server : merge oai.hpp in utils.hpp
This commit is contained in:
Georgi Gerganov
parent
cb3ce0bfff
commit
4a2d5f63f2
5 changed files with 216 additions and 223 deletions
2
Makefile
2
Makefile
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@ -724,7 +724,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
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server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
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@ -1,7 +1,7 @@
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set(TARGET server)
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option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
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include_directories(${CMAKE_CURRENT_SOURCE_DIR})
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add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h)
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add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
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install(TARGETS ${TARGET} RUNTIME)
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target_compile_definitions(${TARGET} PRIVATE
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SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
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@ -1,220 +0,0 @@
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#pragma once
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#include "utils.hpp"
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#include "json.hpp"
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#include <string>
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#include <vector>
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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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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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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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static json format_final_response_oaicompat(const json & request, json result, bool streaming = 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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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 = json {
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{"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", json {
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{"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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}},
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{"id", gen_chatcmplid()}
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};
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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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static std::vector<json> format_partial_response_oaicompat(json result) {
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if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
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return std::vector<json>({result});
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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 {
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{"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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};
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return std::vector<json>({ret});
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}
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static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
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json res = 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", json {
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{"prompt_tokens", 0},
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{"total_tokens", 0}
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}},
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{"data", embeddings}
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};
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return res;
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}
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@ -1,5 +1,4 @@
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#include "utils.hpp"
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#include "oai.hpp"
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#include "common.h"
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#include "llama.h"
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@ -10,6 +10,8 @@
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#include <sstream>
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#include <random>
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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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extern bool server_verbose;
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@ -317,3 +319,215 @@ static json probs_vector_to_json(const llama_context * ctx, const std::vector<co
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return out;
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}
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//
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// OAI utils
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//
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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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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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static json format_final_response_oaicompat(const json & request, json result, bool streaming = 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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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 = json {
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{"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", json {
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{"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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}},
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{"id", gen_chatcmplid()}
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};
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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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static std::vector<json> format_partial_response_oaicompat(json result) {
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if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
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return std::vector<json>({result});
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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{
|
||||
{"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});
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue