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https://github.com/ggerganov/llama.cpp.git
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server : some style changes
This commit is contained in:
Georgi Gerganov
parent
e1516709f2
commit
f25308be5c
@ -383,9 +383,9 @@ struct llama_client_slot
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bool stopped_eos = false;
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bool stopped_word = false;
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bool stopped_limit = false;
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bool oaicompat = false;
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std::string oaicompat_model = "";
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std::string oaicompat_model;
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std::string stopping_word;
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@ -486,7 +486,7 @@ struct llama_client_slot
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};
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}
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void print_timings() {
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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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@ -685,7 +685,7 @@ struct llama_server_context
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bool launch_slot_with_data(llama_client_slot* &slot, json data) {
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slot_params default_params;
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llama_sampling_params default_sparams;
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if (data.count("__oaicompat") != 0) {
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slot->oaicompat = true;
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slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
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@ -693,7 +693,7 @@ struct llama_server_context
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slot->oaicompat = false;
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slot->oaicompat_model = "";
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}
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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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@ -1284,7 +1284,7 @@ struct llama_server_context
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std::lock_guard<std::mutex> lock(mutex_tasks);
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task_server task;
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task.id = id_gen++;
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task.data = data;
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task.data = std::move(data);
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task.infill_mode = infill;
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task.embedding_mode = embedding;
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task.type = COMPLETION_TASK;
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@ -2252,29 +2252,27 @@ json oaicompat_completion_params_parse(
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llama_params["__oaicompat"] = true;
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// Map OpenAI parameters to llama.cpp parameters
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llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
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llama_params["temperature"] = json_value(body, "temperature", 0.8);
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llama_params["top_k"] = json_value(body, "max_tokens", 40);
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llama_params["top_p"] = json_value(body, "top_p", 0.95);
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llama_params["n_predict"] = json_value(body, "max_tokens", -1);
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llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
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llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
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llama_params["temperature"] = json_value(body, "temperature", 0.8);
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llama_params["top_k"] = json_value(body, "top_k", 40);
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llama_params["top_p"] = json_value(body, "top_p", 0.95);
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llama_params["n_predict"] = json_value(body, "max_tokens", -1);
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llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
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llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
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llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
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llama_params["seed"] = json_value(body, "seed", 0);
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llama_params["stream"] =json_value(body, "stream", false);
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llama_params["mirostat"] = json_value(body, "mirostat", false);
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llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
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llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
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llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
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llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
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llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
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llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
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llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
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llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
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llama_params["seed"] = json_value(body, "seed", 0);
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llama_params["stream"] = json_value(body, "stream", false);
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llama_params["mirostat"] = json_value(body, "mirostat", false);
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llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
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llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
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llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
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llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
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llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
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llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
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llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
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if (llama_params.count("grammar") != 0) {
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llama_params["grammar"] = json_value(
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body, "grammar",
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json::object());
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llama_params["grammar"] = json_value(body, "grammar", json::object());
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}
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// Handle 'stop' field
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@ -2287,23 +2285,22 @@ json oaicompat_completion_params_parse(
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body, "stop",
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json::array());
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}
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// Ensure there is ChatML-specific end sequence among stop words
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llama_params["stop"].push_back("<|im_end|>");
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return llama_params;
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}
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static json format_final_response_oaicompat(json request, task_result response,
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bool streaming = false)
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static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
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{
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json result = response.result_json;
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bool stopped_word = result.count("stopped_word") != 0;
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bool stopped_eos = json_value(result, "stopped_eos", false);
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bool stopped_word = result.count("stopped_word") != 0;
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bool stopped_eos = json_value(result, "stopped_eos", false);
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int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
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int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
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std::string content = json_value(result, "content", std::string(""));
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int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
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std::string content = json_value(result, "content", std::string(""));
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std::string finish_reason = "length";
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if (stopped_word || stopped_eos) {
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@ -2314,10 +2311,10 @@ static json format_final_response_oaicompat(json request, task_result response,
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streaming ? json::array({json{{"finish_reason", finish_reason},
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{"index", 0},
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{"delta", json::object()}}})
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: json::array({json{{"finish_reason", finish_reason},
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: json::array({json{{"finish_reason", finish_reason},
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{"index", 0},
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{"message", json{{"content", content},
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{"role", "assistant"}}}}});
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{"role", "assistant"}}}}});
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std::time_t t = std::time(0);
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@ -2345,7 +2342,7 @@ static json format_final_response_oaicompat(json request, task_result response,
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}
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// return value is vector as there is one case where we might need to generate two responses
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static std::vector<json> format_partial_response_oaicompat(task_result response) {
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static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
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json result = response.result_json;
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if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
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@ -2353,15 +2350,14 @@ static std::vector<json> format_partial_response_oaicompat(task_result response)
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}
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bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
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std::string modelname =
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json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
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std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
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bool stopped_word = json_value(result, "stopped_word", false);
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bool stopped_eos = json_value(result, "stopped_eos", false);
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bool stopped_limit = json_value(result, "stopped_limit", false);
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bool stopped_word = json_value(result, "stopped_word", false);
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bool stopped_eos = json_value(result, "stopped_eos", false);
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bool stopped_limit = json_value(result, "stopped_limit", false);
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std::string content = json_value(result, "content", std::string(""));
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std::string finish_reason = "";
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std::string finish_reason;
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if (stopped_word || stopped_eos) {
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finish_reason = "stop";
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}
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@ -2383,7 +2379,7 @@ static std::vector<json> format_partial_response_oaicompat(task_result response)
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choices = json::array({json{{"finish_reason", nullptr},
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{"index", 0},
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{"delta", json{{"role", "assistant"}}}}});
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} else {
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} else {
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// We have to send this as two updates to conform to openai behavior
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json initial_ret = json{{"choices", json::array({json{
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{"finish_reason", nullptr},
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@ -2400,13 +2396,13 @@ static std::vector<json> format_partial_response_oaicompat(task_result response)
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{"choices", json::array({json{{"finish_reason", nullptr},
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{"index", 0},
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{"delta", json{
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{"content", content}}}
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{"content", content}}}
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}})},
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{"created", t},
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{"id", gen_chatcmplid()},
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{"model", modelname},
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{"object", "chat.completion.chunk"}};
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return std::vector<json>({initial_ret, second_ret});
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}
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} else {
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@ -2612,9 +2608,9 @@ int main(int argc, char **argv)
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task_result result = llama.next_result(task_id);
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if (!result.error) {
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const std::string str =
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"data: " +
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result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
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"\n\n";
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"data: " +
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result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
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"\n\n";
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LOG_VERBOSE("data stream", {
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{ "to_send", str }
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});
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@ -2627,9 +2623,9 @@ int main(int argc, char **argv)
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}
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} else {
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const std::string str =
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"error: " +
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result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
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"\n\n";
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"error: " +
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result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
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"\n\n";
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LOG_VERBOSE("data stream", {
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{ "to_send", str }
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});
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@ -2655,13 +2651,13 @@ int main(int argc, char **argv)
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});
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svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req,
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httplib::Response &res)
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// TODO: add mount point without "/v1" prefix -- how?
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svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
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{
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json data = oaicompat_completion_params_parse(json::parse(req.body));
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const int task_id = llama.request_completion(data, false, false);
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if (!json_value(data, "stream", false)) {
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std::string completion_text;
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task_result result = llama.next_result(task_id);
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@ -2683,7 +2679,7 @@ int main(int argc, char **argv)
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task_result llama_result = llama.next_result(task_id);
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if (!llama_result.error) {
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std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
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for (auto it = result_array.begin(); it != result_array.end(); ++it)
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{
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if (!it->empty()) {
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@ -2725,7 +2721,7 @@ int main(int argc, char **argv)
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res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
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}
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});
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svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
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{
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json data = json::parse(req.body);
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