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Fix server.cpp code style according to review

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kir-gadjello 2023-11-22 22:35:57 -03:00 committed by GitHub
parent 9ad4d273e1
commit e1516709f2
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1 changed files with 263 additions and 278 deletions
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examples/server/server.cpp
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@ -61,14 +61,14 @@ static bool server_verbose = false;
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
json oaicompat_completion_params_parse(const json &body);
std::string format_chatml(std::vector<json> messages);
// //
// base64 utils (TODO: move to common in the future) // base64 utils (TODO: move to common in the future)
// //
json oaicompat_completion_params_parse(
const json &body);
std::string format_chatml(std::vector<json> messages);
static const std::string base64_chars = static const std::string base64_chars =
"ABCDEFGHIJKLMNOPQRSTUVWXYZ" "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz" "abcdefghijklmnopqrstuvwxyz"
@ -688,8 +688,7 @@ struct llama_server_context
if (data.count("__oaicompat") != 0) { if (data.count("__oaicompat") != 0) {
slot->oaicompat = true; slot->oaicompat = true;
slot->oaicompat_model = slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
} else { } else {
slot->oaicompat = false; slot->oaicompat = false;
slot->oaicompat_model = ""; slot->oaicompat_model = "";
@ -2209,246 +2208,232 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
} }
static std::string random_string() { static std::string random_string()
std::string str( {
"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
std::random_device rd; std::random_device rd;
std::mt19937 generator(rd()); std::mt19937 generator(rd());
std::shuffle(str.begin(), str.end(), generator); std::shuffle(str.begin(), str.end(), generator);
return str.substr(0, 32); // assumes 32 < number of characters in str return str.substr(0, 32); // assumes 32 < number of characters in str
} }
static std::string gen_chatcmplid() { static std::string gen_chatcmplid()
std::stringstream chatcmplid; {
chatcmplid << "chatcmpl-" << random_string(); std::stringstream chatcmplid;
return chatcmplid.str(); chatcmplid << "chatcmpl-" << random_string();
return chatcmplid.str();
} }
std::string format_chatml(std::vector<json> messages) { std::string format_chatml(std::vector<json> messages)
{
std::ostringstream chatml_msgs;
std::ostringstream chatml_msgs; for (auto it = messages.begin(); it != messages.end(); ++it) {
chatml_msgs << "<|im_start|>"
<< json_value(*it, "role", std::string("user")) << '\n';
chatml_msgs << json_value(*it, "content", std::string(""))
<< "<|im_end|>\n";
}
// iterate the array chatml_msgs << "<|im_start|>assistant" << '\n';
for (auto it = messages.begin(); it != messages.end(); ++it) {
chatml_msgs << "<|im_start|>"
<< json_value(*it, "role", std::string("user")) << '\n';
chatml_msgs << json_value(*it, "content", std::string(""))
<< "<|im_end|>\n";
}
chatml_msgs << "<|im_start|>assistant" << '\n'; return chatml_msgs.str();
return chatml_msgs.str();
} }
/* llama.cpp completion api semantics */ /* llama.cpp completion api semantics */
json oaicompat_completion_params_parse( json oaicompat_completion_params_parse(
const json &body /* openai api json semantics */) { const json &body /* openai api json semantics */)
json llama_params; {
json llama_params;
llama_params["__oaicompat"] = true; llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters // Map OpenAI parameters to llama.cpp parameters
llama_params["prompt"] = format_chatml( llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' llama_params["temperature"] = json_value(body, "temperature", 0.8);
llama_params["temperature"] = llama_params["top_k"] = json_value(body, "max_tokens", 40);
json_value(body, "temperature", 0.8); // Default to 0.8 if not provided llama_params["top_p"] = json_value(body, "top_p", 0.95);
llama_params["top_k"] = llama_params["n_predict"] = json_value(body, "max_tokens", -1);
json_value(body, "max_tokens", 40); // Default to 40 if not provided llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
llama_params["top_p"] = llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
json_value(body, "top_p", 0.95); // Default to 0.95 if not provided llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["n_predict"] = llama_params["seed"] = json_value(body, "seed", 0);
json_value(body, "max_tokens", -1); // Default to -1 if not provided llama_params["stream"] =json_value(body, "stream", false);
llama_params["logit_bias"] = json_value( llama_params["mirostat"] = json_value(body, "mirostat", false);
body, "logit_bias", llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
json::object()); // Default to empty object if not provided llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
llama_params["frequency_penalty"] = json_value( llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
body, "frequency_penalty", 0.0); // Default to 0.0 if not provided llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
llama_params["presence_penalty"] = json_value( llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
body, "presence_penalty", 0.0); // Default to 0.0 if not provided llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["seed"] = json_value(body, "seed", 0); llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
llama_params["stream"] =
json_value(body, "stream", false); // Default to 0 if not provided
llama_params["mirostat"] =
json_value(body, "mirostat", false); // Default to false if not provided
llama_params["mirostat_tau"] =
json_value(body, "mirostat_tau", 0.0); // Default to 0.0 if not provided
llama_params["mirostat_eta"] =
json_value(body, "mirostat_eta", 0.0); // Default to 0.0 if not provided
llama_params["penalize_nl"] = json_value(
body, "penalize_nl", false); // Default to false if not provided
llama_params["typical_p"] =
json_value(body, "typical_p", 0.0); // Default to 0.0 if not provided
llama_params["repeat_last_n"] =
json_value(body, "repeat_last_n", 0); // Default to 0 if not provided
llama_params["ignore_eos"] =
json_value(body, "ignore_eos", false); // Default to false if not provided
llama_params["tfs_z"] =
json_value(body, "tfs_z", 0.0); // Default to 0.0 if not provided
if (llama_params.count("grammar") != 0) {
llama_params["grammar"] = json_value(
body, "grammar",
json::object()); // Default to empty object if not provided
}
// Handle 'stop' field if (llama_params.count("grammar") != 0) {
if (body["stop"].is_null()) { llama_params["grammar"] = json_value(
llama_params["stop"] = json::array({}); body, "grammar",
} else if (body["stop"].is_string()) { json::object());
llama_params["stop"] = json::array({body["stop"].get<std::string>()}); }
} else {
llama_params["stop"] = json_value(
body, "stop",
json::array()); // Default to empty array if not provided
}
llama_params["stop"].push_back("<|im_end|>"); // Handle 'stop' field
if (body["stop"].is_null()) {
llama_params["stop"] = json::array({});
} else if (body["stop"].is_string()) {
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
} else {
llama_params["stop"] = json_value(
body, "stop",
json::array());
}
return llama_params; // Ensure there is ChatML-specific end sequence among stop words
llama_params["stop"].push_back("<|im_end|>");
return llama_params;
} }
static json format_final_response_oaicompat(json request, task_result response, static json format_final_response_oaicompat(json request, task_result response,
bool streaming = false) { bool streaming = false)
{
json result = response.result_json;
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(""));
bool stopped_word = result.count("stopped_word") != 0; std::string finish_reason = "length";
bool stopped_eos = json_value(result, "stopped_eos", false); if (stopped_word || stopped_eos) {
int num_tokens_predicted = json_value(result, "tokens_predicted", 0); finish_reason = "stop";
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); }
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason = "length"; json choices =
if (stopped_word || stopped_eos) { streaming ? json::array({json{{"finish_reason", finish_reason},
finish_reason = "stop"; {"index", 0},
} {"delta", json::object()}}})
: json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"message", json{{"content", content},
{"role", "assistant"}}}}});
json choices = std::time_t t = std::time(0);
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()}};
json res = if (server_verbose) {
json{{"choices", choices}, res["__verbose"] = result;
{"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) { if (result.contains("completion_probabilities")) {
res["__verbose"] = result; res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
} }
if (result.contains("completion_probabilities")) { return res;
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
static std::vector<json> format_partial_response_oaicompat(task_result response) { static std::vector<json> format_partial_response_oaicompat(task_result response) {
json result = response.result_json; json result = response.result_json;
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
return std::vector<json>({response.result_json}); 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 idosyncrasy 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}, bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
{"created", t}, std::string modelname =
{"id", gen_chatcmplid()}, json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
{"model", modelname},
{"object", "chat.completion.chunk"}};
return std::vector<json>({ret}); 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});
} }
static json format_partial_response( static json format_partial_response(
@ -2670,76 +2655,76 @@ int main(int argc, char **argv)
}); });
svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req,
httplib::Response &res) { httplib::Response &res)
json data = oaicompat_completion_params_parse(json::parse(req.body));
const int task_id = llama.request_completion(data, false, false);
if (!json_value(data, "stream", false)) {
std::string completion_text;
task_result result = llama.next_result(task_id);
if (!result.error && result.stop) {
json oaicompat_result = format_final_response_oaicompat(data, result);
res.set_content(oaicompat_result.dump(-1, ' ', false,
json::error_handler_t::replace),
"application/json");
} else {
res.status = 500;
res.set_content(result.result_json["content"], "text/plain");
return;
}
} else {
const auto chunked_content_provider = [task_id, &llama](size_t,
httplib::DataSink &sink) {
while (true) {
task_result llama_result = llama.next_result(task_id);
if (!llama_result.error) {
std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
for (auto it = result_array.begin(); it != result_array.end(); ++it)
{ {
if (!it->empty()) { json data = oaicompat_completion_params_parse(json::parse(req.body));
const std::string str =
"data: " + const int task_id = llama.request_completion(data, false, false);
it->dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n"; if (!json_value(data, "stream", false)) {
LOG_VERBOSE("data stream", {{"to_send", str}}); std::string completion_text;
if (!sink.write(str.c_str(), str.size())) { task_result result = llama.next_result(task_id);
return false;
if (!result.error && result.stop) {
json oaicompat_result = format_final_response_oaicompat(data, result);
res.set_content(oaicompat_result.dump(-1, ' ', false,
json::error_handler_t::replace),
"application/json");
} else {
res.status = 500;
res.set_content(result.result_json["content"], "text/plain");
return;
} }
} else {
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
while (true) {
task_result llama_result = llama.next_result(task_id);
if (!llama_result.error) {
std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
for (auto it = result_array.begin(); it != result_array.end(); ++it)
{
if (!it->empty()) {
const std::string str =
"data: " +
it->dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
return false;
}
}
}
if (llama_result.stop) {
break;
}
} else {
const std::string str =
"error: " +
llama_result.result_json.dump(-1, ' ', false,
json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
return false;
}
break;
}
}
sink.done();
return true;
};
auto on_complete = [task_id, &llama](bool) {
// cancel request
llama.request_cancel(task_id);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
} }
} });
if (llama_result.stop) {
break;
}
} else {
const std::string str =
"error: " +
llama_result.result_json.dump(-1, ' ', false,
json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
return false;
}
break;
}
}
sink.done();
return true;
};
auto on_complete = [task_id, &llama](bool) {
// cancel
llama.request_cancel(task_id);
};
res.set_chunked_content_provider("text/event-stream",
chunked_content_provider, on_complete);
}
});
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
{ {