); @@ -3020,17 +2974,6 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q6_K_reference(x, y, k); } -size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K; - quantize_row_q6_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q6_K)); -} - static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -3120,8 +3063,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri #endif } -size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q6_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row); if (!quant_weights) { quantize_row_q6_K_reference(src, dst, nrow*n_per_row); @@ -3165,9 +3107,10 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri } } -size_t quantize_q4_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q4_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q4_0(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q4_0_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row); char * qrow = (char *)dst; @@ -3209,9 +3152,10 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri } } -size_t quantize_q4_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q4_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q4_1(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q4_1_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row); char * qrow = (char *)dst; @@ -3262,9 +3206,10 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri } } -size_t quantize_q5_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q5_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q5_0(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q5_0_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row); char * qrow = (char *)dst; @@ -3314,9 +3259,10 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri } } -size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q5_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q5_1(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q5_1_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row); char * qrow = (char *)dst; @@ -3328,6 +3274,13 @@ size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int return nrow * row_size; } +size_t quantize_q8_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { + (void)quant_weights; // not used + const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row); + quantize_row_q8_0_reference(src, dst, nrow*n_per_row); + return nrow * row_size; +} + // ====================== "True" 2-bit (de)-quantization void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { @@ -9373,7 +9326,7 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void #endif } -void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); UNUSED(nrc); @@ -9620,7 +9573,7 @@ static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { } #endif -void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); UNUSED(nrc); @@ -10220,7 +10173,7 @@ void iq2xs_init_impl(enum ggml_type type) { int * kmap_q2xs; uint16_t * kneighbors_q2xs; - printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); uint64_t * the_grid = (uint64_t *)malloc(grid_size*sizeof(uint64_t)); for (int k = 0; k < grid_size; ++k) { int8_t * pos = (int8_t *)(the_grid + k); @@ -10275,7 +10228,7 @@ void iq2xs_init_impl(enum ggml_type type) { } num_neighbors += n; } - printf("%s: %d neighbours in total\n", __func__, num_neighbors); + //printf("%s: %d neighbours in total\n", __func__, num_neighbors); kneighbors_q2xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); iq2_data[gindex].neighbours = kneighbors_q2xs; int counter = 0; @@ -10698,8 +10651,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v } } -size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -10711,8 +10663,7 @@ size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, return nrow * nblock * sizeof(block_iq2_xxs); } -size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq2_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -10816,7 +10767,7 @@ void iq3xs_init_impl(int grid_size) { int * kmap_q3xs; uint16_t * kneighbors_q3xs; - printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); uint32_t * the_grid = (uint32_t *)malloc(grid_size*sizeof(uint32_t)); for (int k = 0; k < grid_size; ++k) { int8_t * pos = (int8_t *)(the_grid + k); @@ -10871,7 +10822,7 @@ void iq3xs_init_impl(int grid_size) { } num_neighbors += n; } - printf("%s: %d neighbours in total\n", __func__, num_neighbors); + //printf("%s: %d neighbours in total\n", __func__, num_neighbors); kneighbors_q3xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); iq3_data[gindex].neighbours = kneighbors_q3xs; int counter = 0; @@ -11154,8 +11105,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v } } -size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11361,8 +11311,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } #define IQ3S_BLOCK_SIZE 32 -size_t quantize_iq3_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; float scales[QK_K/IQ3S_BLOCK_SIZE]; @@ -11392,7 +11341,7 @@ void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int k) { assert(k % QK_K == 0); - quantize_iq3_s(x, y, 1, k, NULL, NULL); + quantize_iq3_s(x, y, 1, k, NULL); } @@ -11587,8 +11536,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq1_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11613,7 +11561,7 @@ static inline int best_index_int8(int n, const int8_t * val, float x) { return x - val[mu-1] < val[mu] - x ? mu-1 : mu; } -static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x, +static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * restrict x, ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l, float * scales, float * weight, uint8_t * L, const int8_t * values, @@ -11721,8 +11669,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block } } -size_t quantize_iq4_nl(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK4_NL == 0); int nblock = n_per_row/QK4_NL; char * qrow = (char *)dst; @@ -11752,14 +11699,13 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int k) { assert(k % QK4_NL == 0); - quantize_iq4_nl(x, y, 1, k, NULL, NULL); + quantize_iq4_nl(x, y, 1, k, NULL); } -size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { #if QK_K == 64 - return quantize_iq4_nl(src, dst, nrow, n_per_row, hist, quant_weights); + return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights); #else - (void)hist; GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11788,7 +11734,7 @@ void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) { assert(k % QK_K == 0); - quantize_iq4_xs(x, y, 1, k, NULL, NULL); + quantize_iq4_xs(x, y, 1, k, NULL); } // =============================== 2.5625 bpw @@ -11961,8 +11907,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq2_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11976,7 +11921,7 @@ size_t quantize_iq2_s(const float * src, void * dst, int nrow, int n_per_row, in void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int k) { assert(k % QK_K == 0); - quantize_iq2_s(x, y, 1, k, NULL, NULL); + quantize_iq2_s(x, y, 1, k, NULL); } void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int k) { diff --git a/ggml-quants.h b/ggml-quants.h index d2d25eb44..47dd52856 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -261,6 +261,7 @@ void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGM void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k); void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); + void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k); @@ -280,6 +281,7 @@ void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); + void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -300,6 +302,7 @@ void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRI void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); + void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -321,6 +324,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -330,26 +334,26 @@ void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -// // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") -// -size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q5_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q6_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q4_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q4_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q5_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); + +size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); void iq2xs_init_impl(enum ggml_type type); void iq2xs_free_impl(enum ggml_type type); diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 5a1b3f477..d41aa7d22 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -4102,45 +4102,7 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool } static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) { - std::vector hist_cur(1 << 4, 0); - - switch(quant) { - case GGML_TYPE_F32: - memcpy(to, from, sizeof(float) * ne); - break; - case GGML_TYPE_Q4_0: - ggml_quantize_q4_0(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q4_1: - ggml_quantize_q4_1(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_0: - ggml_quantize_q5_0(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_1: - ggml_quantize_q5_1(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q8_0: - ggml_quantize_q8_0(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q2_K: - ggml_quantize_q2_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q3_K: - ggml_quantize_q3_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q4_K: - ggml_quantize_q4_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_K: - ggml_quantize_q5_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q6_K: - ggml_quantize_q6_K(from, to, ne, ne, hist_cur.data()); - break; - default: - GGML_ASSERT(false); - } + ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr); } static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { diff --git a/ggml.c b/ggml.c index 6eff98ab6..80efa6f2a 100644 --- a/ggml.c +++ b/ggml.c @@ -20159,133 +20159,6 @@ void ggml_quantize_free(void) { ggml_critical_section_end(); } -size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK4_0 == 0); - const int nb = k / QK4_0; - - for (int b = 0; b < n; b += k) { - block_q4_0 * restrict y = (block_q4_0 *) dst + b/QK4_0; - - quantize_row_q4_0_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - for (int j = 0; j < QK4_0; j += 2) { - const uint8_t vi0 = y[i].qs[j/2] & 0x0F; - const uint8_t vi1 = y[i].qs[j/2] >> 4; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK4_0*sizeof(block_q4_0)); -} - -size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK4_1 == 0); - const int nb = k / QK4_1; - - for (int b = 0; b < n; b += k) { - block_q4_1 * restrict y = (block_q4_1 *) dst + b/QK4_1; - - quantize_row_q4_1_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - for (int j = 0; j < QK4_1; j += 2) { - const uint8_t vi0 = y[i].qs[j/2] & 0x0F; - const uint8_t vi1 = y[i].qs[j/2] >> 4; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK4_1*sizeof(block_q4_1)); -} - -size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK5_0 == 0); - const int nb = k / QK5_0; - - for (int b = 0; b < n; b += k) { - block_q5_0 * restrict y = (block_q5_0 *)dst + b/QK5_0; - - quantize_row_q5_0_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, &y[i].qh, sizeof(qh)); - - for (int j = 0; j < QK5_0; j += 2) { - const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4; - const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12)); - - // cast to 16 bins - const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2; - const uint8_t vi1 = ((y[i].qs[j/2] >> 4) | vh1) / 2; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK5_0*sizeof(block_q5_0)); -} - -size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK5_1 == 0); - const int nb = k / QK5_1; - - for (int b = 0; b < n; b += k) { - block_q5_1 * restrict y = (block_q5_1 *)dst + b/QK5_1; - - quantize_row_q5_1_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, &y[i].qh, sizeof(qh)); - - for (int j = 0; j < QK5_1; j += 2) { - const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4; - const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12)); - - // cast to 16 bins - const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2; - const uint8_t vi1 = ((y[i].qs[j/2] >> 4) | vh1) / 2; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK5_1*sizeof(block_q5_1)); -} - -size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - for (int b = 0; b < n; b += k) { - block_q8_0 * restrict y = (block_q8_0 *)dst + b/QK8_0; - - quantize_row_q8_0_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - for (int j = 0; j < QK8_0; ++j) { - const int8_t vi = y[i].qs[j]; - - hist[vi/16 + 8]++; - } - } - } - - return (n/QK8_0*sizeof(block_q8_0)); -} - bool ggml_quantize_requires_imatrix(enum ggml_type type) { return type == GGML_TYPE_IQ2_XXS || @@ -20293,177 +20166,52 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) { type == GGML_TYPE_IQ1_S; } -size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, - int nrows, int n_per_row, int64_t * hist, const float * imatrix) { +size_t ggml_quantize_chunk( + enum ggml_type type, + const float * src, + void * dst, + int start, + int nrows, + int n_per_row, + const float * imatrix) { + const int n = nrows * n_per_row; + + if (ggml_quantize_requires_imatrix(type)) { + GGML_ASSERT(imatrix != NULL); + } + + GGML_ASSERT(start % type_traits[type].blck_size == 0); + GGML_ASSERT(start % n_per_row == 0); + ggml_quantize_init(type); // this is noop if already initialized + + const size_t start_row = start / n_per_row; + const size_t row_size = ggml_row_size(type, n_per_row); + size_t result = 0; - int n = nrows * n_per_row; + switch (type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(start % QK4_0 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q4_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(start % QK4_1 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q4_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q5_0: - { - GGML_ASSERT(start % QK5_0 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q5_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q5_1: - { - GGML_ASSERT(start % QK5_1 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q5_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q8_0: - { - GGML_ASSERT(start % QK8_0 == 0); - block_q8_0 * block = (block_q8_0*)dst + start / QK8_0; - result = ggml_quantize_q8_0(src + start, block, n, n, hist); - } break; - case GGML_TYPE_Q2_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q2_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q3_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q3_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q4_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q4_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q5_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q5_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q6_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q6_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ2_XXS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - GGML_ASSERT(imatrix); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq2_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ2_XS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - GGML_ASSERT(imatrix); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ3_XXS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ3_S: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ2_S: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq2_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ1_S: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq1_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ4_NL: + case GGML_TYPE_Q4_0: result = quantize_q4_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_1: result = quantize_q4_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_0: result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_1: result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q8_0: result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q2_K: result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q3_K: result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_K: result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_K: result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q6_K: result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_XS: result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; #if QK_K == 64 - case GGML_TYPE_IQ4_XS: -#endif - { - GGML_ASSERT(start % QK4_NL == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; -#if QK_K != 64 - case GGML_TYPE_IQ4_XS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; + case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; +#else + case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; #endif case GGML_TYPE_F16: { @@ -20480,6 +20228,9 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i default: assert(false); } + + GGML_ASSERT(result == nrows * row_size); + return result; } diff --git a/ggml.h b/ggml.h index a13b0cec4..1171088a9 100644 --- a/ggml.h +++ b/ggml.h @@ -2194,25 +2194,18 @@ extern "C" { GGML_API void ggml_quantize_init(enum ggml_type type); GGML_API void ggml_quantize_free(void); - // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk - GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist); - - GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); - // some quantization type cannot be used without an importance matrix GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type); // calls ggml_quantize_init internally (i.e. can allocate memory) - GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, - int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix); + GGML_API size_t ggml_quantize_chunk( + enum ggml_type type, + const float * src, + void * dst, + int start, + int nrows, + int n_per_row, + const float * imatrix); // // gguf diff --git a/llama.cpp b/llama.cpp index 8c147a42b..c58a029f7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11890,17 +11890,16 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty return new_type; } -static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, int64_t * hist_cur, const float * imatrix, std::vector & workers, const int nthread) { +static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector & workers, const int nthread) { std::mutex mutex; int counter = 0; size_t new_size = 0; if (nthread < 2) { // single-thread - return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur, imatrix); + return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix); } - auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, + auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix]() { - std::array local_hist = {}; const int nrows_per_chunk = chunk_size / n_per_row; size_t local_size = 0; while (true) { @@ -11908,17 +11907,13 @@ static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const flo int first_row = counter; counter += nrows_per_chunk; if (first_row >= nrows) { if (local_size > 0) { - for (int j=0; j hist_all(1 << 4, 0); std::vector workers; workers.reserve(nthread); @@ -12175,7 +12169,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s work.resize(nelements * 4); // upper bound on size } new_data = work.data(); - std::array hist_cur = {}; const int n_per_row = tensor->ne[0]; const int nrows = nelements / n_per_row; @@ -12185,22 +12178,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const int nchunk = (nelements + chunk_size - 1)/chunk_size; const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; - new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, hist_cur.data(), imatrix, workers, nthread_use); + new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix, workers, nthread_use); - LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); - int64_t tot_count = 0; - for (size_t i = 0; i < hist_cur.size(); i++) { - hist_all[i] += hist_cur[i]; - tot_count += hist_cur[i]; - } - - if (tot_count > 0) { - LLAMA_LOG_INFO(" | hist: "); - for (size_t i = 0; i < hist_cur.size(); i++) { - LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements)); - } - } - LLAMA_LOG_INFO("\n"); + LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); } total_size_org += ggml_nbytes(tensor); total_size_new += new_size; @@ -12229,22 +12209,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); - // print histogram for all tensors - { - int64_t sum_all = 0; - for (size_t i = 0; i < hist_all.size(); i++) { - sum_all += hist_all[i]; - } - - if (sum_all > 0) { - LLAMA_LOG_INFO("%s: hist: ", __func__); - for (size_t i = 0; i < hist_all.size(); i++) { - LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all)); - } - LLAMA_LOG_INFO("\n"); - } - } - if (qs.n_fallback > 0) { LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n", __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 8a6999f21..fc5edcc4b 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -53,7 +53,6 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) { GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0); std::vector dataq(ggml_row_size(tensor->type, size)); - int64_t hist[16]; std::vector imatrix(tensor->ne[0], 1.0f); // dummy importance matrix const float * im = imatrix.data(); if (!ggml_quantize_requires_imatrix(tensor->type)) { @@ -63,7 +62,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m im = nullptr; } } - ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, im); + ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { // This is going to create some weird integers though. From 58308a0ecce7cc261b802f4803c38d420063db21 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 17:34:15 +0200 Subject: [PATCH 81/86] server : fix metrics init (#5964) --- examples/server/server.cpp | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 796f3499c..2374b7e4a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -341,7 +341,7 @@ struct server_slot { }; struct server_metrics { - const int64_t t_start = ggml_time_us(); + int64_t t_start = 0; uint64_t n_prompt_tokens_processed_total = 0; uint64_t t_prompt_processing_total = 0; @@ -354,14 +354,18 @@ struct server_metrics { uint64_t n_tokens_predicted = 0; uint64_t t_tokens_generation = 0; - void on_prompt_eval(const server_slot &slot) { + void init() { + t_start = ggml_time_us(); + } + + void on_prompt_eval(const server_slot & slot) { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; n_prompt_tokens_processed += slot.n_prompt_tokens_processed; t_prompt_processing += slot.t_prompt_processing; t_prompt_processing_total += slot.t_prompt_processing; } - void on_prediction(const server_slot &slot) { + void on_prediction(const server_slot & slot) { n_tokens_predicted_total += slot.n_decoded; n_tokens_predicted += slot.n_decoded; t_tokens_generation += slot.t_token_generation; @@ -690,10 +694,11 @@ struct server_context { return res > 0; } - void initialize() { + void init() { const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); + for (int i = 0; i < params.n_parallel; i++) { server_slot slot; @@ -735,6 +740,8 @@ struct server_context { default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); + + metrics.init(); } std::vector tokenize(const json & json_prompt, bool add_bos) const { @@ -2783,7 +2790,7 @@ int main(int argc, char ** argv) { state.store(SERVER_STATE_ERROR); return 1; } else { - ctx_server.initialize(); + ctx_server.init(); state.store(SERVER_STATE_READY); } From 8380ecfb219c2e73cc706fcc83935b7c806cc7c3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 17:36:20 +0200 Subject: [PATCH 82/86] ggml : fix unnecessary f32 -> f16 -> f32 casts (mmla) (#5951) --- ggml-quants.c | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 4ee4e0604..807c5e391 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -4059,10 +4059,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); // mmla into int32x4_t - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); From 098dbaab449f5309a54871ba7e5acef72ae696de Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 18:14:13 +0200 Subject: [PATCH 83/86] readme : update hot topics --- README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d7dba73e6..e3ec0817a 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,11 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ +> [!IMPORTANT] +> **Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962** +> +> Vote for which quantization type provides better responses, all other parameters being the same. + ### Recent API changes - [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 @@ -16,11 +21,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) -- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) -- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 -- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 -- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 +- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328 ---- From d894f352bf433157232dc8dc54eacd50014e898e Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 9 Mar 2024 19:55:54 +0100 Subject: [PATCH 84/86] perplexity : support using multiple sequences to allow larger batch sizes (#5946) * perplexity : support using multiple sequences to allow larger batch sizes ggml-ci * set cparams.n_parallel to the number of sequences * print tested n_ctx, add assert --- examples/perplexity/perplexity.cpp | 137 +++++++++++++++++++---------- llama.cpp | 22 +++-- 2 files changed, 107 insertions(+), 52 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 52789ee63..293eb52c3 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -442,7 +442,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & return {tokens, std::exp(nll / count), logit_history, prob_history}; } -static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { +static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) { if (params.ppl_stride > 0) { return perplexity_v2(ctx, params); } @@ -453,7 +453,6 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par // BOS tokens will be added for each chunk before eval const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - const int n_ctx = llama_n_ctx(ctx); std::ofstream logits_stream; if (!params.logits_file.empty()) { @@ -499,13 +498,19 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par double nll2 = 0.0; const int num_batches = (n_ctx + n_batch - 1) / n_batch; + const int n_seq = std::max(1, n_batch / n_ctx); + + GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0); + GGML_ASSERT(params.n_ctx == n_seq * n_ctx); + + llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1); std::vector logits; if (num_batches > 1) { logits.reserve((size_t)n_ctx * n_vocab); } - fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + fprintf(stderr, "%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq); std::vector workers(std::thread::hardware_concurrency() - 1); @@ -518,10 +523,26 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par log_probs.resize(n_ctx * nv); } - for (int i = 0; i < n_chunk; ++i) { + // We get the logits for all the tokens in the context window (params.n_ctx) + // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, + // calculate the perplexity over the last half of the window (so the model always has + // some context to predict the token). + // + // We rely on the fact that attention in the forward pass only looks at previous + // tokens here, so the logits returned for each token are an accurate representation + // of what the model would have predicted at that point. + // + // Example, we have a context window of 512, we will compute perplexity for each of the + // last 256 tokens. Then, we split the input up into context window size chunks to + // process the entire prompt. + const int first = n_ctx/2; + + for (int i = 0; i < n_chunk; i += n_seq) { const int start = i * n_ctx; const int end = start + n_ctx; + const int n_seq_batch = std::min(n_seq, n_chunk - i); + const auto t_start = std::chrono::high_resolution_clock::now(); // clear the KV cache @@ -531,22 +552,37 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); - // save original token and restore it after eval - const auto token_org = tokens[batch_start]; + batch.n_tokens = 0; + for (int seq = 0; seq < n_seq_batch; seq++) { + int seq_start = batch_start + seq*n_ctx; - // add BOS token for the first batch of each chunk - if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + // save original token and restore it after eval + const auto token_org = tokens[seq_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[seq_start] = llama_token_bos(llama_get_model(ctx)); + } + + for (int k = 0; k < batch_size; ++k) { + const int idx = seq*n_ctx + k; + batch.token[idx] = tokens[seq_start + k]; + batch.pos[idx] = j*n_batch + k; + batch.n_seq_id[idx] = 1; + batch.seq_id[idx][0] = seq; + batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0; + } + batch.n_tokens += batch_size; + + // restore the original token in case it was set to BOS + tokens[seq_start] = token_org; } - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + if (llama_decode(ctx, batch)) { fprintf(stderr, "%s : failed to eval\n", __func__); return {tokens, -1, logit_history, prob_history}; } - // restore the original token in case it was set to BOS - tokens[batch_start] = token_org; - if (num_batches > 1) { const auto * batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); @@ -558,7 +594,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par if (i == 0) { const float t_total = std::chrono::duration(t_end - t_start).count(); fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); - int total_seconds = (int)(t_total * n_chunk); + int total_seconds = (int)(t_total*n_chunk/n_seq); if (total_seconds >= 60*60) { fprintf(stderr, "%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); @@ -566,37 +602,31 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); } - // We get the logits for all the tokens in the context window (params.n_ctx) - // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, - // calculate the perplexity over the last half of the window (so the model always has - // some context to predict the token). - // - // We rely on the fact that attention in the forward pass only looks at previous - // tokens here, so the logits returned for each token are an accurate representation - // of what the model would have predicted at that point. - // - // Example, we have a context window of 512, we will compute perplexity for each of the - // last 256 tokens. Then, we split the input up into context window size chunks to - // process the entire prompt. - const int first = n_ctx/2; - const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); - if (!params.logits_file.empty()) { - process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, log_probs, nll, nll2); - } else { - process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); - } - count += n_ctx - first - 1; + for (int seq = 0; seq < n_seq_batch; seq++) { + const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx); + llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first; + if (!params.logits_file.empty()) { + process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, + tokens_data, n_ctx - 1 - first, + workers, log_probs, nll, nll2); + } else { + process_logits(n_vocab, all_logits + first*n_vocab, + tokens_data, n_ctx - 1 - first, + workers, nll, nll2, + logit_history.data() + start + seq*n_ctx + first, + prob_history.data() + start + seq*n_ctx + first); + } + count += n_ctx - first - 1; - // perplexity is e^(average negative log-likelihood) - if (params.ppl_output_type == 0) { - printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); - } else { - double av = nll/count; - double av2 = nll2/count - av*av; - if (av2 > 0) av2 = sqrt(av2/(count-1)); - printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + // perplexity is e^(average negative log-likelihood) + if (params.ppl_output_type == 0) { + printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count)); + } else { + double av = nll/count; + double av2 = nll2/count - av*av; + if (av2 > 0) av2 = sqrt(av2/(count-1)); + printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + } } fflush(stdout); @@ -615,6 +645,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par printf("Unexpected negative standard deviation of log(prob)\n"); } + llama_batch_free(batch); + return {tokens, ppl, logit_history, prob_history}; } @@ -1782,13 +1814,24 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { int main(int argc, char ** argv) { gpt_params params; - params.n_batch = 512; if (!gpt_params_parse(argc, argv, params)) { return 1; } params.logits_all = true; - params.n_batch = std::min(params.n_batch, params.n_ctx); + + const int32_t n_ctx = params.n_ctx; + + const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence; + if (ppl) { + int n_seq = std::max(1, params.n_batch / n_ctx); + int32_t n_kv = n_seq * n_ctx; + params.n_parallel = n_seq; + params.n_ctx = n_kv; + params.n_batch = std::min(params.n_batch, n_kv); + } else { + params.n_batch = std::min(params.n_batch, params.n_ctx); + } if (params.ppl_stride > 0) { fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n", @@ -1847,7 +1890,7 @@ int main(int argc, char ** argv) { } else if (params.kl_divergence) { kl_divergence(ctx, params); } else { - results = perplexity(ctx, params); + results = perplexity(ctx, params, n_ctx); } llama_print_timings(ctx); diff --git a/llama.cpp b/llama.cpp index c58a029f7..b19616e8f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8925,17 +8925,29 @@ static int llama_decode_internal( if (batch.logits) { logits_out.resize(n_vocab * n_tokens); + int32_t i_first = -1; for (uint32_t i = 0; i < n_tokens; i++) { - if (batch.logits[i] == 0) { - continue; + if (batch.logits[i] && i_first == -1) { + i_first = (int32_t) i; + } + if (batch.logits[i] == 0 || i == n_tokens - 1) { + if (i_first != -1) { + int i_last = batch.logits[i] == 0 ? i : i + 1; + // extract logits for the range [i_first, i_last) + // group the requests to minimize the number of calls to the backend + ggml_backend_tensor_get_async(backend_res, res, + logits_out.data() + (n_vocab*i_first), + (n_vocab*i_first)*sizeof(float), + (i_last - i_first)*n_vocab*sizeof(float)); + i_first = -1; + } } - ggml_backend_tensor_get_async(backend_res, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG - logits_valid[i] = true; + logits_valid[i] = batch.logits[i] != 0; #endif } } else if (lctx.logits_all) { - logits_out.resize(n_vocab * n_tokens); + logits_out.resize(n_vocab*n_tokens); ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); From 77d1ac7e00bf049b9f2bba1b5a310a78318c49c4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 22:04:00 +0200 Subject: [PATCH 85/86] server : print chat template info --- examples/server/server.cpp | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2374b7e4a..b14cca61b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2197,7 +2197,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); - printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); + printf(" only commonly used templates are accepted:\n"); + printf(" https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n"); printf("\n"); } @@ -2798,13 +2799,30 @@ int main(int argc, char ** argv) { const auto model_meta = ctx_server.model_meta(); - if (sparams.chat_template.empty()) { // custom chat template is not supplied + // if a custom chat template is not supplied, we will use the one that comes with the model (if any) + if (sparams.chat_template.empty()) { if (!ctx_server.validate_model_chat_template()) { LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); sparams.chat_template = "chatml"; } } + // print sample chat example to make it clear which template is used + { + json chat; + chat.push_back({{"role", "system"}, {"content", "You are a helpful assistant"}}); + chat.push_back({{"role", "user"}, {"content", "Hello"}}); + chat.push_back({{"role", "assistant"}, {"content", "Hi there"}}); + chat.push_back({{"role", "user"}, {"content", "How are you?"}}); + + const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat); + + LOG_INFO("chat template", { + {"chat_example", chat_example}, + {"built_in", sparams.chat_template.empty()}, + }); + } + // // Middlewares // From 621e86b331f8b0e71f79fd82a4ae1cd54c3e4396 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 9 Mar 2024 23:41:49 +0100 Subject: [PATCH 86/86] server: benchmark: chat/completions scenario and other llm servers comparison (#5941) * server: bench: Init a bench scenario with K6 See #5827 * server: bench: EOL EOF * server: bench: PR feedback and improved k6 script configuration * server: bench: remove llamacpp_completions_tokens_seconds as it include prompt processing time and it's misleading server: bench: add max_tokens from SERVER_BENCH_MAX_TOKENS server: bench: increase truncated rate to 80% before failing * server: bench: fix doc * server: bench: change gauge custom metrics to trend * server: bench: change gauge custom metrics to trend server: bench: add trend custom metrics for total tokens per second average * server: bench: doc add an option to debug http request * server: bench: filter dataset too short and too long sequences * server: bench: allow to filter out conversation in the dataset based on env variable * server: bench: fix assistant message sent instead of user message * server: bench: fix assistant message sent instead of user message * server : add defrag thold parameter * server: bench: select prompts based on the current iteration id not randomly to make the bench more reproducible --------- Co-authored-by: Georgi Gerganov --- examples/server/bench/README.md | 88 +++++++++++++++++++++++ examples/server/bench/script.js | 120 ++++++++++++++++++++++++++++++++ examples/server/server.cpp | 8 +++ 3 files changed, 216 insertions(+) create mode 100644 examples/server/bench/README.md create mode 100644 examples/server/bench/script.js diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md new file mode 100644 index 000000000..a53ad64d7 --- /dev/null +++ b/examples/server/bench/README.md @@ -0,0 +1,88 @@ +### Server benchmark tools + +Benchmark is using [k6](https://k6.io/). + +##### Install k6 + +Follow instruction from: https://k6.io/docs/get-started/installation/ + +Example for ubuntu: +```shell +snap install k6 +``` + +#### Download a dataset + +This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md). + +```shell +wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json +``` + +#### Download a model +Example for PHI-2 + +```shell +../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf +``` + +#### Start the server +The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`. + +Example: +```shell +server --host localhost --port 8080 \ + --model ggml-model-q4_0.gguf \ + --cont-batching \ + --metrics \ + --parallel 8 \ + --batch-size 512 \ + --ctx-size 4096 \ + --log-format text \ + -ngl 33 +``` + +#### Run the benchmark + +For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run: +```shell +k6 run script.js --duration 10m --iterations 500 --vus 8 +``` + +The benchmark values can be overridden with: +- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1` +- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480` +- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model` +- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512` +- `SERVER_BENCH_DATASET` path to the benchmark dataset file +- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024` +- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048` + +Note: the local tokenizer is just a string space split, real number of tokens will differ. + +Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/): + +```shell +SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8 +``` + +To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`. + +#### Metrics + +Following metrics are available computed from the OAI chat completions response `usage`: +- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration` +- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens` +- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens` +- `llamacpp_completion_tokens` Trend of `usage.completion_tokens` +- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens` +- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'` +- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'` + +The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`. + +K6 metrics might be compared against [server metrics](../README.md), with: + +```shell +curl http://localhost:8080/metrics +``` diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js new file mode 100644 index 000000000..a4f5ac5ab --- /dev/null +++ b/examples/server/bench/script.js @@ -0,0 +1,120 @@ +import http from 'k6/http' +import {check, sleep} from 'k6' +import {SharedArray} from 'k6/data' +import {Counter, Rate, Trend} from 'k6/metrics' +import exec from 'k6/execution'; + +// Server chat completions prefix +const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1' + +// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users +const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8 + +// Model name to request +const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model' + +// Dataset path +const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json' + +// Max tokens to predict +const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512 + +// Max prompt tokens +const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024 + +// Max slot context +const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048 + +export function setup() { + console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`) +} + +const data = new SharedArray('conversations', function () { + const tokenizer = (message) => message.split(/[\s,'".?]/) + + return JSON.parse(open(dataset_path)) + // Filter out the conversations with less than 2 turns. + .filter(data => data["conversations"].length >= 2) + .filter(data => data["conversations"][0]["from"] === "human") + .map(data => { + return { + prompt: data["conversations"][0]["value"], + n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length, + n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length, + } + }) + // Filter out too short sequences + .filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4) + // Filter out too long sequences. + .filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot) + // Keep only first n prompts + .slice(0, n_prompt) +}) + +const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens') +const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens') +const llamacpp_tokens_second = new Trend('llamacpp_tokens_second') + +const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter') +const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter') + +const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate') +const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate') + +export const options = { + thresholds: { + llamacpp_completions_truncated_rate: [ + // more than 80% of truncated input will abort the test + {threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'}, + ], + }, + duration: '10m', + vus: 8, +} + +export default function () { + const conversation = data[exec.scenario.iterationInInstance % data.length] + const payload = { + "messages": [ + { + "role": "system", + "content": "You are ChatGPT, an AI assistant.", + }, + { + "role": "user", + "content": conversation.prompt, + } + ], + "model": model, + "stream": false, + "max_tokens": max_tokens + } + + const body = JSON.stringify(payload) + + let res = http.post(`${server_url}/chat/completions`, body, { + headers: {'Content-Type': 'application/json'}, + timeout: '300s' + }) + + check(res, {'success completion': (r) => r.status === 200}) + + if (res.status === 200) { + const completions = res.json() + + llamacpp_prompt_tokens.add(completions.usage.prompt_tokens) + llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens) + + llamacpp_completion_tokens.add(completions.usage.completion_tokens) + llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens) + + llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length') + llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop') + + llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3) + } else { + console.error(`response: ${res.body} request=${payload}`) + } + + sleep(0.3) +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b14cca61b..c7d3ed01b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2133,6 +2133,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); + printf(" -dt N, --defrag-thold N\n"); + printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); 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"); @@ -2355,6 +2357,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } else { invalid_param = true; break; } + } else if (arg == "--defrag-thold" || arg == "-dt") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.defrag_thold = std::stof(argv[i]); } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) {