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deps(llama.cpp): update (#1714)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto
GitHub
parent
255748bcba
commit
54ec6348fa
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?=f026f8120f97090d34a52b3dc023c82e0ede3f7d
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CPPLLAMA_VERSION?=9350a1cf21b1492c69b20175b73a419b897d6a3a
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# gpt4all version
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GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
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@ -51,6 +51,7 @@ struct server_params
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std::string hostname = "127.0.0.1";
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std::vector<std::string> api_keys;
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std::string public_path = "examples/server/public";
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std::string chat_template = "chatml";
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int32_t port = 8080;
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int32_t read_timeout = 600;
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int32_t write_timeout = 600;
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@ -349,6 +350,7 @@ struct llama_server_context
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// slots / clients
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std::vector<llama_client_slot> slots;
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json default_generation_settings_for_props;
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llama_server_queue queue_tasks;
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llama_server_response queue_results;
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@ -445,6 +447,9 @@ struct llama_server_context
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slots.push_back(slot);
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}
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default_generation_settings_for_props = get_formated_generation(slots.front());
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default_generation_settings_for_props["seed"] = -1;
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batch = llama_batch_init(n_ctx, 0, params.n_parallel);
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// empty system prompt
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@ -527,27 +532,29 @@ struct llama_server_context
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slot_params default_params;
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llama_sampling_params default_sparams;
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slot->params.stream = json_value(data, "stream", false);
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slot->params.cache_prompt = json_value(data, "cache_prompt", false);
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slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
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slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
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slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
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slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
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slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
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slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
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slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
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slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
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slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
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slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
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slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
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slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
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slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
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slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
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slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
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slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
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slot->params.seed = json_value(data, "seed", default_params.seed);
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slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
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slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
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slot->params.stream = json_value(data, "stream", false);
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slot->params.cache_prompt = json_value(data, "cache_prompt", false);
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slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
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slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
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slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
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slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
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slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
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slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
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slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
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slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
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slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
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slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
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slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
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slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
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slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
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slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
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slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
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slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
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slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
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slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
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slot->params.seed = json_value(data, "seed", default_params.seed);
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slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
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slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
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// infill
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if (data.count("input_prefix") != 0)
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@ -626,18 +633,36 @@ struct llama_server_context
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const int n_vocab = llama_n_vocab(model);
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for (const auto &el : *logit_bias)
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{
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if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
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if (el.is_array() && el.size() == 2)
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{
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llama_token tok = el[0].get<llama_token>();
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if (tok >= 0 && tok < n_vocab)
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float bias;
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if (el[1].is_number())
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{
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if (el[1].is_number())
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bias = el[1].get<float>();
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}
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else if (el[1].is_boolean() && !el[1].get<bool>())
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{
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bias = -INFINITY;
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}
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else
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{
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continue;
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}
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if (el[0].is_number_integer())
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{
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llama_token tok = el[0].get<llama_token>();
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if (tok >= 0 && tok < n_vocab)
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{
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slot->sparams.logit_bias[tok] = el[1].get<float>();
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slot->sparams.logit_bias[tok] = bias;
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}
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else if (el[1].is_boolean() && !el[1].get<bool>())
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}
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else if (el[0].is_string())
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{
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auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
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for (auto tok : toks)
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{
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slot->sparams.logit_bias[tok] = -INFINITY;
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slot->sparams.logit_bias[tok] = bias;
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}
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}
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}
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@ -950,18 +975,31 @@ struct llama_server_context
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{
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continue;
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}
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clip_image_f32 * img_res = clip_image_f32_init();
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if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
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clip_image_f32_batch img_res_v;
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img_res_v.size = 0;
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img_res_v.data = nullptr;
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if (!clip_image_preprocess(clp_ctx, img.img_data, img_res_v))
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{
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LOG_TEE("Error processing the given image");
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clip_free(clp_ctx);
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clip_image_f32_batch_free(img_res_v);
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return false;
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}
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if (img_res_v.size == 0)
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{
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LOG_TEE("Error processing the given image");
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return false;
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}
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// note: assumes only one image was returned by clip_image_preprocess
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clip_image_f32 * img_res = img_res_v.data;
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img.image_tokens = clip_n_patches(clp_ctx);
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img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
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if (!img.image_embedding)
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{
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LOG_TEE("Unable to allocate memory for image embeddings\n");
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clip_image_f32_batch_free(img_res_v);
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clip_free(clp_ctx);
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return false;
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}
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@ -969,9 +1007,12 @@ struct llama_server_context
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if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
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{
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LOG_TEE("Unable to encode image\n");
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clip_image_f32_batch_free(img_res_v);
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return false;
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}
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clip_image_f32_free(img_res);
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clip_image_f32_batch_free(img_res_v);
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img.request_encode_image = false;
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}
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@ -990,11 +1031,6 @@ struct llama_server_context
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queue_results.send(res);
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}
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json get_model_props()
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{
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return get_formated_generation(slots[0]);
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}
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json get_formated_generation(llama_client_slot &slot)
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{
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const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
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@ -1005,6 +1041,8 @@ struct llama_server_context
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{"model", params.model_alias},
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{"seed", slot.params.seed},
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{"temperature", slot.sparams.temp},
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{"dynatemp_range", slot.sparams.dynatemp_range},
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{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
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{"top_k", slot.sparams.top_k},
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{"top_p", slot.sparams.top_p},
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{"min_p", slot.sparams.min_p},
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@ -1166,13 +1204,30 @@ struct llama_server_context
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task.multitask_id = multitask_id;
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// when a completion task's prompt array is not a singleton, we split it into multiple requests
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if (task.data.count("prompt") && task.data.at("prompt").size() > 1)
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{
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split_multiprompt_task(task_id, task);
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}
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// otherwise, it's a single-prompt task, we actually queue it
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queue_tasks.post(task);
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// if there's numbers in the prompt array it will be treated as an array of tokens
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if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
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bool numbers = false;
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for (const auto& e : task.data.at("prompt")) {
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if (e.is_number()) {
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numbers = true;
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break;
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}
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}
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// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
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// it will completely stall the server. I don't know where the bug for this is.
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//
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// if there are numbers, it needs to be treated like a single prompt,
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// queue_tasks handles a mix of strings and numbers just fine.
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if (numbers) {
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queue_tasks.post(task);
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} else {
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split_multiprompt_task(task_id, task);
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}
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} else {
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queue_tasks.post(task);
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}
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}
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// for multiple images processing
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@ -1254,7 +1309,10 @@ struct llama_server_context
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void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
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{
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int prompt_count = multiprompt_task.data.at("prompt").size();
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assert(prompt_count > 1);
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if (prompt_count <= 1) {
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send_error(multiprompt_task, "error while handling multiple prompts");
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return;
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}
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// generate all the ID for subtask
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std::vector<int> subtask_ids(prompt_count);
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@ -1566,10 +1624,6 @@ struct llama_server_context
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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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}
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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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slot.cache_tokens = prompt_tokens;
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if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0)
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@ -1583,6 +1637,10 @@ 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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LOG_VERBOSE("prompt ingested", {
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{"n_past", slot.n_past},
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{"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
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