mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
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
|
|
|
|
#include <string>
|
|
#include <vector>
|
|
#include <set>
|
|
#include <mutex>
|
|
#include <condition_variable>
|
|
#include <unordered_map>
|
|
|
|
#include "json.hpp"
|
|
#include "utils.hpp"
|
|
|
|
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
|
|
|
|
using json = nlohmann::json;
|
|
|
|
inline static json oaicompat_completion_params_parse(
|
|
const struct llama_model * model,
|
|
const json &body, /* openai api json semantics */
|
|
const std::string &chat_template)
|
|
{
|
|
json llama_params;
|
|
|
|
llama_params["__oaicompat"] = true;
|
|
|
|
// Map OpenAI parameters to llama.cpp parameters
|
|
//
|
|
// For parameters that are defined by the OpenAI documentation (e.g.
|
|
// temperature), we explicitly specify OpenAI's intended default; we
|
|
// need to do that because sometimes OpenAI disagrees with llama.cpp
|
|
//
|
|
// https://platform.openai.com/docs/api-reference/chat/create
|
|
llama_sampling_params default_sparams;
|
|
llama_params["model"] = json_value(body, "model", std::string("unknown"));
|
|
llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
|
|
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
|
|
llama_params["temperature"] = json_value(body, "temperature", 0.0);
|
|
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
|
|
llama_params["top_p"] = json_value(body, "top_p", 1.0);
|
|
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
|
|
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
|
|
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
|
|
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
|
|
llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
|
|
llama_params["stream"] = json_value(body, "stream", false);
|
|
llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
|
|
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
|
|
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
|
|
llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
|
|
llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
|
|
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
|
|
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
|
|
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
|
|
|
|
if (body.count("grammar") != 0) {
|
|
llama_params["grammar"] = json_value(body, "grammar", json::object());
|
|
}
|
|
|
|
// Handle 'stop' field
|
|
if (body.contains("stop") && body["stop"].is_string()) {
|
|
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
|
|
} else {
|
|
llama_params["stop"] = json_value(body, "stop", json::array());
|
|
}
|
|
|
|
// Ensure there is ChatML-specific end sequence among stop words
|
|
llama_params["stop"].push_back("<|im_end|>");
|
|
|
|
return llama_params;
|
|
}
|
|
|
|
inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
|
|
{
|
|
json result = response.result_json;
|
|
|
|
bool stopped_word = result.count("stopped_word") != 0;
|
|
bool stopped_eos = json_value(result, "stopped_eos", false);
|
|
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
|
|
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
|
|
std::string content = json_value(result, "content", std::string(""));
|
|
|
|
std::string finish_reason = "length";
|
|
if (stopped_word || stopped_eos) {
|
|
finish_reason = "stop";
|
|
}
|
|
|
|
json choices =
|
|
streaming ? json::array({json{{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"delta", json::object()}}})
|
|
: json::array({json{{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"message", json{{"content", content},
|
|
{"role", "assistant"}}}}});
|
|
|
|
std::time_t t = std::time(0);
|
|
|
|
json res =
|
|
json{{"choices", choices},
|
|
{"created", t},
|
|
{"model",
|
|
json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
|
{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
|
|
{"usage",
|
|
json{{"completion_tokens", num_tokens_predicted},
|
|
{"prompt_tokens", num_prompt_tokens},
|
|
{"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
|
|
{"id", gen_chatcmplid()}};
|
|
|
|
if (server_verbose) {
|
|
res["__verbose"] = result;
|
|
}
|
|
|
|
if (result.contains("completion_probabilities")) {
|
|
res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
// return value is vector as there is one case where we might need to generate two responses
|
|
inline static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
|
|
json result = response.result_json;
|
|
|
|
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
|
|
return std::vector<json>({response.result_json});
|
|
}
|
|
|
|
bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
|
|
std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
|
|
|
|
bool stopped_word = json_value(result, "stopped_word", false);
|
|
bool stopped_eos = json_value(result, "stopped_eos", false);
|
|
bool stopped_limit = json_value(result, "stopped_limit", false);
|
|
std::string content = json_value(result, "content", std::string(""));
|
|
|
|
std::string finish_reason;
|
|
if (stopped_word || stopped_eos) {
|
|
finish_reason = "stop";
|
|
}
|
|
if (stopped_limit) {
|
|
finish_reason = "length";
|
|
}
|
|
|
|
std::time_t t = std::time(0);
|
|
|
|
json choices;
|
|
|
|
if (!finish_reason.empty()) {
|
|
choices = json::array({json{{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"delta", json::object()}}});
|
|
} else {
|
|
if (first) {
|
|
if (content.empty()) {
|
|
choices = json::array({json{{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", json{{"role", "assistant"}}}}});
|
|
} else {
|
|
// We have to send this as two updates to conform to openai behavior
|
|
json initial_ret = json{{"choices", json::array({json{
|
|
{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", json{
|
|
{"role", "assistant"}
|
|
}}}})},
|
|
{"created", t},
|
|
{"id", gen_chatcmplid()},
|
|
{"model", modelname},
|
|
{"object", "chat.completion.chunk"}};
|
|
|
|
json second_ret = json{
|
|
{"choices", json::array({json{{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", json{
|
|
{"content", content}}}
|
|
}})},
|
|
{"created", t},
|
|
{"id", gen_chatcmplid()},
|
|
{"model", modelname},
|
|
{"object", "chat.completion.chunk"}};
|
|
|
|
return std::vector<json>({initial_ret, second_ret});
|
|
}
|
|
} else {
|
|
// Some idiosyncrasy in task processing logic makes several trailing calls
|
|
// with empty content, we ignore these at the calee site.
|
|
if (content.empty()) {
|
|
return std::vector<json>({json::object()});
|
|
}
|
|
|
|
choices = json::array({json{
|
|
{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta",
|
|
json{
|
|
{"content", content},
|
|
}},
|
|
}});
|
|
}
|
|
}
|
|
|
|
json ret = json{{"choices", choices},
|
|
{"created", t},
|
|
{"id", gen_chatcmplid()},
|
|
{"model", modelname},
|
|
{"object", "chat.completion.chunk"}};
|
|
|
|
return std::vector<json>({ret});
|
|
}
|
|
|
|
inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings)
|
|
{
|
|
json res =
|
|
json{
|
|
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
|
{"object", "list"},
|
|
{"usage",
|
|
json{{"prompt_tokens", 0},
|
|
{"total_tokens", 0}}},
|
|
{"data", embeddings}
|
|
};
|
|
return res;
|
|
}
|
|
|