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llama: remove server static assets (#3174)

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Jeffrey Morgan 2024-03-15 19:24:12 -07:00 committed by GitHub
parent 2dce1ab40b
commit e95ffc7448
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GPG key ID: B5690EEEBB952194
6 changed files with 3 additions and 6014 deletions
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485
llm/ext_server/completion.js.hpp
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@ -1,485 +0,0 @@
unsigned char completion_js[] = {
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};
unsigned int completion_js_len = 5782;

2791
llm/ext_server/index.html.hpp
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File diff suppressed because it is too large Load diff

1903
llm/ext_server/index.js.hpp
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File diff suppressed because it is too large Load diff

311
llm/ext_server/json-schema-to-grammar.mjs.hpp
View file

@ -1,311 +0,0 @@
unsigned char json_schema_to_grammar_mjs[] = {
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0x6d, 0x61, 0x72, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a
};
unsigned int json_schema_to_grammar_mjs_len = 3695;

225
llm/ext_server/oai.hpp
View file

@ -1,225 +0,0 @@
#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;
}

302
llm/ext_server/server.cpp
View file

@ -2,7 +2,6 @@
#include "llama.h"
#include "grammar-parser.h"
#include "utils.hpp"
#include "oai.hpp"
#include "../llava/clip.h"
#include "../llava/llava.h"
@ -18,12 +17,6 @@
#include "httplib.h"
#include "json.hpp"
// auto generated files (update with ./deps.sh)
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
#include <cstddef>
#include <thread>
#include <chrono>
@ -129,9 +122,6 @@ struct server_slot {
bool stopped_word = false;
bool stopped_limit = false;
bool oaicompat = false;
std::string oaicompat_model;
std::string stopping_word;
// sampling
@ -543,14 +533,6 @@ struct llama_server_context
slot_params default_params;
llama_sampling_params default_sparams;
if (data.count("__oaicompat") != 0) {
slot->oaicompat = true;
slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
} else {
slot->oaicompat = false;
slot->oaicompat_model = "";
}
slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
@ -1148,12 +1130,6 @@ struct llama_server_context
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
}
if (slot.oaicompat)
{
res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
res.result_json["model"] = slot.oaicompat_model;
}
queue_results.send(res);
}
@ -1201,12 +1177,6 @@ struct llama_server_context
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
}
if (slot.oaicompat)
{
res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
res.result_json["model"] = slot.oaicompat_model;
}
queue_results.send(res);
}
@ -3075,41 +3045,9 @@ int _main(int argc, char **argv)
// this is only called if no index.html is found in the public --path
svr.Get("/", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
return false;
});
// this is only called if no index.js is found in the public --path
svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
return false;
});
// this is only called if no index.html is found in the public --path
svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
return false;
});
// this is only called if no index.html is found in the public --path
svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
return false;
});
svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = {
{ "user_name", llama.name_user.c_str() },
{ "assistant_name", llama.name_assistant.c_str() },
{ "default_generation_settings", llama.default_generation_settings_for_props },
{ "total_slots", llama.params.n_parallel }
};
res.set_content(data.dump(), "application/json; charset=utf-8");
res.set_content("server running", "text/plain; charset=utf-8");
res.status = 200; // Unauthorized
return true;
});
svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
@ -3189,180 +3127,6 @@ int _main(int argc, char **argv)
}
});
svr.Get("/v1/models", [&params, &model_meta](const httplib::Request& req, httplib::Response& res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
std::time_t t = std::time(0);
json models = {
{"object", "list"},
{"data", {
{
{"id", params.model_alias},
{"object", "model"},
{"created", t},
{"owned_by", "llamacpp"},
{"meta", model_meta}
},
}}
};
res.set_content(models.dump(), "application/json; charset=utf-8");
});
const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
if (!validate_api_key(req, res)) {
return;
}
json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template);
const int task_id = llama.queue_tasks.get_new_id();
llama.queue_results.add_waiting_task_id(task_id);
llama.request_completion(task_id, data, false, false, -1);
if (!json_value(data, "stream", false)) {
std::string completion_text;
task_result result = llama.queue_results.recv(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; charset=utf-8");
} else {
res.status = 500;
res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
}
llama.queue_results.remove_waiting_task_id(task_id);
} else {
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
while (true) {
task_result llama_result = llama.queue_results.recv(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())) {
llama.queue_results.remove_waiting_task_id(task_id);
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())) {
llama.queue_results.remove_waiting_task_id(task_id);
return false;
}
break;
}
}
sink.done();
llama.queue_results.remove_waiting_task_id(task_id);
return true;
};
auto on_complete = [task_id, &llama](bool) {
// cancel request
llama.request_cancel(task_id);
llama.queue_results.remove_waiting_task_id(task_id);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
}
};
svr.Post("/chat/completions", chat_completions);
svr.Post("/v1/chat/completions", chat_completions);
svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.queue_tasks.get_new_id();
llama.queue_results.add_waiting_task_id(task_id);
llama.request_completion(task_id, data, true, false, -1);
if (!json_value(data, "stream", false)) {
std::string completion_text;
task_result result = llama.queue_results.recv(task_id);
if (!result.error && result.stop)
{
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
}
else
{
res.status = 404;
res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
}
llama.queue_results.remove_waiting_task_id(task_id);
} else {
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) {
while (true)
{
task_result result = llama.queue_results.recv(task_id);
if (!result.error) {
const std::string str =
"data: " +
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()))
{
llama.queue_results.remove_waiting_task_id(task_id);
return false;
}
if (result.stop)
{
break;
}
}
else
{
break;
}
}
llama.queue_results.remove_waiting_task_id(task_id);
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.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
{ return res.set_content("", "application/json; charset=utf-8"); });
svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
@ -3427,66 +3191,6 @@ int _main(int argc, char **argv)
return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
});
svr.Post("/v1/embeddings", [&llama](const httplib::Request &req, httplib::Response &res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
const json body = json::parse(req.body);
json prompt;
if (body.count("input") != 0)
{
prompt = body["input"];
// batch
if(prompt.is_array()) {
json data = json::array();
int i = 0;
for (const json &elem : prompt) {
const int task_id = llama.queue_tasks.get_new_id();
llama.queue_results.add_waiting_task_id(task_id);
llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1);
// get the result
task_result result = llama.queue_results.recv(task_id);
llama.queue_results.remove_waiting_task_id(task_id);
json embedding = json{
{"embedding", json_value(result.result_json, "embedding", json::array())},
{"index", i++},
{"object", "embedding"}
};
data.push_back(embedding);
}
json result = format_embeddings_response_oaicompat(body, data);
return res.set_content(result.dump(), "application/json; charset=utf-8");
}
}
else
{
prompt = "";
}
// create and queue the task
const int task_id = llama.queue_tasks.get_new_id();
llama.queue_results.add_waiting_task_id(task_id);
llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1);
// get the result
task_result result = llama.queue_results.recv(task_id);
llama.queue_results.remove_waiting_task_id(task_id);
json data = json::array({json{
{"embedding", json_value(result.result_json, "embedding", json::array())},
{"index", 0},
{"object", "embedding"}
}}
);
json root = format_embeddings_response_oaicompat(body, data);
// send the result
return res.set_content(root.dump(), "application/json; charset=utf-8");
});
// GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
// "Bus error: 10" - this is on macOS, it does not crash on Linux
//std::thread t2([&]()