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deps(llama.cpp): update (#1759)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto
GitHub
parent
05818e0425
commit
bc5f5aa538
2
Makefile
2
Makefile
@ -8,7 +8,7 @@ GOLLAMA_VERSION?=aeba71ee842819da681ea537e78846dc75949ac0
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GOLLAMA_STABLE_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7
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CPPLLAMA_VERSION?=9e359a4f47c1b2dceb99e29706c9f7403d32ab5e
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CPPLLAMA_VERSION?=c39373398803c669056304090050fe3f44b41bf9
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# gpt4all version
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GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
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@ -58,9 +58,11 @@ struct server_params
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int32_t read_timeout = 600;
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int32_t write_timeout = 600;
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bool slots_endpoint = true;
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bool metrics_endpoint = false;
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};
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bool server_verbose = false;
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bool server_log_json = true;
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static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
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{
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@ -316,12 +318,76 @@ struct llama_client_slot
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}
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void print_timings() const {
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LOG_TEE("\n");
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LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
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__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
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LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
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__func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
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LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
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char buffer[512];
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double t_token = t_prompt_processing / num_prompt_tokens_processed;
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double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed;
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sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
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t_prompt_processing, num_prompt_tokens_processed,
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t_token, n_tokens_second);
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LOG_INFO(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_prompt_processing", t_prompt_processing},
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{"num_prompt_tokens_processed", num_prompt_tokens_processed},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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t_token = t_token_generation / n_decoded;
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n_tokens_second = 1e3 / t_token_generation * n_decoded;
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sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
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t_token_generation, n_decoded,
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t_token, n_tokens_second);
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LOG_INFO(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_token_generation", t_token_generation},
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{"n_decoded", n_decoded},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
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LOG_INFO(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_prompt_processing", t_prompt_processing},
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{"t_token_generation", t_token_generation},
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{"t_total", t_prompt_processing + t_token_generation},
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});
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}
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};
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struct llama_metrics {
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uint64_t n_prompt_tokens_processed_total = 0;
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uint64_t n_tokens_predicted_total = 0;
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uint64_t n_prompt_tokens_processed = 0;
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uint64_t t_prompt_processing = 0;
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uint64_t n_tokens_predicted = 0;
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uint64_t t_tokens_generation = 0;
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void on_prompt_eval(const llama_client_slot &slot) {
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n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed;
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n_prompt_tokens_processed += slot.num_prompt_tokens_processed;
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t_prompt_processing += slot.t_prompt_processing;
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}
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void on_prediction(const llama_client_slot &slot) {
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n_tokens_predicted_total += slot.n_decoded;
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n_tokens_predicted += slot.n_decoded;
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t_tokens_generation += slot.t_token_generation;
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}
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void reset_bucket() {
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n_prompt_tokens_processed = 0;
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t_prompt_processing = 0;
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n_tokens_predicted = 0;
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t_tokens_generation = 0;
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}
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};
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@ -359,6 +425,8 @@ struct llama_server_context
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llama_server_queue queue_tasks;
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llama_server_response queue_results;
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llama_metrics metrics;
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~llama_server_context()
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{
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if (ctx)
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@ -378,7 +446,7 @@ struct llama_server_context
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params = params_;
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if (!params.mmproj.empty()) {
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multimodal = true;
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LOG_TEE("Multi Modal Mode Enabled");
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LOG_INFO("Multi Modal Mode Enabled", {});
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clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
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if(clp_ctx == nullptr) {
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LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
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@ -415,13 +483,23 @@ struct llama_server_context
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return true;
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}
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void validate_model_chat_template(server_params & sparams) {
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llama_chat_message chat[] = {{"user", "test"}};
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std::vector<char> buf(1);
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int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size());
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if (res < 0) {
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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", {});
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sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template
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}
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}
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void initialize() {
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// create slots
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all_slots_are_idle = true;
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const int32_t n_ctx_slot = n_ctx / params.n_parallel;
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LOG_TEE("Available slots:\n");
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LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
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for (int i = 0; i < params.n_parallel; i++)
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{
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llama_client_slot slot;
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@ -430,7 +508,10 @@ struct llama_server_context
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slot.n_ctx = n_ctx_slot;
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slot.n_predict = params.n_predict;
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LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
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LOG_INFO("new slot", {
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{"slot_id", slot.id},
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{"n_ctx_slot", slot.n_ctx}
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});
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const int ga_n = params.grp_attn_n;
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const int ga_w = params.grp_attn_w;
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@ -440,7 +521,12 @@ struct llama_server_context
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GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
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//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
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//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
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LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w);
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LOG_INFO("slot self-extend", {
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{"slot_id", slot.id},
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{"ga_n", ga_n},
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{"ga_w", ga_w}
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});
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}
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slot.ga_i = 0;
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@ -726,10 +812,16 @@ struct llama_server_context
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img_sl.img_data = clip_image_u8_init();
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if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
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{
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LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
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LOG_ERROR("failed to load image", {
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{"slot_id", slot->id},
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{"img_sl_id", img_sl.id}
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});
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return false;
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}
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LOG_TEE("slot %i - loaded image\n", slot->id);
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LOG_VERBOSE("image loaded", {
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{"slot_id", slot->id},
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{"img_sl_id", img_sl.id}
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});
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img_sl.request_encode_image = true;
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slot->images.push_back(img_sl);
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}
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@ -789,7 +881,10 @@ struct llama_server_context
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all_slots_are_idle = false;
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LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
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LOG_INFO("slot is processing task", {
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{"slot_id", slot->id},
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{"task_id", slot->task_id},
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});
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return true;
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}
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@ -814,10 +909,24 @@ struct llama_server_context
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llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
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}
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if (llama_decode(ctx, batch) != 0)
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for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch)
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{
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LOG_TEE("%s: llama_decode() failed\n", __func__);
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return;
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const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
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llama_batch batch_view = {
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n_tokens,
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batch.token + i,
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nullptr,
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batch.pos + i,
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batch.n_seq_id + i,
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batch.seq_id + i,
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batch.logits + i,
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0, 0, 0, // unused
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};
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if (llama_decode(ctx, batch_view) != 0)
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{
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LOG_TEE("%s: llama_decode() failed\n", __func__);
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return;
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}
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}
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// assign the system KV cache to all parallel sequences
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@ -1351,7 +1460,7 @@ struct llama_server_context
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if (slot == nullptr)
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{
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// if no slot is available, we defer this task for processing later
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LOG_VERBOSE("no slot is available", {});
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LOG_VERBOSE("no slot is available", {{"task_id", task.id}});
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queue_tasks.defer(task);
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break;
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}
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@ -1425,7 +1534,7 @@ struct llama_server_context
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bool update_slots() {
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if (system_need_update)
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{
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LOG_TEE("updating system prompt\n");
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LOG_INFO("updating system prompt", {});
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update_system_prompt();
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}
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@ -1435,12 +1544,13 @@ struct llama_server_context
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{
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if (system_prompt.empty() && clean_kv_cache)
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{
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LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
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LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {});
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kv_cache_clear();
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}
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return true;
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}
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LOG_VERBOSE("posting NEXT_RESPONSE", {});
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task_server task;
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task.type = TASK_TYPE_NEXT_RESPONSE;
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task.target_id = -1;
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@ -1471,6 +1581,7 @@ struct llama_server_context
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}
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// decode any currently ongoing sequences
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LOG_VERBOSE("decoding ongoing sequences", {});
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for (auto & slot : slots)
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{
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// release the slot
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@ -1480,7 +1591,15 @@ struct llama_server_context
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slot.command = NONE;
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slot.t_last_used = ggml_time_us();
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LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());
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LOG_INFO("slot released", {
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{"slot_id", slot.id},
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{"task_id", slot.task_id},
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{"n_ctx", n_ctx},
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{"n_past", slot.n_past},
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{"n_system_tokens", system_tokens.size()},
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{"n_cache_tokens", slot.cache_tokens.size()},
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{"truncated", slot.truncated}
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});
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queue_tasks.notify_slot_changed();
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continue;
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@ -1607,6 +1726,14 @@ struct llama_server_context
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}
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slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
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// the last token of the cache is not in the KV cache until the next call to llama_decode
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// (it was sampled, pushed into the "cache_tokens", but not yet put in the context)
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if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size())
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{
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slot.n_past -= 1;
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}
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slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
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if (slot.ga_n != 1)
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@ -1628,7 +1755,12 @@ struct llama_server_context
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slot.ga_i = ga_i;
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}
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LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
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LOG_INFO("slot progression", {
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{ "slot_id", slot.id },
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{ "task_id", slot.task_id },
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{ "n_past", slot.n_past },
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{ "num_prompt_tokens_processed", slot.num_prompt_tokens_processed }
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});
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}
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slot.cache_tokens = prompt_tokens;
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@ -1636,7 +1768,10 @@ struct llama_server_context
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if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0)
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{
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// we have to evaluate at least 1 token to generate logits.
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LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
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LOG_INFO("we have to evaluate at least 1 token to generate logits", {
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{ "slot_id", slot.id },
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{ "task_id", slot.task_id }
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});
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slot.n_past--;
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if (slot.ga_i > 0)
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{
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@ -1644,9 +1779,13 @@ struct llama_server_context
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}
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}
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LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
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llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
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int p0 = (int) system_tokens.size() + slot.n_past;
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LOG_INFO("kv cache rm [p0, end)", {
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{ "slot_id", slot.id },
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{ "task_id", slot.task_id },
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{ "p0", p0 }
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});
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llama_kv_cache_seq_rm(ctx, slot.id, p0, -1);
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LOG_VERBOSE("prompt ingested", {
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{"n_past", slot.n_past},
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@ -1681,7 +1820,13 @@ struct llama_server_context
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if (has_images && !ingest_images(slot, n_batch))
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{
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LOG_TEE("failed processing images\n");
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LOG_ERROR("failed processing images", {
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"slot_id", slot.id,
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"task_id", slot.task_id,
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});
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// FIXME @phymbert: to be properly tested
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// early returning without changing the slot state will block the slot for ever
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// no one at the moment is checking the return value
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return false;
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}
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@ -1723,9 +1868,9 @@ struct llama_server_context
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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);
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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);
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llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
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llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
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llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
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llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
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llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
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slot.n_past_se -= bd;
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@ -1781,7 +1926,7 @@ struct llama_server_context
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send_embedding(slot);
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slot.release();
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slot.i_batch = -1;
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return true;
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continue;
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}
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completion_token_output result;
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@ -1794,6 +1939,7 @@ struct llama_server_context
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{
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slot.t_start_genereration = ggml_time_us();
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slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
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metrics.on_prompt_eval(slot);
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}
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llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
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@ -1816,11 +1962,14 @@ struct llama_server_context
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slot.release();
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slot.print_timings();
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send_final_response(slot);
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metrics.on_prediction(slot);
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}
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slot.i_batch = -1;
|
||||
}
|
||||
}
|
||||
|
||||
LOG_VERBOSE("slots updated", {});
|
||||
return true;
|
||||
}
|
||||
|
||||
@ -1849,18 +1998,6 @@ static json format_partial_response(
|
||||
return res;
|
||||
}
|
||||
|
||||
static json format_tokenizer_response(const std::vector<llama_token> &tokens)
|
||||
{
|
||||
return json{
|
||||
{"tokens", tokens}};
|
||||
}
|
||||
|
||||
static json format_detokenized_response(std::string content)
|
||||
{
|
||||
return json{
|
||||
{"content", content}};
|
||||
}
|
||||
|
||||
struct token_translator
|
||||
{
|
||||
llama_context * ctx;
|
||||
@ -2119,9 +2256,9 @@ static void params_parse(const backend::ModelOptions* request,
|
||||
params.use_mmap = request->mmap();
|
||||
params.embedding = request->embeddings();
|
||||
|
||||
if (request->ropescaling() == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
|
||||
else if (request->ropescaling() == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
|
||||
else { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
|
||||
if (request->ropescaling() == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
|
||||
else if (request->ropescaling() == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
|
||||
else { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
|
||||
if ( request->yarnextfactor() != 0.0f ) {
|
||||
params.yarn_ext_factor = request->yarnextfactor();
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user