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https://github.com/ggerganov/llama.cpp.git
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9c405c9f9a
* server: use llama_chat_apply_template * server: remove trailing space * server: fix format_chat * server: fix help message Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server: fix formatted_chat --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
226 lines
9.2 KiB
C++
226 lines
9.2 KiB
C++
#pragma once
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#include <string>
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#include <vector>
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#include <set>
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#include <mutex>
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#include <condition_variable>
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#include <unordered_map>
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#include "json.hpp"
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#include "utils.hpp"
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#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
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using json = nlohmann::json;
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inline static json oaicompat_completion_params_parse(
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const struct llama_model * model,
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const json &body, /* openai api json semantics */
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const std::string &chat_template)
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{
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json llama_params;
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llama_params["__oaicompat"] = true;
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// Map OpenAI parameters to llama.cpp parameters
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//
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// For parameters that are defined by the OpenAI documentation (e.g.
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// temperature), we explicitly specify OpenAI's intended default; we
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// need to do that because sometimes OpenAI disagrees with llama.cpp
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//
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// https://platform.openai.com/docs/api-reference/chat/create
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llama_sampling_params default_sparams;
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llama_params["model"] = json_value(body, "model", std::string("unknown"));
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llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
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llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
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llama_params["temperature"] = json_value(body, "temperature", 0.0);
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llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
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llama_params["top_p"] = json_value(body, "top_p", 1.0);
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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", LLAMA_DEFAULT_SEED);
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llama_params["stream"] = json_value(body, "stream", false);
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llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
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llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
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llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
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llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
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llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
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llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
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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", default_sparams.tfs_z);
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if (body.count("grammar") != 0) {
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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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if (body.contains("stop") && body["stop"].is_string()) {
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llama_params["stop"] = json::array({body["stop"].get<std::string>()});
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} else {
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llama_params["stop"] = json_value(body, "stop", 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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inline 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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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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std::string finish_reason = "length";
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if (stopped_word || stopped_eos) {
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finish_reason = "stop";
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}
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json choices =
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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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{"index", 0},
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{"message", json{{"content", content},
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{"role", "assistant"}}}}});
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std::time_t t = std::time(0);
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json res =
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json{{"choices", choices},
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{"created", t},
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{"model",
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json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
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{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
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{"usage",
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json{{"completion_tokens", num_tokens_predicted},
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{"prompt_tokens", num_prompt_tokens},
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{"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
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{"id", gen_chatcmplid()}};
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if (server_verbose) {
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res["__verbose"] = result;
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}
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if (result.contains("completion_probabilities")) {
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res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
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}
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return res;
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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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inline 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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return std::vector<json>({response.result_json});
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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 = 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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std::string content = json_value(result, "content", std::string(""));
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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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if (stopped_limit) {
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finish_reason = "length";
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}
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std::time_t t = std::time(0);
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json choices;
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if (!finish_reason.empty()) {
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choices = json::array({json{{"finish_reason", finish_reason},
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{"index", 0},
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{"delta", json::object()}}});
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} else {
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if (first) {
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if (content.empty()) {
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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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// 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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{"index", 0},
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{"delta", json{
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{"role", "assistant"}
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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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json second_ret = json{
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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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}})},
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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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// Some idiosyncrasy in task processing logic makes several trailing calls
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// with empty content, we ignore these at the calee site.
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if (content.empty()) {
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return std::vector<json>({json::object()});
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}
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choices = json::array({json{
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{"finish_reason", nullptr},
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{"index", 0},
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{"delta",
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json{
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{"content", content},
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}},
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}});
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}
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}
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json ret = json{{"choices", choices},
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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>({ret});
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}
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inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings)
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{
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json res =
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json{
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{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
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{"object", "list"},
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{"usage",
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json{{"prompt_tokens", 0},
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{"total_tokens", 0}}},
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{"data", embeddings}
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};
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return res;
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}
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