mirror of
https://github.com/mudler/LocalAI.git
synced 2024-06-07 19:40:48 +00:00
2286 lines
84 KiB
C++
2286 lines
84 KiB
C++
// llama.cpp gRPC C++ backend server
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//
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// Ettore Di Giacinto <mudler@localai.io> and llama.cpp authors
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//
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// This is a gRPC server for llama.cpp compatible with the LocalAI proto
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// Note: this is a re-adaptation of the original llama.cpp example/server.cpp for HTTP (https://github.com/ggerganov/llama.cpp/tree/master/examples/server),
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// but modified to work with gRPC
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//
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#include <iostream>
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#include <memory>
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#include <string>
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#include <getopt.h>
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#include "clip.h"
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#include "llava.h"
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#include "stb_image.h"
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#include "common.h"
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#include "json.hpp"
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#include "llama.h"
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#include "grammar-parser.h"
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#include "backend.pb.h"
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#include "backend.grpc.pb.h"
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#include "utils.hpp"
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// include std::regex
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#include <cstddef>
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#include <thread>
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#include <mutex>
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#include <chrono>
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#include <regex>
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#include <condition_variable>
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#include <grpcpp/ext/proto_server_reflection_plugin.h>
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#include <grpcpp/grpcpp.h>
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#include <grpcpp/health_check_service_interface.h>
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#include <atomic>
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#include <signal.h>
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using grpc::Server;
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using grpc::ServerBuilder;
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using grpc::ServerContext;
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using grpc::Status;
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using backend::HealthMessage;
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///// LLAMA.CPP server code below
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using json = nlohmann::json;
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struct server_params
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{
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std::string hostname = "127.0.0.1";
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std::vector<std::string> api_keys;
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std::string public_path = "examples/server/public";
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std::string chat_template = "";
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int32_t port = 8080;
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int32_t read_timeout = 600;
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int32_t write_timeout = 600;
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bool slots_endpoint = true;
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};
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bool server_verbose = false;
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static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
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{
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size_t i;
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for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
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{
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}
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return i;
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}
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enum stop_type
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{
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STOP_FULL,
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STOP_PARTIAL,
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};
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static bool ends_with(const std::string &str, const std::string &suffix)
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{
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return str.size() >= suffix.size() &&
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0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
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}
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static size_t find_partial_stop_string(const std::string &stop,
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const std::string &text)
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{
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if (!text.empty() && !stop.empty())
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{
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const char text_last_char = text.back();
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for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
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{
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if (stop[char_index] == text_last_char)
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{
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const std::string current_partial = stop.substr(0, char_index + 1);
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if (ends_with(text, current_partial))
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{
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return text.size() - char_index - 1;
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}
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}
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}
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}
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return std::string::npos;
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}
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// TODO: reuse llama_detokenize
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template <class Iter>
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static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
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{
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std::string ret;
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for (; begin != end; ++begin)
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{
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ret += llama_token_to_piece(ctx, *begin);
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}
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return ret;
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}
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// format incomplete utf-8 multibyte character for output
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static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
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{
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std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
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// if the size is 1 and first bit is 1, meaning it's a partial character
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// (size > 1 meaning it's already a known token)
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if (out.size() == 1 && (out[0] & 0x80) == 0x80)
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{
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std::stringstream ss;
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ss << std::hex << (out[0] & 0xff);
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std::string res(ss.str());
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out = "byte: \\x" + res;
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}
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return out;
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}
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// convert a vector of completion_token_output to json
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static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
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{
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json out = json::array();
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for (const auto &prob : probs)
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{
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json probs_for_token = json::array();
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for (const auto &p : prob.probs)
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{
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std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
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probs_for_token.push_back(json
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{
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{"tok_str", tok_str},
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{"prob", p.prob},
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});
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}
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std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
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out.push_back(json{
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{"content", tok_str},
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{"probs", probs_for_token},
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});
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}
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return out;
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}
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struct llama_client_slot
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{
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int id;
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int task_id = -1;
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struct slot_params params;
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slot_state state = IDLE;
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slot_command command = NONE;
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// used to determine the slot that has been used the longest
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int64_t t_last_used = -1;
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// generation props
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int32_t n_ctx = 0; // context size per slot
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int32_t n_past = 0;
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int32_t n_decoded = 0;
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int32_t n_remaining = -1;
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int32_t i_batch = -1;
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int32_t n_predict = -1;
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int32_t num_prompt_tokens = 0;
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int32_t num_prompt_tokens_processed = 0;
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json prompt;
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std::string generated_text;
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llama_token sampled;
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std::vector<llama_token> cache_tokens;
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std::vector<completion_token_output> generated_token_probs;
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bool infill = false;
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bool embedding = false;
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bool has_next_token = true;
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bool truncated = false;
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bool stopped_eos = false;
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bool stopped_word = false;
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bool stopped_limit = false;
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bool oaicompat = false;
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std::string oaicompat_model;
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std::string stopping_word;
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// sampling
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struct llama_sampling_params sparams;
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llama_sampling_context *ctx_sampling = nullptr;
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int32_t ga_i = 0; // group-attention state
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int32_t ga_n = 1; // group-attention factor
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int32_t ga_w = 512; // group-attention width
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int32_t n_past_se = 0; // self-extend
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// multimodal
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std::vector<slot_image> images;
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// stats
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size_t sent_count = 0;
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size_t sent_token_probs_index = 0;
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int64_t t_start_process_prompt;
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int64_t t_start_genereration;
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double t_prompt_processing; // ms
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double t_token_generation; // ms
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// multitasks
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int multitask_id = -1;
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void reset() {
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num_prompt_tokens = 0;
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generated_text = "";
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truncated = false;
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stopped_eos = false;
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stopped_word = false;
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stopped_limit = false;
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stopping_word = "";
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n_past = 0;
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sent_count = 0;
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sent_token_probs_index = 0;
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infill = false;
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ga_i = 0;
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n_past_se = 0;
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generated_token_probs.clear();
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for (slot_image & img : images)
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{
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free(img.image_embedding);
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if (img.img_data) {
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clip_image_u8_free(img.img_data);
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}
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img.prefix_prompt = "";
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}
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images.clear();
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}
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bool has_budget(gpt_params &global_params) {
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if (params.n_predict == -1 && global_params.n_predict == -1)
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{
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return true; // limitless
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}
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n_remaining = -1;
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if (params.n_predict != -1)
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{
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n_remaining = params.n_predict - n_decoded;
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}
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else if (global_params.n_predict != -1)
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{
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n_remaining = global_params.n_predict - n_decoded;
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}
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return n_remaining > 0; // no budget
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}
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bool available() const {
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return state == IDLE && command == NONE;
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}
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bool is_processing() const {
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return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
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}
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void add_token_string(const completion_token_output &token) {
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if (command == RELEASE)
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{
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return;
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}
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cache_tokens.push_back(token.tok);
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generated_token_probs.push_back(token);
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}
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void release() {
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if (state == PROCESSING)
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{
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t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
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command = RELEASE;
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}
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}
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json get_formated_timings() {
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return json
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{
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{"prompt_n", num_prompt_tokens_processed},
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{"prompt_ms", t_prompt_processing},
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{"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed},
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{"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed},
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{"predicted_n", n_decoded},
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{"predicted_ms", t_token_generation},
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{"predicted_per_token_ms", t_token_generation / n_decoded},
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{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
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};
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}
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void print_timings() const {
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LOG_TEE("\n");
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LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
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__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
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LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
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__func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
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LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
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}
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};
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struct llama_server_context
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{
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llama_model *model = nullptr;
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llama_context *ctx = nullptr;
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clip_ctx *clp_ctx = nullptr;
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gpt_params params;
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llama_batch batch;
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bool multimodal = false;
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bool clean_kv_cache = true;
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bool all_slots_are_idle = false;
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bool add_bos_token = true;
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int32_t n_ctx; // total context for all clients / slots
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// system prompt
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bool system_need_update = false;
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std::string system_prompt;
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std::vector<llama_token> system_tokens;
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std::string name_user; // this should be the antiprompt
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std::string name_assistant;
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// slots / clients
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std::vector<llama_client_slot> slots;
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json default_generation_settings_for_props;
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llama_server_queue queue_tasks;
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llama_server_response queue_results;
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~llama_server_context()
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{
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if (ctx)
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{
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llama_free(ctx);
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ctx = nullptr;
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}
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if (model)
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{
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llama_free_model(model);
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model = nullptr;
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}
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}
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bool load_model(const gpt_params ¶ms_)
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{
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params = params_;
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if (!params.mmproj.empty()) {
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multimodal = true;
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LOG_TEE("Multi Modal Mode Enabled");
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clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
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if(clp_ctx == nullptr) {
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LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
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return false;
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}
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if (params.n_ctx < 2048) { // request larger context for the image embedding
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params.n_ctx = 2048;
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}
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}
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == nullptr)
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{
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LOG_ERROR("unable to load model", {{"model", params.model}});
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return false;
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}
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if (multimodal) {
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const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
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const int n_embd_llm = llama_n_embd(model);
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if (n_embd_clip != n_embd_llm) {
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LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
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llama_free(ctx);
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llama_free_model(model);
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return false;
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}
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}
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n_ctx = llama_n_ctx(ctx);
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add_bos_token = llama_should_add_bos_token(model);
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return true;
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}
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void initialize() {
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// create slots
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all_slots_are_idle = true;
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const int32_t n_ctx_slot = n_ctx / params.n_parallel;
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LOG_TEE("Available slots:\n");
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for (int i = 0; i < params.n_parallel; i++)
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{
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llama_client_slot slot;
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slot.id = i;
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slot.n_ctx = n_ctx_slot;
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slot.n_predict = params.n_predict;
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LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
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const int ga_n = params.grp_attn_n;
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const int ga_w = params.grp_attn_w;
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if (ga_n != 1) {
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GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
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GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
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//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
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//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
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LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w);
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}
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slot.ga_i = 0;
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slot.ga_n = ga_n;
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slot.ga_w = ga_w;
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slot.reset();
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slots.push_back(slot);
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}
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default_generation_settings_for_props = get_formated_generation(slots.front());
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default_generation_settings_for_props["seed"] = -1;
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batch = llama_batch_init(n_ctx, 0, params.n_parallel);
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}
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std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
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{
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// TODO: currently, we tokenize using special tokens by default
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// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
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// but it's better compared to completely ignoring ChatML and other chat templates
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const bool TMP_FORCE_SPECIAL = true;
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// If `add_bos` is true, we only add BOS, when json_prompt is a string,
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// or the first element of the json_prompt array is a string.
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std::vector<llama_token> prompt_tokens;
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if (json_prompt.is_array())
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{
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bool first = true;
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for (const auto& p : json_prompt)
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{
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if (p.is_string())
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{
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auto s = p.template get<std::string>();
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std::vector<llama_token> p;
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if (first)
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{
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p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
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first = false;
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}
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else
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{
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p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
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}
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prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
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}
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else
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{
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if (first)
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{
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first = false;
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}
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prompt_tokens.push_back(p.template get<llama_token>());
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}
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}
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}
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else
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{
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auto s = json_prompt.template get<std::string>();
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prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
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}
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return prompt_tokens;
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}
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llama_client_slot* get_slot(int id) {
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int64_t t_last = ggml_time_us();
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llama_client_slot *last_used = nullptr;
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for (llama_client_slot & slot : slots)
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{
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if (slot.id == id && slot.available())
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{
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return &slot;
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}
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if (slot.available() && slot.t_last_used < t_last)
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{
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last_used = &slot;
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t_last = slot.t_last_used;
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}
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}
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|
|
return last_used;
|
|
}
|
|
|
|
bool launch_slot_with_data(llama_client_slot* &slot, json data) {
|
|
slot_params default_params;
|
|
llama_sampling_params default_sparams;
|
|
|
|
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);
|
|
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
|
|
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
|
|
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
|
|
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
|
|
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
|
|
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
|
|
slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
|
|
slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
|
|
slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
|
|
slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
|
|
slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
|
|
slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
|
|
slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
|
|
slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
|
|
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
|
|
slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
|
|
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
|
|
slot->params.seed = json_value(data, "seed", default_params.seed);
|
|
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
|
|
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
|
|
slot->sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
|
|
|
|
if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) {
|
|
// Might be better to reject the request with a 400 ?
|
|
LOG_WARNING("Max tokens to predict exceeds server configuration", {
|
|
{"params.n_predict", slot->params.n_predict},
|
|
{"slot.n_predict", slot->n_predict},
|
|
});
|
|
slot->params.n_predict = slot->n_predict;
|
|
}
|
|
|
|
// infill
|
|
if (data.count("input_prefix") != 0)
|
|
{
|
|
slot->params.input_prefix = data["input_prefix"];
|
|
}
|
|
else
|
|
{
|
|
slot->params.input_prefix = "";
|
|
}
|
|
|
|
if (data.count("input_suffix") != 0)
|
|
{
|
|
slot->params.input_suffix = data["input_suffix"];
|
|
}
|
|
else
|
|
{
|
|
slot->params.input_suffix = "";
|
|
}
|
|
|
|
if (data.count("prompt") != 0)
|
|
{
|
|
slot->prompt = data["prompt"];
|
|
}
|
|
else
|
|
{
|
|
slot->prompt = "";
|
|
}
|
|
|
|
slot->sparams.penalty_prompt_tokens.clear();
|
|
slot->sparams.use_penalty_prompt_tokens = false;
|
|
const auto &penalty_prompt = data.find("penalty_prompt");
|
|
if (penalty_prompt != data.end())
|
|
{
|
|
if (penalty_prompt->is_string())
|
|
{
|
|
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
|
|
auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
|
|
slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
|
|
if (slot->params.n_predict > 0)
|
|
{
|
|
slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
|
|
}
|
|
slot->sparams.use_penalty_prompt_tokens = true;
|
|
}
|
|
else if (penalty_prompt->is_array())
|
|
{
|
|
const auto n_tokens = penalty_prompt->size();
|
|
slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto &penalty_token : *penalty_prompt)
|
|
{
|
|
if (penalty_token.is_number_integer())
|
|
{
|
|
const auto tok = penalty_token.get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab)
|
|
{
|
|
slot->sparams.penalty_prompt_tokens.push_back(tok);
|
|
}
|
|
}
|
|
}
|
|
slot->sparams.use_penalty_prompt_tokens = true;
|
|
}
|
|
}
|
|
|
|
slot->sparams.logit_bias.clear();
|
|
|
|
if (json_value(data, "ignore_eos", false))
|
|
{
|
|
slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
const auto &logit_bias = data.find("logit_bias");
|
|
if (logit_bias != data.end() && logit_bias->is_array())
|
|
{
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto &el : *logit_bias)
|
|
{
|
|
if (el.is_array() && el.size() == 2)
|
|
{
|
|
float bias;
|
|
if (el[1].is_number())
|
|
{
|
|
bias = el[1].get<float>();
|
|
}
|
|
else if (el[1].is_boolean() && !el[1].get<bool>())
|
|
{
|
|
bias = -INFINITY;
|
|
}
|
|
else
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (el[0].is_number_integer())
|
|
{
|
|
llama_token tok = el[0].get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab)
|
|
{
|
|
slot->sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
else if (el[0].is_string())
|
|
{
|
|
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
|
|
for (auto tok : toks)
|
|
{
|
|
slot->sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
slot->params.antiprompt.clear();
|
|
|
|
const auto &stop = data.find("stop");
|
|
if (stop != data.end() && stop->is_array())
|
|
{
|
|
for (const auto &word : *stop)
|
|
{
|
|
if (!word.empty())
|
|
{
|
|
slot->params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
|
|
const auto &samplers_sequence = data.find("samplers");
|
|
if (samplers_sequence != data.end() && samplers_sequence->is_array())
|
|
{
|
|
std::vector<std::string> sampler_names;
|
|
for (const auto &sampler_name : *samplers_sequence)
|
|
{
|
|
if (sampler_name.is_string())
|
|
{
|
|
sampler_names.emplace_back(sampler_name);
|
|
}
|
|
}
|
|
slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
|
|
}
|
|
else
|
|
{
|
|
slot->sparams.samplers_sequence = default_sparams.samplers_sequence;
|
|
}
|
|
|
|
if (multimodal)
|
|
{
|
|
const auto &images_data = data.find("image_data");
|
|
if (images_data != data.end() && images_data->is_array())
|
|
{
|
|
for (const auto &img : *images_data)
|
|
{
|
|
const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
|
|
|
|
slot_image img_sl;
|
|
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
|
|
img_sl.img_data = clip_image_u8_init();
|
|
if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
|
|
{
|
|
LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
|
|
return false;
|
|
}
|
|
LOG_TEE("slot %i - loaded image\n", slot->id);
|
|
img_sl.request_encode_image = true;
|
|
slot->images.push_back(img_sl);
|
|
}
|
|
// process prompt
|
|
// example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
|
|
if (slot->images.size() > 0 && !slot->prompt.is_array())
|
|
{
|
|
std::string prompt = slot->prompt.get<std::string>();
|
|
size_t pos = 0, begin_prefix = 0;
|
|
std::string pattern = "[img-";
|
|
while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
|
|
size_t end_prefix = pos;
|
|
pos += pattern.length();
|
|
size_t end_pos = prompt.find(']', pos);
|
|
if (end_pos != std::string::npos)
|
|
{
|
|
std::string image_id = prompt.substr(pos, end_pos - pos);
|
|
try
|
|
{
|
|
int img_id = std::stoi(image_id);
|
|
bool found = false;
|
|
for (slot_image &img : slot->images)
|
|
{
|
|
if (img.id == img_id) {
|
|
found = true;
|
|
img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
|
|
begin_prefix = end_pos + 1;
|
|
break;
|
|
}
|
|
}
|
|
if (!found) {
|
|
LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
|
|
slot->images.clear();
|
|
return false;
|
|
}
|
|
} catch (const std::invalid_argument& e) {
|
|
LOG_TEE("Invalid image number id in prompt\n");
|
|
slot->images.clear();
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
slot->prompt = "";
|
|
slot->params.input_suffix = prompt.substr(begin_prefix);
|
|
slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot->ctx_sampling != nullptr)
|
|
{
|
|
llama_sampling_free(slot->ctx_sampling);
|
|
}
|
|
slot->ctx_sampling = llama_sampling_init(slot->sparams);
|
|
llama_set_rng_seed(ctx, slot->params.seed);
|
|
slot->command = LOAD_PROMPT;
|
|
|
|
all_slots_are_idle = false;
|
|
|
|
LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
|
|
|
|
return true;
|
|
}
|
|
|
|
void kv_cache_clear() {
|
|
// clear the entire KV cache
|
|
llama_kv_cache_clear(ctx);
|
|
clean_kv_cache = false;
|
|
}
|
|
|
|
void update_system_prompt() {
|
|
kv_cache_clear();
|
|
system_tokens.clear();
|
|
|
|
if (!system_prompt.empty()) {
|
|
system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
for (int i = 0; i < (int)system_tokens.size(); ++i)
|
|
{
|
|
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
|
|
}
|
|
|
|
if (llama_decode(ctx, batch) != 0)
|
|
{
|
|
LOG_TEE("%s: llama_decode() failed\n", __func__);
|
|
return;
|
|
}
|
|
|
|
// assign the system KV cache to all parallel sequences
|
|
for (int32_t i = 1; i < params.n_parallel; ++i)
|
|
{
|
|
llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
|
|
}
|
|
}
|
|
|
|
LOG_TEE("system prompt updated\n");
|
|
system_need_update = false;
|
|
}
|
|
|
|
void notify_system_prompt_changed() {
|
|
// release all slots
|
|
for (llama_client_slot &slot : slots)
|
|
{
|
|
slot.release();
|
|
}
|
|
|
|
system_need_update = true;
|
|
}
|
|
|
|
void process_system_prompt_data(const json &sys_props) {
|
|
system_prompt = sys_props.value("prompt", "");
|
|
name_user = sys_props.value("anti_prompt", "");
|
|
name_assistant = sys_props.value("assistant_name", "");
|
|
|
|
|
|
notify_system_prompt_changed();
|
|
}
|
|
|
|
static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
|
|
const stop_type type, llama_client_slot &slot)
|
|
{
|
|
size_t stop_pos = std::string::npos;
|
|
|
|
for (const std::string &word : slot.params.antiprompt)
|
|
{
|
|
size_t pos;
|
|
if (type == STOP_FULL)
|
|
{
|
|
const size_t tmp = word.size() + last_token_size;
|
|
const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
|
|
pos = text.find(word, from_pos);
|
|
}
|
|
else
|
|
{
|
|
pos = find_partial_stop_string(word, text);
|
|
}
|
|
if (pos != std::string::npos &&
|
|
(stop_pos == std::string::npos || pos < stop_pos))
|
|
{
|
|
if (type == STOP_FULL)
|
|
{
|
|
slot.stopped_word = true;
|
|
slot.stopping_word = word;
|
|
slot.has_next_token = false;
|
|
}
|
|
stop_pos = pos;
|
|
}
|
|
}
|
|
|
|
return stop_pos;
|
|
}
|
|
|
|
bool process_token(completion_token_output &result, llama_client_slot &slot) {
|
|
// remember which tokens were sampled - used for repetition penalties during sampling
|
|
const std::string token_str = llama_token_to_piece(ctx, result.tok);
|
|
slot.sampled = result.tok;
|
|
|
|
// search stop word and delete it
|
|
slot.generated_text += token_str;
|
|
slot.has_next_token = true;
|
|
|
|
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
|
|
{
|
|
// we can change penalty_prompt_tokens because it is always created from scratch each request
|
|
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
|
|
}
|
|
|
|
// check if there is incomplete UTF-8 character at the end
|
|
bool incomplete = false;
|
|
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
|
|
{
|
|
unsigned char c = slot.generated_text[slot.generated_text.size() - i];
|
|
if ((c & 0xC0) == 0x80)
|
|
{
|
|
// continuation byte: 10xxxxxx
|
|
continue;
|
|
}
|
|
if ((c & 0xE0) == 0xC0)
|
|
{
|
|
// 2-byte character: 110xxxxx ...
|
|
incomplete = i < 2;
|
|
}
|
|
else if ((c & 0xF0) == 0xE0)
|
|
{
|
|
// 3-byte character: 1110xxxx ...
|
|
incomplete = i < 3;
|
|
}
|
|
else if ((c & 0xF8) == 0xF0)
|
|
{
|
|
// 4-byte character: 11110xxx ...
|
|
incomplete = i < 4;
|
|
}
|
|
// else 1-byte character or invalid byte
|
|
break;
|
|
}
|
|
|
|
if (!incomplete)
|
|
{
|
|
size_t pos = std::min(slot.sent_count, slot.generated_text.size());
|
|
const std::string str_test = slot.generated_text.substr(pos);
|
|
bool is_stop_full = false;
|
|
size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
|
|
if (stop_pos != std::string::npos)
|
|
{
|
|
is_stop_full = true;
|
|
slot.generated_text.erase(
|
|
slot.generated_text.begin() + pos + stop_pos,
|
|
slot.generated_text.end());
|
|
pos = std::min(slot.sent_count, slot.generated_text.size());
|
|
}
|
|
else
|
|
{
|
|
is_stop_full = false;
|
|
stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
|
|
}
|
|
|
|
// check if there is any token to predict
|
|
if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
|
|
{
|
|
// no send the stop word in the response
|
|
result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
|
|
slot.sent_count += result.text_to_send.size();
|
|
// add the token to slot queue and cache
|
|
}
|
|
slot.add_token_string(result);
|
|
if (slot.params.stream)
|
|
{
|
|
send_partial_response(slot, result);
|
|
}
|
|
}
|
|
|
|
if (incomplete)
|
|
{
|
|
slot.has_next_token = true;
|
|
}
|
|
|
|
// check the limits
|
|
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
|
|
{
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false;
|
|
}
|
|
|
|
if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
|
|
{
|
|
slot.stopped_eos = true;
|
|
slot.has_next_token = false;
|
|
LOG_VERBOSE("eos token found", {});
|
|
}
|
|
|
|
LOG_VERBOSE("next token", {
|
|
{"token", result.tok},
|
|
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"num_tokens_predicted", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
});
|
|
|
|
return slot.has_next_token; // continue
|
|
}
|
|
|
|
bool process_images(llama_client_slot &slot) const
|
|
{
|
|
for (slot_image &img : slot.images)
|
|
{
|
|
if (!img.request_encode_image)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) {
|
|
LOG_TEE("Error processing the given image");
|
|
return false;
|
|
}
|
|
|
|
img.request_encode_image = false;
|
|
}
|
|
|
|
return slot.images.size() > 0;
|
|
}
|
|
|
|
void send_error(task_server& task, const std::string &error)
|
|
{
|
|
LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
|
|
task_result res;
|
|
res.id = task.id;
|
|
res.multitask_id = task.multitask_id;
|
|
res.stop = false;
|
|
res.error = true;
|
|
res.result_json = { { "content", error } };
|
|
queue_results.send(res);
|
|
}
|
|
|
|
json get_formated_generation(llama_client_slot &slot)
|
|
{
|
|
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
|
|
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
|
|
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
std::vector<std::string> samplers_sequence;
|
|
for (const auto &sampler_type : slot.sparams.samplers_sequence)
|
|
{
|
|
samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
|
|
}
|
|
|
|
return json {
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_predict", slot.n_predict},
|
|
{"model", params.model_alias},
|
|
{"seed", slot.params.seed},
|
|
{"temperature", slot.sparams.temp},
|
|
{"dynatemp_range", slot.sparams.dynatemp_range},
|
|
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
|
|
{"top_k", slot.sparams.top_k},
|
|
{"top_p", slot.sparams.top_p},
|
|
{"min_p", slot.sparams.min_p},
|
|
{"tfs_z", slot.sparams.tfs_z},
|
|
{"typical_p", slot.sparams.typical_p},
|
|
{"repeat_last_n", slot.sparams.penalty_last_n},
|
|
{"repeat_penalty", slot.sparams.penalty_repeat},
|
|
{"presence_penalty", slot.sparams.penalty_present},
|
|
{"frequency_penalty", slot.sparams.penalty_freq},
|
|
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
|
|
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
|
|
{"mirostat", slot.sparams.mirostat},
|
|
{"mirostat_tau", slot.sparams.mirostat_tau},
|
|
{"mirostat_eta", slot.sparams.mirostat_eta},
|
|
{"penalize_nl", slot.sparams.penalize_nl},
|
|
{"stop", slot.params.antiprompt},
|
|
{"n_predict", slot.params.n_predict},
|
|
{"n_keep", params.n_keep},
|
|
{"ignore_eos", ignore_eos},
|
|
{"stream", slot.params.stream},
|
|
{"logit_bias", slot.sparams.logit_bias},
|
|
{"n_probs", slot.sparams.n_probs},
|
|
{"min_keep", slot.sparams.min_keep},
|
|
{"grammar", slot.sparams.grammar},
|
|
{"samplers", samplers_sequence}
|
|
};
|
|
}
|
|
|
|
void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
|
|
{
|
|
task_result res;
|
|
res.id = slot.task_id;
|
|
res.multitask_id = slot.multitask_id;
|
|
res.error = false;
|
|
res.stop = false;
|
|
|
|
res.result_json = json
|
|
{
|
|
{"content", tkn.text_to_send},
|
|
{"stop", false},
|
|
{"slot_id", slot.id},
|
|
{"multimodal", multimodal}
|
|
};
|
|
|
|
if (slot.sparams.n_probs > 0)
|
|
{
|
|
std::vector<completion_token_output> probs_output = {};
|
|
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
|
|
size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
|
|
size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
|
|
if (probs_pos < probs_stop_pos)
|
|
{
|
|
probs_output = std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos);
|
|
}
|
|
slot.sent_token_probs_index = probs_stop_pos;
|
|
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);
|
|
}
|
|
|
|
void send_final_response(llama_client_slot &slot)
|
|
{
|
|
task_result res;
|
|
res.id = slot.task_id;
|
|
res.multitask_id = slot.multitask_id;
|
|
res.error = false;
|
|
res.stop = true;
|
|
|
|
res.result_json = json
|
|
{
|
|
{"content", !slot.params.stream ? slot.generated_text : ""},
|
|
{"slot_id", slot.id},
|
|
{"stop", true},
|
|
{"model", params.model_alias},
|
|
{"tokens_predicted", slot.n_decoded},
|
|
{"tokens_evaluated", slot.num_prompt_tokens},
|
|
{"generation_settings", get_formated_generation(slot)},
|
|
{"prompt", slot.prompt},
|
|
{"truncated", slot.truncated},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
{"tokens_cached", slot.n_past},
|
|
{"timings", slot.get_formated_timings()}
|
|
};
|
|
|
|
if (slot.sparams.n_probs > 0)
|
|
{
|
|
std::vector<completion_token_output> probs = {};
|
|
if (!slot.params.stream && slot.stopped_word)
|
|
{
|
|
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
|
|
probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size());
|
|
}
|
|
else
|
|
{
|
|
probs = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end());
|
|
}
|
|
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);
|
|
}
|
|
|
|
void send_embedding(llama_client_slot &slot)
|
|
{
|
|
task_result res;
|
|
res.id = slot.task_id;
|
|
res.multitask_id = slot.multitask_id;
|
|
res.error = false;
|
|
res.stop = true;
|
|
|
|
const int n_embd = llama_n_embd(model);
|
|
if (!params.embedding)
|
|
{
|
|
LOG_WARNING("embedding disabled", {
|
|
{"params.embedding", params.embedding},
|
|
});
|
|
res.result_json = json
|
|
{
|
|
{"embedding", std::vector<float>(n_embd, 0.0f)},
|
|
};
|
|
}
|
|
else
|
|
{
|
|
const float *data = llama_get_embeddings(ctx);
|
|
std::vector<float> embedding(data, data + n_embd);
|
|
res.result_json = json
|
|
{
|
|
{"embedding", embedding },
|
|
};
|
|
}
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id)
|
|
{
|
|
task_server task;
|
|
task.id = task_id;
|
|
task.target_id = 0;
|
|
task.data = std::move(data);
|
|
task.infill_mode = infill;
|
|
task.embedding_mode = embedding;
|
|
task.type = TASK_TYPE_COMPLETION;
|
|
task.multitask_id = multitask_id;
|
|
|
|
// when a completion task's prompt array is not a singleton, we split it into multiple requests
|
|
// otherwise, it's a single-prompt task, we actually queue it
|
|
// if there's numbers in the prompt array it will be treated as an array of tokens
|
|
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
|
|
bool numbers = false;
|
|
for (const auto& e : task.data.at("prompt")) {
|
|
if (e.is_number()) {
|
|
numbers = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
|
|
// it will completely stall the server. I don't know where the bug for this is.
|
|
//
|
|
// if there are numbers, it needs to be treated like a single prompt,
|
|
// queue_tasks handles a mix of strings and numbers just fine.
|
|
if (numbers) {
|
|
queue_tasks.post(task);
|
|
} else {
|
|
split_multiprompt_task(task_id, task);
|
|
}
|
|
} else {
|
|
queue_tasks.post(task);
|
|
}
|
|
}
|
|
|
|
// for multiple images processing
|
|
bool ingest_images(llama_client_slot &slot, int n_batch)
|
|
{
|
|
int image_idx = 0;
|
|
|
|
while (image_idx < (int) slot.images.size())
|
|
{
|
|
slot_image &img = slot.images[image_idx];
|
|
|
|
// process prefix prompt
|
|
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
|
|
{
|
|
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
|
|
llama_batch batch_view = {
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
0, 0, 0, // unused
|
|
};
|
|
if (llama_decode(ctx, batch_view))
|
|
{
|
|
LOG_TEE("%s : failed to eval\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// process image with llm
|
|
for (int i = 0; i < img.image_tokens; i += n_batch)
|
|
{
|
|
int n_eval = img.image_tokens - i;
|
|
if (n_eval > n_batch)
|
|
{
|
|
n_eval = n_batch;
|
|
}
|
|
|
|
const int n_embd = llama_n_embd(model);
|
|
llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
|
|
if (llama_decode(ctx, batch_img))
|
|
{
|
|
LOG_TEE("%s : failed to eval image\n", __func__);
|
|
return false;
|
|
}
|
|
slot.n_past += n_eval;
|
|
}
|
|
image_idx++;
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
// append prefix of next image
|
|
const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
|
|
slot.params.input_suffix : // no more images, then process suffix prompt
|
|
(json)(slot.images[image_idx].prefix_prompt);
|
|
|
|
std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
|
|
for (int i = 0; i < (int) append_tokens.size(); ++i)
|
|
{
|
|
llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true);
|
|
slot.n_past += 1;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void request_cancel(int task_id)
|
|
{
|
|
task_server task;
|
|
task.type = TASK_TYPE_CANCEL;
|
|
task.target_id = task_id;
|
|
queue_tasks.post(task);
|
|
}
|
|
|
|
void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
|
|
{
|
|
int prompt_count = multiprompt_task.data.at("prompt").size();
|
|
if (prompt_count <= 1) {
|
|
send_error(multiprompt_task, "error while handling multiple prompts");
|
|
return;
|
|
}
|
|
|
|
// generate all the ID for subtask
|
|
std::vector<int> subtask_ids(prompt_count);
|
|
for (int i = 0; i < prompt_count; i++)
|
|
{
|
|
subtask_ids[i] = queue_tasks.get_new_id();
|
|
}
|
|
|
|
// queue up the multitask so we can track its subtask progression
|
|
queue_tasks.add_multitask(multitask_id, subtask_ids);
|
|
|
|
// add subtasks
|
|
for (int i = 0; i < prompt_count; i++)
|
|
{
|
|
json subtask_data = multiprompt_task.data;
|
|
subtask_data["prompt"] = subtask_data["prompt"][i];
|
|
|
|
// subtasks inherit everything else (infill mode, embedding mode, etc.)
|
|
request_completion(subtask_ids[i], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
|
|
}
|
|
}
|
|
|
|
void process_single_task(task_server& task)
|
|
{
|
|
switch (task.type)
|
|
{
|
|
case TASK_TYPE_COMPLETION: {
|
|
llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
|
|
if (slot == nullptr)
|
|
{
|
|
// if no slot is available, we defer this task for processing later
|
|
LOG_VERBOSE("no slot is available", {});
|
|
queue_tasks.defer(task);
|
|
break;
|
|
}
|
|
|
|
if (task.data.contains("system_prompt"))
|
|
{
|
|
if (!all_slots_are_idle) {
|
|
send_error(task, "system prompt can only be updated when all slots are idle");
|
|
break;
|
|
}
|
|
process_system_prompt_data(task.data["system_prompt"]);
|
|
|
|
// reset cache_tokens for all slots
|
|
for (llama_client_slot &slot : slots)
|
|
{
|
|
slot.cache_tokens.clear();
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
}
|
|
}
|
|
|
|
slot->reset();
|
|
|
|
slot->infill = task.infill_mode;
|
|
slot->embedding = task.embedding_mode;
|
|
slot->task_id = task.id;
|
|
slot->multitask_id = task.multitask_id;
|
|
|
|
if (!launch_slot_with_data(slot, task.data))
|
|
{
|
|
// send error result
|
|
send_error(task, "internal_error");
|
|
break;
|
|
}
|
|
} break;
|
|
case TASK_TYPE_CANCEL: { // release slot linked with the task id
|
|
for (auto & slot : slots)
|
|
{
|
|
if (slot.task_id == task.target_id)
|
|
{
|
|
slot.release();
|
|
break;
|
|
}
|
|
}
|
|
} break;
|
|
case TASK_TYPE_NEXT_RESPONSE: {
|
|
// do nothing
|
|
} break;
|
|
}
|
|
}
|
|
|
|
void on_finish_multitask(task_multi& multitask)
|
|
{
|
|
// all subtasks done == multitask is done
|
|
task_result result;
|
|
result.id = multitask.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
|
|
// collect json results into one json result
|
|
std::vector<json> result_jsons;
|
|
for (auto& subres : multitask.results)
|
|
{
|
|
result_jsons.push_back(subres.result_json);
|
|
result.error = result.error && subres.error;
|
|
}
|
|
result.result_json = json{ { "results", result_jsons } };
|
|
queue_results.send(result);
|
|
}
|
|
|
|
bool update_slots() {
|
|
if (system_need_update)
|
|
{
|
|
LOG_TEE("updating system prompt\n");
|
|
update_system_prompt();
|
|
}
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
if (all_slots_are_idle)
|
|
{
|
|
if (system_prompt.empty() && clean_kv_cache)
|
|
{
|
|
LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
|
|
kv_cache_clear();
|
|
}
|
|
return true;
|
|
}
|
|
|
|
task_server task;
|
|
task.type = TASK_TYPE_NEXT_RESPONSE;
|
|
task.target_id = -1;
|
|
queue_tasks.post(task);
|
|
|
|
for (llama_client_slot &slot : slots)
|
|
{
|
|
if (slot.ga_n == 1)
|
|
{
|
|
if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx)
|
|
{
|
|
// START LOCALAI changes
|
|
// Temporary disable context-shifting as it can lead to infinite loops (issue: https://github.com/ggerganov/llama.cpp/issues/3969)
|
|
// See: https://github.com/mudler/LocalAI/issues/1333
|
|
// Context is exhausted, release the slot
|
|
slot.release();
|
|
send_final_response(slot);
|
|
slot.cache_tokens.clear();
|
|
slot.n_past = 0;
|
|
slot.truncated = false;
|
|
slot.has_next_token = true;
|
|
LOG_TEE("Context exhausted. Slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());
|
|
|
|
continue;
|
|
// END LOCALAI changes
|
|
}
|
|
}
|
|
}
|
|
|
|
// decode any currently ongoing sequences
|
|
for (auto & slot : slots)
|
|
{
|
|
// release the slot
|
|
if (slot.command == RELEASE)
|
|
{
|
|
slot.state = IDLE;
|
|
slot.command = NONE;
|
|
slot.t_last_used = ggml_time_us();
|
|
|
|
LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());
|
|
queue_tasks.notify_slot_changed();
|
|
|
|
continue;
|
|
}
|
|
|
|
if (slot.state == IDLE)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
slot.i_batch = batch.n_tokens;
|
|
|
|
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
// TODO: we always have to take into account the "system_tokens"
|
|
// this is not great and needs to be improved somehow
|
|
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
|
|
slot.n_past += 1;
|
|
}
|
|
|
|
// process in chunks of params.n_batch
|
|
int32_t n_batch = params.n_batch;
|
|
|
|
// assign workload to the slots
|
|
if (params.cont_batching || batch.n_tokens == 0)
|
|
{
|
|
for (auto & slot : slots)
|
|
{
|
|
const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();
|
|
|
|
// empty prompt passed -> release the slot and send empty response
|
|
// note: infill mode allows empty prompt
|
|
if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill)
|
|
{
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
continue;
|
|
}
|
|
|
|
// need process the prompt
|
|
if (slot.state == IDLE && slot.command == LOAD_PROMPT)
|
|
{
|
|
slot.state = PROCESSING;
|
|
slot.command = NONE;
|
|
std::vector<llama_token> prompt_tokens;
|
|
slot.t_start_process_prompt = ggml_time_us();
|
|
slot.t_start_genereration = 0;
|
|
|
|
if (slot.infill)
|
|
{
|
|
bool suff_rm_leading_spc = true;
|
|
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
|
|
{
|
|
params.input_suffix.erase(0, 1);
|
|
suff_rm_leading_spc = false;
|
|
}
|
|
auto prefix_tokens = tokenize(slot.params.input_prefix, false);
|
|
auto suffix_tokens = tokenize(slot.params.input_suffix, false);
|
|
|
|
const int space_token = 29871; // TODO: this should not be hardcoded
|
|
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
|
|
suffix_tokens.erase(suffix_tokens.begin());
|
|
}
|
|
|
|
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
|
|
prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
|
|
prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
|
|
prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
|
|
prefix_tokens.push_back(llama_token_middle(model));
|
|
prompt_tokens = prefix_tokens;
|
|
}
|
|
else
|
|
{
|
|
prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt
|
|
}
|
|
|
|
slot.num_prompt_tokens = prompt_tokens.size();
|
|
|
|
if (slot.params.n_keep < 0)
|
|
{
|
|
slot.params.n_keep = slot.num_prompt_tokens;
|
|
}
|
|
slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
|
|
|
|
// if input prompt is too big, truncate it
|
|
if (slot.num_prompt_tokens >= slot.n_ctx)
|
|
{
|
|
const int n_left = slot.n_ctx - slot.params.n_keep;
|
|
const int n_block_size = n_left / 2;
|
|
const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
|
|
|
|
std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
|
|
new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
|
|
|
|
LOG_VERBOSE("input truncated", {
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_left", n_left},
|
|
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
|
|
});
|
|
slot.truncated = true;
|
|
prompt_tokens = new_tokens;
|
|
|
|
slot.num_prompt_tokens = prompt_tokens.size();
|
|
GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
|
|
}
|
|
|
|
if (!slot.params.cache_prompt)
|
|
{
|
|
llama_sampling_reset(slot.ctx_sampling);
|
|
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
|
|
}
|
|
else
|
|
{
|
|
// push the prompt into the sampling context (do not apply grammar)
|
|
for (auto &token : prompt_tokens)
|
|
{
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
|
|
}
|
|
|
|
slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
|
|
slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
|
|
|
|
if (slot.ga_n != 1)
|
|
{
|
|
int ga_i = 0;
|
|
int32_t ga_n = slot.ga_n;
|
|
int32_t ga_w = slot.ga_w;
|
|
int32_t slot_npast = 0;
|
|
for (int k = 0; k < slot.n_past; ++k)
|
|
{
|
|
while (slot_npast >= ga_i + ga_w) {
|
|
const int bd = (ga_w/ga_n)*(ga_n - 1);
|
|
slot_npast -= bd;
|
|
ga_i += ga_w/ga_n;
|
|
}
|
|
slot_npast++;
|
|
}
|
|
slot.n_past_se = slot_npast;
|
|
slot.ga_i = ga_i;
|
|
}
|
|
|
|
LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
|
|
}
|
|
|
|
slot.cache_tokens = prompt_tokens;
|
|
|
|
if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0)
|
|
{
|
|
// we have to evaluate at least 1 token to generate logits.
|
|
LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
|
|
slot.n_past--;
|
|
if (slot.ga_i > 0)
|
|
{
|
|
slot.n_past_se--;
|
|
}
|
|
}
|
|
|
|
LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
|
|
|
|
llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
|
|
|
|
LOG_VERBOSE("prompt ingested", {
|
|
{"n_past", slot.n_past},
|
|
{"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
|
|
{"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
|
|
});
|
|
|
|
const bool has_images = process_images(slot);
|
|
|
|
// process the prefix of first image
|
|
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
|
|
|
|
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
int32_t ga_i = slot.ga_i;
|
|
int32_t ga_n = slot.ga_n;
|
|
int32_t ga_w = slot.ga_w;
|
|
|
|
for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
|
|
{
|
|
if (slot.ga_n != 1)
|
|
{
|
|
while (slot_npast >= ga_i + ga_w) {
|
|
const int bd = (ga_w/ga_n)*(ga_n - 1);
|
|
slot_npast -= bd;
|
|
ga_i += ga_w/ga_n;
|
|
}
|
|
}
|
|
llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false);
|
|
slot_npast++;
|
|
}
|
|
|
|
if (has_images && !ingest_images(slot, n_batch))
|
|
{
|
|
LOG_TEE("failed processing images\n");
|
|
return false;
|
|
}
|
|
|
|
// extract the logits only for the last token
|
|
if (batch.n_tokens > 0)
|
|
{
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
}
|
|
|
|
slot.n_decoded = 0;
|
|
slot.i_batch = batch.n_tokens - 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens == 0)
|
|
{
|
|
all_slots_are_idle = true;
|
|
return true;
|
|
}
|
|
|
|
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
|
|
{
|
|
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
|
|
|
|
for (auto & slot : slots)
|
|
{
|
|
if (slot.ga_n != 1)
|
|
{
|
|
// context extension via Self-Extend
|
|
while (slot.n_past_se >= slot.ga_i + slot.ga_w)
|
|
{
|
|
const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
|
|
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
|
|
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
|
|
|
|
LOG_TEE("\n");
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
|
|
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
|
|
|
|
llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
|
|
llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
|
|
llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
|
|
|
|
slot.n_past_se -= bd;
|
|
|
|
slot.ga_i += slot.ga_w / slot.ga_n;
|
|
|
|
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
|
|
}
|
|
slot.n_past_se += n_tokens;
|
|
}
|
|
}
|
|
|
|
llama_batch batch_view =
|
|
{
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
0, 0, 0, // unused
|
|
};
|
|
|
|
const int ret = llama_decode(ctx, batch_view);
|
|
|
|
if (ret != 0)
|
|
{
|
|
if (n_batch == 1 || ret < 0)
|
|
{
|
|
// if you get here, it means the KV cache is full - try increasing it via the context size
|
|
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
|
|
return false;
|
|
}
|
|
|
|
LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
|
|
|
|
// retry with half the batch size to try to find a free slot in the KV cache
|
|
n_batch /= 2;
|
|
i -= n_batch;
|
|
continue;
|
|
}
|
|
|
|
for (auto & slot : slots)
|
|
{
|
|
if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
|
|
{
|
|
continue;
|
|
}
|
|
|
|
// prompt evaluated for embedding
|
|
if (slot.embedding)
|
|
{
|
|
send_embedding(slot);
|
|
slot.release();
|
|
slot.i_batch = -1;
|
|
return true;
|
|
}
|
|
|
|
completion_token_output result;
|
|
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);
|
|
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
|
|
|
slot.n_decoded += 1;
|
|
if (slot.n_decoded == 1)
|
|
{
|
|
slot.t_start_genereration = ggml_time_us();
|
|
slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
|
|
}
|
|
|
|
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
|
|
result.tok = id;
|
|
|
|
const int32_t n_probs = slot.sparams.n_probs;
|
|
if (slot.sparams.temp <= 0 && n_probs > 0)
|
|
{
|
|
// for llama_sample_token_greedy we need to sort candidates
|
|
llama_sample_softmax(ctx, &cur_p);
|
|
}
|
|
|
|
for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
|
|
{
|
|
result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
|
|
}
|
|
|
|
if (!process_token(result, slot))
|
|
{
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
}
|
|
|
|
slot.i_batch = -1;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void run_on_all_tasks_finished() {
|
|
update_slots();
|
|
}
|
|
};
|
|
|
|
/* llama.cpp completion api semantics */
|
|
static json format_partial_response(
|
|
llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
|
|
) {
|
|
json res = json
|
|
{
|
|
{"content", content },
|
|
{"stop", false},
|
|
{"slot_id", slot->id },
|
|
{"multimodal", llama.multimodal }
|
|
};
|
|
|
|
if (slot->sparams.n_probs > 0)
|
|
{
|
|
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
static json format_tokenizer_response(const std::vector<llama_token> &tokens)
|
|
{
|
|
return json{
|
|
{"tokens", tokens}};
|
|
}
|
|
|
|
static json format_detokenized_response(std::string content)
|
|
{
|
|
return json{
|
|
{"content", content}};
|
|
}
|
|
|
|
struct token_translator
|
|
{
|
|
llama_context * ctx;
|
|
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
|
|
std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); }
|
|
};
|
|
|
|
static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot)
|
|
{
|
|
auto & gtps = slot->generated_token_probs;
|
|
auto translator = token_translator{llama.ctx};
|
|
auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
|
|
const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
|
|
if (slot->generated_text.capacity() < slot->generated_text.size() + len)
|
|
{
|
|
slot->generated_text.reserve(slot->generated_text.size() + len);
|
|
}
|
|
for (const completion_token_output & cto : gtps)
|
|
{
|
|
slot->generated_text += translator(cto);
|
|
}
|
|
}
|
|
|
|
std::function<void(int)> shutdown_handler;
|
|
inline void signal_handler(int signal) { shutdown_handler(signal); }
|
|
|
|
/////////////////////////////////
|
|
////////////////////////////////
|
|
//////// LOCALAI code starts below here
|
|
/////////////////////////////////
|
|
////////////////////////////////
|
|
|
|
bool loaded_model; // TODO: add a mutex for this, but happens only once loading the model
|
|
|
|
// The class has a llama instance that is shared across all RPCs
|
|
llama_server_context llama;
|
|
|
|
static void start_llama_server() {
|
|
// Wait for model to be loaded first
|
|
while (!loaded_model) {
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(100));
|
|
}
|
|
|
|
llama.queue_tasks.on_new_task(std::bind(
|
|
&llama_server_context::process_single_task, &llama, std::placeholders::_1));
|
|
llama.queue_tasks.on_finish_multitask(std::bind(
|
|
&llama_server_context::on_finish_multitask, &llama, std::placeholders::_1));
|
|
llama.queue_tasks.on_all_tasks_finished(std::bind(
|
|
&llama_server_context::run_on_all_tasks_finished, &llama));
|
|
llama.queue_results.on_multitask_update(std::bind(
|
|
&llama_server_queue::update_multitask,
|
|
&llama.queue_tasks,
|
|
std::placeholders::_1,
|
|
std::placeholders::_2,
|
|
std::placeholders::_3
|
|
));
|
|
llama.queue_tasks.start_loop();
|
|
}
|
|
|
|
json parse_options(bool streaming, const backend::PredictOptions* predict, llama_server_context &llama)
|
|
{
|
|
|
|
// This is for example a slot data from the json data
|
|
// 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);
|
|
// slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
|
|
// slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
|
|
// slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
|
|
// slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
|
|
// slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
|
|
// slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
|
|
// slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
|
|
// slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
|
|
// slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
|
|
// slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
|
|
// slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
|
|
// slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
|
|
// slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
|
|
// slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
|
|
// slot->params.seed = json_value(data, "seed", default_params.seed);
|
|
// slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
|
|
// slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
|
|
|
|
// Create now a json data from the prediction options instead
|
|
//
|
|
json data;
|
|
data["stream"] = streaming;
|
|
data["cache_prompt"] = predict->promptcacheall();
|
|
data["n_predict"] = predict->tokens() == 0 ? -1 : predict->tokens();
|
|
data["top_k"] = predict->topk();
|
|
data["top_p"] = predict->topp();
|
|
data["tfs_z"] = predict->tailfreesamplingz();
|
|
data["typical_p"] = predict->typicalp();
|
|
data["temperature"] = predict->temperature();
|
|
data["repeat_last_n"] = predict->repeat();
|
|
data["repeat_penalty"] = predict->penalty();
|
|
data["frequency_penalty"] = predict->frequencypenalty();
|
|
data["presence_penalty"] = predict->presencepenalty();
|
|
data["mirostat"] = predict->mirostat();
|
|
data["mirostat_tau"] = predict->mirostattau();
|
|
data["mirostat_eta"] = predict->mirostateta();
|
|
data["penalize_nl"] = predict->penalizenl();
|
|
data["n_keep"] = predict->nkeep();
|
|
data["seed"] = predict->seed();
|
|
data["grammar"] = predict->grammar();
|
|
data["prompt"] = predict->prompt();
|
|
data["ignore_eos"] = predict->ignoreeos();
|
|
|
|
// for each image in the request, add the image data
|
|
//
|
|
for (int i = 0; i < predict->images_size(); i++) {
|
|
data["image_data"].push_back(json
|
|
{
|
|
{"id", i},
|
|
{"data", predict->images(i)},
|
|
});
|
|
}
|
|
|
|
data["stop"] = predict->stopprompts();
|
|
// data["n_probs"] = predict->nprobs();
|
|
//TODO: images,
|
|
|
|
return data;
|
|
}
|
|
|
|
// static void parse_options_completion(bool streaming,const backend::PredictOptions* predict, llama_server_context &llama)
|
|
// {
|
|
// // https://github.com/ggerganov/llama.cpp/blob/d9b33fe95bd257b36c84ee5769cc048230067d6f/examples/server/server.cpp#L673
|
|
// gpt_params default_params;
|
|
|
|
// llama.stream = streaming;
|
|
// llama.params.n_predict = predict->tokens() == 0 ? -1 : predict->tokens();
|
|
// llama.params.sparams.top_k = predict->topk();
|
|
// llama.params.sparams.top_p = predict->topp();
|
|
// llama.params.sparams.tfs_z = predict->tailfreesamplingz();
|
|
// llama.params.sparams.typical_p = predict->typicalp();
|
|
// llama.params.sparams.penalty_last_n = predict->repeat();
|
|
// llama.params.sparams.temp = predict->temperature();
|
|
// llama.params.sparams.penalty_repeat = predict->penalty();
|
|
// llama.params.sparams.penalty_present = predict->presencepenalty();
|
|
// llama.params.sparams.penalty_freq = predict->frequencypenalty();
|
|
// llama.params.sparams.mirostat = predict->mirostat();
|
|
// llama.params.sparams.mirostat_tau = predict->mirostattau();
|
|
// llama.params.sparams.mirostat_eta = predict->mirostateta();
|
|
// llama.params.sparams.penalize_nl = predict->penalizenl();
|
|
// llama.params.n_keep = predict->nkeep();
|
|
// llama.params.seed = predict->seed();
|
|
// llama.params.sparams.grammar = predict->grammar();
|
|
// // llama.params.n_probs = predict->
|
|
// llama.params.prompt = predict->prompt();
|
|
|
|
// llama.params.sparams.logit_bias.clear();
|
|
|
|
// if (predict->ignoreeos())
|
|
// {
|
|
// llama.params.sparams.logit_bias[llama_token_eos(llama.model)] = -INFINITY;
|
|
// }
|
|
|
|
// // const auto &logit_bias = body.find("logit_bias");
|
|
// // if (logit_bias != body.end() && logit_bias->is_array())
|
|
// // {
|
|
// // const int n_vocab = llama_n_vocab(llama.model);
|
|
// // for (const auto &el : *logit_bias)
|
|
// // {
|
|
// // if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
|
|
// // {
|
|
// // llama_token tok = el[0].get<llama_token>();
|
|
// // if (tok >= 0 && tok < n_vocab)
|
|
// // {
|
|
// // if (el[1].is_number())
|
|
// // {
|
|
// // llama.params.logit_bias[tok] = el[1].get<float>();
|
|
// // }
|
|
// // else if (el[1].is_boolean() && !el[1].get<bool>())
|
|
// // {
|
|
// // llama.params.logit_bias[tok] = -INFINITY;
|
|
// // }
|
|
// // }
|
|
// // }
|
|
// // }
|
|
// // }
|
|
|
|
// llama.params.antiprompt.clear();
|
|
// for (const std::string& stopPrompt : predict->stopprompts()) {
|
|
// if (!stopPrompt.empty())
|
|
// {
|
|
// llama.params.antiprompt.push_back(stopPrompt);
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
static void params_parse(const backend::ModelOptions* request,
|
|
gpt_params & params) {
|
|
|
|
// this is comparable to: https://github.com/ggerganov/llama.cpp/blob/d9b33fe95bd257b36c84ee5769cc048230067d6f/examples/server/server.cpp#L1809
|
|
|
|
params.model = request->modelfile();
|
|
if (!request->mmproj().empty()) {
|
|
// get the directory of modelfile
|
|
std::string model_dir = params.model.substr(0, params.model.find_last_of("/\\"));
|
|
params.mmproj = model_dir + "/"+ request->mmproj();
|
|
}
|
|
// params.model_alias ??
|
|
params.model_alias = request->modelfile();
|
|
params.n_ctx = request->contextsize();
|
|
//params.memory_f16 = request->f16memory();
|
|
params.n_threads = request->threads();
|
|
params.n_gpu_layers = request->ngpulayers();
|
|
params.n_batch = request->nbatch();
|
|
// Set params.n_parallel by environment variable (LLAMA_PARALLEL), defaults to 1
|
|
//params.n_parallel = 1;
|
|
const char *env_parallel = std::getenv("LLAMACPP_PARALLEL");
|
|
if (env_parallel != NULL) {
|
|
params.n_parallel = std::stoi(env_parallel);
|
|
params.cont_batching = true;
|
|
} else {
|
|
params.n_parallel = 1;
|
|
}
|
|
// TODO: Add yarn
|
|
|
|
if (!request->tensorsplit().empty()) {
|
|
std::string arg_next = request->tensorsplit();
|
|
|
|
// split string by , and /
|
|
const std::regex regex{ R"([,/]+)" };
|
|
std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
|
|
std::vector<std::string> split_arg{ it, {} };
|
|
|
|
GGML_ASSERT(split_arg.size() <= llama_max_devices());
|
|
|
|
for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) {
|
|
if (i_device < split_arg.size()) {
|
|
params.tensor_split[i_device] = std::stof(split_arg[i_device]);
|
|
}
|
|
else {
|
|
params.tensor_split[i_device] = 0.0f;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!request->maingpu().empty()) {
|
|
params.main_gpu = std::stoi(request->maingpu());
|
|
}
|
|
if (!request->loraadapter().empty() && !request->lorabase().empty()) {
|
|
float scale_factor = 1.0f;
|
|
if (request->lorascale() != 0.0f) {
|
|
scale_factor = request->lorascale();
|
|
}
|
|
// get the directory of modelfile
|
|
std::string model_dir = params.model.substr(0, params.model.find_last_of("/\\"));
|
|
params.lora_adapter.push_back(std::make_tuple(model_dir + "/"+request->loraadapter(), scale_factor));
|
|
params.lora_base = model_dir + "/"+request->lorabase();
|
|
}
|
|
params.use_mlock = request->mlock();
|
|
params.use_mmap = request->mmap();
|
|
params.embedding = request->embeddings();
|
|
|
|
if (request->ropescaling() == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
|
|
else if (request->ropescaling() == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
|
|
else { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
|
|
if ( request->yarnextfactor() != 0.0f ) {
|
|
params.yarn_ext_factor = request->yarnextfactor();
|
|
}
|
|
if ( request->yarnattnfactor() != 0.0f ) {
|
|
params.yarn_attn_factor = request->yarnattnfactor();
|
|
}
|
|
if ( request->yarnbetafast() != 0.0f ) {
|
|
params.yarn_beta_fast = request->yarnbetafast();
|
|
}
|
|
if ( request->yarnbetaslow() != 0.0f ) {
|
|
params.yarn_beta_slow = request->yarnbetaslow();
|
|
}
|
|
if ( request->ropefreqbase() != 0.0f ) {
|
|
params.rope_freq_base = request->ropefreqbase();
|
|
}
|
|
if ( request->ropefreqscale() != 0.0f ) {
|
|
params.rope_freq_scale = request->ropefreqscale();
|
|
}
|
|
}
|
|
|
|
|
|
// GRPC Server start
|
|
class BackendServiceImpl final : public backend::Backend::Service {
|
|
public:
|
|
grpc::Status Health(ServerContext* context, const backend::HealthMessage* request, backend::Reply* reply) {
|
|
// Implement Health RPC
|
|
reply->set_message("OK");
|
|
return Status::OK;
|
|
}
|
|
|
|
grpc::Status LoadModel(ServerContext* context, const backend::ModelOptions* request, backend::Result* result) {
|
|
// Implement LoadModel RPC
|
|
gpt_params params;
|
|
params_parse(request, params);
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
// load the model
|
|
if (!llama.load_model(params))
|
|
{
|
|
result->set_message("Failed loading model");
|
|
result->set_success(false);
|
|
return Status::CANCELLED;
|
|
}
|
|
llama.initialize();
|
|
result->set_message("Loading succeeded");
|
|
result->set_success(true);
|
|
loaded_model = true;
|
|
return Status::OK;
|
|
}
|
|
grpc::Status PredictStream(grpc::ServerContext* context, const backend::PredictOptions* request, grpc::ServerWriter<backend::Reply>* writer) override {
|
|
json data = parse_options(true, request, llama);
|
|
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);
|
|
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 }
|
|
});
|
|
|
|
backend::Reply reply;
|
|
// print it
|
|
std::string completion_text = result.result_json.value("content", "");
|
|
|
|
reply.set_message(completion_text);
|
|
|
|
// Send the reply
|
|
writer->Write(reply);
|
|
|
|
if (result.stop) {
|
|
break;
|
|
}
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return grpc::Status::OK;
|
|
}
|
|
|
|
|
|
grpc::Status Predict(ServerContext* context, const backend::PredictOptions* request, backend::Reply* reply) {
|
|
json data = parse_options(false, request, llama);
|
|
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);
|
|
std::string completion_text;
|
|
task_result result = llama.queue_results.recv(task_id);
|
|
if (!result.error && result.stop) {
|
|
completion_text = result.result_json.value("content", "");
|
|
reply->set_message(completion_text);
|
|
}
|
|
else
|
|
{
|
|
return grpc::Status::OK;
|
|
}
|
|
|
|
return grpc::Status::OK;
|
|
}
|
|
};
|
|
|
|
void RunServer(const std::string& server_address) {
|
|
BackendServiceImpl service;
|
|
|
|
ServerBuilder builder;
|
|
builder.AddListeningPort(server_address, grpc::InsecureServerCredentials());
|
|
builder.RegisterService(&service);
|
|
|
|
std::unique_ptr<Server> server(builder.BuildAndStart());
|
|
std::cout << "Server listening on " << server_address << std::endl;
|
|
server->Wait();
|
|
}
|
|
|
|
int main(int argc, char** argv) {
|
|
std::string server_address("localhost:50051");
|
|
|
|
// Define long and short options
|
|
struct option long_options[] = {
|
|
{"addr", required_argument, nullptr, 'a'},
|
|
{nullptr, 0, nullptr, 0}
|
|
};
|
|
|
|
// Parse command-line arguments
|
|
int option;
|
|
int option_index = 0;
|
|
while ((option = getopt_long(argc, argv, "a:", long_options, &option_index)) != -1) {
|
|
switch (option) {
|
|
case 'a':
|
|
server_address = optarg;
|
|
break;
|
|
default:
|
|
std::cerr << "Usage: " << argv[0] << " [--addr=<address>] or [-a <address>]" << std::endl;
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
// run the HTTP server in a thread - see comment below
|
|
std::thread t([&]()
|
|
{
|
|
RunServer(server_address);
|
|
return 0;
|
|
});
|
|
|
|
|
|
//);
|
|
start_llama_server();
|
|
std::cout << "stopping" << std::endl;
|
|
|
|
t.join();
|
|
|
|
llama_backend_free();
|
|
return 0;
|
|
}
|